(en)Provided herein are fetal diagnostic methods, kits and computational products useful for non-invasively detecting genetic variations for which maternal nucleic acid sequences are utilized as a reference.

1.ApplicationNumber: US-201113333842-A
1.PublishNumber: US-2012184449-A1
2.Date Publish: 20120719
3.Inventor: HIXSON HARRY F.
CANTOR CHARLES R.

4.Inventor Harmonized: HIXSON HARRY F(US)
CANTOR CHARLES R(US)

5.Country: US
6.Claims:
(en)Provided herein are fetal diagnostic methods, kits and computational products useful for non-invasively detecting genetic variations for which maternal nucleic acid sequences are utilized as a reference.

7.Description:
(en)RELATED PATENT APPLICATION
This patent application claims the benefit of U.S. Provisional Application No. 61/427,054 filed on Dec. 23, 2010, entitled FETAL ANEUPLOIDY DIAGNOSTICS, naming Harry Hixson and Charles R. Cantor as inventors, and designated by attorney docket no. SEQ-6030-PV. The entirety of the foregoing provisional patent application is incorporated herein by reference.


FIELD
The technology in part relates to methods and compositions for identifying genetic variations, which include, without limitation, prenatal tests for detecting a chromosome aneuploidy (e.g., trisomy 21 (Down syndrome), trisomy 18 (Edward syndrome), trisomy 13 (Patau syndrome)).
BACKGROUND
Genetic information of living organisms (e.g., animals, plants and microorganisms) and other forms of replicating genetic information (e.g., viruses) is encoded in deoxyribonucleic acid (DNA) or ribonucleic acid (RNA). Genetic information is a succession of nucleotides or modified nucleotides representing the primary structure of chemical or hypothetical nucleic acids. In humans, the complete genome contains about 30,000 genes located on twenty-four (24) chromosomes (see The Human Genome, T. Strachan, BIOS Scientific Publishers, 1992). Each gene encodes a specific protein, which after expression via transcription and translation, fulfills a specific biochemical function within a living cell.
Many medical conditions are caused by one or more genetic variations. Certain genetic variations cause medical conditions that include, for example, hemophilia, thalassemia, Duchenne Muscular Dystrophy (DMD), Huntington's Disease (HD), Alzheimer's Disease and Cystic Fibrosis (CF) (Human Genome Mutations, D. N. Cooper and M. Krawczak, BIOS Publishers, 1993). Such genetic diseases can result from an addition, substitution, or deletion of a single nucleotide in DNA of a particular gene. Certain birth defects are caused by a chromosomal abnormality, also referred to as an aneuploidy, such as Trisomy 21 (Down's Syndrome), Trisomy 13 (Patau Syndrome), Trisomy 18 (Edward's Syndrome), Monosomy X (Turner's Syndrome) and certain sex chromosome aneuploidies such as Klinefelter's Syndrome (XXY), for example. Some genetic variations may predispose an individual to, or cause, any of a number of diseases such as, for example, diabetes, arteriosclerosis, obesity, various autoimmune diseases and cancer (e.g., colorectal, breast, ovarian, lung).
Identifying one or more genetic variations or variances can lead to diagnosis of, or determining predisposition to, a particular medical condition. Identifying a genetic variance can result in facilitating a medical decision and/or employing a helpful medical procedure. In some cases, identification of one or more genetic variations or variances involves the analysis of cell-free DNA. Cell-free DNA (CF-DNA) is composed of DNA fragments that originate from cell death and circulate in peripheral blood. High concentrations of CF-DNA can be indicative of certain clinical conditions such as cancer, trauma, burns, myocardial infarction, stroke, sepsis, infection, and other illnesses. Additionally, cell-free fetal DNA (CFF-DNA) can be detected in the maternal bloodstream and used for various noninvasive prenatal diagnostics.
The presence of fetal nucleic acid in maternal plasma allows for non-invasive prenatal diagnosis through the analysis of a maternal blood sample. For example, quantitative abnormalities of fetal DNA in maternal plasma can be associated with a number of pregnancy-associated disorders, including preeclampsia, preterm labor, antepartum hemorrhage, invasive placentation, fetal Down syndrome, and other fetal chromosomal aneuploidies. Hence, fetal nucleic acid analysis in maternal plasma is a useful mechanism for the monitoring of fetomaternal well-being.
Early detection of pregnancy-related conditions, including complications during pregnancy and genetic defects of the fetus is important, as it allows early medical intervention necessary for the safety of both the mother and the fetus. Prenatal diagnosis traditionally has been conducted using cells isolated from the fetus through procedures such as chorionic villus sampling (CVS) or amniocentesis. However, these conventional methods are invasive and present an appreciable risk to both the mother and the fetus. The National Health Service currently cites a miscarriage rate of between 1 and 2 percent following the invasive amniocentesis and chorionic villus sampling (CVS) tests. An alternative to these invasive approaches is the use of non-invasive screening techniques that analyze circulating CFF-DNA.
SUMMARY
Provided in some embodiments are methods for detecting the presence or absence of a chromosomal aneuploidy in a fetus of a pregnant female, comprising: (a) determining nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid; (b) determining nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid; (c) assembling the nucleotide sequences of (b) into a maternal reference sequence; (d) aligning the nucleotide sequences of (a) to a portion of or all of the maternal reference sequence and counting the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence; and (e) detecting the presence or absence of the chromosomal aneuploidy in the fetus of the pregnant female based on the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence. Also provided in some embodiments are methods for detecting the presence or absence of a chromosomal aneuploidy in a fetus of a pregnant female, comprising: (a) determining nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid; (b) determining nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid; (c) assembling the nucleotide sequences of (b) into a maternal reference sequence; (d) aligning the nucleotide sequences of (a) to a portion of or all of the maternal reference sequence and counting the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence; and (e) providing an outcome determinative of the presence or absence of a chromosomal aneuploidy from the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence. In certain embodiments, the nucleotide sequences of (a) that map to the portion of or all of the maternal reference sequence and are counted consist of (i) maternal nucleotide sequences, (ii) fetal nucleotide sequences inherited from the pregnant female, and (iii) fetal nucleotide sequences inherited from either parent but where no information about which parent provided such nucleotide sequences is discernable.
In some embodiments, methods can comprise comparing the number of nucleotide sequences of (a) that map to the portion of or all of the maternal reference sequence to a predetermined value for chromosomal euploidy, with respect to a particular target chromosome. In certain embodiments, the portion of the maternal reference sequence is in a particular target chromosome or other portion of genomic nucleic acid. A portion of the maternal reference sequence sometimes is a bin or plurality of bins, and sometimes a bin is about 30K base pairs to about 100K base pairs in length. In some embodiments, the target chromosome is chromosome 21, chromosome 18, chromosome 13, chromosome X and/or chromosome Y.
In certain embodiments, extracellular nucleic acid, or cell-free nucleic acid, is from blood, and sometimes from blood plasma or blood serum. In some embodiments, the extracellular nucleic acid, or cell free nucleic acid, is from a pregnant female in the first trimester of pregnancy. Extracellular nucleic acid, or cell-free nucleic acid, sometimes contains about 1% to about 40% fetal nucleic acid, and sometimes contains about 15% or more of fetal nucleic acid. The number of fetal nucleic acid copies in the extracellular nucleic acid sometimes is about 10 copies to about 2000 copies of the total extracellular nucleic acid. In some embodiments, a method comprises determining the fetal nucleic acid concentration in the extracellular nucleic acid, or cell-free nucleic acid, and sometimes a method comprises enriching the extracellular nucleic acid, or cell-free nucleic acid, for fetal nucleic acid.
In some embodiments, the extracellular nucleic acid, the nucleic acid from the pregnant female containing substantially no fetal nucleic acid, or the extracellular nucleic acid and the nucleic acid from the pregnant female containing substantially no fetal nucleic acid, is not fragmented, not size fractionated, or is not fragmented and not size fractionated, prior to determining the nucleotide sequences in (a), (b), or (a) and (b). In certain embodiments, the extracellular nucleic acid, the nucleic acid from the pregnant female containing substantially no fetal nucleic acid, or the extracellular nucleic acid and the nucleic acid from the pregnant female containing substantially no fetal nucleic acid, is fragmented, size fractionated, or is fragmented and size fractionated, prior to determining the nucleotide sequences in (a), (b), or (a) and (b).
In some embodiments, the nucleic acid from the pregnant female containing substantially no fetal nucleic acid is cellular nucleic acid from the pregnant female. The cellular nucleic acid sometimes is from a buccal swab or skin sample, and can be obtained from any other suitable source and method. In some embodiments, a method comprises fragmenting, size-fractionating, or fragmenting and size-fractionating, the nucleic acid from the pregnant female containing substantially no fetal nucleic acid. In certain embodiments, a method comprises not fragmenting, not size-fractionating, or not fragmenting and not size-fractionating, the nucleic acid from the pregnant female containing substantially no fetal nucleic acid.
In some embodiments, the nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid, is all or a portion of the pregnant female's genomic nucleic acid. In certain embodiments, the nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid cover about 0.1-fold to about 20-fold of the pregnant female's genomic nucleic acid (e.g., about 0.2-fold, 0.3-fold, 0.4-fold, 0.5-fold, 0.6-fold, 0.7-fold, 0.8-fold, 0.9-fold, 1-fold, 2-fold, 4-fold, 6-fold, 8-fold, 10-fold, 12-fold, 14-fold, 16-fold, 18-fold). In some embodiments, the nucleotide sequences in (a), (b), or (a) and (b), are determined by a massively parallel sequencing method.
In certain embodiments, the maternal reference sequence is assembled by aligning nucleotide sequences of (b) to an external reference sequence. The external reference sequence sometimes has been assembled from nucleotide sequences having about 6-fold to about 60-fold coverage (e.g., about 10-fold, 15-fold, 20-fold, 25-fold, 30-fold, 35-fold, 40-fold, 45-fold, 50-fold, 55-fold). In some embodiments, the external reference sequence is from a subject or subjects of substantially the same ethnicity as the pregnant female. The maternal reference sequence sometimes is not completely aligned to the external reference sequence. In some embodiments, the maternal reference sequence is substantially completely aligned to the external reference sequence.
In some embodiments, a method comprises aligning the nucleotide sequences of (b) to a portion of the maternal reference sequence and counting the nucleotide sequences of (b) that map to the portion of the maternal reference sequence. In certain embodiments, the nucleotide sequences of (b) that map substantially exactly to the portion of the maternal reference sequence are counted. In some embodiments, the nucleotide sequences of (a) that map substantially exactly to the portion of the maternal reference sequence are counted.
In certain embodiments, a method comprises comparing the number of nucleotide sequences of (a) that map to the maternal reference sequence with respect to one or more chromosomal positions with the number of nucleotide sequences of (a) that map to the maternal reference sequence with respect to one or more different chromosomal positions. In some embodiments, a method comprises comparing the number of nucleotide sequences of (b) that map to the maternal reference sequence with respect to one or more chromosomal positions with the number of nucleotide sequences of (b) that map to the maternal reference sequence with respect to one or more different chromosomal positions. In some methods, the presence or absence of a difference between (i) the counted number of nucleotide sequences in (a) that map to the portion of the maternal reference sequence, and (ii) the counted number of nucleotide sequences in (b) that map to the portion of the maternal reference sequence, is determined. In certain embodiments, the presence of the chromosomal aneuploidy is detected based on determining the presence or absence of a statistically significant difference. In some embodiments, a method comprises comparing the difference for one or more different chromosomal positions.
In certain embodiments, the presence or absence of the chromosomal aneuploidy is determined with a confidence level of about 95% or more. Sometimes the presence or absence of the chromosomal aneuploidy is determined with a specificity of about 95% or more. In some embodiments, the presence or absence of the chromosomal aneuploidy is determined with a sensitivity of about 95% or more.
In some embodiments, the nucleotide sequences of (a), (b), or (a) and (b) comprise single-end reads. The nominal, average, mean or absolute length of the single-end reads sometimes is about 20 contiguous nucleotides to about 50 contiguous nucleotides, sometimes about 30 contiguous nucleotides to about 40 contiguous nucleotides, and sometimes about 35 contiguous nucleotides or about 36 contiguous nucleotides.
In certain embodiments, the nucleotide sequences of (a), (b), or (a) and (b) comprise double-end reads. The nominal, average, mean or absolute length of the single-end reads sometimes is about 10 contiguous nucleotides to about 25 contiguous nucleotides, sometimes is about 15 contiguous nucleotides to about 20 contiguous nucleotides, and sometimes is about 17 contiguous nucleotides or about 18 contiguous nucleotides.
When appropriate, a method provided herein comprises indicating that the presence or absence of an aneuploidy cannot be determined, in some embodiments.
In some embodiments, methods comprise isolating nucleic acid from a sample from a pregnant female. Sometimes the isolated nucleic acid is extracellular nucleic acid, or cell-free nucleic acid, from a sample, and sometimes the sample is blood plasma, blood serum, urine and the like. Sometimes the isolated nucleic acid is cellular nucleic acid from a sample, and the sample is a suitable cellular sample from the pregnant female such as blood cells for example. In certain embodiments, methods comprise isolating a sample from the pregnant female.
Provided in some embodiments are methods for detecting the presence or absence of a genetic variation in a fetus of a pregnant female, comprising: (a) determining nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid; (b) determining nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid; (c) assembling the nucleotide sequences of (b) into a maternal reference sequence; (d) aligning the nucleotide sequences of (a) to a portion of or all of the maternal reference sequence and counting the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence; and (e) detecting the presence or absence of the genetic variation in the fetus of the pregnant female based on the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence. Also provided in some embodiments are methods for detecting the presence or absence of a genetic variation in a fetus of a pregnant female, comprising: (a) determining nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid; (b) determining nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid; (c) assembling the nucleotide sequences of (b) into a maternal reference sequence; (d) aligning the nucleotide sequences of (a) to a portion of or all of the maternal reference sequence and counting the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence; and (e) providing an outcome determinative of the presence or absence of the genetic variation from the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence.
Provided in certain embodiments are computer program products, comprising a computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method for identifying the presence or absence of a chromosomal aneuploidy in a fetus of a pregnant female, the method comprising: providing a system that comprises distinct software modules comprising a detection module, a logic processing module, and a data display organization module; collecting, by the detection module, (a) nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid; and (b) nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid; receiving, by the logic processing module, the nucleotide sequences; aligning, by the logic processing module, the nucleotide sequences of (a) to a portion of a maternal reference sequence and counting the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence, thereby determining a number of counts; calling the presence or absence of a chromosomal aneuploidy in the fetus by the logic processing module based on the number of counts; organizing, by the data display organization model in response to being called by the logic processing module, a data display indicating the presence or absence of the chromosomal aneuploidy.
Also provided in some embodiments are computer program products, comprising a computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method for identifying the presence or absence of a chromosomal aneuploidy in a fetus of a pregnant female, the method comprising: providing a system that comprises distinct software modules comprising a data processing module, a logic processing module and a data display organization module; parsing, by the data processing module, a configuration file comprising (a) nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid, and (b) nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid into definition data; receiving, by the logic processing module, the definition data; aligning, by the logic processing module, nucleotide sequences of (a) to a portion of a maternal reference sequence and counting the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence, thereby determining a number of counts; calling the presence or absence of a chromosomal aneuploidy by the logic processing module based on the number of counts; organizing, by the data display organization model in response to being called by the logic processing module, a data display indicating the presence or absence of the chromosomal aneuploidy in the fetus of the pregnant female.
In some embodiments, a computer program product comprises assembling, by the logic processing module, the maternal reference sequence from the nucleotide sequences of (b). Also provided in certain embodiments are apparatus comprising memory in which a computer program product described herein is stored. In certain embodiments, the apparatus comprises a processor that implements one or more functions of the computer program product described herein.
Provided in certain embodiments are kits comprising one or more components for (a) determining nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid; and (b) determining nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid. In some embodiments, a kit comprises one or more components for processing a nucleic acid sample from the pregnant female, and sometimes, a kit comprises directions, or information for obtaining directions, which directions are for conducting a method described herein.
Certain embodiments are described further in the following description, claims and drawings.


DETAILED DESCRIPTION
Provided herein are improved processes and kits for identifying presence or absence of one or more fetal genetic variations (e.g., one or more chromosome abnormalities). Such processes and kits impart advantages of (i) decreasing risk of pregnancy complications as they are non-invasive; (ii) providing rapid results; and (iii) providing results with a relatively high degree of one or more of confidence, specificity and sensitivity, for example. Processes and kits described herein can be applied to identifying presence or absence of a variety of chromosome abnormalities, such as trisomy 21, trisomy 18 and/or trisomy 13, and aneuploid states associated with particular cancers, for example. Further, such processes and kits are useful for applications including, but not limited to, non-invasive prenatal screening and diagnostics, cancer detection, copy number variation detection, and as quality control tools for molecular biology methods relating to cellular replication (e.g., stem cells).
Genetic Variations and Medical Conditions
The presence or absence of a genetic variance can be determined using a method, kit or apparatus described herein. In certain embodiments, the presence or absence of one or more genetic variations is determined according to an outcome provided by methods, kits and apparatuses described herein. A genetic variation generally is a particular genetic phenotype present in certain individuals, and often a genetic variation is present in a statistically significant sub-population of individuals. Non-limiting examples of genetic variations include one or more deletions (e.g., micro-deletions), duplications (e.g., micro-duplications), insertions, mutations, polymorphisms (e.g., single-nucleotide polymorphisms), fusions, repeats (e.g., short tandem repeats), distinct methylation sites, distinct methylation patterns, the like and combinations thereof. An insertion, repeat, deletion, duplication, mutation or polymorphism can be of any observed length, and in some embodiments, is about 1 base or base pair (bp) to 1,000 kilobases (kb) in length (e.g., about 10 bp, 50 bp, 100 bp, 500 bp, 1 kb, 5 kb, 10 kb, 50 kb, 100 kb, 500 kb, or 1000 kb in length). In some embodiments, a genetic variation is a chromosome abnormality (e.g., aneuploidy), partial chromosome abnormality or mosaicism, which are described in greater detail hereafter.
A genetic variation for which the presence or absence is identified for a subject is associated with a medical condition in certain embodiments. Thus, technology described herein can be used to identify the presence or absence of one or more genetic variations that are associated with a medical condition or medical state. Non-limiting examples of medical conditions include those associated with intellectual disability (e.g., Down Syndrome), aberrant cell-proliferation (e.g., cancer), presence of a micro-organism nucleic acid (e.g., virus, bacterium, fungus, yeast), and preeclampsia.
Non-limiting examples of genetic variations, medical conditions and states are described hereafter.
Fetal Gender
In some embodiments, the prediction of fetal gender can be determined by a method, kit or apparatus described herein. Gender determination generally is based on a sex chromosome. In humans, there are two sex chromosomes, the X and Y chromosomes. Individuals with XX are female and XY are male and non-limiting variations include XO, XYY, XXX and XXY.
Chromosome Abnormalities
In some embodiments, the presence or absence of a fetal chromosome abnormality can be determined by using a method, kit or apparatus described herein. Chromosome abnormalities include, without limitation, a gain or loss of an entire chromosome or a region of a chromosome comprising one or more genes. Chromosome abnormalities include monosomies, trisomies, polysomies, loss of heterozygosity, deletions and/or duplications of one or more nucleotide sequences (e.g., one or more genes), including deletions and duplications caused by unbalanced translocations. The terms “aneuploidy” and “aneuploid” as used herein refer to an abnormal number of chromosomes in cells of an organism. As different organisms have widely varying chromosome complements, the term “aneuploidy” does not refer to a particular number of chromosomes, but rather to the situation in which the chromosome content within a given cell or cells of an organism is abnormal.
The term “monosomy” as used herein refers to lack of one chromosome of the normal complement. Partial monosomy can occur in unbalanced translocations or deletions, in which only a portion of the chromosome is present in a single copy. Monosomy of sex chromosomes (45, X) causes Turner syndrome, for example.
The term “disomy” refers to the presence of two copies of a chromosome. For organisms such as humans that have two copies of each chromosome (those that are diploid or “euploid”), disomy is the normal condition. For organisms that normally have three or more copies of each chromosome (those that are triploid or above), disomy is an aneuploid chromosome state. In uniparental disomy, both copies of a chromosome come from the same parent (with no contribution from the other parent).
The term “trisomy” as used herein refers to the presence of three copies, instead of two copies, of a particular chromosome. The presence of an extra chromosome 21, which is found in human Down syndrome, is referred to as “Trisomy 21.” Trisomy 18 and Trisomy 13 are two other human autosomal trisomies. Trisomy of sex chromosomes can be seen in females (e.g., 47, XXX) or males (e.g., 47, XXY in Klinefelter's syndrome; or 47, XYY).
The terms “tetrasomy” and “pentasomy” as used herein refer to the presence of four or five copies of a chromosome, respectively. Although rarely seen with autosomes, sex chromosome tetrasomy and pentasomy have been reported in humans, including XXXX, XXXY, XXYY, XYYY, XXXXX, XXXXY, XXXYY, XXYYY and XYYYY.
Chromosome abnormalities can be caused by a variety of mechanisms. Mechanisms include, but are not limited to (i) nondisjunction occurring as the result of a weakened mitotic checkpoint, (ii) inactive mitotic checkpoints causing non-disjunction at multiple chromosomes, (iii) merotelic attachment occurring when one kinetochore is attached to both mitotic spindle poles, (iv) a multipolar spindle forming when more than two spindle poles form, (v) a monopolar spindle forming when only a single spindle pole forms, and (vi) a tetraploid intermediate occurring as an end result of the monopolar spindle mechanism.
The terms “partial monosomy” and “partial trisomy” as used herein refer to an imbalance of genetic material caused by loss or gain of part of a chromosome. A partial monosomy or partial trisomy can result from an unbalanced translocation, where an individual carries a derivative chromosome formed through the breakage and fusion of two different chromosomes. In this situation, the individual would have three copies of part of one chromosome (two normal copies and the portion that exists on the derivative chromosome) and only one copy of part of the other chromosome involved in the derivative chromosome.
The term “mosaicism” as used herein refers to aneuploidy in some cells, but not all cells, of an organism. Certain chromosome abnormalities can exist as mosaic and non-mosaic chromosome abnormalities. For example, certain trisomy 21 individuals have mosaic Down syndrome and some have non-mosaic Down syndrome. Different mechanisms can lead to mosaicism. For example, (i) an initial zygote may have three 21st chromosomes, which normally would result in simple trisomy 21, but during the course of cell division one or more cell lines lost one of the 21st chromosomes; and (ii) an initial zygote may have two 21st chromosomes, but during the course of cell division one of the 21st chromosomes were duplicated. Somatic mosaicism likely occurs through mechanisms distinct from those typically associated with genetic syndromes involving complete or mosaic aneuploidy. Somatic mosaicism has been identified in certain types of cancers and in neurons, for example. In certain instances, trisomy 12 has been identified in chronic lymphocytic leukemia (CLL) and trisomy 8 has been identified in acute myeloid leukemia (AML). Also, genetic syndromes in which an individual is predisposed to breakage of chromosomes (chromosome instability syndromes) are frequently associated with increased risk for various types of cancer, thus highlighting the role of somatic aneuploidy in carcinogenesis. Methods and protocols described herein can identify presence or absence of non-mosaic and mosaic chromosome abnormalities.
Tables 1A and 1B present a non-limiting list of chromosome conditions, syndromes and/or abnormalities that can be potentially identified by methods, kits and apparatus described herein. Table 1B is from the DECIPHER database as of Oct. 6, 2011 (e.g., version 5.1, based on positions mapped to GRCh37; available at uniform resource locator (URL) dechipher.sanger.ac.uk).







TABLE 1A



Chro-



mo-



some
Abnormality
Disease Association



X
XO
Turner's Syndrome

Y
XXY
Klinefelter syndrome

Y
XYY
Double Y syndrome

Y
XXX
Trisomy X syndrome

Y
XXXX
Four X syndrome

Y
Xp21 deletion
Duchenne's/Becker syndrome, congenital adrenal



hypoplasia, chronic granulomatus disease

Y
Xp22 deletion
steroid sulfatase deficiency

Y
Xq26 deletion
X-linked lymphproliferative disease

1
1p (somatic)
neuroblastoma


monosomy



trisomy


2
monosomy
growth retardation, developmental and


trisomy 2q
mental delay, and minor physical abnormalities

3
monosomy
Non-Hodgkin's lymphoma


trisomy (somatic)


4
monosomy
Acute non lymphocytic leukemia (ANLL)


trisomy (somatic)


5
5p
Cri du chat; Lejeune syndrome

5
5q
myelodysplastic syndrome


(somatic)



monosomy



trisomy


6
monosomy
clear-cell sarcoma


trisomy (somatic)


7
7q11.23 deletion
William's syndrome

7
monosomy
monosomy 7 syndrome of childhood;


trisomy
somatic: renal cortical adenomas;



myelodysplastic syndrome

8
8q24.1 deletion
Langer-Giedon syndrome

8
monosomy
myelodysplastic syndrome; Warkany syndrome;


trisomy
somatic: chronic myelogenous leukemia

9
monosomy 9p
Alfi's syndrome

9
monosomy 9p
Rethore syndrome


partial trisomy


9
trisomy
complete trisomy 9 syndrome; mosaic trisomy 9



syndrome

10
Monosomy
ALL or ANLL


trisomy (somatic)


11
11p-
Aniridia; Wilms tumor

11
11q-
Jacobson Syndrome

11
monosomy
myeloid lineages affected (ANLL, MDS)


(somatic) trisomy


12
monosomy
CLL, Juvenile granulosa cell tumor (JGCT)


trisomy (somatic)


13
13q-
13q-syndrome; Orbeli syndrome

13
13q14 deletion
retinoblastoma

13
monosomy
Patau's syndrome


trisomy


14
monosomy
myeloid disorders (MDS, ANLL, atypical CML)


trisomy (somatic)


15
15q11-q13
Prader-Willi, Angelman's syndrome


deletion



monosomy


15
trisomy (somatic)
myeloid and lymphoid lineages affected, e.g.,



MDS, ANLL, ALL, CLL)

16
16q13.3 deletion
Rubenstein-Taybi


monosomy
papillary renal cell carcinomas (malignant)


trisomy (somatic)


17
17p-(somatic)
17p syndrome in myeloid malignancies

17
17q11.2 deletion
Smith-Magenis

17
17q13.3
Miller-Dieker

17
monosomy
renal cortical adenomas


trisomy (somatic)


17
17p11.2-12
Charcot-Marie Tooth Syndrome type 1; HNPP


trisomy


18
18p-
18p partial monosomy syndrome or



Grouchy Lamy Thieffry syndrome

18
18q-
Grouchy Lamy Salmon Landry Syndrome

18
monosomy
Edwards Syndrome


trisomy


19
monosomy



trisomy


20
20p-
trisomy 20p syndrome

20
20p11.2-12
Alagille


deletion


20
20q-
somatic: MDS, ANLL, polycythemia vera,



chronic neutrophilic leukemia

20
monosomy
papillary renal cell carcinomas (malignant)


trisomy (somatic)


21
monosomy
Down's syndrome


trisomy


22
22q11.2 deletion
DiGeorge's syndrome, velocardiofacial



syndrome, conotruncal anomaly face



syndrome, autosomal dominant Opitz



G/BBB syndrome, Caylor cardiofacial



syndrome

22
monosomy
complete trisomy 22 syndrome


trisomy





















































































































TABLE 1B



Syndrome
Chromosome
Start
End
Interval (Mb)
Grade












12q14 microdeletion
12
65,071,919
68,645,525
3.57


syndrome






15q13.3
15
30,769,995
32,701,482
1.93


microdeletion






syndrome






15q24 recurrent
15
74,377,174
76,162,277
1.79


microdeletion






syndrome






15q26 overgrowth
15
99,357,970
102,521,392
3.16


syndrome






16p11.2
16
29,501,198
30,202,572
0.70


microduplication






syndrome






16p11.2-p12.2
16
21,613,956
29,042,192
7.43


microdeletion






syndrome






16p13.11 recurrent
16
15,504,454
16,284,248
0.78


microdeletion






(neurocognitive






disorder






susceptibility locus)






16p13.11 recurrent
16
15,504,454
16,284,248
0.78


microduplication






(neurocognitive






disorder






susceptibility locus)






17q21.3 recurrent
17
43,632,466
44,210,205
0.58
1

microdeletion






syndrome






1p36 microdeletion
1
10,001
5,408,761
5.40
1

syndrome






1q21.1 recurrent
1
146,512,930
147,737,500
1.22
3

microdeletion






(susceptility locus






for






neurodevelopmental






disorders)






1q21.1 recurrent
1
146,512,930
147,737,500
1.22
3

microduplication






(possible






susceptiblity locus






for






neurodevelopmental






disorders)






1q21.1 susceptibility
1
145,401,253
145,928,123
0.53
3

locus for






Thrombocytopenia-






Absent Radius






(TAR) syndrome






22q11 deletion
22
18,546,349
22,336,469
3.79
1

syndrome






(Velocardiofacial/






DiGeorge






syndrome)






22q11 duplication
22
18,546,349
22,336,469
3.79
3

syndrome






22q11.2 distal
22
22,115,848
23,696,229
1.58


deletion syndrome






22q13 deletion
22
51,045,516
51,187,844
0.14
1

syndrome (Phelan-






Mcdermid






syndrome)






2p15-16.1
2
57,741,796
61,738,334
4.00


microdeletion






syndrome






2q33.1 deletion
2
196,925,089
205,206,940
8.28
1

syndrome






2q37 monosomy
2
239,954,693
243,102,476
3.15
1

3q29 microdeletion
3
195,672,229
197,497,869
1.83


syndrome






3q29
3
195,672,229
197,497,869
1.83


microduplication






syndrome






7q11.23 duplication
7
72,332,743
74,616,901
2.28


syndrome






8p23.1 deletion
8
8,119,295
11,765,719
3.65


syndrome






9q subtelomeric
9
140,403,363
141,153,431
0.75
1

deletion syndrome






Adult-onset
5
126,063,045
126,204,952
0.14


autosomal dominant






leukodystrophy






(ADLD)






Angelman
15
22,876,632
28,557,186
5.68
1

syndrome (Type 1)






Angelman
15
23,758,390
28,557,186
4.80
1

syndrome (Type 2)






ATR-16 syndrome
16
60,001
834,372
0.77
1

AZFa
Y
14,352,761
15,154,862
0.80


AZFb
Y
20,118,045
26,065,197
5.95


AZFb + AZFc
Y
19,964,826
27,793,830
7.83


AZFc
Y
24,977,425
28,033,929
3.06


Cat-Eye Syndrome
22
1
16,971,860
16.97


(Type I)






Charcot-Marie-
17
13,968,607
15,434,038
1.47
1

Tooth syndrome






type 1A (CMT1A)






Cri du Chat
5
10,001
11,723,854
11.71
1

Syndrome (5p






deletion)






Early-onset

21
27,037,956
27,548,479
0.51

Alzheimer disease






with cerebral






amyloid angiopathy






Familial

5
112,101,596
112,221,377
0.12

Adenomatous






Polyposis






Hereditary Liability
17
13,968,607
15,434,038
1.47
1

to Pressure Palsies






(HNPP)






Leri-Weill
X
751,878
867,875
0.12


dyschondrostosis






(LWD)-SHOX






deletion






Leri-Weill
X
460,558
753,877
0.29


dyschondrostosis






(LWD)-SHOX






deletion






Miller-Dieker
17
1
2,545,429
2.55
1

syndrome (MDS)






NF1-microdeletion
17
29,162,822
30,218,667
1.06
1

syndrome






Pelizaeus-
X
102,642,051
103,131,767
0.49


Merzbacher disease






Potocki-Lupski
17
16,706,021
20,482,061
3.78


syndrome (17p11.2






duplication






syndrome)






Potocki-Shaffer
11
43,985,277
46,064,560
2.08
1

syndrome






Prader-Willi
15
22,876,632
28,557,186
5.68
1

syndrome (Type 1)






Prader-Willi
15
23,758,390
28,557,186
4.80
1

Syndrome (Type 2)






ROAD (renal cysts
17
34,907,366
36,076,803
1.17


and diabetes)






Rubinstein-Taybi
16
3,781,464
3,861,246
0.08
1

Syndrome






Smith-Magenis
17
16,706,021
20,482,061
3.78
1

Syndrome






Sotos syndrome
5
175,130,402
177,456,545
2.33
1

Split hand/foot
7
95,533,860
96,779,486
1.25


malformation 1






(SHFM1)






Steroid sulphatase
X
6,441,957
8,167,697
1.73


deficiency (STS)






WAGR 11p13
11
31,803,509
32,510,988
0.71


deletion syndrome






Williams-Beuren
7
72,332,743
74,616,901
2.28
1

Syndrome (WBS)






Wolf-Hirschhorn
4
10,001
2,073,670
2.06
1

Syndrome






Xq28 (MECP2)
X
152,749,900
153,390,999
0.64


duplication



















































































































































































Grade 1 conditions often have one or more of the following characteristics; pathogenic anomaly; strong agreement amongst geneticists; highly penetrant; may still have variable phenotype but some common features; all cases in the literature have a clinical phenotype; no cases of healthy individuals with the anomaly; not reported on DVG databases or found in healthy population; functional data confirming single gene or multi-gene dosage effect; confirmed or strong candidate genes; clinical management implications defined; known cancer risk with implication for surveillance; multiple sources of information (OMIM, Genereviews, Orphanet, Unique, Wikipedia); and/or available for diagnostic use (reproductive counseling).
Grade 2 conditions often have one or more of the following characteristics; likely pathogenic anomaly; highly penetrant; variable phenotype with no consistent features other than DD; small number of cases/reports in the literature; all reported cases have a clinical phenotype; no functional data or confirmed pathogenic genes; multiple sources of information (OMIM, Genereviews, Orphanet, Unique, Wikipedia); and/or may be used for diagnostic purposes and reproductive counseling.
Grade 3 conditions often have one or more of the following characteristics; susceptibility locus; healthy individuals or unaffected parents of a proband described; present in control populations; non penetrant; phenotype mild and not specific; features less consistent; no functional data or confirmed pathogenic genes; more limited sources of data; possibility of second diagnosis remains a possibility for cases deviating from the majority or if novel clinical finding present; and/or caution when using for diagnostic purposes and guarded advice for reproductive counseling.
Preeclampsia
In some embodiments, the presence or absence of preeclampsia is determined by using a method, kit or apparatus described herein. Preeclampsia is a condition in which hypertension arises in pregnancy (i.e. pregnancy-induced hypertension) and is associated with significant amounts of protein in the urine. In some cases, preeclampsia also is associated with elevated levels of extracellular nucleic acid and/or alterations in methylation patterns. For example, a positive correlation between extracellular fetal-derived hypermethylated RASSF1A levels and the severity of pre-eclampsia has been observed. In certain examples, increased DNA methylation is observed for the H19 gene in preeclamptic placentas compared to normal controls.
Preeclampsia is one of the leading causes of maternal and fetal/neonatal mortality and morbidity worldwide. Circulating cell-free nucleic acids in plasma and serum are novel biomarkers with promising clinical applications in different medical fields, including prenatal diagnosis. Quantitative changes of cell-free fetal (cff) DNA in maternal plasma as an indicator for impending preeclampsia have been reported in different studies, for example, using real-time quantitative PCR for the male-specific SRY or DYS 14 loci. In cases of early onset preeclampsia, elevated levels may be seen in the first trimester. The increased levels of cffDNA before the onset of symptoms may be due to hypoxia/reoxygenation within the intervillous space leading to tissue oxidative stress and increased placental apoptosis and necrosis. In addition to the evidence for increased shedding of cffDNA into the maternal circulation, there is also evidence for reduced renal clearance of cffDNA in preeclampsia. As the amount of fetal DNA is currently determined by quantifying Y-chromosome specific sequences, alternative approaches such as measurement of total cell-free DNA or the use of gender-independent fetal epigenetic markers, such as DNA methylation, offer an alternative. Cell-free RNA of placental origin is another alternative biomarker that may be used for screening and diagnosing preeclampsia in clinical practice. Fetal RNA is associated with subcellular placental particles that protect it from degradation. Fetal RNA levels sometimes are ten-fold higher in pregnant females with preeclampsia compared to controls, and therefore is an alternative biomarker that may be used for screening and diagnosing preeclampsia in clinical practice.
Pathogens
In some embodiments, the presence or absence of a pathogenic condition is determined by a method, kit or apparatus described herein. A pathogenic condition can be caused by infection of a host by a pathogen including, but not limited to, a bacterium, virus or fungus. Since pathogens typically possess nucleic acid (e.g., genomic DNA, genomic RNA, mRNA) that can be distinguishable from host nucleic acid, methods, kits and apparatus provided herein can be used to determine the presence or absence of a pathogen. Often, pathogens possess nucleic acid with characteristics unique to a particular pathogen such as, for example, epigenetic state and/or one or more sequence variations, duplications and/or deletions. Thus, methods provided herein may be used to identify a particular pathogen or pathogen variant (e.g. strain).
Cancers
In some embodiments, the presence or absence of a cell proliferation disorder (e.g., a cancer) is determined by using a method, kit or apparatus described herein. For example, levels of cell-free nucleic acid in serum can be elevated in patients with various types of cancer compared with healthy patients. Patients with metastatic diseases, for example, can sometimes have serum DNA levels approximately twice as high as non-metastatic patients. Patients with metastatic diseases may also be identified by cancer-specific markers and/or certain single nucleotide polymorphisms or short tandem repeats, for example. Non-limiting examples of cancer types that may be positively correlated with elevated levels of circulating DNA include breast cancer, colorectal cancer, gastrointestinal cancer, hepatocellular cancer, lung cancer, melanoma, non-Hodgkin lymphoma, leukemia, multiple myeloma, bladder cancer, hepatoma, cervical cancer, esophageal cancer, pancreatic cancer, and prostate cancer. Various cancers can possess, and can sometimes release into the bloodstream, nucleic acids with characteristics that are distinguishable from nucleic acids from non-cancerous healthy cells, such as, for example, epigenetic state and/or sequence variations, duplications and/or deletions. Such characteristics can, for example, be specific to a particular type of cancer. Thus, it is further contemplated that the methods provided herein can be used to identify a particular type of cancer.
Other Genetic Variations
In some embodiments, the presence or absence of a genetic variation can be determined by using a method, kit or apparatus described herein. The term “genetic variation” as used herein refers to one or more conditions chosen from copy number variations (CNV's), microdeletions, duplications, or any condition which causes or results in a genetic dosage variation from an expected genetic dosage observed in an unaffected individual. The term “copy number variation” as used herein refers to structural rearrangements of one or more genomic sections, chromosomes, or parts of chromosomes, which rearrangement often is caused by deletions, duplications, inversions, and/or translocations. CNV's can be inherited or caused by de novo mutation, and typically result in an abnormal number of copies of one or more genomic sections (e.g., abnormal gene dosage with respect to an unaffected sample). Copy number variation can occur in regions that range from as small as one kilobase to several megabases, in some embodiments. CNV's can be detected using various cytogenetic methods (FISH, CGH, aCGH, karyotype analysis) and/or sequencing methods.
The term “microdeletion” as used herein refers to a decreased dosage, with respect to unaffected regions, of genetic material (e.g., DNA, genes, nucleic acid representative of a particular region) located in a selected genomic section or segment. Microdeletions, and syndromes caused by microdeletions, often are characterized by a small deletion (e.g., generally less than five megabases) of one or more chromosomal segments, spanning one or more genes, the absence of which sometimes confers a disease condition. Microdeletions sometimes are caused by errors in chromosomal crossover during meiosis. In many instances, microdeletions are not detectable by currently utilized karyotyping methods.
The terms “chromosomal duplication”, “microduplication”, or “duplication” as used herein refer to one or more regions of genetic material (e.g., DNA, genes, nucleic acid representative of a particular region) for which the dosage is increased relative to unaffected regions. Duplications frequently occur as the result of an error in homologous recombination or due to a retrotransposon event. Duplications can range from small regions (thousands of base pairs) to whole chromosomes in some instances. Duplications have been associated with certain types of proliferative diseases. Duplications can be characterized using genomic microarrays or comparative genetic hybridization (CGH). A duplication sometimes is characterized as a genetic region repeated one or more times (e.g., repeated 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 times).
Samples
Nucleic acid utilized in methods, kits and apparatus described herein often is isolated from a sample obtained from a subject. In some embodiments, a subject is referred to as a test subject, and in certain embodiments a subject is referred to as a sample subject or reference subject. The term “test subject” as used herein refers to a subject being evaluated for the presence or absence of a genetic variation. The terms “sample subject” and “reference subject” as used herein refer to a subject utilized as a basis for comparison to the test subject, and a reference subject sometimes is selected based on knowledge that the reference subject is known to be free of, or have, the genetic variation being evaluated for the test subject. A subject can be any living or non-living organism, including but not limited to a human, a non-human animal, a plant, a bacterium, a fungus or a protist. Any human or non-human animal can be selected, including but not limited to mammal, reptile, avian, amphibian, fish, ungulate, ruminant, bovine (e.g., cattle), equine (e.g., horse), caprine and ovine (e.g., sheep, goat), swine (e.g., pig), camelid (e.g., camel, llama, alpaca), monkey, ape (e.g., gorilla, chimpanzee), ursid (e.g., bear), poultry, dog, cat, mouse, rat, fish, dolphin, whale and shark. A subject may be a male or female (e.g., woman).
Nucleic acid may be isolated from any type of suitable biological specimen or sample. Non-limiting examples of specimens include fluid or tissue from a subject, including, without limitation, umbilical cord blood, chorionic villi, amniotic fluid, cerbrospinal fluid, spinal fluid, lavage fluid (e.g., bronchoalveolar, gastric, peritoneal, ductal, ear, athroscopic), biopsy sample (e.g., from pre-implantation embryo), celocentesis sample, fetal nucleated cells or fetal cellular remnants, washings of female reproductive tract, urine, feces, sputum, saliva, nasal mucous, prostate fluid, lavage, semen, lymphatic fluid, bile, tears, sweat, breast milk, breast fluid, embryonic cells and fetal cells (e.g. placental cells). In some embodiments, a biological sample may be blood and sometimes plasma or serum. As used herein, the term “blood” encompasses whole blood or any fractions of blood, such as serum and plasma as conventionally defined, for example. Blood plasma refers to the fraction of whole blood resulting from centrifugation of blood treated with anticoagulants. Blood serum refers to the watery portion of fluid remaining after a blood sample has coagulated. Fluid or tissue samples often are collected in accordance with standard protocols hospitals or clinics generally follow. For blood, an appropriate amount of peripheral blood (e.g., between 3-40 milliliters) often is collected and can be stored according to standard procedures prior to further preparation. A fluid or tissue sample from which nucleic acid is extracted may be acellular. In some embodiments, a fluid or tissue sample may contain cellular elements or cellular remnants. In some embodiments fetal cells or cancer cells may be included in the sample.
A sample may be heterogeneous, by which is meant that more than one type of nucleic acid species is present in the sample. For example, heterogeneous nucleic acid can include, but is not limited to, (i) fetally derived and maternally derived nucleic acid, (ii) cancer and non-cancer nucleic acid, (iii) pathogen and host nucleic acid, and more generally, (iv) mutated and wild-type nucleic acid. A sample may be heterogeneous because more than one cell type is present, such as a fetal cell and a maternal cell, a cancer and non-cancer cell, or a pathogenic and host cell. In some embodiments, a minority nucleic acid species and a majority nucleic acid species is present.
For prenatal applications of technology described herein, fluid or tissue sample may be collected from a female at a gestational age suitable for testing, or from a female who is being tested for possible pregnancy. Suitable gestational age may vary depending on the prenatal test being performed. In certain embodiments, a pregnant female subject sometimes is in the first trimester of pregnancy, at times in the second trimester of pregnancy, or sometimes in the third trimester of pregnancy. In certain embodiments, a fluid or tissue is collected from a pregnant female between about 1 to about 45 weeks of fetal gestation (e.g., at 1-4, 4-8, 8-12, 12-16, 16-20, 20-24, 24-28, 28-32, 32-36, 36-40 or 40-44 weeks of fetal gestation), and sometimes between about 5 to about 28 weeks of fetal gestation (e.g., at 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27 weeks of fetal gestation).
Nucleic Acid Isolation and Processing
Nucleic acid may be derived from one or more sources (e.g., cells, soil, etc.) by methods known in the art. Cell lysis procedures and reagents are known in the art and may generally be performed by chemical, physical, or electrolytic lysis methods. For example, chemical methods generally employ lysing agents to disrupt cells and extract the nucleic acids from the cells, followed by treatment with chaotropic salts. Physical methods such as freeze/thaw followed by grinding, the use of cell presses and the like also are useful. High salt lysis procedures also are commonly used. For example, an alkaline lysis procedure may be utilized. The latter procedure traditionally incorporates the use of phenol-chloroform solutions, and an alternative phenol-chloroform-free procedure involving three solutions can be utilized. In the latter procedures, one solution can contain 15 mM Tris, pH 8.0; 10 mM EDTA and 100 ug/ml Rnase A; a second solution can contain 0.2N NaOH and 1% SDS; and a third solution can contain 3M KOAc, pH 5.5. These procedures can be found in Current Protocols in Molecular Biology, John Wiley & Sons, N.Y., 6.3.1-6.3.6 (1989), incorporated herein in its entirety.
The terms “nucleic acid” and “nucleic acid molecule” are used interchangeably. The terms refer to nucleic acids of any composition form, such as deoxyribonucleic acid (DNA, e.g., complementary DNA (cDNA), genomic DNA (gDNA) and the like), ribonucleic acid (RNA, e.g., message RNA (mRNA), short inhibitory RNA (siRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), microRNA, RNA highly expressed by the fetus or placenta, and the like), and/or DNA or RNA analogs (e.g., containing base analogs, sugar analogs and/or a non-native backbone and the like), RNA/DNA hybrids and polyamide nucleic acids (PNAs), all of which can be in single- or double-stranded form. Unless otherwise limited, a nucleic acid can comprise known analogs of natural nucleotides, some of which can function in a similar manner as naturally occurring nucleotides. A nucleic acid can be in any form useful for conducting processes herein (e.g., linear, circular, supercoiled, single-stranded, double-stranded and the like). A nucleic acid may be, or may be from, a plasmid, phage, autonomously replicating sequence (ARS), centromere, artificial chromosome, chromosome, or other nucleic acid able to replicate or be replicated in vitro or in a host cell, a cell, a cell nucleus or cytoplasm of a cell in certain embodiments. A nucleic acid in some embodiments can be from a single chromosome (e.g., a nucleic acid sample may be from one chromosome of a sample obtained from a diploid organism). Nucleic acids also can include derivatives, variants and analogs of RNA or DNA synthesized, replicated or amplified from single-stranded (“sense” or “antisense”, “plus” strand or “minus” strand, “forward” reading frame or “reverse” reading frame) and double-stranded polynucleotides. Deoxyribonucleotides often include deoxyadenosine, deoxycytidine, deoxyguanosine and deoxythymidine. For RNA, the base cytosine is replaced with uracil and the sugar 2′ position includes a hydroxyl moiety. A nucleic acid may be prepared using a nucleic acid obtained from a subject as a template.
Nucleic acid may be isolated at a different time point as compared to another nucleic acid, where each of the samples is from the same or a different source. A nucleic acid may be from a nucleic acid library, such as a cDNA or RNA library, for example. A nucleic acid may be a result of nucleic acid purification or isolation and/or amplification of nucleic acid molecules from the sample. Nucleic acid provided for processes described herein may contain nucleic acid from one sample or from two or more samples (e.g., from 1 or more, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, or 20 or more samples).
Nucleic acid can include extracellular nucleic acid in certain embodiments. The term “extracellular nucleic acid” as used herein refers to nucleic acid isolated from a source having substantially no cells, and extracellular nucleic acid often is substantially cell-free nucleic acid. Extracellular nucleic acid often includes no detectable cells and may contain cellular elements or cellular remnants. Non-limiting examples of acellular sources for extracellular nucleic acid are blood plasma, blood serum and urine. Without being limited by theory, extracellular nucleic acid may be a product of cell apoptosis and cell breakdown, which provides basis for extracellular nucleic acid often having a series of lengths across a large spectrum (e.g., a “ladder”).
Extracellular nucleic acid can include different nucleic acid species, and therefore is referred to herein as “heterogeneous” in certain embodiments. For example, blood serum or plasma from a person having cancer can include nucleic acid from cancer cells and nucleic acid from non-cancer cells. In another example, blood serum or plasma from a pregnant female can include maternal nucleic acid and fetal nucleic acid. In some instances, fetal nucleic acid sometimes is about 5% to about 50% of the overall nucleic acid (e.g., about 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, or 49% of the total nucleic acid is fetal nucleic acid). In some embodiments, the majority of fetal nucleic acid in nucleic acid is of a length of about 500 base pairs or less (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of fetal nucleic acid is of a length of about 500 base pairs or less). In some embodiments, the majority of fetal nucleic acid in nucleic acid is of a length of about 250 base pairs or less (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of fetal nucleic acid is of a length of about 250 base pairs or less). In some embodiments, the majority of fetal nucleic acid in nucleic acid is of a length of about 200 base pairs or less (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of fetal nucleic acid is of a length of about 200 base pairs or less). In some embodiments, the majority of fetal nucleic acid in nucleic acid is of a length of about 150 base pairs or less (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of fetal nucleic acid is of a length of about 150 base pairs or less). In some embodiments, the majority of fetal nucleic acid in nucleic acid is of a length of about 100 base pairs or less (e.g., about 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of fetal nucleic acid is of a length of about 100 base pairs or less).
Nucleic acid may be provided for conducting methods described herein without processing of the sample(s) containing the nucleic acid, in certain embodiments. In some embodiments, nucleic acid is provided for conducting methods described herein after processing of the sample(s) containing the nucleic acid. For example, a nucleic acid may be extracted, isolated, purified or amplified from the sample(s). The term “isolated” as used herein refers to nucleic acid removed from its original environment (e.g., the natural environment if it is naturally occurring, or a host cell if expressed exogenously), and thus is altered by human intervention (e.g., “by the hand of man”) from its original environment. An isolated nucleic acid is provided with fewer non-nucleic acid components (e.g., protein, lipid) than the amount of components present in a source sample. A composition comprising isolated nucleic acid can be about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or greater than 99% free of non-nucleic acid components. The term “purified” as used herein refers to nucleic acid provided that contains fewer nucleic acid species than in the sample source from which the nucleic acid is derived. A composition comprising nucleic acid may be about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or greater than 99% free of other nucleic acid species. The term “amplified” as used herein refers to subjecting nucleic acid of a sample to a process that linearly or exponentially generates amplicon nucleic acids having the same or substantially the same nucleotide sequence as the nucleotide sequence of the nucleic acid in the sample, or portion thereof.
Nucleic acid also may be processed by subjecting nucleic acid to a method that generates nucleic acid fragments, in certain embodiments, before providing nucleic acid for a process described herein. In some embodiments, nucleic acid subjected to fragmentation or cleavage may have a nominal, average or mean length of about 5 to about 10,000 base pairs, about 100 to about 1,000 base pairs, about 100 to about 500 base pairs, or about 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000 or 9000 base pairs. Fragments can be generated by any suitable method known in the art, and the average, mean or nominal length of nucleic acid fragments can be controlled by selecting an appropriate fragment-generating procedure. In certain embodiments, nucleic acid of a relatively shorter length can be utilized to analyze sequences that contain little sequence variation and/or contain relatively large amounts of known nucleotide sequence information. In some embodiments, nucleic acid of a relatively longer length can be utilized to analyze sequences that contain greater sequence variation and/or contain relatively small amounts of nucleotide sequence information.
Nucleic acid fragments may contain overlapping nucleotide sequences, and such overlapping sequences can facilitate construction of a nucleotide sequence of the non-fragmented counterpart nucleic acid, or a portion thereof. For example, one fragment may have subsequences x and y and another fragment may have subsequences y and z, where x, y and z are nucleotide sequences that can be 5 nucleotides in length or greater. Overlap sequence y can be utilized to facilitate construction of the x-y-z nucleotide sequence in nucleic acid from a sample in certain embodiments. Nucleic acid may be partially fragmented (e.g., from an incomplete or terminated specific cleavage reaction) or fully fragmented in certain embodiments.
Nucleic acid can be fragmented by various methods known in the art, which include without limitation, physical, chemical and enzymatic processes. Non-limiting examples of such processes are described in U.S. Patent Application Publication No. 20050112590 (published on May 26, 2005, entitled “Fragmentation-based methods and systems for sequence variation detection and discovery,” naming Van Den Boom et al.). Certain processes can be selected to generate non-specifically cleaved fragments or specifically cleaved fragments. Non-limiting examples of processes that can generate non-specifically cleaved fragment nucleic acid include, without limitation, contacting nucleic acid with apparatus that expose nucleic acid to shearing force (e.g., passing nucleic acid through a syringe needle; use of a French press); exposing nucleic acid to irradiation (e.g., gamma, x-ray, UV irradiation; fragment sizes can be controlled by irradiation intensity); boiling nucleic acid in water (e.g., yields about 500 base pair fragments) and exposing nucleic acid to an acid and base hydrolysis process.
As used herein, “fragmentation” or “cleavage” refers to a procedure or conditions in which a nucleic acid molecule, such as a nucleic acid template gene molecule or amplified product thereof, may be severed into two or more smaller nucleic acid molecules. Such fragmentation or cleavage can be sequence specific, base specific, or nonspecific, and can be accomplished by any of a variety of methods, reagents or conditions, including, for example, chemical, enzymatic, physical fragmentation.
As used herein, “fragments”, “cleavage products”, “cleaved products” or grammatical variants thereof, refers to nucleic acid molecules resultant from a fragmentation or cleavage of a nucleic acid template gene molecule or amplified product thereof. While such fragments or cleaved products can refer to all nucleic acid molecules resultant from a cleavage reaction, typically such fragments or cleaved products refer only to nucleic acid molecules resultant from a fragmentation or cleavage of a nucleic acid template gene molecule or the portion of an amplified product thereof containing the corresponding nucleotide sequence of a nucleic acid template gene molecule. For example, an amplified product can contain one or more nucleotides more than the amplified nucleotide region of a nucleic acid template sequence (e.g., a primer can contain “extra” nucleotides such as a transcriptional initiation sequence, in addition to nucleotides complementary to a nucleic acid template gene molecule, resulting in an amplified product containing “extra” nucleotides or nucleotides not corresponding to the amplified nucleotide region of the nucleic acid template gene molecule). Accordingly, fragments can include fragments arising from portions of amplified nucleic acid molecules containing, at least in part, nucleotide sequence information from or based on the representative nucleic acid template molecule.
As used herein, the term “complementary cleavage reactions” refers to cleavage reactions that are carried out on the same nucleic acid using different cleavage reagents or by altering the cleavage specificity of the same cleavage reagent such that alternate cleavage patterns of the same target or reference nucleic acid or protein are generated. In certain embodiments, nucleic acid may be treated with one or more specific cleavage agents (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more specific cleavage agents) in one or more reaction vessels (e.g., nucleic acid is treated with each specific cleavage agent in a separate vessel).
Nucleic acid may be specifically cleaved by contacting the nucleic acid with one or more specific cleavage agents. The term “specific cleavage agent” as used herein refers to an agent, sometimes a chemical or an enzyme that can cleave a nucleic acid at one or more specific sites. Specific cleavage agents often cleave specifically according to a particular nucleotide sequence at a particular site.
Examples of enzymatic specific cleavage agents include without limitation endonucleases (e.g., DNase (e.g., DNase I, II); RNase (e.g., RNase E, F, H, P); Cleavase™ enzyme; Taq DNA polymerase; E. coli DNA polymerase I and eukaryotic structure-specific endonucleases; murine FEN-1 endonucleases; type I, II or III restriction endonucleases such as Acc I, Afl III, Alu I, Alw44 I, Apa I, Asn I, Ava I, Ava II, BamH I, Ban II, Bcl I, Bgl I. Bgl II, Bln I, Bsm I, BssH II, BstE II, Cfo I, Cla I, Dde I, Dpn I, Dra I, EclX I, EcoR I, EcoR I, EcoR II, EcoR V, Hae II, Hae II, Hind III, Hind III, Hpa I, Hpa II, Kpn I, Ksp I, Mlu I, MluN I, Msp I, Nci I, Nco I, Nde I, Nde II, Nhe I, Not I, Nru I, Nsi I, Pst I, Pvu I, Pvu II, Rsa I, Sac I, Sal I, Sau3A I, Sca I, ScrF I, Sfi I, Sma I, Spe I, Sph I, Ssp I, Stu I, Sty I, Swa I, Taq I, Xba I, Xho I; glycosylases (e.g., uracil-DNA glycolsylase (UDG), 3-methyladenine DNA glycosylase, 3-methyladenine DNA glycosylase II, pyrimidine hydrate-DNA glycosylase, FaPy-DNA glycosylase, thymine mismatch-DNA glycosylase, hypoxanthine-DNA glycosylase, 5-Hydroxymethyluracil DNA glycosylase (HmUDG), 5-Hydroxymethylcytosine DNA glycosylase, or 1,N6-etheno-adenine DNA glycosylase); exonucleases (e.g., exonuclease III); ribozymes, and DNAzymes. Nucleic acid may be treated with a chemical agent, and the modified nucleic acid may be cleaved. In non-limiting examples, nucleic acid may be treated with (i) alkylating agents such as methylnitrosourea that generate several alkylated bases, including N3-methyladenine and N3-methylguanine, which are recognized and cleaved by alkyl purine DNA-glycosylase; (ii) sodium bisulfite, which causes deamination of cytosine residues in DNA to form uracil residues that can be cleaved by uracil N-glycosylase; and (iii) a chemical agent that converts guanine to its oxidized form, 8-hydroxyguanine, which can be cleaved by formamidopyrimidine DNA N-glycosylase. Examples of chemical cleavage processes include without limitation alkylation, (e.g., alkylation of phosphorothioate-modified nucleic acid); cleavage of acid lability of P3′-N5′-phosphoroamidate-containing nucleic acid; and osmium tetroxide and piperidine treatment of nucleic acid.
In some embodiments, fragmented nucleic acid can be subjected to a size fractionation procedure and all or part of the fractionated pool may be isolated or analyzed. Size fractionation procedures are known in the art (e.g., separation on an array, separation by a molecular sieve, separation by gel electrophoresis, separation by column chromatography).
Nucleic acid also may be exposed to a process that modifies certain nucleotides in the nucleic acid before providing nucleic acid for a method described herein. A process that selectively modifies nucleic acid based upon the methylation state of nucleotides therein can be applied to nucleic acid, for example. In addition, conditions such as high temperature, ultraviolet radiation, x-radiation, can induce changes in the sequence of a nucleic acid molecule. Nucleic acid may be provided in any form useful for conducting a sequence analysis or manufacture process described herein, such as solid or liquid form, for example. In certain embodiments, nucleic acid may be provided in a liquid form optionally comprising one or more other components, including without limitation one or more buffers or salts.
Determining Fetal Nucleic Acid Content and Enriching for Fetal Nucleic Acid
The amount of fetal nucleic acid (e.g., concentration) in nucleic acid is determined in some embodiments. In certain embodiments, the amount of fetal nucleic acid is determined according to markers specific to a male fetus (e.g., Y-chromosome STR markers (e.g., DYS 19, DYS 385, DYS 392 markers); RhD marker in RhD-negative females), or according to one or more markers specific to fetal nucleic acid and not maternal nucleic acid (e.g., differential methylation between mother and fetus, or fetal RNA markers in maternal blood plasma; Lo, 2005, Journal of Histochemistry and Cytochemistry 53 (3): 293-296). Methylation-based fetal quantifier compositions and processes are described in U.S. application Ser. No. 12/561,241, filed Sep. 16, 2009, which is hereby incorporated by reference. Determination of fetal fraction sometimes is performed using a fetal quantifier assay (FQA) (e.g., U.S. Patent Application Publication No: US 2010-0105049 A1, entitled “PROCESSES AND COMPOSITIONS FOR METHYLATION-BASED ENRICHMENT OF FETAL NUCLEIC ACIDS”).
The amount of fetal nucleic acid in extracellular nucleic acid can be quantified and used in conjunction with the determination methods provided herein. Thus, in certain embodiments, methods of the technology comprise an additional step of determining the amount of fetal nucleic acid. The amount of fetal nucleic acid can be determined in a nucleic acid sample from a subject before or after processing to prepare sample nucleic acid. In certain embodiments, the amount of fetal nucleic acid is determined in a sample after sample nucleic acid is processed and prepared, which amount is utilized for further assessment. In some embodiments, an outcome comprises factoring the fraction of fetal nucleic acid in the sample nucleic acid (e.g., adjusting counts, removing samples, making a call or not making a call). The determination step can be performed before, during or after aneuploidy detection methods described herein. For example, to achieve an aneuploidy detection method with a given sensitivity or specificity, a fetal nucleic acid quantification method may be implemented prior to, during or after aneuploidy detection to identify those samples with greater than about 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25% or more fetal nucleic acid. In some embodiments, samples determined as having a certain threshold amount of fetal nucleic acid (e.g., about 15% or more fetal nucleic acid) are further analyzed for the presence or absence of aneuploidy. In certain embodiments, determinations of the presence or absence of aneuploidy are selected (e.g., selected and communicated to a patient) only for samples having a certain threshold amount of fetal nucleic acid (e.g., about 15% or more fetal nucleic acid).
In some embodiments, extracellular nucleic acid is enriched or relatively enriched for fetal nucleic acid. Methods for enriching a sample for a particular species of nucleic acid are described in U.S. Pat. No. 6,927,028, filed Aug. 31, 2001, Patent Application Number PCT/US07/69991, filed May 30, 2007, Patent Application Number PCT/US2007/071232 (filed Jun. 15, 2007), Patent Application Number PCT/US2008/074689, Patent Application Number PCT/US2008/074692, Patent Application Number PCT/US2009/057215, Patent Application Number PCT/US2010/027879, and Patent Application Number PCT/EP05/012707 (filed Nov. 28, 2005). In certain embodiments, maternal nucleic acid is selectively removed (partially, substantially, almost completely or completely) from the sample. In certain embodiments, fetal nucleic acid is differentiated and separated from maternal nucleic acid based on methylation differences. In certain embodiments, fetal nucleic is enriched by size enrichment (e.g., amplification of smaller size nucleic acid) or size separation (e.g., isolated cell-free nucleic having a size of about 300 base pairs or less, about 200 base pairs or less or about 150 base pairs or less can be enriched for fetal nucleic acid). Enriching for a particular low copy number species nucleic acid may also improve quantitative sensitivity. For example, the most sensitive peak ratio detection area sometimes is within 10% from center point.
Obtaining Sequence Reads
Sequencing, mapping and related analytical methods are known in the art (e.g., U.S. Patent Application Publication US2009/0029377, incorporated by reference). Certain aspects of such processes are described hereafter.
As used herein, “reads” are short nucleotide sequences produced by any sequencing process described herein or known in the art. Reads can be generated from one end of nucleic acid fragments (“single-end reads”), and sometimes are generated from both ends of nucleic acids (“double-end reads”). In certain embodiments, “obtaining” nucleic acid sequence reads of a sample from a subject and/or “obtaining” nucleic acid sequence reads of a biological specimen from one or more reference persons can involve directly sequencing nucleic acid to obtain the sequence information. In some embodiments, “obtaining” can involve receiving sequence information obtained directly from a nucleic acid by another.
In some embodiments, one nucleic acid sample from one individual is sequenced. In certain embodiments, nucleic acid samples from two or more biological samples, where each biological sample is from one individual or two or more individuals, are pooled and the pool is sequenced. In the latter embodiments, a nucleic acid sample from each biological sample often is identified by one or more unique identification tags.
In some embodiments, a fraction of the genome is sequenced, which sometimes is expressed in the amount of the genome covered by the determined nucleotide sequences (e.g., “fold” coverage less than 1). When a genome is sequenced with about 1-fold coverage, roughly 100% of the nucleotide sequence of the genome is represented by reads. A genome also can be sequenced with redundancy, where a given region of the genome can be covered by two or more reads or overlapping reads (e.g., “fold” coverage greater than 1). In some embodiments, a genome is sequenced with about 0.1-fold to about 100-fold coverage, about 0.2-fold to 20-fold coverage, or about 0.2-fold to about 1-fold coverage (e.g., about 0.2-, 0.3-, 0.4-, 0.5-, 0.6-, 0.7-, 0.8-, 0.9-, 1-, 2-, 3-, 4-, 5-, 6-, 7-, 8-, 9-, 10-, 15-, 20-, 30-, 40-, 50-, 60-, 70-, 80-, 90-fold coverage).
In certain embodiments, a fraction of a nucleic acid pool that is sequenced in a run is further sub-selected prior to sequencing. In certain embodiments, hybridization-based techniques (e.g., using oligonucleotide arrays) can be used to first sub-select for nucleic acid sequences from certain chromosomes (e.g., a potentially aneuploid chromosome and other chromosome(s) not involved in the aneuploidy tested). In some embodiments, nucleic acid can be fractionated by size (e.g., by gel electrophoresis, size exclusion chromatography or by microfluidics-based approach) and in certain instances, fetal nucleic acid can be enriched by selecting for nucleic acid having a lower molecular weight (e.g., less than 300 base pairs, less than 200 base pairs, less than 150 base pairs, less than 100 base pairs). In some embodiments, fetal nucleic acid can be enriched by suppressing maternal background nucleic acid, such as by the addition of formaldehyde. In some embodiments, a portion or subset of a pre-selected pool of nucleic acids is sequenced randomly. In some embodiments, the nucleic acid is amplified prior to sequencing. In some embodiments, a portion or subset of the nucleic acid is amplified prior to sequencing.
Any sequencing method suitable for conducting methods described herein can be utilized. In some embodiments, a high-throughput sequencing method is used. High-throughput sequencing methods generally involve clonally amplified DNA templates or single DNA molecules that are sequenced in a massively parallel fashion within a flow cell (e.g. as described in Metzker M Nature Rev 11:31-46 (2010); Volkerding et al. Clin Chem 55:641-658 (2009)). Such sequencing methods also can provide digital quantitative information, where each sequence read is a countable “sequence tag” representing an individual clonal DNA template or a single DNA molecule. High-throughput sequencing technologies include, for example, sequencing-by-synthesis with reversible dye terminators, sequencing by oligonucleotide probe ligation, pyrosequencing and real time sequencing.
Systems utilized for high-throughput sequencing methods are commercially available and include, for example, the Roche 454 platform, the Applied Biosystems SOLID platform, the Helicos True Single Molecule DNA sequencing technology, the sequencing-by-hybridization platform from Affymetrix Inc., the single molecule, real-time (SMRT) technology of Pacific Biosciences, the sequencing-by-synthesis platforms from 454 Life Sciences, Illumina/Solexa and Helicos Biosciences, and the sequencing-by-ligation platform from Applied Biosystems. The ION TORRENT technology from Life technologies and nanopore sequencing also can be used in high-throughput sequencing approaches.
In some embodiments, first generation technology, such as, for example, Sanger sequencing including the automated Sanger sequencing, can be used in the methods provided herein. Additional sequencing technologies that include the use of developing nucleic acid imaging technologies (e.g. transmission electron microscopy (TEM) and atomic force microscopy (AFM)), are also contemplated herein. Examples of various sequencing technologies are described below.
A nucleic acid sequencing technology that may be used in the methods described herein is sequencing-by-synthesis and reversible terminator-based sequencing (e.g. Illumina's Genome Analyzer and Genome Analyzer II). With this technology, millions of nucleic acid (e.g. DNA) fragments can be sequenced in parallel. In one example of this type of sequencing technology, a flow cell is used which contains an optically transparent slide with 8 individual lanes on the surfaces of which are bound oligonucleotide anchors (e.g., adapter primers). The term “flow cell” as described herein refers to any solid support that can be configured to retain and/or allow the orderly passage of reagent solutions over bound analytes. Flow cells frequently are planar in shape, optically transparent, generally in the millimeter or sub-millimeter scale, and often have channels or lanes in which the analyte/reagent interaction occurs.
In certain sequencing by synthesis procedures, for example, template DNA (e.g., circulating cell-free DNA (ccfDNA)) sometimes is fragmented into lengths of several hundred base pairs in preparation for library generation. In some embodiments, library preparation can be performed without further fragmentation or size selection of the template DNA (e.g., ccfDNA). In certain embodiments, library generation is performed using a modification of the manufacturers protocol, as described in Example 2. Sample isolation and library generation are performed using automated methods and apparatus, in certain embodiments. Briefly, ccfDNA is end repaired by a fill-in reaction, exonuclease reaction or a combination of a fill-in reaction and exonuclease reaction. The resulting blunt-end repaired ccfDNA is extended by a single nucleotide, which is complementary to a single nucleotide overhang on the 3′ end of an adapter primer, and often increase ligation efficiency. Any complementary nucleotides can be used for the extension/overhang nucleotides (e.g., A/T, C/G), however adenine frequently is used to extend the end-repaired DNA, and thymine often is used as the 3′ end overhang nucleotide.
In certain sequencing by synthesis procedures, for example, adapter oligonucleotides are complementary to the flow-cell anchors, and sometimes are utilized to associate the modified ccfDNA (e.g., end-repaired and single nucleotide extended) with a solid support, the inside surface of a flow cell for example. In some embodiments, the adapter primer includes indexing nucleotides, or “barcode” nucleotides (e.g., a unique sequence of nucleotides usable as an indexing primer to allow unambiguous identification of a sample), one or more sequencing primer hybridization sites (e.g., sequences complementary to universal sequencing primers, single end sequencing primers, paired end sequencing primers, multiplexed sequencing primers, and the like), or combinations thereof (e.g., adapter/sequencing, adapter/indexing, adapter/indexing/sequencing). Indexing primers or nucleotides contained in an adapter primer often are six or more nucleotides in length, and frequently are positioned in the primer such that the indexing nucleotides are sequenced.
In certain sequencing by synthesis procedures, utilization of index primers allows multiplexing of sequence reactions in a flow cell lane, thereby allowing analysis of multiple samples per flow cell lane. The number of samples that can be analyzed in a given flow cell lane often is dependent on the number of unique index primers utilized during library preparation. Index primers are available from a number of commercial sources (e.g., Illumina, Life Technologies, NEB). Reactions can be performed using a commercially available kit (e.g., Multiplexing Sample Preparation Oligonucleotide Kit (Kitted oligonucleotides used to prepare up to 96 samples for multiplexed sequencing) Illumina catalog Number PE-400-1001; Multiplexing Sequencing Primers and PhiX Control Kit (Kitted multiplexing sequencing primers, multiplexing control DNA, and buffer set, sufficient for up to 10 Genome Analyzer runs) Illumina catalog Number PE-400-1002. Methods described herein are not limited to 12 index primers and can be performed using any number of unique indexing primers (e.g., 4, 8, 12, 24, 48, 96, or more). The greater the number of unique indexing primers, the greater the number of samples that can be multiplexed in a single flow cell lane. Multiplexing using 12 index primers allows 96 samples (e.g., equal to the number of wells in a 96 well microwell plate) to be analyzed simultaneously in an 8 lane flow cell. Similarly, multiplexing using 48 index primers allows 384 samples (e.g., equal to the number of wells in a 384 well microwell plate) to be analyzed simultaneously in an 8 lane flow cell.
In certain sequencing by synthesis procedures, adapter-modified, single-stranded template DNA is added to the flow cell and immobilized by hybridization to the anchors under limiting-dilution conditions. In contrast to emulsion PCR, DNA templates are amplified in the flow cell by “bridge” amplification, which relies on captured DNA strands “arching” over and hybridizing to an adjacent anchor oligonucleotide. Multiple amplification cycles convert the single-molecule DNA template to a clonally amplified arching “cluster,” with each cluster containing approximately 1000 clonal molecules. Approximately 50×10 6 separate clusters can be generated per flow cell. For sequencing, the clusters are denatured, and a subsequent chemical cleavage reaction and wash leave only forward strands for single-end sequencing. Sequencing of the forward strands is initiated by hybridizing a primer complementary to the adapter sequences, which is followed by addition of polymerase and a mixture of four differently colored fluorescent reversible dye terminators. The terminators are incorporated according to sequence complementarity in each strand in a clonal cluster. After incorporation, excess reagents are washed away, the clusters are optically interrogated, and the fluorescence is recorded. With successive chemical steps, the reversible dye terminators are unblocked, the fluorescent labels are cleaved and washed away, and the next sequencing cycle is performed. This iterative, sequencing-by-synthesis process sometimes requires approximately 2.5 days to generate read lengths of 36 bases. With 50×10 6 clusters per flow cell, the overall sequence output can be greater than 1 billion base pairs (Gb) per analytical run.
Another nucleic acid sequencing technology that may be used with the methods described herein is 454 sequencing (Roche). 454 sequencing uses a large-scale parallel pyrosequencing system capable of sequencing about 400-600 megabases of DNA per run. The process typically involves two steps. In the first step, sample nucleic acid (e.g. DNA) is sometimes fractionated into smaller fragments (300-800 base pairs) and polished (made blunt at each end). Short adaptors are then ligated onto the ends of the fragments. These adaptors provide priming sequences for both amplification and sequencing of the sample-library fragments. One adaptor (Adaptor B) contains a 5′-biotin tag for immobilization of the DNA library onto streptavidin-coated beads. After nick repair, the non-biotinylated strand is released and used as a single-stranded template DNA (sstDNA) library. The sstDNA library is assessed for its quality and the optimal amount (DNA copies per bead) needed for emPCR is determined by titration. The sstDNA library is immobilized onto beads. The beads containing a library fragment carry a single sstDNA molecule. The bead-bound library is emulsified with the amplification reagents in a water-in-oil mixture. Each bead is captured within its own microreactor where PCR amplification occurs. This results in bead-immobilized, clonally amplified DNA fragments.
In the second step of 454 sequencing, single-stranded template DNA library beads are added to an incubation mix containing DNA polymerase and are layered with beads containing sulfurylase and luciferase onto a device containing pico-liter sized wells. Pyrosequencing is performed on each DNA fragment in parallel. Addition of one or more nucleotides generates a light signal that is recorded by a CCD camera in a sequencing instrument. The signal strength is proportional to the number of nucleotides incorporated. Pyrosequencing exploits the release of pyrophosphate (PPi) upon nucleotide addition. PPi is converted to ATP by ATP sulfurylase in the presence of adenosine 5′ phosphosulfate. Luciferase uses ATP to convert luciferin to oxyluciferin, and this reaction generates light that is discerned and analyzed (see, for example, Margulies, M. et al. Nature 437:376-380 (2005)).
Another nucleic acid sequencing technology that may be used in the methods provided herein is Applied Biosystems' SOLiD™ technology. In SOLiD™ sequencing-by-ligation, a library of nucleic acid fragments is prepared from the sample and is used to prepare clonal bead populations. With this method, one species of nucleic acid fragment will be present on the surface of each bead (e.g. magnetic bead). Sample nucleic acid (e.g. genomic DNA) is sheared into fragments, and adaptors are subsequently attached to the 5′ and 3′ ends of the fragments to generate a fragment library. The adapters are typically universal adapter sequences so that the starting sequence of every fragment is both known and identical. Emulsion PCR takes place in microreactors containing all the necessary reagents for PCR. The resulting PCR products attached to the beads are then covalently bound to a glass slide. Primers then hybridize to the adapter sequence within the library template. A set of four fluorescently labeled di-base probes compete for ligation to the sequencing primer. Specificity of the di-base probe is achieved by interrogating every 1st and 2nd base in each ligation reaction. Multiple cycles of ligation, detection and cleavage are performed with the number of cycles determining the eventual read length. Following a series of ligation cycles, the extension product is removed and the template is reset with a primer complementary to the n−1 position for a second round of ligation cycles. Often, five rounds of primer reset are completed for each sequence tag. Through the primer reset process, each base is interrogated in two independent ligation reactions by two different primers. For example, the base at read position 5 is assayed by primer number 2 in ligation cycle 2 and by primer number 3 in ligation cycle 1.
Another nucleic acid sequencing technology that may be used in the methods described herein is the Helicos True Single Molecule Sequencing (tSMS). In the tSMS technique, a polyA sequence is added to the 3′ end of each nucleic acid (e.g. DNA) strand from the sample. Each strand is labeled by the addition of a fluorescently labeled adenosine nucleotide. The DNA strands are then hybridized to a flow cell, which contains millions of oligo-T capture sites that are immobilized to the flow cell surface. The templates can be at a density of about 100 million templates/cm 2 . The flow cell is then loaded into a sequencing apparatus and a laser illuminates the surface of the flow cell, revealing the position of each template. A CCD camera can map the position of the templates on the flow cell surface. The template fluorescent label is then cleaved and washed away. The sequencing reaction begins by introducing a DNA polymerase and a fluorescently labeled nucleotide. The oligo-T nucleic acid serves as a primer. The polymerase incorporates the labeled nucleotides to the primer in a template directed manner. The polymerase and unincorporated nucleotides are removed. The templates that have directed incorporation of the fluorescently labeled nucleotide are detected by imaging the flow cell surface. After imaging, a cleavage step removes the fluorescent label, and the process is repeated with other fluorescently labeled nucleotides until the desired read length is achieved. Sequence information is collected with each nucleotide addition step (see, for example, Harris T. D. et al., Science 320:106-109 (2008)).
Another nucleic acid sequencing technology that may be used in the methods provided herein is the single molecule, real-time (SMRT™) sequencing technology of Pacific Biosciences. With this method, each of the four DNA bases is attached to one of four different fluorescent dyes. These dyes are phospholinked. A single DNA polymerase is immobilized with a single molecule of template single stranded DNA at the bottom of a zero-mode waveguide (ZMW). A ZMW is a confinement structure which enables observation of incorporation of a single nucleotide by DNA polymerase against the background of fluorescent nucleotides that rapidly diffuse in an out of the ZMW (in microseconds). It takes several milliseconds to incorporate a nucleotide into a growing strand. During this time, the fluorescent label is excited and produces a fluorescent signal, and the fluorescent tag is cleaved off. Detection of the corresponding fluorescence of the dye indicates which base was incorporated. The process is then repeated.
Another nucleic acid sequencing technology that may be used in the methods described herein is ION TORRENT (Life Technologies) single molecule sequencing which pairs semiconductor technology with a simple sequencing chemistry to directly translate chemically encoded information (A, C, G, T) into digital information (0, 1) on a semiconductor chip. ION TORRENT uses a high-density array of micro-machined wells to perform nucleic acid sequencing in a massively parallel way. Each well holds a different DNA molecule. Beneath the wells is an ion-sensitive layer and beneath that an ion sensor. Typically, when a nucleotide is incorporated into a strand of DNA by a polymerase, a hydrogen ion is released as a byproduct. If a nucleotide, for example a C, is added to a DNA template and is then incorporated into a strand of DNA, a hydrogen ion will be released. The charge from that ion will change the pH of the solution, which can be detected by an ion sensor. A sequencer can call the base, going directly from chemical information to digital information. The sequencer then sequentially floods the chip with one nucleotide after another. If the next nucleotide that floods the chip is not a match, no voltage change will be recorded and no base will be called. If there are two identical bases on the DNA strand, the voltage will be double, and the chip will record two identical bases called. Because this is direct detection (i.e. detection without scanning, cameras or light), each nucleotide incorporation is recorded in seconds.
Another nucleic acid sequencing technology that may be used in the methods described herein is the chemical-sensitive field effect transistor (CHEMFET) array. In one example of this sequencing technique, DNA molecules are placed into reaction chambers, and the template molecules can be hybridized to a sequencing primer bound to a polymerase. Incorporation of one or more triphosphates into a new nucleic acid strand at the 3′ end of the sequencing primer can be detected by a change in current by a CHEMFET sensor. An array can have multiple CHEMFET sensors. In another example, single nucleic acids are attached to beads, and the nucleic acids can be amplified on the bead, and the individual beads can be transferred to individual reaction chambers on a CHEMFET array, with each chamber having a CHEMFET sensor, and the nucleic acids can be sequenced (see, for example, U.S. Patent Publication No. 2009/0026082).
Another nucleic acid sequencing technology that may be used in the methods described herein is electron microscopy. In one example of this sequencing technique, individual nucleic acid (e.g. DNA) molecules are labeled using metallic labels that are distinguishable using an electron microscope. These molecules are then stretched on a flat surface and imaged using an electron microscope to measure sequences (see, for example, Moudrianakis E. N. and Beer M. Proc Natl Acad Sci USA. 1965 March; 53:564-71). In some cases, transmission electron microscopy (TEM) is used (e.g. Halcyon Molecular's TEM method). This method, termed Individual Molecule Placement Rapid Nano Transfer (IMPRNT), includes utilizing single atom resolution transmission electron microscope imaging of high-molecular weight (e.g. about 150 kb or greater) DNA selectively labeled with heavy atom markers and arranging these molecules on ultra-thin films in ultra-dense (3 nm strand-to-strand) parallel arrays with consistent base-to-base spacing. The electron microscope is used to image the molecules on the films to determine the position of the heavy atom markers and to extract base sequence information from the DNA (see, for example, PCT patent publication WO 2009/046445).
Other sequencing methods that may be used to conduct methods herein include digital PCR and sequencing by hybridization. Digital polymerase chain reaction (digital PCR or dPCR) can be used to directly identify and quantify nucleic acids in a sample. Digital PCR can be performed in an emulsion, in some embodiments. For example, individual nucleic acids are separated, e.g., in a microfluidic chamber device, and each nucleic acid is individually amplified by PCR. Nucleic acids can be separated such that there is no more than one nucleic acid per well. In some embodiments, different probes can be used to distinguish various alleles (e.g. fetal alleles and maternal alleles). Alleles can be enumerated to determine copy number. In sequencing by hybridization, the method involves contacting a plurality of polynucleotide sequences with a plurality of polynucleotide probes, where each of the plurality of polynucleotide probes can be optionally tethered to a substrate. The substrate can be a flat surface with an array of known nucleotide sequences, in some embodiments. The pattern of hybridization to the array can be used to determine the polynucleotide sequences present in the sample. In some embodiments, each probe is tethered to a bead, e.g., a magnetic bead or the like. Hybridization to the beads can be identified and used to identify the plurality of polynucleotide sequences within the sample.
In some embodiments, nanopore sequencing can be used in the methods described herein. Nanopore sequencing is a single-molecule sequencing technology whereby a single nucleic acid molecule (e.g. DNA) is sequenced directly as it passes through a nanopore. A nanopore is a small hole or channel, of the order of 1 nanometer in diameter. Certain transmembrane cellular proteins can act as nanopores (e.g. alpha-hemolysin). In some cases, nanopores can be synthesized (e.g. using a silicon platform). Immersion of a nanopore in a conducting fluid and application of a potential across it results in a slight electrical current due to conduction of ions through the nanopore. The amount of current which flows is sensitive to the size of the nanopore. As a DNA molecule passes through a nanopore, each nucleotide on the DNA molecule obstructs the nanopore to a different degree and generates characteristic changes to the current. The amount of current which can pass through the nanopore at any given moment therefore varies depending on whether the nanopore is blocked by an A, a C, a G, a T, or in some cases, methyl-C. The change in the current through the nanopore as the DNA molecule passes through the nanopore represents a direct reading of the DNA sequence. In some cases a nanopore can be used to identify individual DNA bases as they pass through the nanopore in the correct order (see, for example, Soni G V and Meller A. Clin Chem 53: 1996-2001 (2007); PCT publication no. WO2010/004265).
There are a number of ways that nanopores can be used to sequence nucleic acid molecules. In some embodiments, an exonuclease enzyme, such as a deoxyribonuclease, is used. In this case, the exonuclease enzyme is used to sequentially detach nucleotides from a nucleic acid (e.g. DNA) molecule. The nucleotides are then detected and discriminated by the nanopore in order of their release, thus reading the sequence of the original strand. For such an embodiment, the exonuclease enzyme can be attached to the nanopore such that a proportion of the nucleotides released from the DNA molecule is capable of entering and interacting with the channel of the nanopore. The exonuclease can be attached to the nanopore structure at a site in close proximity to the part of the nanopore that forms the opening of the channel. In some cases, the exonuclease enzyme can be attached to the nanopore structure such that its nucleotide exit trajectory site is orientated towards the part of the nanopore that forms part of the opening.
In some embodiments, nanopore sequencing of nucleic acids involves the use of an enzyme that pushes or pulls the nucleic acid (e.g. DNA) molecule through the pore. In this case, the ionic current fluctuates as a nucleotide in the DNA molecule passes through the pore. The fluctuations in the current are indicative of the DNA sequence. For such an embodiment, the enzyme can be attached to the nanopore structure such that it is capable of pushing or pulling the target nucleic acid through the channel of a nanopore without interfering with the flow of ionic current through the pore. The enzyme can be attached to the nanopore structure at a site in close proximity to the part of the structure that forms part of the opening. The enzyme can be attached to the subunit, for example, such that its active site is orientated towards the part of the structure that forms part of the opening.
In some embodiments, nanopore sequencing of nucleic acids involves detection of polymerase bi-products in close proximity to a nanopore detector. In this case, nucleoside phosphates (nucleotides) are labeled so that a phosphate labeled species is released upon the addition of a polymerase to the nucleotide strand and the phosphate labeled species is detected by the pore. Typically, the phosphate species contains a specific label for each nucleotide. As nucleotides are sequentially added to the nucleic acid strand, the bi-products of the base addition are detected. The order that the phosphate labeled species are detected can be used to determine the sequence of the nucleic acid strand.
The length of the sequence read is often associated with the particular sequencing technology. High-throughput methods, for example, provide sequence reads that can vary in size from tens to hundreds of base pairs (bp). Nanopore sequencing, for example, can provide sequence reads that can vary in size from tens to hundreds to thousands of base pairs. In some embodiments, the sequence reads are of a mean, median or average length of about 15 bp to 900 bp long (e.g. about 20 bp, about 25 bp, about 30 bp, about 35 bp, about 40 bp, about 45 bp, about 50 bp, about 55 bp, about 60 bp, about 65 bp, about 70 bp, about 75 bp, about 80 bp, about 85 bp, about 90 bp, about 95 bp, about 100 bp, about 110 bp, about 120 bp, about 130, about 140 bp, about 150 bp, about 200 bp, about 250 bp, about 300 bp, about 350 bp, about 400 bp, about 450 bp, or about 500 bp. In some embodiments, the sequence reads are of a mean, median or average length of about 1000 bp or more.
In some embodiments, nucleic acids may include a fluorescent signal or sequence tag information. Quantification of the signal or tag may be used in a variety of techniques such as, for example, flow cytometry, quantitative polymerase chain reaction (qPCR), gel electrophoresis, gene-chip analysis, microarray, mass spectrometry, cytofluorimetric analysis, fluorescence microscopy, confocal laser scanning microscopy, laser scanning cytometry, affinity chromatography, manual batch mode separation, electric field suspension, sequencing, and combination thereof.
Mapping Sequencing Reads
Mapping shotgun sequence information (i.e., sequence information from a fragment whose physical genomic position is unknown) can be done in a number of ways, which involve alignment of the obtained sequence reads with a matching sequence in a reference genome. See, Li et al., “Mapping short DNA sequencing reads and calling variants using mapping quality score,” Genome Res., 2008 Aug. 19. Sequence reads are aligned to a reference sequence and those that align are designated as being “mapped” or a “sequence tag.”
A “sequence tag” is a DNA sequence assigned specifically to one of chromosomes 1-22, X or Y. A sequence tag may be repetitive or non-repetitive within a single portion of the reference genome (e.g., a chromosome). A certain, small degree of mismatch (0-1) may be allowed to account for minor polymorphisms that may exist between the reference genome and the reads from individual genomes (maternal and fetal) being mapped, in certain embodiments. In some embodiments, no degree of mismatch is allowed for a read to be mapped to a reference sequence.
“Sequence tag density” refers to the normalized value of sequence tags for a defined window of a sequence on a chromosome where the sequence tag density is used for comparing different samples and for subsequent analysis. In some embodiments, the window is about 10 kilobases (kb) to about 100 kb, about 20 kb to about 80 kb, about 30 kb to about 70 kb, about 40 kb to about 60 kb, and sometimes about 50 kb. A sequence window also can be referred to as a “bin.”
The value of the sequence tag density often is normalized within a sample. Normalization can be performed by counting the number of tags falling within each window on a chromosome; obtaining a median value of the total sequence tag count for each chromosome; obtaining a median value of all of the autosomal values; and using this value as a normalization constant to account for the differences in total number of sequence tags obtained for different samples. A sequence tag density sometimes is about 1 for a disomic chromosome. Sequence tag densities can vary according to sequencing artifacts, most notably G/C bias, which can be corrected by use of an external standard or internal reference (e.g., derived from substantially all of the sequence tags (genomic sequences), which may be, for example, a single chromosome or a calculated value from all autosomes). Thus, dosage imbalance of a chromosome or chromosomal regions can be inferred from the percentage representation of the locus among other mappable sequenced tags of the specimen. Dosage imbalance of a particular chromosome or chromosomal regions therefore can be quantitatively determined and be normalized.
A reference sequence often is an assembled or partially assembled genomic sequence from an individual or multiple individuals. A reference sequence sometimes is not from the fetus, the mother of the fetus or the father of the fetus, and is referred to herein as an “external reference.” When a reference from the pregnant female is prepared (“maternal reference sequence”) based on an external reference, reads from DNA of the pregnant female that contains substantially no fetal DNA are mapped to the external reference sequence and assembled. In certain embodiments the external reference is from DNA of an individual having substantially the same ethnicity as the pregnant female. A maternal reference sequence may not completely cover the maternal genomic DNA (e.g., it may cover about 50%, 60%, 70%, 80%, 90% or more of the maternal genomic DNA), and the maternal reference may not perfectly match the maternal genomic DNA sequence (e.g., the maternal reference sequence may include multiple mismatches). Use of a maternal reference sequence can facilitate providing an outcome due to the similarity of chromosome over or under representation in one or more bins in the pure maternal sequence and the maternal component of the maternal plus fetal plasma sequence, in certain embodiments. Comparison of the abundance of fetal plus maternal sequence reads in one or more genomic sections or bins with the abundance of maternal sequence reads from maternal only sequence reads sometimes is used to arrive at an outcome with respect to fetal aneuploidy. Use of maternal sequence reads to generate a maternal reference and mapping of fetal plus maternal sequence reads to the maternal reference to arrive at an outcome are described in Example 1.
In some embodiments, a proportion of all of the sequence reads are from the chromosome involved in an aneuploidy (e.g., chromosome 21), and other sequence reads are from other chromosomes. By taking into account the relative size of the chromosome involved in the aneuploidy (e.g., “target chromosome”: chromosome 21) compared to other chromosomes, one could obtain a normalized frequency, within a reference range, of target chromosome-specific sequences. If the fetus has an aneuploidy in the target chromosome, then the normalized frequency of the target chromosome-derived sequences is statistically greater than the normalized frequency of non-target chromosome-derived sequences, thus allowing the detection of the aneupolidy. The degree of change in the normalized frequency will be dependent on the fractional concentration of fetal nucleic acids in the analyzed sample.
Counting
Sequence reads that have been mapped or partitioned based on a selected feature or variable can be quantified to determine the number of reads that were mapped to each genomic section (e.g., bin, partition, genomic segment and the like), in some embodiments. In certain embodiments, the total number of mapped sequence reads is determined by counting all mapped sequence reads. In some embodiments the total number of mapped sequence reads is determined by summing counts mapped to each bin or partition. In certain embodiments, a subset of mapped sequence reads is determined by counting a predetermined subset of mapped sequence reads, and in some embodiments a predetermined subset of mapped sequence reads is determined by summing counts mapped to each predetermined bin or partition. In some embodiments, predetermined subsets of mapped sequence reads can include from 1 to n sequence reads, where n represents a number equal to the sum of all sequence reads generated from a test subject or reference subject sample. In certain embodiments, predetermined subsets of mapped sequence reads can be selected utilizing any suitable feature or variable.
Sequence reads that have been mapped and counted for a test subject sample (e.g., isolated fetal DNA, circulating cell-free DNA that includes maternal and fetal DNA), one or more reference subject samples (e.g., external reference, maternal DNA mapped to an external reference), all samples processed in a flow cell, or all samples prepared in a plate sometimes are referred to as a sample count. Sample counts sometimes are further distinguished by reference to the subject from which the sample was isolated (e.g., test subject sample count, reference subject sample count, and the like). In some embodiments, a test sample also is used as a reference sample. A median expected count and/or a derivative of the median expected count for one or more selected genomic sections (e.g., a first genomic section a second genomic section, a third genomic section, 5 or more genomic sections, 50 or more genomic sections, 500 or more genomic sections, and the like) known to be free from genetic variation (e.g., do not have any microdelections, duplications, aneuploidies, and the like in the one or more selected genomic sections) sometimes is determined for a test sample and/or a reference sample. The median expected count or a derivative of the median expected count for the one or more genomic sections free of genetic variation can be used to evaluate the statistical significance of counts obtained from other selected genomic sections of the test sample. In some embodiments, the median absolute deviation also is determined, and in certain embodiments, the median absolute deviation also is used to evaluate the statistical significance of counts obtained from other selected genomic sections of the test sample.
Quantifying or counting sequence reads can be done in any suitable manner including but not limited to manual counting methods and automated counting methods. In some embodiments, an automated counting method can be embodied in software that determines or counts the number of sequence reads or sequence tags mapping to each chromosome and/or one or more selected genomic sections. As used herein, software refers to computer readable program instructions that, when executed by a computer, perform computer operations.
The number of sequence reads mapped to each bin and the total number of sequence reads for samples derived from test subject and/or reference subjects can be further analyzed and processed to provide an outcome determinative of the presence or absence of a genetic variation. Mapped sequence reads that have been counted sometimes are referred to as “data” or “data sets”. In some embodiments, data or data sets can be characterized by one or more features or variables (e.g., sequence based [e.g., GC content, specific nucleotide sequence, the like], function specific [e.g., expressed genes, cancer genes, the like], location based [genome specific, chromosome specific, genomic section or bin specific], the like and combinations thereof). In certain embodiments, data or data sets can be organized into a matrix having two or more dimensions based on one or more features of variables. Data organized into matrices can be stratified using any suitable features or variables. A non-limiting example of data organized into a matrix includes data that is stratified by maternal age, maternal ploidy, and fetal contribution. In certain embodiments, data sets characterized by one or more features or variables sometimes are processed after counting.
Amplification Methods
A process utilized to detect the presence or absence of a chromosomal aneuploidy can include an amplification process in some embodiments, and may include no amplification process in certain embodiments. Certain nucleic acid amplification methods can be utilized with respect to technology described herein. Described hereafter are various aspects of amplification and primer technologies that can be utilized.
Amplification
In some embodiments, one or more nucleic acids are amplified using a suitable amplification process. It may be desirable to amplify a nucleic acid particularly if one or more of the nucleic acid exists at low copy number. In some embodiments amplification of sequences or regions of interest may aid in detection of gene dosage imbalances. An amplification product (amplicon) of a particular nucleic acid is referred to herein as an “amplified nucleic acid.”
Nucleic acid amplification often involves enzymatic synthesis of nucleic acid amplicons (copies), which contain a sequence complementary to a nucleic acid being amplified. Amplifying nucleic acid and detecting the amplicons synthesized, can improve the sensitivity of an assay, since fewer target sequences are needed at the beginning of the assay, and can improve detection of a nucleic acid.
Any suitable amplification technique can be utilized. Amplification of polynucleotides include, but are not limited to, polymerase chain reaction (PCR); ligation amplification (or ligase chain reaction (LCR)); amplification methods based on the use of Q-beta replicase or template-dependent polymerase (see U.S. Patent Publication Number US20050287592); helicase-dependant isothermal amplification (Vincent et al., “Helicase-dependent isothermal DNA amplification”. EMBO reports 5 (8): 795-800 (2004)); strand displacement amplification (SDA); thermophilic SDA nucleic acid sequence based amplification (3SR or NASBA) and transcription-associated amplification (TAA). Non-limiting examples of PCR amplification methods include standard PCR, AFLP-PCR, Allele-specific PCR, Alu-PCR, Asymmetric PCR, Colony PCR, Hot start PCR, Inverse PCR (IPCR), In situ PCR (ISH), Intersequence-specific PCR (ISSR-PCR), Long PCR, Multiplex PCR, Nested PCR, Quantitative PCR, Reverse Transcriptase PCR(RT-PCR), Real Time PCR, Single cell PCR, Solid phase PCR, combinations thereof, and the like. Reagents and hardware for conducting PCR are commercially available.
The terms “amplify”, “amplification”, “amplification reaction”, or “amplifying” refers to any in vitro processes for multiplying the copies of a target sequence of nucleic acid. Amplification sometimes refers to an “exponential” increase in target nucleic acid. However, “amplifying” as used herein can also refer to linear increases in the numbers of a select target sequence of nucleic acid, but is different than a one-time, single primer extension step. In some embodiments a limited amplification reaction, also known as pre-amplification, can be performed. Pre-amplification is a method in which a limited amount of amplification occurs due to a small number of cycles, for example 10 cycles, being performed. Pre-amplification can allow some amplification, but stops amplification prior to the exponential phase, and typically produces about 500 copies of the desired nucleotide sequence(s). Use of pre-amplification may also limit inaccuracies associated with depleted reactants in standard PCR reactions, and also may reduce amplification biases due to nucleotide sequence or species abundance of the target. In some embodiments a one-time primer extension may be used may be performed as a prelude to linear or exponential amplification. A generalized description of an amplification process is presented herein. Primers and target nucleic acid are contacted, and complementary sequences anneal to one another, for example. Primers can anneal to a target nucleic acid, at or near (e.g., adjacent to, abutting, and the like) a sequence of interest. A reaction mixture, containing components necessary for enzymatic functionality, is added to the primer—target nucleic acid hybrid, and amplification can occur under suitable conditions. Components of an amplification reaction may include, but are not limited to, e.g., primers (e.g., individual primers, primer pairs, primer sets and the like) a polynucleotide template (e.g., target nucleic acid), polymerase, nucleotides, dNTPs and the like. In some embodiments, non-naturally occurring nucleotides or nucleotide analogs, such as analogs containing a detectable label (e.g., fluorescent or colorimetric label), may be used for example. Polymerases can be selected and include polymerases for thermocycle amplification (e.g., Taq DNA Polymerase; Q-Bio™ Taq DNA Polymerase (recombinant truncated form of Taq DNA Polymerase lacking 5′-3′ exo activity); SurePrime™ Polymerase (chemically modified Taq DNA polymerase for “hot start” PCR); Arrow™ Taq DNA Polymerase (high sensitivity and long template amplification)) and polymerases for thermostable amplification (e.g., RNA polymerase for transcription-mediated amplification (TMA) described at World Wide Web URL “gen-probe.com/pdfs/tma_whiteppr.pdf”). Other enzyme components can be added, such as reverse transcriptase for transcription mediated amplification (TMA) reactions, for example.
The terms “near” or “adjacent to” when referring to a nucleotide sequence of interest refers to a distance or region between the end of the primer and the nucleotide or nucleotides of interest. As used herein adjacent is in the range of about 5 nucleotides to about 500 nucleotides (e.g., about 5 nucleotides away from nucleotide of interest, about 10, about 20, about 30, about 40, about 50, about 60, about 70, about 80, about 90, about 100, about 150, about 200, about 250, about 300, abut 350, about 400, about 450 or about 500 nucleotides from a nucleotide of interest). In some embodiments the primers in a set hybridize within about 10 to 30 nucleotides from a nucleic acid sequence of interest and produce amplified products.
Each amplified nucleic acid independently is about 10 to about 500 base pairs in length in some embodiments. In certain embodiments, an amplified nucleic acid is about 20 to about 250 base pairs in length, sometimes is about 50 to about 150 base pairs in length and sometimes is about 100 base pairs in length. Thus, in some embodiments, the length of each of the amplified nucleic acid products independently is about 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 125, 130, 135, 140, 145, 150, 175, 200, 250, 300, 350, 400, 450, or 500 base pairs (bp) in length.
An amplification product may include naturally occurring nucleotides, non-naturally occurring nucleotides, nucleotide analogs and the like and combinations of the foregoing. An amplification product often has a nucleotide sequence that is identical to or substantially identical to a sample nucleic acid nucleotide sequence or complement thereof. A “substantially identical” nucleotide sequence in an amplification product will generally have a high degree of sequence identity to the nucleic acid being amplified or complement thereof (e.g., about 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or greater than 99% sequence identity), and variations sometimes are a result of infidelity of the polymerase used for extension and/or amplification, or additional nucleotide sequence(s) added to the primers used for amplification.
PCR conditions can be dependent upon primer sequences, target abundance, and the desired amount of amplification, and therefore, one of skill in the art may choose from a number of PCR protocols available (see, e.g., U.S. Pat. Nos. 4,683,195 and 4,683,202; and PCR Protocols: A Guide to Methods and Applications, Innis et al., eds, 1990. Digital PCR is also known to those of skill in the art; see, e.g., U.S. Patent Application Publication Number 20070202525, filed Feb. 2, 2007, which is hereby incorporated by reference). PCR often is carried out as an automated process with a thermostable enzyme. In this process, the temperature of the reaction mixture is cycled through a denaturing region, a primer-annealing region, and an extension reaction region automatically. Machines specifically adapted for this purpose are commercially available. A non-limiting example of a PCR protocol that may be suitable for embodiments described herein is, treating the sample at 95° C. for 5 minutes; repeating forty-five cycles of 95° C. for 1 minute, 59° C. for 1 minute, 10 seconds, and 72° C. for 1 minute 30 seconds; and then treating the sample at 72° C. for 5 minutes. Multiple cycles frequently are performed using a commercially available thermal cycler. Suitable isothermal amplification processes known and selected also may be applied, in certain embodiments.
In some embodiments, multiplex amplification processes may be used to amplify target nucleic acids, such that multiple amplicons are simultaneously amplified in a single, homogenous reaction. As used herein “multiplex amplification” refers to a variant of PCR where simultaneous amplification of many targets of interest in one reaction vessel may be accomplished by using more than one pair of primers (e.g., more than one primer set). Multiplex amplification may be useful for analysis of deletions, mutations, and polymorphisms, or quantitative assays, in some embodiments. In certain embodiments multiplex amplification may be used for detecting paralog sequence imbalance, genotyping applications where simultaneous analysis of multiple markers is required, detection of pathogens or genetically modified organisms, or for microsatellite analyses.
In some embodiments multiplex amplification may be combined with another amplification (e.g., PCR) method (e.g., nested PCR or hot start PCR, for example) to increase amplification specificity and reproducibility. In other embodiments multiplex amplification may be done in replicates, for example, to reduce the variance introduced by said amplification.
In certain embodiments, nucleic acid amplification can generate additional nucleic acid species of different or substantially similar nucleic acid sequence. In certain embodiments described herein, contaminating or additional nucleic acid species, which may contain sequences substantially complementary to, or may be substantially identical to, the sequence of interest, can be useful for sequence quantification, with the proviso that the level of contaminating or additional sequences remains constant and therefore can be a reliable marker whose level can be substantially reproduced. Additional considerations that may affect sequence amplification reproducibility are: PCR conditions (number of cycles, volume of reactions, melting temperature difference between primers pairs, and the like), concentration of target nucleic acid in sample, the number of chromosomes on which the nucleotide species of interest resides, variations in quality of prepared sample, and the like. The terms “substantially reproduced” or “substantially reproducible” as used herein refer to a result (e.g., quantifiable amount of nucleic acid) that under substantially similar conditions would occur in substantially the same way about 75% of the time or greater, about 80%, about 85%, about 90%, about 95%, or about 99% of the time or greater.
In some embodiments where a target nucleic acid is RNA, prior to the amplification step, a DNA copy (cDNA) of the RNA transcript of interest may be synthesized. A cDNA can be synthesized by reverse transcription, which can be carried out as a separate step, or in a homogeneous reverse transcription-polymerase chain reaction (RT-PCR), a modification of the polymerase chain reaction for amplifying RNA. Methods suitable for PCR amplification of ribonucleic acids are described by Romero and Rotbart in Diagnostic Molecular Biology: Principles and Applications pp. 401-406; Persing et al., eds., Mayo Foundation, Rochester, Minn., 1993; Egger et al., J. Clin. Microbiol. 33:1442-1447, 1995; and U.S. Pat. No. 5,075,212. Branched-DNA technology may be used to amplify the signal of RNA markers in maternal blood. For a review of branched-DNA (bDNA) signal amplification for direct quantification of nucleic acid sequences in clinical samples, see Nolte, Adv. Clin. Chem. 33:201-235, 1998.
Amplification also can be accomplished using digital PCR, in certain embodiments (e.g., Kalinina and colleagues (Kalinina et al., “Nanoliter scale PCR with TaqMan detection.” Nucleic Acids Research. 25; 1999-2004, (1997); Vogelstein and Kinzler (Digital PCR. Proc Natl Acad Sci USA. 96; 9236-41, (1999); PCT Patent Publication No. WO05023091A2; U.S. Patent Publication No. US 20070202525). Digital PCR takes advantage of nucleic acid (DNA, cDNA or RNA) amplification on a single molecule level, and offers a highly sensitive method for quantifying low copy number nucleic acid. Systems for digital amplification and analysis of nucleic acids are available (e.g., Fluidigm® Corporation).
Use of a primer extension reaction also can be applied in methods of the technology. A primer extension reaction operates, for example, by discriminating nucleic acid sequences at a single nucleotide mismatch, in some embodiments. The mismatch is detected by the incorporation of one or more deoxynucleotides and/or dideoxynucleotides to an extension oligonucleotide, which hybridizes to a region adjacent to the mismatch site. The extension oligonucleotide generally is extended with a polymerase. In some embodiments, a detectable tag or detectable label is incorporated into the extension oligonucleotide or into the nucleotides added on to the extension oligonucleotide (e.g., biotin or streptavidin). The extended oligonucleotide can be detected by any known suitable detection process (e.g., mass spectrometry; sequencing processes). In some embodiments, the mismatch site is extended only by one or two complementary deoxynucleotides or dideoxynucleotides that are tagged by a specific label or generate a primer extension product with a specific mass, and the mismatch can be discriminated and quantified.
In some embodiments, amplification may be performed on a solid support. In some embodiments, primers may be associated with a solid support. In certain embodiments, target nucleic acid (e.g., nucleic acid) may be associated with a solid support. A nucleic acid (primer or target) in association with a solid support often is referred to as a solid phase nucleic acid.
In some embodiments, nucleic acid molecules provided for amplification and in a “microreactor”. As used herein, the term “microreactor” refers to a partitioned space in which a nucleic acid molecule can hybridize to a solid support nucleic acid molecule. Examples of microreactors include, without limitation, an emulsion globule (described hereafter) and a void in a substrate. A void in a substrate can be a pit, a pore or a well (e.g., microwell, nanowell, picowell, micropore, or nanopore) in a substrate constructed from a solid material useful for containing fluids (e.g., plastic (e.g., polypropylene, polyethylene, polystyrene) or silicon) in certain embodiments. Emulsion globules are partitioned by an immiscible phase as described in greater detail hereafter. In some embodiments, the microreactor volume is large enough to accommodate one solid support (e.g., bead) in the microreactor and small enough to exclude the presence of two or more solid supports in the microreactor.
The term “emulsion” as used herein refers to a mixture of two immiscible and unblendable substances, in which one substance (the dispersed phase) often is dispersed in the other substance (the continuous phase). The dispersed phase can be an aqueous solution (i.e., a solution comprising water) in certain embodiments. In some embodiments, the dispersed phase is composed predominantly of water (e.g., greater than 70%, greater than 75%, greater than 80%, greater than 85%, greater than 90%, greater than 95%, greater than 97%, greater than 98% and greater than 99% water (by weight)). Each discrete portion of a dispersed phase, such as an aqueous dispersed phase, is referred to herein as a “globule” or “microreactor.” A globule sometimes may be spheroidal, substantially spheroidal or semi-spheroidal in shape, in certain embodiments.
The terms “emulsion apparatus” and “emulsion component(s)” as used herein refer to apparatus and components that can be used to prepare an emulsion. Non-limiting examples of emulsion apparatus include without limitation counter-flow, cross-current, rotating drum and membrane apparatus suitable for use to prepare an emulsion. An emulsion component forms the continuous phase of an emulsion in certain embodiments, and includes without limitation a substance immiscible with water, such as a component comprising or consisting essentially of an oil (e.g., a heat-stable, biocompatible oil (e.g., light mineral oil)). A biocompatible emulsion stabilizer can be utilized as an emulsion component. Emulsion stabilizers include without limitation Atlox 4912, Span 80 and other biocompatible surfactants.
In some embodiments, components useful for biological reactions can be included in the dispersed phase. Globules of the emulsion can include (i) a solid support unit (e.g., one bead or one particle); (ii) sample nucleic acid molecule; and (iii) a sufficient amount of extension agents to elongate solid phase nucleic acid and amplify the elongated solid phase nucleic acid (e.g., extension nucleotides, polymerase, primer). Inactive globules in the emulsion may include a subset of these components (e.g., solid support and extension reagents and no sample nucleic acid) and some can be empty (i.e., some globules will include no solid support, no sample nucleic acid and no extension agents).
Emulsions may be prepared using known suitable methods (e.g., Nakano et al. “Single-molecule PCR using water-in-oil emulsion;” Journal of Biotechnology 102 (2003) 117-124). Emulsification methods include without limitation adjuvant methods, counter-flow methods, cross-current methods, rotating drum methods, membrane methods, and the like. In certain embodiments, an aqueous reaction mixture containing a solid support (hereafter the “reaction mixture”) is prepared and then added to a biocompatible oil. In certain embodiments, the reaction mixture may be added dropwise into a spinning mixture of biocompatible oil (e.g., light mineral oil (Sigma)) and allowed to emulsify. In some embodiments, the reaction mixture may be added dropwise into a cross-flow of biocompatible oil. The size of aqueous globules in the emulsion can be adjusted, such as by varying the flow rate and speed at which the components are added to one another, for example.
The size of emulsion globules can be selected in certain embodiments based on two competing factors: (i) globules are sufficiently large to encompass one solid support molecule, one sample nucleic acid molecule, and sufficient extension agents for the degree of elongation and amplification required; and (ii) globules are sufficiently small so that a population of globules can be amplified by conventional laboratory equipment (e.g., thermocycling equipment, test tubes, incubators and the like). Globules in the emulsion can have a nominal, mean or average diameter of about 5 microns to about 500 microns, about 10 microns to about 350 microns, about 50 to 250 microns, about 100 microns to about 200 microns, or about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 200, 300, 400 or 500 microns in certain embodiments.
In certain embodiments, amplified nucleic acid in a set are of identical length, and sometimes the amplified nucleic acid in a set are of a different length. For example, one amplified nucleic acid may be longer than one or more other amplified nucleic acid in the set by about 1 to about 100 nucleotides (e.g., about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 30, 40, 50, 60, 70, 80 or 90 nucleotides longer).
In some embodiments, a ratio can be determined for the amount of one amplified nucleic acid in a set to the amount of another amplified nucleic acid in the set (hereafter a “set ratio”). In some embodiments, the amount of one amplified nucleic acid in a set is about equal to the amount of another amplified nucleic acid in the set (i.e., amounts of amplified nucleic acid in a set are about 1:1), which generally is the case when the number of chromosomes in a sample bearing each nucleic acid amplified is about equal. The term “amount” as used herein with respect to amplified nucleic acid refers to any suitable measurement, including, but not limited to, copy number, weight (e.g., grams) and concentration (e.g., grams per unit volume (e.g., milliliter); molar units). In certain embodiments, the amount of one amplified nucleic acid in a set can differ from the amount of another amplified nucleic acid in a set, even when the number of chromosomes in a sample bearing each nucleic acid amplified is about equal. In some embodiments, amounts of amplified nucleic acid within a set may vary up to a threshold level at which a chromosome abnormality can be detected with a confidence level of about 95% (e.g., about 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or greater than 99%). In certain embodiments, the amounts of the amplified nucleic acid in a set vary by about 50% or less (e.g., about 45, 40, 35, 30, 25, 20, 15, 10, 5, 4, 3, 2 or 1%, or less than 1%). Thus, in certain embodiments amounts of amplified nucleic acid in a set may vary from about 1:1 to about 1:1.5. Without being limited by theory, certain factors can lead to the observation that the amount of one amplified nucleic acid in a set can differ from the amount of another amplified nucleic acid in a set, even when the number of chromosomes in a sample bearing each nucleic acid amplified is about equal. Such factors may include different amplification efficiency rates and/or amplification from a chromosome not intended in the assay design.
Each amplified nucleic acid in a set generally is amplified under conditions that amplify that species at a substantially reproducible level. The term “substantially reproducible level” as used herein refers to consistency of amplification levels for a particular amplified nucleic acid per unit nucleic acid (e.g., per unit nucleic acid that contains the particular nucleic acid amplified). A substantially reproducible level varies by about 1% or less in certain embodiments, after factoring the amount of nucleic acid giving rise to a particular amplification nucleic acid species (e.g., normalized for the amount of nucleic acid). In some embodiments, a substantially reproducible level varies by 10%, 5%, 4%, 3%, 2%, 1.5%, 1%, 0.5%, 0.1%, 0.05%, 0.01%, 0.005% or 0.001% after factoring the amount of nucleic acid giving rise to a particular amplification nucleic acid species. Alternatively, substantially reproducible means that any two or more measurements of an amplification level are within a particular coefficient of variation (“CV”) from a given mean. Such CV may be 20% or less, sometimes 10% or less and at times 5% or less. The two or more measurements of an amplification level may be determined between two or more reactions and/or two or more of the same sample types (for example, two normal samples or two trisomy samples)
Primers
Primers useful for detection, quantification, amplification, sequencing and analysis of nucleic acid are provided. In some embodiments primers are used in sets, where a set contains at least a pair.
In some embodiments a set of primers may include a third or a fourth nucleic acid (e.g., two pairs of primers or nested sets of primers, for example). A plurality of primer pairs may constitute a primer set in certain embodiments (e.g., about 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 or 100 pairs). In some embodiments a plurality of primer sets, each set comprising pair(s) of primers, may be used. The term “primer” as used herein refers to a nucleic acid that comprises a nucleotide sequence capable of hybridizing or annealing to a target nucleic acid, at or near (e.g., adjacent to) a specific region of interest. Primers can allow for specific determination of a target nucleic acid nucleotide sequence or detection of the target nucleic acid (e.g., presence or absence of a sequence or copy number of a sequence), or feature thereof, for example. A primer may be naturally occurring or synthetic. The term “specific” or “specificity”, as used herein, refers to the binding or hybridization of one molecule to another molecule, such as a primer for a target polynucleotide. That is, “specific” or “specificity” refers to the recognition, contact, and formation of a stable complex between two molecules, as compared to substantially less recognition, contact, or complex formation of either of those two molecules with other molecules. As used herein, the term “anneal” refers to the formation of a stable complex between two molecules. The terms “primer”, “oligo”, or “oligonucleotide” may be used interchangeably throughout the document, when referring to primers.
A primer nucleic acid can be designed and synthesized using suitable processes, and may be of any length suitable for hybridizing to a nucleotide sequence of interest (e.g., where the nucleic acid is in liquid phase or bound to a solid support) and performing analysis processes described herein. Primers may be designed based upon a target nucleotide sequence. A primer in some embodiments may be about 10 to about 100 nucleotides, about 10 to about 70 nucleotides, about 10 to about 50 nucleotides, about 15 to about 30 nucleotides, or about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 or 100 nucleotides in length. A primer may be composed of naturally occurring and/or non-naturally occurring nucleotides (e.g., labeled nucleotides), or a mixture thereof. Primers suitable for use with embodiments described herein, may be synthesized and labeled using known techniques. Oligonucleotides (e.g., primers) may be chemically synthesized according to the solid phase phosphoramidite triester method first described by Beaucage and Caruthers, Tetrahedron Letts., 22:1859-1862, 1981, using an automated synthesizer, as described in Needham-VanDevanter et al., Nucleic Acids Res. 12:6159-6168, 1984. Purification of oligonucleotides can be effected by native acrylamide gel electrophoresis or by anion-exchange high-performance liquid chromatography (HPLC), for example, as described in Pearson and Regnier, J. Chrom., 255:137-149, 1983.
All or a portion of a primer nucleic acid sequence (naturally occurring or synthetic) may be substantially complementary to a target nucleic acid, in some embodiments. As referred to herein, “substantially complementary” with respect to sequences refers to nucleotide sequences that will hybridize with each other. The stringency of the hybridization conditions can be altered to tolerate varying amounts of sequence mismatch. Included are regions of counterpart, target and capture nucleotide sequences 55% or more, 56% or more, 57% or more, 58% or more, 59% or more, 60% or more, 61% or more, 62% or more, 63% or more, 64% or more, 65% or more, 66% or more, 67% or more, 68% or more, 69% or more, 70% or more, 71% or more, 72% or more, 73% or more, 74% or more, 75% or more, 76% or more, 77% or more, 78% or more, 79% or more, 80% or more, 81% or more, 82% or more, 83% or more, 84% or more, 85% or more, 86% or more, 87% or more, 88% or more, 89% or more, 90% or more, 91% or more, 92% or more, 93% or more, 94% or more, 95% or more, 96% or more, 97% or more, 98% or more or 99% or more complementary to each other. Primers that are substantially complimentary to a target nucleic acid sequence are also substantially identical to the compliment of the target nucleic acid sequence. That is, primers are substantially identical to the anti-sense strand of the nucleic acid. As referred to herein, “substantially identical” with respect to sequences refers to nucleotide sequences that are 55% or more, 56% or more, 57% or more, 58% or more, 59% or more, 60% or more, 61% or more, 62% or more, 63% or more, 64% or more, 65% or more, 66% or more, 67% or more, 68% or more, 69% or more, 70% or more, 71% or more, 72% or more, 73% or more, 74% or more, 75% or more, 76% or more, 77% or more, 78% or more, 79% or more, 80% or more, 81% or more, 82% or more, 83% or more, 84% or more, 85% or more, 86% or more, 87% or more, 88% or more, 89% or more, 90% or more, 91% or more, 92% or more, 93% or more, 94% or more, 95% or more, 96% or more, 97% or more, 98% or more or 99% or more identical to each other. One test for determining whether two nucleotide sequences are substantially identical is to determine the percent of identical nucleotide sequences shared.
Primer sequences and length may affect hybridization to target nucleic acid sequences. Depending on the degree of mismatch between the primer and target nucleic acid, low, medium or high stringency conditions may be used to effect primer/target annealing. As used herein, the term “stringent conditions” refers to conditions for hybridization and washing. Methods for hybridization reaction temperature condition optimization are known to those of skill in the art, and may be found in Current Protocols in Molecular Biology, John Wiley & Sons, N.Y., 6.3.1-6.3.6 (1989). Aqueous and non-aqueous methods are described in that reference and either can be used. Non-limiting examples of stringent hybridization conditions are hybridization in 6× sodium chloride/sodium citrate (SSC) at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 50° C. Another example of stringent hybridization conditions are hybridization in 6× sodium chloride/sodium citrate (SSC) at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 55° C. A further example of stringent hybridization conditions is hybridization in 6× sodium chloride/sodium citrate (SSC) at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 60° C. Often, stringent hybridization conditions are hybridization in 6× sodium chloride/sodium citrate (SSC) at about 45° C., followed by one or more washes in 0.2×SSC, 0.1% SDS at 65° C. More often, stringency conditions are 0.5M sodium phosphate, 7% SDS at 65° C., followed by one or more washes at 0.2×SSC, 1% SDS at 65° C. Stringent hybridization temperatures can also be altered (i.e. lowered) with the addition of certain organic solvents, formamide for example. Organic solvents, like formamide, reduce the thermal stability of double-stranded polynucleotides, so that hybridization can be performed at lower temperatures, while still maintaining stringent conditions and extending the useful life of nucleic acids that may be heat labile.
As used herein, the phrase “hybridizing” or grammatical variations thereof, refers to binding of a first nucleic acid molecule to a second nucleic acid molecule under low, medium or high stringency conditions, or under nucleic acid synthesis conditions. Hybridizing can include instances where a first nucleic acid molecule binds to a second nucleic acid molecule, where the first and second nucleic acid molecules are complementary. As used herein, “specifically hybridizes” refers to preferential hybridization under nucleic acid synthesis conditions of a primer, to a nucleic acid molecule having a sequence complementary to the primer compared to hybridization to a nucleic acid molecule not having a complementary sequence. For example, specific hybridization includes the hybridization of a primer to a target nucleic acid sequence that is complementary to the primer. In some embodiments primers can include a nucleotide subsequence that may be complementary to a solid phase nucleic acid primer hybridization sequence or substantially complementary to a solid phase nucleic acid primer hybridization sequence (e.g., about 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 910%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or greater than 99% identical to the primer hybridization sequence complement when aligned). A primer may contain a nucleotide subsequence not complementary to or not substantially complementary to a solid phase nucleic acid primer hybridization sequence (e.g., at the 3′ or 5′ end of the nucleotide subsequence in the primer complementary to or substantially complementary to the solid phase primer hybridization sequence).
A primer, in certain embodiments, may contain a modification such as inosines, abasic sites, locked nucleic acids, minor groove binders, duplex stabilizers (e.g., acridine, spermidine), Tm modifiers or any modifier that changes the binding properties of the primers or probes.
A primer, in certain embodiments, may contain a detectable molecule or entity (e.g., a fluorophore, radioisotope, colorimetric agent, particle, enzyme and the like). When desired, the nucleic acid can be modified to include a detectable label using any method known to one of skill in the art. The label may be incorporated as part of the synthesis, or added on prior to using the primer in any of the processes described herein. Incorporation of label may be performed either in liquid phase or on solid phase. In some embodiments the detectable label may be useful for detection of targets. In some embodiments the detectable label may be useful for the quantification target nucleic acids (e.g., determining copy number of a particular sequence or species of nucleic acid). Any detectable label suitable for detection of an interaction or biological activity in a system can be appropriately selected and utilized by the artisan. Examples of detectable labels are fluorescent labels such as fluorescein, rhodamine, and others (e.g., Anantha, et al., Biochemistry (1998) 37:2709 2714; and Qu & Chaires, Methods Enzymol. (2000) 321:353 369); radioactive isotopes (e.g., 125I, 131I, 35S, 31P, 32P, 33P, 14C, 3H, 7Be, 28Mg, 57Co, 65Zn, 67Cu, 68Ge, 82Sr, 83Rb, 95Tc, 96Tc, 103Pd, 109Cd, and 127Xe); light scattering labels (e.g., U.S. Pat. No. 6,214,560, and commercially available from Genicon Sciences Corporation, CA); chemiluminescent labels and enzyme substrates (e.g., dioxetanes and acridinium esters), enzymic or protein labels (e.g., green fluorescence protein (GFP) or color variant thereof, luciferase, peroxidase); other chromogenic labels or dyes (e.g., cyanine), and other cofactors or biomolecules such as digoxigenin, strepdavidin, biotin (e.g., members of a binding pair such as biotin and avidin for example), affinity capture moieties and the like. In some embodiments a primer may be labeled with an affinity capture moiety. Also included in detectable labels are those labels useful for mass modification for detection with mass spectrometry (e.g., matrix-assisted laser desorption ionization (MALDI) mass spectrometry and electrospray (ES) mass spectrometry).
A primer also may refer to a polynucleotide sequence that hybridizes to a subsequence of a target nucleic acid or another primer and facilitates the detection of a primer, a target nucleic acid or both, as with molecular beacons, for example. The term “molecular beacon” as used herein refers to detectable molecule, where the detectable property of the molecule is detectable only under certain specific conditions, thereby enabling it to function as a specific and informative signal. Non-limiting examples of detectable properties are, optical properties, electrical properties, magnetic properties, chemical properties and time or speed through an opening of known size.
In some embodiments a molecular beacon can be a single-stranded oligonucleotide capable of forming a stem-loop structure, where the loop sequence may be complementary to a target nucleic acid sequence of interest and is flanked by short complementary arms that can form a stem. The oligonucleotide may be labeled at one end with a fluorophore and at the other end with a quencher molecule. In the stem-loop conformation, energy from the excited fluorophore is transferred to the quencher, through long-range dipole-dipole coupling similar to that seen in fluorescence resonance energy transfer, or FRET, and released as heat instead of light. When the loop sequence is hybridized to a specific target sequence, the two ends of the molecule are separated and the energy from the excited fluorophore is emitted as light, generating a detectable signal. Molecular beacons offer the added advantage that removal of excess probe is unnecessary due to the self-quenching nature of the unhybridized probe. In some embodiments molecular beacon probes can be designed to either discriminate or tolerate mismatches between the loop and target sequences by modulating the relative strengths of the loop-target hybridization and stem formation. As referred to herein, the term “mismatched nucleotide” or a “mismatch” refers to a nucleotide that is not complementary to the target sequence at that position or positions. A probe may have at least one mismatch, but can also have 2, 3, 4, 5, 6 or 7 or more mismatched nucleotides.
Data Processing and Identifying Presence or Absence of a Genetic Variation
Mapped sequence reads that have been counted are referred to herein as raw data, since the data represent unmanipulated counts (e.g., raw counts). In some embodiments, sequence read data in a data set can be adjusted and/or processed further (e.g., mathematically and/or statistically manipulated) and/or displayed to facilitate providing an outcome. The term “adjusted” as used herein sometimes refers to a manipulation of a portion of, or all sequences reads, data in a data set, and/or sample nucleic acid. Any suitable manipulation can be used to adjust a portion of or all sequence reads, data in a data set and/or sample nucleic acid. In some embodiments, an adjustment to sequence reads, data in a data set and/or sample nucleic acid is a process chosen from filtering (e.g., removing a portion of the data based on a selected feature or variable; removing repetitive sequences, removing uninformative bins or bins having zero median counts, for example), adjusting (e.g., rescaling and/or re-weighting a portion of or all data based on G/C content, rescaling and/or re-weighting a portion of or all data based on fetal fraction, for example), normalizing using one or more estimators or statistical manipulations (e.g., normalizing all data in a data set to the fetal contribution), and the like. In certain embodiments, a portion of the sequence read data is adjusted and/or processed, and in some embodiments, all of the sequence read data is adjusted and/or processed.
Adjusted or processed sequence reads, data in a data set and/or sample nucleic acid sometimes are referred to as a derivative (e.g., a derivative of the counts, derivative data, derivative of the sequence reads, and the like). A derivative of counts, data or sequence reads often is generated by the use of one or more mathematical and/or statistical manipulations on the counts, data or sequence reads. Any suitable mathematic and/or statistical manipulation described herein or known in the art can be used to generate a derivative counts, data, or sequence reads. Non-limiting examples of mathematical and/or statistical manipulations that can be utilized to filter, adjust, normalize or manipulate counts, data, or sequence reads to generate a derivative include, average, mean, median, median absolute deviation, other methods described herein and known in the art, the like or combinations thereof.
In certain embodiments, data sets, including larger data sets, may benefit from pre-processing to facilitate further analysis. Pre-processing of data sets sometimes involves removal of redundant and/or uninformative genomic sections or bins (e.g., bins with uninformative data, redundant mapped reads, genomic sections or bins with zero median counts, over represented or under represented sequences [e.g., G/C sequences], repetitive sequences). Without being limited by theory, data processing and/or preprocessing may (i) remove noisy data, (ii) remove uninformative data, (iii) remove redundant data, (iv) reduce the complexity of larger data sets, (v) rescale and/or re-weight a portion of or all data in a data set, and/or (vi) facilitate transformation of the data from one form into one or more other forms. The terms “pre-processing” and “processing” when utilized with respect to data or data sets are collectively referred to herein as “processing”. Processing can render data more amenable to further analysis, and can generate an outcome in some embodiments.
The term “noisy data” as used herein refers to (a) data that has a significant variance between data points when analyzed or plotted, (b) data that has a significant standard deviation, (c) data that has a significant standard error of the mean, the like, and combinations of the foregoing. Noisy data sometimes occurs due to the quantity and/or quality of starting material (e.g., nucleic acid sample), and sometimes occurs as part of processes for preparing or replicating DNA used to generate sequence reads. In certain embodiments, noise results from certain sequences being over represented when prepared using PCR-based methods. Methods described herein can reduce or eliminate the contribution of noisy data, and therefore reduce the effect of noisy data on the provided outcome.
The terms “uninformative data”, “uninformative bins”, and “uninformative genomic sections” as used herein refer to genomic sections, or data derived therefrom, having a numerical value that is significantly different from a predetermined cutoff threshold value or falls outside a predetermined cutoff range of values. A cutoff threshold value or range of values often is calculated by mathematically and/or statistically manipulating sequence read data (e.g., from a reference and/or subject), in some embodiments, and in certain embodiments, sequence read data manipulated to generate a threshold cutoff value or range of values is sequence read data (e.g., from a reference and/or subject). In some embodiments, a threshold cutoff value is obtained by calculating the standard deviation and/or median absolute deviation (e.g., MAD) of a raw or normalized count profile and multiplying the standard deviation for the profile by a constant representing the number of standard deviations chosen as a cutoff threshold (e.g., multiply by 3 for 3 standard deviations), whereby a value for an uncertainty is generated. In certain embodiments, a portion or all of the genomic sections exceeding the calculated uncertainty threshold cutoff value, or outside the range of threshold cutoff values, are removed as part of, prior to, or after the normalization process. In some embodiments, a portion or all of the genomic sections exceeding the calculated uncertainty threshold cutoff value, or outside the range of threshold cutoff values or raw data points, are weighted as part of, or prior to the normalization or classification process. Examples of weighting are described herein. The terms “redundant data”, and “redundant mapped reads” as used herein refer to sample derived sequences reads that are identified as having already been assigned to a genomic location (e.g., base position) and/or counted for a genomic section.
Any suitable procedure can be utilized for adjusting and/or processing counted, mapped sequence reads (e.g., data or data sets) described herein. Non-limiting examples of procedures suitable for use for processing data sets include filtering, normalizing, weighting, monitoring peak heights, monitoring peak areas, monitoring peak edges, determining area ratios, mathematical processing of data, statistical processing of data, application of statistical algorithms, analysis with fixed variables, analysis with optimized variables, plotting data to identify patterns or trends for additional processing, the like and combinations of the foregoing. In some embodiments, data sets are processed based on various features (e.g., GC content, repetitive sequences, redundant mapped reads, centromere regions, telomere regions, the like and combinations thereof) and/or variables (e.g., fetal gender, maternal age, maternal ploidy, percent contribution of fetal nucleic acid, the like or combinations thereof). In certain embodiments, processing data sets as described herein can reduce the complexity and/or dimensionality of large and/or complex data sets. A non-limiting example of a complex data set includes sequence read data generated from one or more test subjects and a plurality of reference subjects of different ages and ethnic backgrounds. In some embodiments, data sets can include from thousands to millions of sequence reads for each test and/or reference subject.
Data adjustment and/or processing can be performed in any suitable number of steps, in certain embodiments, and in those embodiments with more than one step, the steps can be performed in any order. For example, data may be adjusted and/or processed using only a single processing procedure in some embodiments, and in certain embodiments data may be processed using 1 or more, 5 or more, 10 or more or 20 or more processing steps (e.g., 1 or more processing steps, 2 or more processing steps, 3 or more processing steps, 4 or more processing steps, 5 or more processing steps, 6 or more processing steps, 7 or more processing steps, 8 or more processing steps, 9 or more processing steps, 10 or more processing steps, 11 or more processing steps, 12 or more processing steps, 13 or more processing steps, 14 or more processing steps, 15 or more processing steps, 16 or more processing steps, 17 or more processing steps, 18 or more processing steps, 19 or more processing steps, or 20 or more processing steps). In some embodiments, adjustment and/or processing steps may be the same step repeated two or more times (e.g., filtering two or more times, normalizing two or more times), and in certain embodiments, adjustment/processing steps may be two or more different adjustment/processing steps (e.g., removal of repetitive sequences, normalization to for GC content; filtering, normalizing; normalizing, monitoring peak heights and edges; filtering, normalizing, normalizing to a reference, statistical manipulation to determine p-values, and the like), carried out simultaneously or sequentially. In some embodiments, any suitable number and/or combination of the same or different processing steps can be utilized to process sequence read data to facilitate providing an outcome. In certain embodiments, processing data sets by the criteria described herein may reduce the complexity and/or dimensionality of a data set. In some embodiments, one or more processing steps can comprise one or more filtering steps.
The term “filtering” as used herein refers to removing genomic sections or bins from consideration. Bins can be selected for removal based on any suitable criteria, including but not limited to redundant data (e.g., redundant or overlapping mapped reads), non-informative data (e.g., bins with zero median counts), bins with over represented or under represented sequences, noisy data, the like, or combinations of the foregoing. A filtering process often involves removing one or more bins from consideration and subtracting the counts in the one or more bins selected for removal from the counted or summed counts for the bins, chromosome or chromosomes, or genome under consideration. In some embodiments, bins can be removed successively (e.g., one at a time to allow evaluation of the effect of removal of each individual bin), and in certain embodiments all bins marked for removal can be removed at the same time.
In some embodiments, one or more adjustment/processing steps can comprise one or more normalization steps. The term “normalization” as used herein refers to division of one or more data sets by a predetermined variable. Any suitable number of normalizations can be used. In some embodiments, data sets can be normalized 1 or more, 5 or more, 10 or more or even 20 or more times. Data sets can be normalized to values (e.g., normalizing value) representative of any suitable feature or variable (e.g., sample data, reference data, or both). Non-limiting examples of types of data normalizations that can be used include normalizing raw count data for one or more selected test or reference genomic sections to the total number of counts mapped to the chromosome or the entire genome on which the selected genomic section or sections are mapped; normalizing raw count data for one or more selected genomic segments to a median reference count for one or more genomic sections or the chromosome on which a selected genomic segment or segments is mapped; normalizing raw count data to previously normalized data or derivatives thereof; and normalizing previously normalized data to one or more other predetermined normalization variables. Normalizing a data set sometimes has the effect of isolating statistical error, depending on the feature or property selected as the predetermined normalization variable. Normalizing a data set sometimes also allows comparison of data characteristics of data having different scales, by bringing the data to a common scale (e.g., predetermined normalization variable). In some embodiments, one or more normalizations to a statistically derived value can be utilized to minimize data differences and diminish the importance of outlying data.
In some embodiments, a processing step comprises a weighting. The terms “weighted”, “weighting” or “weight function” or grammatical derivatives or equivalents thereof, as used herein, refer to a mathematical manipulation of a portion or all of a data set sometimes utilized to alter the influence of certain data set features or variables with respect to other data set features or variables (e.g., increase or decrease the significance and/or contribution of data contained in one or more genomic sections or bins, based on the quality or usefulness of the data in the selected bin or bins). A weighting function can be used to increase the influence of data with a relatively small measurement variance, and/or to decrease the influence of data with a relatively large measurement variance, in some embodiments. For example, bins with under represented or low quality sequence data can be “down weighted” to minimize the influence on a data set, whereas selected bins can be “up weighted” to increase the influence on a data set. A non-limiting example of a weighting function is [1/(standard deviation) 2 ]. A weighting step sometimes is performed in a manner substantially similar to a normalizing step. In some embodiments, a data set is divided by a predetermined variable (e.g., weighting variable). A predetermined variable (e.g., minimized target function, Phi) often is selected to weigh different parts of a data set differently (e.g., increase the influence of certain data types while decreasing the influence of other data types).
In some embodiments, one or more adjustment/processing steps can comprise adjustment for G/C content. As noted herein, sequences with high G/C content sometimes are over or under represented in a raw or processed data set. In certain embodiments, G/C content for a portion of or all of a data set (e.g., selected bins, selected portions of chromosomes, selected chromosomes) is adjusted to minimize or eliminate G/C content bias by adjusting or normalizing a portion of, or all of a data set with reference to an expected value. In some embodiments, the expected value is the G/C content of the nucleotide sequence reads, and in certain embodiments, the expected value is the G/C content of the sample nucleic acid. In some embodiments, the expected value is calculated for a portion of, or all chromosomes using one or more estimators chosen from; average, median, median absolute deviation, (MAD), standard deviation, z-score, ANOVA, and the like. Adjusting a portion of or all of a data set to reduce or eliminate the effect of G/C content bias can facilitate providing an outcome, and/or reduce the complexity and/or dimensionality of a data set, in some embodiments.
An adjusted/normalized dataset can be generated by one or more manipulations of counted mapped sequence read data. Sequence reads are mapped and the number of sequence tags mapping to each genomic bin are determined (e.g., counted). In certain embodiments, sequence reads are mapped to the maternal genome, thereby using the ploidy of the maternal genome as a filter or reference for identifying regions in the fetal genome that deviate from an expected chromosome representation value for one or more selected genomic sections. In some embodiments, datasets are repeat masking adjusted to remove uninformative and/or repetitive genomic sections prior to mapping, and in certain embodiments, the reference genome is repeat masking adjusted prior to mapping. Performing either masking procedure yields substantially the same results. In some embodiments, a dataset is repeat masking adjusted prior to G/C content adjustment, and in certain embodiments, a dataset is G/C content adjusted prior to repeat masking adjustment. After adjustment, the remaining counts typically are summed to generate an adjusted data set. In certain embodiments, dataset adjustment facilitates classification and/or providing an outcome. In some embodiments, an adjusted data set profile is generated from an adjusted dataset and utilized to facilitate classification and/or providing an outcome.
By way of a non-limiting example, an adjusted/normalized dataset can be generated from raw sequence read data by (a) obtaining total counts for all chromosomes, selected chromosomes, genomic sections and/or portions thereof for all samples from one or more flow cells, or all samples from one or more plates; (b) adjusting, filtering and/or removing one or more of (i) uninformative and/or repetitive genomic sections (e.g., repeat masking; described in Example 2) (ii) G/C content bias (iii) over or under represented sequences, (iv) noisy data; and (c) adjusting/normalizing a portion of or all remaining data in (b) with respect to an expected value for the selected chromosome or selected genomic location, thereby generating an adjusted/normalized value. In some embodiments, adjusting, filtering and/or removing one or more of (i) uninformative and/or repetitive genomic sections (e.g., repeat masking) (ii) G/C content bias (iii) over or under represented sequences, (iv) noisy data can be performed in any order (e.g.,(i); (ii); (iii); (iv); (i), (ii); (ii), (i); (iii), (i); (ii), (iii), (i); (i), (iv), (iii); (ii), (i) (iii); (i), (ii), (iii), (iv); (ii), (i), (iii), (v); (ii), (iv), (iii), (i); and the like). In some embodiments, sequences adjusted by one method can impact a portion of sequences substantially completely adjusted by a different method (e.g., G/C content bias adjustment sometimes removes up to 50% of sequences removed substantially completely by repeat masking).
In certain embodiments, a processing step can comprise one or more mathematical and/or statistical manipulations. Any suitable mathematical and/or statistical manipulation, alone or in combination, may be used to analyze and/or manipulate a data set described herein. Any suitable number of mathematical and/or statistical manipulations can be used. In some embodiments, a data set can be mathematically and/or statistically manipulated 1 or more, 5 or more, 10 or more or 20 or more times. Non-limiting examples of mathematical and statistical manipulations that can be used include addition, subtraction, multiplication, division, algebraic functions, least squares estimators, curve fitting, differential equations, rational polynomials, double polynomials, orthogonal polynomials, z-scores, p-values, chi values, phi values, analysis of peak elevations, determination of peak edge locations, calculation of peak area ratios, analysis of median chromosomal elevation, calculation of mean absolute deviation, sum of squared residuals, mean, standard deviation, standard error, the like or combinations thereof. A mathematical and/or statistical manipulation can be performed on all or a portion of sequence read data, or processed products thereof. Non-limiting examples of data set variables or features that can be statistically manipulated include raw counts, filtered counts, normalized counts, peak heights, peak widths, peak areas, peak edges, lateral tolerances, P-values, median elevations, mean elevations, count distribution within a genomic region, relative representation of nucleic acid species, the like or combinations thereof.
In some embodiments, a processing step can include the use of one or more statistical algorithms. Any suitable statistical algorithm, alone or in combination, may be used to analyze and/or manipulate a data set described herein. Any suitable number of statistical algorithms can be used. In some embodiments, a data set can be analyzed using 1 or more, 5 or more, 10 or more or 20 or more statistical algorithms. Non-limiting examples of statistical algorithms suitable for use with methods described herein include decision trees, counternulls, multiple comparisons, omnibus test, Behrens-Fisher problem, bootstrapping, Fisher's method for combining independent tests of significance, null hypothesis, type I error, type II error, exact test, one-sample Z test, two-sample Z test, one-sample t-test, paired t-test, two-sample pooled t-test having equal variances, two-sample unpooled t-test having unequal variances, one-proportion z-test, two-proportion z-test pooled, two-proportion z-test unpooled, one-sample chi-square test, two-sample F test for equality of variances, confidence interval, credible interval, significance, meta analysis, simple linear regression, robust linear regression, the like or combinations of the foregoing. Non-limiting examples of data set variables or features that can be analyzed using statistical algorithms include raw counts, filtered counts, normalized counts, peak heights, peak widths, peak edges, lateral tolerances, P-values, median elevations, mean elevations, count distribution within a genomic region, relative representation of nucleic acid species, the like or combinations thereof.
In some embodiments, analysis and processing of data can include the use of one or more assumptions. Any suitable number or type of assumptions can be utilized to analyze or process a data set. Non-limiting examples of assumptions that can be used for data processing and/or analysis include maternal ploidy, fetal contribution, prevalence of certain sequences in a reference population, ethnic background, prevalence of a selected medical condition in related family members, parallelism between raw count profiles from different patients and/or runs after GC-normalization and repeat masking (e.g., GCRM), identical matches represent PCR artifacts (e.g., identical base position), assumptions inherent in a fetal quantifier assay (e.g., FQA), assumptions regarding twins (e.g., if 2 twins and only 1 is affected the effective fetal fraction is only 50% of the total measured fetal fraction (similarly for triplets, quadruplets and the like)), fetal cell free DNA (e.g., cfDNA) uniformly covers the entire genome, the like and combinations thereof.
In those instances where the quality and/or depth of mapped sequence reads does not permit an outcome prediction of the presence or absence of a genetic variation at a desired confidence level (e.g., 95% or higher confidence level), based on the normalized count profiles, one or more additional mathematical manipulation algorithms and/or statistical prediction algorithms, can be utilized to generate additional numerical values useful for data analysis and/or providing an outcome. The term “normalized count profile” as used herein refers to a profile generated using normalized counts. Examples of methods that can be used to generate normalized counts and normalized count profiles are described herein. As noted, mapped sequence reads that have been counted can be normalized with respect to test sample counts or reference sample counts. In some embodiments, a normalized count profile can be presented as a plot.
As noted above, data sometimes is transformed from one form into another form. The terms “transformed”, “transformation”, and grammatical derivations or equivalents thereof, as used herein refer to an alteration of data from a physical starting material (e.g., test subject and/or reference subject sample nucleic acid) into a digital representation of the physical starting material (e.g., sequence read data), and in some embodiments includes a further transformation into one or more numerical values or graphical representations of the digital representation that can be utilized to provide an outcome. In certain embodiments, the one or more numerical values and/or graphical representations of digitally represented data can be utilized to represent the appearance of a test subject's physical genome (e.g., virtually represent or visually represent the presence or absence of a genomic insertion or genomic deletion; represent the presence or absence of a variation in the physical amount of a sequence associated with medical conditions). A virtual representation sometimes is further transformed into one or more numerical values or graphical representations of the digital representation of the starting material. These procedures can transform physical starting material into a numerical value or graphical representation, or a representation of the physical appearance of a test subject's genome.
In some embodiments, transformation of a data set facilitates providing an outcome by reducing data complexity and/or data dimensionality. Data set complexity sometimes is reduced during the process of transforming a physical starting material into a virtual representation of the starting material (e.g., sequence reads representative of physical starting material). Any suitable feature or variable can be utilized to reduce data set complexity and/or dimensionality. Non-limiting examples of features that can be chosen for use as a target feature for data processing include GC content, fetal gender prediction, identification of chromosomal aneuploidy, identification of particular genes or proteins, identification of cancer, diseases, inherited genes/traits, chromosomal abnormalities, a biological category, a chemical category, a biochemical category, a category of genes or proteins, a gene ontology, a protein ontology, co-regulated genes, cell signaling genes, cell cycle genes, proteins pertaining to the foregoing genes, gene variants, protein variants, co-regulated genes, co-regulated proteins, amino acid sequence, nucleotide sequence, protein structure data and the like, and combinations of the foregoing. Non-limiting examples of data set complexity and/or dimensionality reduction include: reduction of a plurality of sequence reads to profile plots, reduction of a plurality of sequence reads to numerical values (e.g., normalized values, Z-scores, p-values); reduction of multiple analysis methods to probability plots or single points; principle component analysis of derived quantities; and the like or combinations thereof.
The term “detection” of a chromosome abnormality as used herein refers to identification of a genetic variation (e.g., imbalance of chromosomes) by processing data arising from sequence analyses described herein. In certain aspects, detection of a genetic variation (e.g., chromosome abnormality) can comprise providing an outcome determinative of the presence or absence of the variation. An outcome pertaining to the presence or absence of a genetic variation can be expressed in any suitable form, including, without limitation, probability (e.g., odds ratio, p-value), likelihood, percentage, value over a threshold, or risk factor, associated with the presence of a genetic variation for a subject or sample. An outcome may be provided with one or more of sensitivity, specificity, standard deviation, coefficient of variation (CV) and/or confidence level, or combinations of the foregoing, in certain embodiments.
Outcomes
Analysis, adjustment and processing of data can provide one or more outcomes. The term “outcome” as used herein refers to a result of data adjustment and processing that facilitates determining whether a subject was, or is at risk of having, a genetic variation. An outcome often comprises one or more numerical values generated using an adjustment/processing method described herein in the context of one or more considerations of probability or estimators. A consideration of probability includes but is not limited to: measure of variability, confidence level, sensitivity, specificity, standard deviation, coefficient of variation (CV) and/or confidence level, Z-scores, robust Z-scores, percent chromosome representation, median absolute deviation, or alternates to median absolute deviation, Chi values, Phi values, ploidy values, fetal fraction, fitted fetal fraction, area ratios, median elevation, the like or combinations thereof. A consideration of probability can facilitate determining whether a subject is at risk of having, or has, a genetic variation, and an outcome determinative of a presence or absence of a genetic disorder often includes such a consideration. In some embodiments, an outcome comprises factoring the fraction of fetal nucleic acid in the sample nucleic acid (e.g., addressed above).
An outcome often is a phenotype with an associated level of confidence (e.g., fetus is positive for trisomy 21 with a confidence level of 99%, test subject is negative for a cancer associated with a genetic variation at a confidence level of 95%). Different methods of generating outcome values sometimes can produce different types of results. Generally, there are four types of possible scores or calls that can be made based on outcome values generated using methods described herein: true positive, false positive, true negative and false negative. The terms “score”, “scores”, “call” and “calls” as used herein refer to calculating the probability that a particular genetic variation is present or absent in a subject/sample. The value of a score may be used to determine, for example, a variation, difference, or ratio of mapped sequence reads that may correspond to a genetic variation. For example, calculating a positive score for a selected genetic variation or genomic section from a data set, with respect to a reference genome can lead to an identification of the presence or absence of a genetic variation, which genetic variation sometimes is associated with a medical condition (e.g., cancer, preeclampsia, trisomy, monosomy, and the like). In certain embodiments, an outcome is generated from an adjusted data set. In some embodiments, a provided outcome that is determinative of the presence or absence of a genetic variation and/or fetal aneuploidy is based on a normalized sample count. In some embodiments, an outcome comprises a profile. In those embodiments in which an outcome comprises a profile, any suitable profile or combination of profiles can be used for an outcome. Non-limiting examples of profiles that can be used for an outcome include z-score profiles, robust Z-score profiles, p-value profiles, chi value profiles, phi value profiles, the like, and combinations thereof
An outcome generated for determining the presence or absence of a genetic variation sometimes includes a null result (e.g., a data point between two clusters, a numerical value with a standard deviation that encompasses values for both the presence and absence of a genetic variation, a data set with a profile plot that is not similar to profile plots for subjects having or free from the genetic variation being investigated). In some embodiments, an outcome indicative of a null result still is a determinative result, and the determination can include the need for additional information and/or a repeat of the data generation and/or analysis for determining the presence or absence of a genetic variation.
An outcome can be generated after performing one or more processing steps described herein, in some embodiments. In certain embodiments, an outcome is generated as a result of one of the processing steps described herein, and in some embodiments, an outcome can be generated after each statistical and/or mathematical manipulation of a data set is performed. An outcome pertaining to the determination of the presence or absence of a genetic variation can be expressed in any suitable form, which form comprises without limitation, a probability (e.g., odds ratio, p-value), likelihood, value in or out of a cluster, value over or under a threshold value, value with a measure of variance or confidence, or risk factor, associated with the presence or absence of a genetic variation for a subject or sample. In certain embodiments, comparison between samples allows confirmation of sample identity (e.g., allows identification of repeated samples and/or samples that have been mixed up (e.g., mislabeled, combined, and the like)).
In some embodiments, an outcome comprises a value above or below a predetermined threshold or cutoff value (e.g., greater than 1, less than 1), and an uncertainty or confidence level associated with the value. An outcome also can describe any assumptions used in data processing. In certain embodiments, an outcome comprises a value that falls within or outside a predetermined range of values and the associated uncertainty or confidence level for that value being inside or outside the range. In some embodiments, an outcome comprises a value that is equal to a predetermined value (e.g., equal to 1, equal to zero), or is equal to a value within a predetermined value range, and its associated uncertainty or confidence level for that value being equal or within or outside a range. An outcome sometimes is graphically represented as a plot (e.g., profile plot).
As noted above, an outcome can be characterized as a true positive, true negative, false positive or false negative. The term “true positive” as used herein refers to a subject correctly diagnosed as having a genetic variation. The term “false positive” as used herein refers to a subject wrongly identified as having a genetic variation. The term “true negative” as used herein refers to a subject correctly identified as not having a genetic variation. The term “false negative” as used herein refers to a subject wrongly identified as not having a genetic variation. Two measures of performance for any given method can be calculated based on the ratios of these occurrences: (i) a sensitivity value, which generally is the fraction of predicted positives that are correctly identified as being positives; and (ii) a specificity value, which generally is the fraction of predicted negatives correctly identified as being negative. The term “sensitivity” as used herein refers to the number of true positives divided by the number of true positives plus the number of false negatives, where sensitivity (sens) may be within the range of 0≦sens≦1. Ideally, the number of false negatives equal zero or close to zero, so that no subject is wrongly identified as not having at least one genetic variation when they indeed have at least one genetic variation. Conversely, an assessment often is made of the ability of a prediction algorithm to classify negatives correctly, a complementary measurement to sensitivity. The term “specificity” as used herein refers to the number of true negatives divided by the number of true negatives plus the number of false positives, where sensitivity (spec) may be within the range of 0≦spec≦1. Ideally, the number of false positives equal zero or close to zero, so that no subject is wrongly identified as having at least one genetic variation when they do not have the genetic variation being assessed.
In certain embodiments, one or more of sensitivity, specificity and/or confidence level are expressed as a percentage. In some embodiments, the percentage, independently for each variable, is greater than about 90% (e.g., about 90, 91, 92, 93, 94, 95, 96, 97, 98 or 99%, or greater than 99% (e.g., about 99.5%, or greater, about 99.9% or greater, about 99.95% or greater, about 99.99% or greater)). Coefficient of variation (CV) in some embodiments is expressed as a percentage, and sometimes the percentage is about 10% or less (e.g., about 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1%, or less than 1% (e.g., about 0.5% or less, about 0.1% or less, about 0.05% or less, about 0.01% or less)). A probability (e.g., that a particular outcome is not due to chance) in certain embodiments is expressed as a Z-score, a p-value, or the results of a t-test. In some embodiments, a measured variance, confidence interval, sensitivity, specificity and the like (e.g., referred to collectively as confidence parameters) for an outcome can be generated using one or more data processing manipulations described herein.
A method that has sensitivity and specificity equaling one, or 100%, or near one (e.g., between about 90% to about 99%) sometimes is selected. In some embodiments, a method having a sensitivity equaling 1, or 100% is selected, and in certain embodiments, a method having a sensitivity near 1 is selected (e.g., a sensitivity of about 90%, a sensitivity of about 91%, a sensitivity of about 92%, a sensitivity of about 93%, a sensitivity of about 94%, a sensitivity of about 95%, a sensitivity of about 96%, a sensitivity of about 97%, a sensitivity of about 98%, or a sensitivity of about 99%). In some embodiments, a method having a specificity equaling 1, or 100% is selected, and in certain embodiments, a method having a specificity near 1 is selected (e.g., a specificity of about 90%, a specificity of about 91%, a specificity of about 92%, a specificity of about 93%, a specificity of about 94%, a specificity of about 95%, a specificity of about 96%, a specificity of about 97%, a specificity of about 98%, or a specificity of about 99%).
In some embodiments, an outcome based on counted mapped sequence reads or derivations thereof is determinative of the presence or absence of one or more conditions, syndromes or abnormalities listed in Table 1A and 1B (e.g., chromosome abnormality (e.g., trisomy)). In certain embodiments, an outcome generated utilizing one or more data processing methods described herein is determinative of the presence or absence of one or more conditions, syndromes or abnormalities listed in Table 1A and 1B. In some embodiments, an outcome determinative of the presence or absence of a condition, syndrome or abnormality is, or includes, detection of a condition, syndrome or abnormality listed in Table 1A and 1B.
In certain embodiments, an outcome is based on a comparison between: a test sample and reference sample (e.g., maternal reference); a test sample and other samples; two or more test samples; the like; and combinations thereof. In some embodiments, the comparison between samples facilitates providing an outcome. In certain embodiments, an outcome is based on a Z-score generated as described herein or as is known in the art. In some embodiments, a Z-score is generated using a normalized sample count. In some embodiments, the Z-score generated to facilitate providing an outcome is a robust Z-score generated using a robust estimator. In certain embodiments, an outcome is based on a normalized sample count.
After one or more outcomes have been generated, an outcome often is used to provide a determination of the presence or absence of a genetic variation and/or associated medical condition. An outcome typically is provided to a health care professional (e.g., laboratory technician or manager; physician or assistant). In some embodiments, an outcome determinative of the presence or absence of a genetic variation is provided to a healthcare professional in the form of a report, and in certain embodiments the report comprises a display of an outcome value and an associated confidence parameter. Generally, an outcome can be displayed in any suitable format that facilitates determination of the presence or absence of a genetic variation and/or medical condition. Non-limiting examples of formats suitable for use for reporting and/or displaying data sets or reporting an outcome include digital data, a graph, a 2D graph, a 3D graph, and 4D graph, a picture, a pictograph, a chart, a bar graph, a pie graph, a diagram, a flow chart, a scatter plot, a map, a histogram, a density chart, a function graph, a circuit diagram, a block diagram, a bubble map, a constellation diagram, a contour diagram, a cartogram, spider chart, Venn diagram, nomogram, and the like, and combination of the foregoing. Various examples of outcome representations are shown in the drawings and are described in the Examples.
Use of Outcomes
A health care professional, or other qualified individual, receiving a report comprising one or more outcomes determinative of the presence or absence of a genetic variation can use the displayed data in the report to make a call regarding the status of the test subject or patient. The healthcare professional can make a recommendation based on the provided outcome, in some embodiments. A health care professional or qualified individual can provide a test subject or patient with a call or score with regards to the presence or absence of the genetic variation based on the outcome value or values and associated confidence parameters provided in a report, in some embodiments. In certain embodiments, a score or call is made manually by a healthcare professional or qualified individual, using visual observation of the provided report. In certain embodiments, a score or call is made by an automated routine, sometimes embedded in software, and reviewed by a healthcare professional or qualified individual for accuracy prior to providing information to a test subject or patient. The term “receiving a report” as used herein refers to obtaining, by any communication means, a written and/or graphical representation comprising an outcome, which upon review allows a healthcare professional or other qualified individual to make a determination as to the presence or absence of a genetic variation in a test subject or patient. The report may be generated by a computer or by human data entry, and can be communicated using electronic means (e.g., over the internet, via computer, via fax, from one network location to another location at the same or different physical sites), or by any other method of sending or receiving data (e.g., mail service, courier service and the like). In some embodiments the outcome is transmitted to a health care professional in a suitable medium, including, without limitation, in verbal, document, or file form. The file may be, for example, but not limited to, an auditory file, a computer readable file, a paper file, a laboratory file or a medical record file.
The term “providing an outcome” and grammatical equivalents thereof, as used herein also can refer to any method for obtaining such information, including, without limitation, obtaining the information from a laboratory file. A laboratory file can be generated by a laboratory that carried out one or more assays or one or more data processing steps to determine the presence or absence of the medical condition. The laboratory may be in the same location or different location (e.g., in another country) as the personnel identifying the presence or absence of the medical condition from the laboratory file. For example, the laboratory file can be generated in one location and transmitted to another location in which the information therein will be transmitted to the pregnant female subject. The laboratory file may be in tangible form or electronic form (e.g., computer readable form), in certain embodiments.
A healthcare professional or qualified individual, can provide any suitable recommendation based on the outcome or outcomes provided in the report. Non-limiting examples of recommendations that can be provided based on the provided outcome report includes, surgery, radiation therapy, chemotherapy, genetic counseling, after birth treatment solutions (e.g., life planning, long term assisted care, medicaments, symptomatic treatments), pregnancy termination, organ transplant, blood transfusion, the like or combinations of the foregoing. In some embodiments the recommendation is dependent on the outcome based classification provided (e.g., Down's syndrome, Turner syndrome, medical conditions associated with genetic variations in T13, medical conditions associated with genetic variations in T18).
Software can be used to perform one or more steps in the process described herein, including but not limited to; counting, data processing, generating an outcome, and/or providing one or more recommendations based on generated outcomes.
Machines, Software and Interfaces
Apparatuses, software and interfaces may be used to conduct methods described herein. Using apparatuses, software and interfaces, a user may enter, request, query or determine options for using particular information, programs or processes (e.g., mapping sequence reads, processing mapped data and/or providing an outcome), which can involve implementing statistical analysis algorithms, statistical significance algorithms, statistical algorithms, iterative steps, validation algorithms, and graphical representations, for example. In some embodiments, a data set may be entered by a user as input information, a user may download one or more data sets by any suitable hardware media (e.g., flash drive), and/or a user may send a data set from one system to another for subsequent processing and/or providing an outcome (e.g., send sequence read data from a sequencer to a computer system for sequence read mapping; send mapped sequence data to a computer system for processing and yielding an outcome and/or report).
A user may, for example, place a query to software which then may acquire a data set via internet access, and in certain embodiments, a programmable processor may be prompted to acquire a suitable data set based on given parameters. A programmable processor also may prompt a user to select one or more data set options selected by the processor based on given parameters. A programmable processor may prompt a user to select one or more data set options selected by the processor based on information found via the internet, other internal or external information, or the like. Options may be chosen for selecting one or more data feature selections, one or more statistical algorithms, one or more statistical analysis algorithms, one or more statistical significance algorithms, one or more robust estimator algorithms, iterative steps, one or more validation algorithms, and one or more graphical representations of methods, apparatuses, or computer programs.
Systems addressed herein may comprise general components of computer systems, such as, for example, network servers, laptop systems, desktop systems, handheld systems, personal digital assistants, computing kiosks, and the like. A computer system may comprise one or more input means such as a keyboard, touch screen, mouse, voice recognition or other means to allow the user to enter data into the system. A system may further comprise one or more outputs, including, but not limited to, a display screen (e.g., CRT or LCD), speaker, FAX machine, printer (e.g., laser, ink jet, impact, black and white or color printer), or other output useful for providing visual, auditory and/or hardcopy output of information (e.g., outcome and/or report).
In a system, input and output means may be connected to a central processing unit which may comprise among other components, a microprocessor for executing program instructions and memory for storing program code and data. In some embodiments, processes may be implemented as a single user system located in a single geographical site. In certain embodiments, processes may be implemented as a multi-user system. In the case of a multi-user implementation, multiple central processing units may be connected by means of a network. The network may be local, encompassing a single department in one portion of a building, an entire building, span multiple buildings, span a region, span an entire country or be worldwide. The network may be private, being owned and controlled by a provider, or it may be implemented as an internet based service where the user accesses a web page to enter and retrieve information. Accordingly, in certain embodiments, a system includes one or more machines, which may be local or remote with respect to a user. More than one machine in one location or multiple locations may be accessed by a user, and data may be mapped and/or processed in series and/or in parallel. Thus, any suitable configuration and control may be utilized for mapping and/or processing data using multiple machines, such as in local network, remote network and/or “cloud” computing platforms.
In some embodiments, an apparatus may comprise a web-based system in which a computer program product described herein is implemented. A web-based system sometimes comprises computers, telecommunications equipment (e.g., communications interfaces, routers, network switches), and the like sufficient for web-based functionality. In certain embodiments, a web-based system includes network cloud computing, network cloud storage or network cloud computing and network cloud storage. The term “network cloud storage” as used herein refers to web-based data storage on virtual servers located on the internet. The term “network cloud computing” as used herein refers to network-based software and/or hardware usage that occurs in a remote network environment (e.g., software available for use for a few located on a remote server). In some embodiments, one or more functions of a computer program product described herein is implemented in a web-based environment.
A system can include a communications interface in some embodiments. A communications interface allows for transfer of software and data between a computer system and one or more external devices. Non-limiting examples of communications interfaces include a modem, a network interface (such as an Ethernet card), a communications port, a PCMCIA slot and card, and the like. Software and data transferred via a communications interface generally are in the form of signals, which can be electronic, electromagnetic, optical and/or other signals capable of being received by a communications interface. Signals often are provided to a communications interface via a channel. A channel often carries signals and can be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link and/or other communications channels. Thus, in an example, a communications interface may be used to receive signal information that can be detected by a signal detection module.
Data may be input by any suitable device and/or method, including, but not limited to, manual input devices or direct data entry devices (DDEs). Non-limiting examples of manual devices include keyboards, concept keyboards, touch sensitive screens, light pens, mouse, tracker balls, joysticks, graphic tablets, scanners, digital cameras, video digitizers and voice recognition devices. Non-limiting examples of DDEs include bar code readers, magnetic strip codes, smart cards, magnetic ink character recognition, optical character recognition, optical mark recognition, and turnaround documents.
In some embodiments, output from a sequencing apparatus may serve as data that can be input via an input device. In certain embodiments, mapped sequence reads may serve as data that can be input via an input device. In certain embodiments, simulated data is generated by an in silico process and the simulated data serves as data that can be input via an input device. The term “in silico” refers to research and experiments performed using a computer. In silico processes include, but are not limited to, mapping sequence reads and processing mapped sequence reads according to processes described herein.
A system may include software useful for performing a process described herein, and software can include one or more modules for performing such processes (e.g., data acquisition module, data processing module, data display module). The term “software” refers to computer readable program instructions that, when executed by a computer, perform computer operations. The term “module” refers to a self-contained functional unit that can be used in a larger software system. For example, a software module is a part of a program that performs a particular process or task. Software often is provided on a program product containing program instructions recorded on a computer readable medium, including, but not limited to, magnetic media including floppy disks, hard disks, and magnetic tape; and optical media including CD-ROM discs, DVD discs, magneto-optical discs, flash drives, RAM, floppy discs, the like, and other such media on which the program instructions can be recorded. In online implementation, a server and web site maintained by an organization can be configured to provide software downloads to remote users, or remote users may access a remote system maintained by an organization to remotely access software. Software may obtain or receive input information. Software may include a module that specifically obtains or receives data (e.g., a data receiving module that receives sequence read data and/or mapped read data) and may include a module that specifically adjusts and/or processes the data (e.g., a processing module that adjusts and/or processes received data (e.g., filters, normalizes, provides an outcome and/or report). The terms “obtaining” and “receiving” input information refers to receiving data (e.g., sequence reads, mapped reads) by computer communication means from a local, or remote site, human data entry, or any other method of receiving data. The input information may be generated in the same location at which it is received, or it may be generated in a different location and transmitted to the receiving location. In some embodiments, input information is modified before it is processed (e.g., placed into a format amenable to processing (e.g., tabulated)).
In some embodiments, provided are computer program products, such as, for example, a computer program product comprising a computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method comprising: (a) obtaining sequence reads of sample nucleic acid from a test subject; (b) mapping the sequence reads obtained in (a) to a known genome, which known genome has been divided into genomic sections; (c) counting the mapped sequence reads within the genomic sections; (d) generating an adjusted data set by adjusting the counts or a derivative of the counts for the genomic sections obtained in (c); and (e) providing an outcome determinative of the presence or absence of a genetic variation from the adjusted count profile in (d).
Software can include one or more algorithms in certain embodiments. An algorithm may be used for processing data and/or providing an outcome or report according to a finite sequence of instructions. An algorithm often is a list of defined instructions for completing a task. Starting from an initial state, the instructions may describe a computation that proceeds through a defined series of successive states, eventually terminating in a final ending state. The transition from one state to the next is not necessarily deterministic (e.g., some algorithms incorporate randomness). By way of example, and without limitation, an algorithm can be a search algorithm, sorting algorithm, merge algorithm, numerical algorithm, graph algorithm, string algorithm, modeling algorithm, computational genometric algorithm, combinatorial algorithm, machine learning algorithm, cryptography algorithm, data compression algorithm, parsing algorithm and the like. An algorithm can include one algorithm or two or more algorithms working in combination. An algorithm can be of any suitable complexity class and/or parameterized complexity. An algorithm can be used for calculation and/or data processing, and in some embodiments, can be used in a deterministic or probabilistic/predictive approach. An algorithm can be implemented in a computing environment by use of a suitable programming language, non-limiting examples of which are C, C++, Java, Perl, Python, Fortran, and the like. In some embodiments, an algorithm can be configured or modified to include margin of errors, statistical analysis, statistical significance, and/or comparison to other information or data sets (e.g., applicable when using a neural net or clustering algorithm).
In certain embodiments, several algorithms may be implemented for use in software. These algorithms can be trained with raw data in some embodiments. For each new raw data sample, the trained algorithms may produce a representative adjusted and/or processed data set or outcome. An adjusted or processed data set sometimes is of reduced complexity compared to the parent data set that was processed. Based on an adjusted and/or processed set, the performance of a trained algorithm may be assessed based on sensitivity and specificity, in some embodiments. An algorithm with the highest sensitivity and/or specificity may be identified and utilized, in certain embodiments.
In certain embodiments, simulated (or simulation) data can aid data adjustment and/or processing, for example, by training an algorithm or testing an algorithm. In some embodiments, simulated data includes hypothetical various samplings of different groupings of sequence reads. Simulated data may be based on what might be expected from a real population or may be skewed to test an algorithm and/or to assign a correct classification. Simulated data also is referred to herein as “virtual” data. Simulations can be performed by a computer program in certain embodiments. One possible step in using a simulated data set is to evaluate the confidence of an identified result, e.g., how well a random sampling matches or best represents the original data. One approach is to calculate a probability value (p-value), which estimates the probability of a random sample having better score than the selected samples. In some embodiments, an empirical model may be assessed, in which it is assumed that at least one sample matches a reference sample (with or without resolved variations). In some embodiments, another distribution, such as a Poisson distribution for example, can be used to define the probability distribution.
A system may include one or more processors in certain embodiments. A processor can be connected to a communication bus. A computer system may include a main memory, often random access memory (RAM), and can also include a secondary memory. Secondary memory can include, for example, a hard disk drive and/or a removable storage drive, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, memory card and the like. A removable storage drive often reads from and/or writes to a removable storage unit. Non-limiting examples of removable storage units include a floppy disk, magnetic tape, optical disk, and the like, which can be read by and written to by, for example, a removable storage drive. A removable storage unit can include a computer-usable storage medium having stored therein computer software and/or data.
A processor may implement software in a system. In some embodiments, a processor may be programmed to automatically perform a task described herein that a user could perform. Accordingly, a processor, or algorithm conducted by such a processor, can require little to no supervision or input from a user (e.g., software may be programmed to implement a function automatically). In some embodiments, the complexity of a process is so large that a single person or group of persons could not perform the process in a timeframe short enough for providing an outcome determinative of the presence or absence of a genetic variation.
In some embodiments, secondary memory may include other similar means for allowing computer programs or other instructions to be loaded into a computer system. For example, a system can include a removable storage unit and an interface device. Non-limiting examples of such systems include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units and interfaces that allow software and data to be transferred from the removable storage unit to a computer system.
Combination Diagnostic Assays
Results from assays described in sections herein can be combined with results from one or more other assays, referred to herein as “secondary assays,” and results from the combination of the assays can be utilized to identify the presence or absence of aneuploidy. Results from a non-invasive assay described herein may be combined with results from one or more other non-invasive assays and/or one or more invasive assays. In certain embodiments, results from a secondary assay are combined with results from a non-invasive assay described above when a sample contains an amount of fetal nucleic acid below a certain threshold amount. A threshold amount of fetal nucleic acid sometimes is about 15% in certain embodiments.
In some embodiments, a non-invasive assay described in sections herein may be combined with a secondary nucleic acid-based allele counting assay. Allele-based methods for diagnosing, monitoring, or predicting chromosomal abnormalities rely on determining the ratio of the alleles found in maternal sample comprising free, fetal nucleic acid. The ratio of alleles refers to the ratio of the population of one allele and the population of the other allele in a biological sample. In some cases, it is possible that in trisomies a fetus may be tri-allelic for a particular locus, and these tri-allelic events may be detected to diagnose aneuploidy. In some embodiments, a secondary assay detects a paternal allele, and in certain embodiments, the mother is homozygous at the polymorphic site and the fetus is heterozygous at the polymorphic site detected in the secondary assay. In a related embodiment, the mother is first genotyped (for example, using peripheral blood mononuclear cells (PBMC) from a maternal whole blood sample) to determine the non-target allele that will be targeted by the cleavage agent in a secondary assay.
In certain embodiments, a non-invasive assay described herein may be combined with a secondary RNA-based diagnostic method. RNA-based methods for diagnosing, monitoring, or predicting chromosomal abnormalities often rely on the use of pregnancy-specificity of fetal-expressed transcripts to develop a method which allows the genetic determination of fetal chromosomal aneuploidy and thus the establishment of its diagnosis non-invasively. In one embodiment, the fetal-expressed transcripts are those expressed in the placenta. Specifically, a secondary assay may detect one or more single nucleotide polymorphisms (SNPs) from RNA transcripts with tissue-specific expression patterns that are encoded by genes on the aneuploid chromosome. Other polymorphisms also may be detected by a secondary assay, such as an insertion/deletion polymorphism and a simple tandem repeat polymorphism, for example. The status of the locus may be determined through the assessment of the ratio between informative SNPs on the RNA transcribed from the genetic loci of interest in a secondary assay. Genetic loci of interest may include, but are not limited to, COL6A1, SOD1, COL6A2, ATP50, BTG3, ADAMTS1, BACE2, ITSN1, APP, ATP5J, DSCR5, PLAC4, LOC90625, RPL17, SERPINB2 or COL4A2, in a secondary assay.
In some embodiments, a non-invasive assay described herein may be combined with a secondary methylation-based assay. Methylation-based tests sometimes are directed to detecting a fetal-specific DNA methylation marker for detection in maternal plasma. It has been demonstrated that fetal and maternal DNA can be distinguished by differences in methylation status (see U.S. Pat. No. 6,927,028, issued Aug. 9, 2005). Methylation is an epigenetic phenomenon, which refers to processes that alter a phenotype without involving changes in the DNA sequence. Poon et al. further showed that epigenetic markers can be used to detect fetal-derived maternally-inherited DNA sequence from maternal plasma (Clin. Chem. 48:35-41, 2002). Epigenetic markers may be used for non-invasive prenatal diagnosis by determining the methylation status of at least a portion of a differentially methylated gene in a blood sample, where the portion of the differentially methylated gene from the fetus and the portion from the pregnant female are differentially methylated, thereby distinguishing the gene from the female and the gene from the fetus in the blood sample; determining the level of the fetal gene; and comparing the level of the fetal gene with a standard control. In some cases, an increase from the standard control indicates the presence or progression of a pregnancy-associated disorder. In other cases, a decrease from the standard control indicates the presence or progression of a pregnancy-associated disorder.
In certain embodiments, a non-invasive assay described herein may be combined with another secondary molecular assay. Other molecular methods for the diagnosis of aneuploidies are also known (Hulten et al., 2003, Reproduction, 126(3):279-97; Armour et al., 2002, Human Mutation 20(5):325-37; Eiben and Glaubitz, J Histochem Cytochem. 2005 March; 53(3):281-3); and Nicolaides et al., J Matern Fetal Neonatal Med. 2002 July; 12(1):9-18)). Alternative molecular methods include PCR based methods such as QF-PCR (Verma et al., 1998, Lancet 352(9121):9-12; Pertl et al., 1994, Lancet 343(8907):1197-8; Mann et al., 2001, Lancet 358(9287):1057-61; Adinolfi et al., 1997, Prenatal Diagnosis 17(13):1299-311), multiple amplifiable probe hybridization (MAPH) (Armour et al., 2000, Nucleic Acids Res 28(2):605-9), multiplex probe ligation assay (MPLA) (Slater et al., 2003, J Med Genet 40(12)907-12; Schouten et al., 2002 30(12:e57), all of which are hereby incorporated by reference. Non PCR-based technologies such as comparative genome hybridization (CGH) offer another approach to aneuploidy detection (Veltman et al., 2002, Am J Hum Genet. 70(5):1269-76; Snijders et al., 2001 Nat Genet. 29(3):263-4).
In some embodiments, a non-invasive assay described herein may be combined with a secondary non-nucleic acid-based chromosome test. Non-limiting examples of non-nucleic acid-based tests include, but are not limited to, invasive amniocentesis or chorionic villus sampling-based test, a maternal age-based test, a biomarker screening test, and an ultrasonography-based test. A biomarker screening test may be performed where nucleic acid (e.g., fetal or maternal) is detected. However, as used herein “biomarker tests” are considered a non-nucleic acid-based test.
Amniocentesis and chorionic villus sampling (CVS)-based tests offer relatively definitive prenatal diagnosis of fetal aneuploidies, but require invasive sampling by amniocentesis or Chorionic Villus Sampling (CVS). These sampling methods are associated with a 0.5% to 1% procedure-related risk of pregnancy loss (D'Alton, M. E., Semin Perinatol 18(3):140-62 (1994)).
While different approaches have been employed in connection with specific aneuploidies, in the case of Down's syndrome, screening initially was based entirely on maternal age, with an arbitrary cut-off of 35 years used to define a population of women at sufficiently high risk to warrant offering invasive fetal testing.
Maternal biomarkers offer another strategy for testing of fetal Down's syndrome and other chromosomal aneuploidies, based upon the proteomic profile of a maternal biological fluid. “Maternal biomarkers” as used herein refer to biomarkers present in a pregnant female whose level of a transcribed mRNA or level of a translated protein is detected and can be correlated with presence or absence of a chromosomal abnormality.
Second-trimester serum screening techniques were introduced to improve detection rate and to reduce invasive testing rate. One type of screening for Down's syndrome requires offering patients a triple-marker serum test between 15 and 18 weeks gestation, which, together with maternal age (MA), is used for risk calculation. This test assays alpha-fetoprotein (AFP), human chorionic gonadotropin (beta-hCG), and unconjugated estriol (uE3). This “triple screen” for Down's syndrome has been modified as a “quad test”, in which the serum marker inhibin-A is tested in combination with the other three analytes. First-trimester concentrations of a variety of pregnancy-associated proteins and hormones have been identified as differing in chromosomally normal and abnormal pregnancies. Two first-trimester serum markers that can be tested for Down's syndrome and Edwards syndrome are PAPP-A and free .beta.hCG (Wapner, R., et al., N Engl J Med 349(15):1405-1413 (2003)). It has been reported that first-trimester serum levels of PAPP-A are significantly lower in Down's syndrome, and this decrease is independent of nuchal translucency (NT) thickness (Brizot, M. L., et al., Obstet Gynecol 84(6):918-22 (1994)). In addition, it has been shown that first-trimester serum levels of both total and free .beta.-hCG are higher in fetal Down's syndrome, and this increase is also independent of NT thickness (Brizot, M. L., Br J Obstet Gynaecol 102(2):127-32 (1995)).
Ultrasonography-based tests provide a non-molecular-based approach for diagnosing chromosomal abnormalities. Certain fetal structural abnormalities are associated with significant increases in the risk of Down's syndrome and other aneuploidies. Further work has been performed evaluating the role of sonographic markers of aneuploidy, which are not structural abnormalities per se. Such sonographic markers employed in Down's syndrome screening include choroid plexus cysts, echogenic bowel, short femur, short humerus, minimal hydronephrosis, and thickened nuchal fold. An 80% detection rate for Down's syndrome has been reported by a combination of screening MA and first-trimester ultrasound evaluation of the fetus (Pandya, P. P. et al., Br J Obstet Gyneacol 102(12):957-62 (1995); Snijders, R. J., et al., Lancet 352(9125):343-6 (1998)). This evaluation relies on the measurement of the translucent space between the back of the fetal neck and overlying skin, which has been reported as increased in fetuses with Down's syndrome and other aneuploidies. This nuchal translucency (NT) measurement is reportedly obtained by transabdominal or transvaginal ultrasonography between 10 and 14 weeks gestation (Snijders, R. J., et al., Ultrasound Obstet Gynecol 7(3):216-26 (1996)).
Kits
Kits often comprise one or more containers that contain one or more components described herein. A kit comprises one or more components in any number of separate containers, packets, tubes, vials, multiwell plates and the like, or components may be combined in various combinations in such containers. One or more of the following components, for example, may be included in a kit: (i) one or more amplification primers and other reagents for amplifying a nucleic acid, (ii) one or more reagents for detecting amplified nucleic acid; (iii) one or more reagents and/or devices for conducting massively parallel sequencing; (iv) one or more reagents and/or equipment for quantifying fetal nucleic acid in extracellular nucleic acid from a pregnant female; (v) reagents and/or equipment for enriching fetal nucleic acid from extracellular nucleic acid from a pregnant female; (vi) software and/or machines for manipulating nucleotide sequences, and/or assembling, aligning, and/or mapping nucleotide sequences; (vii) software, machines and/or information for identifying presence or absence of a chromosome abnormality (e.g., a table or file that convert signal information or ratios into outcomes), (viii) equipment for drawing blood); (ix) equipment for generating cell-free blood; (x) reagents for isolating nucleic acid (e.g., DNA, RNA) from plasma, serum or urine; (xi) reagents for stabilizing serum, plasma, urine or nucleic acid for shipment and/or processing.
A kit sometimes is utilized in conjunction with a process, and can include instructions for performing one or more processes and/or a description of one or more compositions. A kit may be utilized to carry out a process described herein. Instructions and/or descriptions may be in tangible form (e.g., paper and the like) or electronic form (e.g., computer readable file on a tangle medium (e.g., compact disc) and the like) and may be included in a kit insert. A kit also may include a written description of an internet location that provides such instructions or descriptions (e.g., a URL for the World-Wide Web).
EXAMPLES
The following Examples are provided for illustration only and are not limiting.
Example 1
Fetal Aneuploidy Determinations Utilizing Maternal DNA as a Reference
Provided is a method for determining fetal aneuploidy without utilizing any alleles (or other genetic information, such as insertions, deletions, copy number variations and the like) which are uniquely inherited from the father to contribute to the quantification of the number of chromosomes present in a sample containing circulating cell-free fetal (“ccff”) nucleic acid. This method promises an accurate assessment of fetal chromosome number representation (e.g., two or three copies of a chromosome) for reasons addressed hereafter.
Nucleic acid, often DNA, from the mother is used to construct a reference sequence of all or some of the maternal genome. Maternal DNA is isolated from plasma (or other suitable biological sample) prior to pregnancy or often from a buccal swab or skin cell sample (or other biological sample which will contain only or predominantly maternal DNA, and no or undetectable or essentially undetectable fetal DNA), during (or prior to) a current pregnancy, or after a current pregnancy but before a subsequent pregnancy. The maternal DNA is converted into a sequencing library using standard protocols for the DNA sequence reader to be employed. For example, Illumina kits and procedures may be used to construct a maternal DNA library. If plasma is the source, no DNA fragmentation often is needed, because the size of the fragments needed to generate the library exists naturally in the plasma. If the source is a buccal swab or other suitable biological sample, the DNA often is fragmented and size-fractionated using, for example, Illumina's reagents and protocols prior to construction of the sequencing library.
The maternal sample is sequenced using standard 36 base single end reads at a genomic coverage in the range of 0.1 to 20. Ideally the coverage is high enough to achieve a reasonable assembly of the maternal reads by aligning against a known DNA sequence assembled at much higher coverage—.i.e., 6 to 60 fold coverage. Ideally the reference genome is the same ethnic background as the mother. It is not necessary to create a perfectly aligned maternal sequence. Maternal sequence reads are assigned to bins. A bin is some portion of the maternal genome; for example, a portion of a chromosome, as is known in the art. The quantitative abundance of maternal sequence reads in each bin is registered.
DNA often is isolated from the plasma of the pregnant woman according to standard published methods (e.g., U.S. Pat. No. 6,258,540). A library is made without additional fragmentation, and sequenced by standard Illumina sequencing using, for example, 36 base single end reads. The reads are “blasted” or aligned against the previously obtained maternal sequence to assign plasma, or other extracellularly-derived, DNA sequence reads to chromosomes. In this way, each plasma DNA sequence read can be assigned to a particular chromosomal origin.
Unlike previously described methods, a perfect sequence match between the maternal reference sequence and the biological sample containing the fetal DNA often is a requirement. This approach should lead to a gain in efficiency over prior methods using a third party reference sequence because sequence reads that would be discarded because of SNPs, or other sequence differences between the third person reference sequence, and the mother and fetus sequences are not discarded. No sequence reads containing paternally inherited SNPs, or other sequence differences, that differ from the maternal sequence are counted because these can never be perfect matches. Thus all counted reads are reads from maternal DNA, fetal DNA inherited from the mother, or fetal DNA inherited from either parent but where no information about which parent provided that DNA fragment is discernable (i.e. the paternal DNA contains no uniquely paternal sequence information).
This approach differs from those previously described where DNA sequence reads that can be assigned to unique paternal origin are used. The abundance of fetal plus maternal reads in each chromosomal bin is compared to the abundance seen in the sequence reads from the reference sequence sample containing only maternal DNA. Differential populations of chromosomal bins from the maternal and the maternal plus fetal sample are accumulated over (i.e., assigned to) each chromosome (e.g., by summing the bins associated with each chromosome), and compared. Statistically significant deviation in chromosome populations in the pair of samples is evidence for aneuploidy. For example, the number of counts summed across the bins associated with a given chromosome, such as chromosome 21, for the maternal reference sample is X, and the number of counts summed across the bins associated with a given chromosome, such as chromosome 21, for the mixed maternal fetal sample is X plus Y. If X plus Y is statistically significantly larger than X, then the fetus may be said to be aneuploid with respect to chromosome 21. Alternative methods of comparison might also be used, such as comparing the X plus Y value to a value derived from X values or X plus Y values generally found to be indicative of a euploid fetus. Also, X plus Y values may be determined for a chromosome assayed for aneuploidy (e.g., chromosome 21) and another reference chromosome not expected to display aneuploidy (e.g., chromosome 1 or 3). These methods should have sufficient statistical sensitivity to discriminate partial chromosome aneuploidies, such as mosaicisms.
The use of a maternal reference sequence should not be subject to quantitative errors caused by maternal heteromorphisms. In an example of where the mother has a particular chromosome with a large amplified region, that chromosome will be over-represented in one or more bins in the pure maternal sequence and the maternal component of the maternal plus fetal plasma sequence will show the same over-representation. Paternal heteromorphisms would not be accounted for in this way but they are far less significant since they only affect the fetal component which typically is about 10%-20% of the maternal plus fetal sample.
A number of variations on the scheme described above are also envisioned. The single end sequence reads can be longer or shorter than 36 bases. Paired end reads ideally of 18 bases per end, but covering a range of read lengths could alternatively be used. Alternative DNA sequencing platforms and the means to feed them are also envisioned, including platforms provided by LifeTechnologies, Pacific Biosystems, Ion Torrent, Complete Genomics, Nanopore sequencing, for example. Instead of sequencing the maternal and the maternal plus fetal DNA samples separately, they can be coded (e.g., bar coded), mixed and sequenced simultaneously in the same flow cell or equivalent, taking care to adjust the relative amount of each library used in the mixture to achieve the desired difference in genome coverage for the two samples.
The approaches described in this Example 1 can be adapted for more subtle or more complex discriminations between the genomic content of tumors and normal samples by comparing sequences from a patient source uncompromised by tumor (e.g. buccal swab) and compromised by tumor (e.g. plasma).
Example 2
Examples of Certain Embodiments
Provided hereafter are non-limiting examples of certain embodiments of the technology.
A1. A method for detecting the presence or absence of a chromosomal aneuploidy in a fetus of a pregnant female, comprising:
(a) determining nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid; (b) determining nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid; (c) assembling the nucleotide sequences of (b) into a maternal reference sequence; (d) aligning the nucleotide sequences of (a) to portion of or all of the maternal reference sequence and counting the number of nucleotide sequences of (a) that map to the portion of or all of the maternal reference sequence; and (e) detecting the presence or absence of the chromosomal aneuploidy in the fetus of the pregnant female based on the number of nucleotide sequences of (a) that map to the portion of or all of the maternal reference sequence.

A1.1. A method for detecting the presence or absence of a chromosomal aneuploidy in a fetus of a pregnant female, comprising:
(a) determining nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid; (b) determining nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid; (c) assembling the nucleotide sequences of (b) into a maternal reference sequence; (d) aligning the nucleotide sequences of (a) to portion of or all of the maternal reference sequence and counting the number of nucleotide sequences of (a) that map to the portion of or all of the maternal reference sequence; and (e) providing an outcome determinative of the presence or absence of a chromosomal aneuploidy from the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence.

A1.2. A method for detecting the presence or absence of a genetic variation in a fetus of a pregnant female, comprising:
(a) determining nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid; (b) determining nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid; (c) assembling the nucleotide sequences of (b) into a maternal reference sequence; (d) aligning the nucleotide sequences of (a) to portion of or all of the maternal reference sequence and counting the number of nucleotide sequences of (a) that map to the portion of or all of the maternal reference sequence; and (e) detecting the presence or absence of the genetic variation in the fetus of the pregnant female based on the number of nucleotide sequences of (a) that map to the portion of or all of the maternal reference sequence.

A1.3. A method for detecting the presence or absence of a genetic variation in a fetus of a pregnant female, comprising:
(a) determining nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid; (b) determining nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid; (c) assembling the nucleotide sequences of (b) into a maternal reference sequence; (d) aligning the nucleotide sequences of (a) to portion of or all of the maternal reference sequence and counting the number of nucleotide sequences of (a) that map to the portion of or all of the maternal reference sequence; and (e) providing an outcome determinative of the presence or absence of a genetic variation from the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence.

A1.4. The method of any one of embodiments A1 to A1.3, wherein the nucleotide sequences of (a) that map to the portion of or all of the maternal reference sequence and are counted consist of (i) maternal nucleotide sequences, (ii) fetal nucleotide sequences inherited from the pregnant female, and (iii) fetal nucleotide sequences inherited from either parent but where no information about which parent provided such nucleotide sequences is discernable.
A2. The method of any one of embodiments A1 to A1.4, which comprises comparing the number of nucleotide sequences of (a) that map to the portion of or all of the maternal reference sequence to a predetermined value for chromosomal euploidy, with respect to a particular target chromosome.
A3. The method of any one of embodiments A1 to A1.4, wherein the portion of the maternal reference sequence is in a particular target chromosome.
A4. The method of embodiment A3, wherein the portion of the maternal reference sequence is a bin or plurality of bins.
A5. The method of embodiment A4, wherein the bin is about 30K base pairs to about 100K base pairs in length.
A6. The method of any one of embodiments A2 to A5, wherein the target chromosome is chromosome 21.
A7. The method of any one of embodiments A2 to A5, wherein the target chromosome is chromosome 18.
A8. The method of any one of embodiments A2 to A5, wherein the target chromosome is chromosome 13.
A9. The method of any one of embodiments A2 to A5, wherein the target chromosome is chromosome X.
A10. The method of any one of embodiments A2 to A5, wherein the target chromosome is chromosome Y.
A11. The method of any one of embodiments A1 to A10, wherein the extracellular nucleic acid is from blood.
A12. The method of embodiment A11, wherein the extracellular nucleic acid is from blood plasma.
A13. The method of embodiment A11, wherein the extracellular nucleic acid is from blood serum.
A14. The method of any one of embodiments A1 and A11 to A13, wherein the extracellular nucleic acid is from a pregnant female in the first trimester of pregnancy.
A15. The method of any one of embodiments A1 to A14, wherein the extracellular nucleic acid contains about 1% to about 40% fetal nucleic acid.
A16. The method of any one of embodiments A1 to A14, wherein the extracellular nucleic acid fetal nucleic acid contains about 15% or more of fetal nucleic acid.
A17. The method of any one of embodiments A1 to A16, wherein the number of fetal nucleic acid copies in the extracellular nucleic acid is about 10 copies to about 2000 copies of the total extracellular nucleic acid.
A18. The method of any one of embodiments A1 to A17, wherein the extracellular nucleic acid, the nucleic acid from the pregnant female containing substantially no fetal nucleic acid, or the extracellular nucleic acid and the nucleic acid from the pregnant female containing substantially no fetal nucleic acid, is not fragmented, not size fractionated, or is not fragmented and not size fractionated, prior to determining the nucleotide sequences in (a), (b), or (a) and (b).
A19. The method of any one of embodiments A1 to A18, wherein the extracellular nucleic acid, the nucleic acid from the pregnant female containing substantially no fetal nucleic acid, or the extracellular nucleic acid and the nucleic acid from the pregnant female containing substantially no fetal nucleic acid, is fragmented, size fractionated, or is fragmented and size fractionated, prior to determining the nucleotide sequences in (a), (b), or (a) and (b).
A20. The method of any one of embodiments A1 to A19.1, which comprises determining the fetal nucleic acid concentration in the extracellular nucleic acid.
A21. The method of any one of embodiments A1 to A20, which comprises enriching the extracellular nucleic acid for fetal nucleic acid.
A22. The method of any one of embodiments A1 to A21, wherein the nucleic acid from the pregnant female containing substantially no fetal nucleic acid is cellular nucleic acid from the pregnant female.
A23. The method of embodiment A22, wherein the cellular nucleic acid is from a buccal swab.
A24. The method of any one of embodiments A1 or A23, comprising fragmenting, size-fractionating, or fragmenting and size-fractionating, the nucleic acid from the pregnant female containing substantially no fetal nucleic acid.
A25. The method of any one of embodiments A1 to A24, wherein the nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid, is all or a portion of the pregnant female's genomic nucleic acid.
A26. The method of any one of embodiments A1 to A25, wherein the nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid cover about 0.1-fold to about 20-fold of the pregnant female's genomic nucleic acid.
A27. The method of any one of embodiments A1 to A26, wherein the nucleotide sequences in (a), (b), or (a) and (b), are determined by a massively parallel sequencing method.
A28. The method of any one of embodiments A1 to A27, wherein the maternal reference sequence is assembled by aligning nucleotide sequences of (b) to an external reference sequence.
A29. The method of embodiment A28, wherein the external reference sequence has been assembled from nucleotide sequences having about 6-fold to about 60-fold coverage.
A30. The method of embodiment A28 or A29, wherein the external reference sequence is from a subject or subjects of substantially the same ethnicity as the pregnant female.
A31. The method of any one of embodiments A28 to A30, wherein the maternal reference sequence is not completely aligned to the external reference sequence.
A32. The method of any one of embodiments A28 to A30, wherein the maternal reference sequence is substantially completely aligned to the external reference sequence.
A33. The method of any one of embodiments A1 to A32, which comprises aligning the nucleotide sequences of (b) to a portion of or all of the maternal reference sequence and counting the nucleotide sequences of (b) that map to the portion of the maternal reference sequence.
A34. The method of embodiment A33, wherein nucleotide sequences of (b) that map substantially exactly to the portion of the maternal reference sequence are counted.
A35. The method of any one of embodiments A1 to A34, wherein nucleotide sequences of (a) that map substantially exactly to the portion of the maternal reference sequence are counted.
A36. The method of any one of embodiments A1 to A35, which comprises comparing the number of nucleotide sequences of (a) that map to the maternal reference sequence with respect to one or more chromosomal positions with the number of nucleotide sequences of (a) that map to the maternal reference sequence with respect to one or more different chromosomal positions.
A37. The method of any one of embodiments A1 to A36, which comprises comparing the number of nucleotide sequences of (b) that map to the maternal reference sequence with respect to one or more chromosomal positions with the number of nucleotide sequences of (b) that map to the maternal reference sequence with respect to one or more different chromosomal positions.
A38. The method of any one of embodiments A33 to A37, wherein the presence or absence of a difference between (i) the counted number of nucleotide sequences in (a) that map to the portion of the maternal reference sequence, and (ii) the counted number of nucleotide sequences in (b) that map to the portion of the maternal reference sequence, is determined.
A39. The method of embodiment A38, wherein the presence of the chromosomal aneuploidy is detected based on determining the presence or absence of a statistically significant difference.
A40. The method of A38 or A39, which comprises comparing the difference for one or more different chromosomal positions.
A41. The method of any one of embodiments A1 to A40, wherein the presence or absence of the chromosomal aneuploidy is determined with a confidence level of about 95% or more.
A42. The method of any one of embodiments A1 to A40, wherein the presence or absence of the chromosomal aneuploidy is determined with a specificity of about 95% or more.
A43. The method of any one of embodiments A1 to A40, wherein the presence or absence of the chromosomal aneuploidy is determined with a sensitivity of about 95% or more.
A44. The method of any one of embodiments A1 to A43, wherein the nucleotide sequences of (a), (b), or (a) and (b) comprise single-end reads.
A45. The method of embodiment A44, wherein the nominal, average, mean or absolute length of the single-end reads is about 20 contiguous nucleotides to about 50 contiguous nucleotides.
A46. The method of embodiment A45, wherein the nominal, average, mean or absolute length of the single-end reads is about 30 contiguous nucleotides to about 40 contiguous nucleotides.
A47. The method of embodiment A46, wherein the nominal, average, mean or absolute length of the single-end reads is about 35 contiguous nucleotides or about 36 contiguous nucleotides.
A48. The method of any one of embodiments A1 to A47, wherein the nucleotide sequences of (a), (b), or (a) and (b) comprise double-end reads.
A49. The method of embodiment A48, wherein the nominal, average, mean or absolute length of the single-end reads is about 10 contiguous nucleotides to about 25 contiguous nucleotides.
A50. The method of embodiment A49, wherein the nominal, average, mean or absolute length of the single-end reads is about 15 contiguous nucleotides to about 20 contiguous nucleotides.
A51. The method of embodiment A50, wherein the nominal, average, mean or absolute length of the single-end reads is about 17 contiguous nucleotides or about 18 contiguous nucleotides.
A52. The method of embodiment A1, which comprises indicating that the presence or absence of an aneuploidy cannot be determined when appropriate.
B1. A computer program product, comprising a computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method for identifying the presence or absence of a chromosomal aneuploidy in a fetus of a pregnant female, the method comprising:
providing a system that comprises distinct software modules comprising a detection module, a logic processing module, and a data display organization module; collecting, by the detection module, (a) nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid; and (b) nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid; receiving, by the logic processing module, the nucleotide sequences; aligning, by the logic processing module, the nucleotide sequences of (a) to a portion of a maternal reference sequence and counting the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence, thereby determining a number of counts; calling the presence or absence of a chromosomal aneuploidy in the fetus by the logic processing module based on the number of counts; organizing, by the data display organization model in response to being called by the logic processing module, a data display indicating the presence or absence of the chromosomal aneuploidy.

B2. A computer program product, comprising a computer usable medium having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method for identifying the presence or absence of a chromosomal aneuploidy in a fetus of a pregnant female, the method comprising:
providing a system that comprises distinct software modules comprising a data processing module, a logic processing module and a data display organization module; parsing, by the data processing module, a configuration file comprising (a) nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid, and (b) nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid into definition data; receiving, by the logic processing module, the definition data; aligning, by the logic processing module, nucleotide sequences of (a) to a portion of a maternal reference sequence and counting the number of nucleotide sequences of (a) that map to the portion of the maternal reference sequence, thereby determining a number of counts; calling the presence or absence of a chromosomal aneuploidy by the logic processing module based on the number of counts; organizing, by the data display organization model in response to being called by the logic processing module, a data display indicating the presence or absence of the chromosomal aneuploidy in the fetus of the pregnant female.

B3. The computer program produce of embodiment B1 or B2, comprising assembling, by the logic processing module, the maternal reference sequence from the nucleotide sequences of (b).
B4. An apparatus, comprising memory in which a computer program product of any one of embodiments B1 to B3 is stored.
B5. The apparatus of embodiment B4, which comprises a processor that implements one or more functions of the computer program product specified in any one of embodiments B1 to B3.
C1. A kit comprising one or more components for (a) determining nucleotide sequences corresponding to extracellular nucleic acid from the pregnant female, the extracellular nucleic acid including cell-free fetal nucleic acid; and (b) determining nucleotide sequences corresponding to all or a portion of nucleic acid from the pregnant female containing substantially no fetal nucleic acid.
C2. The kit of embodiment C1, comprising one or more components for processing a nucleic acid sample from the pregnant female.
C3. The kit of embodiment C1 or C2, comprising directions, or information for obtaining directions, which directions are for conducting a method of any one of embodiments A1 to A52.
The entirety of each patent, patent application, publication and document referenced herein hereby is incorporated by reference. Citation of the above patents, patent applications, publications and documents is not an admission that any of the foregoing is pertinent prior art, nor does it constitute any admission as to the contents or date of these publications or documents.
Modifications may be made to the foregoing without departing from the basic aspects of the technology. Although the technology has been described in substantial detail with reference to one or more specific embodiments, those of ordinary skill in the art will recognize that changes may be made to the embodiments specifically disclosed in this application, yet these modifications and improvements are within the scope and spirit of the technology.
The technology illustratively described herein suitably may be practiced in the absence of any element(s) not specifically disclosed herein. Thus, for example, in each instance herein the term “comprising,” may be replaced with “consisting essentially of” and “consisting of”. The terms and expressions which have been employed are used as terms of description and not of limitation, and use of such terms and expressions do not exclude any equivalents of the features shown and described or portions thereof, and various modifications are possible within the scope of the technology claimed. The term “a” or “an” can refer to one of or a plurality of the elements it modifies (e.g., “a reagent” can mean one or more reagents) unless it is contextually clear either one of the elements or more than one of the elements is described. Further, when a listing of values is described herein (e.g., about 50%, 60%, 70%, 80%, 85% or 86%) the listing includes all intermediate and fractional values thereof (e.g., 54%, 85.4%), and use of the term “about” at the beginning of a list of values pertains to each of the values in the listing (e.g., “about 1, 2 and 3” refers to about 1, about 2 and about 3. Thus, it should be understood that although the present technology has been specifically disclosed by representative embodiments and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and such modifications and variations are considered within the scope of this technology.
Certain embodiments of the technology are set forth in the claim(s) that follow(s).