TW201421273A - Non-invasive detection of fetus genetic abnormality - Google Patents

Abstract

The invention relates to non-invasive method of detecting fetal genetic polymorphism through large-scale sequencing of maternal Biological sample nucleotides. The invention also provides method of removing sequencing GC deviation caused by chromosome GC content difference. The invention not only makes detection more accurate, but also provides an integrated approach to detecting fetal aneuploidy including sex chromosome disease such as XO, XXX, XXY, XYY.

Description

Non-invasive detection of fetal genetic abnormalities

The present invention relates to a non-invasive method for detecting fetal genetic abnormalities by DNA sequencing of samples from pregnant women. More specifically, the present invention relates to data analysis to remove GC bias introduced by amplification and sequencing of DNA samples. The invention also relates to statistical analysis for the purpose of detecting fetal genetic abnormalities, such as chromosomal abnormalities including aneuploidy.

Conventional prenatal diagnostic methods including invasive procedures such as chorionic villus sampling and amniocentesis have potential risks for both the fetus and the mother. Non-invasive screening for fetal aneuploidy using maternal serum markers and ultrasound is feasible, but with limited sensitivity and specificity (Kagan, et al. , Human Reproduction (2008) 23: 1968-1975; Malone, et Al. , N Engl J Med (2005) 353: 2001-2011).

Recent studies have demonstrated that non-invasive detection of fetal aneuploidy is feasible by large-scale parallel sequencing of DNA molecules in maternal plasma. Fetal DNA has been detected and quantified in maternal plasma and serum (Lo, et al. , Lancet (1997) 350:485487; Lo, et al. , Am. J. hum. Genet. (1998) 62:768 -775). A variety of fetal cell types appear in the maternal circulatory system, including fetal granulocytes, lymphocytes, nucleated red blood cells, blood cells, and trophoblasts (Pertl and Bianchi, Obstetrics and Gynecology (2001) 98: 483-490). Fetal DNA can be detected in serum at week 7 of pregnancy and increases with gestation. The fetal DNA present in maternal serum and plasma is comparable to the DNA obtained from the method of separation from fetal cells.

Cyclic fetal DNA has been used to determine the sex of the fetus (Lo, et al. , Am. J. hum. Genet. (1998) 62:768-775). At the same time, the fetal rhesus D genotype has been detected using fetal DNA. However, diagnostic applications and clinical applications of circulating fetal DNA are limited to genes that are present in the fetus but not present in the mother (Pertl and Bianchi, Obstetrics and Gynecology (2001) 98: 483-490). Therefore, there remains a need for a non-invasive method that can determine fetal DNA sequences and provide a definitive diagnosis of fetal chromosomal abnormalities.

The discovery of fetal cells and cell-free fetal nucleic acids in maternal blood over the past few decades and high-throughput shotgun sequencing of maternal plasma cell-free DNA makes it feasible to detect chromosomes in maternal plasma samples caused by aneuploidy fetuses. Small changes. Non-invasive detection of trisomy 13, 18 and 21 pregnancies has been achieved.

However, as some studies have shown, the sensitivity of GC bias introduced by amplification and sequencing to aneuploidy detection creates operational limitations. Under different conditions such as reagent composition, cluster density and temperature, GC bias may be introduced during sample preparation and sequencing, which results in differential sampling of DNA molecules with different GC compositions and sequencing data for GC-rich or GC-free chromosomes. Significant deviation.

In order to increase sensitivity, methods for removing GC bias effects have been developed. Fan and Quake developed a method to remove GC bias by calculation, which weights each GC density based on local genomic GC content to improve the mapping mapped to each bin by multiplying the corresponding weights (read) number (Fan and Quake PLoS ONE (2010) 5: e10439). However, this method has difficulty in dealing with sexual chromosomal disorders, particularly Y-chromosome-related disorders, because the method may cause slight distortion of the data, which may interfere with the accuracy of the assay.

In this context, the inventors describe a method for removing GC bias by calculation, in order to obtain a higher sensitivity to fetal genetic abnormality detection in addition to avoiding data distortion. The method defines parameters for statistical testing based on GC content. In addition, the inventors introduced the estimated fetal fraction into the diagnosis by a binary hypothesis showing higher sensitivity and specificity. The inventors' method also shows that for parent samples containing low fetal DNA fractions, by sequencing more polynucleotide fragments, it is possible to increase the sensitivity to non-invasive detection of fetal genetic abnormalities to a preset accuracy. Re-sampling the maternal plasma at a later gestational age can also increase the sensitivity of the diagnosis.

The present invention relates to a method for non-invasive detection of fetal genetic abnormalities by large scale sequencing of nucleotides from maternal biological samples. A method of removing the GC bias of the sequencing result due to the difference in the GC content of the chromosome is also provided.

Thus, in one aspect, provided herein is a method for establishing a relationship between a depth of coverage of a chromosome and a GC content, the method comprising: obtaining a sequence of a plurality of polynucleotide fragments encompassing the chromosome from more than one sample a message; assigning the segment to a chromosome based on the sequence message; calculating a depth of coverage and a GC content of the chromosome based on the sequence message for each sample; and determining a relationship between a depth of coverage of the chromosome and a GC content .

In one embodiment, the polynucleotide fragment has a length interval of from about 10 to about 1000 bp. In another embodiment, the polynucleotide fragment has a length interval of from about 15 to about 500 bp. In yet another embodiment, the polynucleotide fragment has a length interval of from about 20 to about 200 bp. In still another embodiment, the polynucleotide fragment The length interval is from about 25 to about 100 bp. In another embodiment, the polynucleotide fragment is about 35 bp in length.

In one embodiment, the sequence message is obtained by parallel genome sequencing. In another embodiment, assigning the fragment to a chromosome is performed by comparing the sequence of the fragment to a reference human genomic sequence. The reference human genomic sequence can be any suitable and/or published human genome build, such as hg18 or hg19. Fragments assigned to more than one chromosome or not assigned to either chromosome can be ignored.

In one embodiment, the depth of coverage of a chromosome is the ratio between the number of fragments assigned to the chromosome and the number of reference unique reads of the chromosome. In another embodiment, the depth of coverage is standardized. In yet another embodiment, normalization is calculated relative to coverage of all other autosomes. In yet another embodiment, the normalization is calculated relative to the coverage of all other chromosomes.

In one embodiment, the relationship is the following formula: cr _{i , j} = f ( GC _{i , j} ) + ε _{i , j} , j =1, 2, ..., 22, X , Y

Where f (GC _i,j ) represents the relationship between the normalized depth of coverage of sample i, chromosome j and the corresponding GC content, ε _i,j represents the residual of sample i, chromosome j. In some embodiments, the relationship between depth of coverage and GC content is calculated by local polynomial regression. In some embodiments, the relationship can be a weak linear relationship. In some embodiments, the relationship is determined by a loess algorithm.

In some embodiments, the method further comprises calculating a fit coverage depth according to the following formula:

In some embodiments, the method further comprises calculating a standard deviation according to the following formula: Where ns represents the number of reference samples.

In some embodiments, the method further comprises calculating a student t statistic according to the following formula:

In one embodiment, the GC content of the chromosome is the average GC content of all fragments assigned to the chromosome. The GC content of a fragment can be calculated by dividing the number of G/C nucleotides of the fragment by the total number of nucleotides of the fragment. In another embodiment, the GC content of the chromosome is the aggregated GC content of the reference unique reads of the chromosome.

In some embodiments, at least 2, 5, 10, 20, 50, 100, 200, 500 or 1000 samples are used. In some embodiments, the chromosome is chromosome 1, 2, ..., 22, X or Y.

In one embodiment, the sample is from a pregnant female subject. In another embodiment, the sample is from a male subject. In yet another embodiment, the sample is from both a pregnant female subject and a male subject.

In some embodiments, the sample is a biological sample. In some embodiments, the sample is a peripheral blood sample.

Also provided herein is a method of detecting a genetic abnormality in a fetus comprising: a) obtaining a sequence message of a plurality of polynucleotide fragments from a sample; b) assigning the fragment to a chromosome based on the sequence message; c) Sequence message calculates chromosome coverage Depth and GC content; d) calculating the fitted coverage depth of the chromosome using the GC content of the chromosome and the established relationship between the depth of coverage of the chromosome and the GC content; and e) fitting the chromosome The depth of coverage is compared to the depth of coverage, where the difference between them indicates fetal genetic abnormalities.

In some embodiments, the method further comprises the step of f) determining the sex of the fetus. The fetal gender can be determined according to the following formula: Where cr.a _{i , x} and cr.a _{i , y} are the normalized relative coverage of the X and Y chromosomes _, respectively.

In some embodiments, the method further comprises the step of g) estimating the fetal fraction. The fetal score can be calculated according to the following formula: among them Is the fitted coverage depth calculated from the relationship between the chromosome Y coverage depth and the corresponding GC content of a sample from a pregnant woman with a female fetus, Refers to the fitted coverage depth calculated from the relationship between the chromosome Y coverage depth of the male subject and the corresponding GC content. Alternatively, the fetal fraction can be calculated according to the following formula: among them Is the fitted coverage depth calculated from the relationship between the chromosome X coverage depth and the corresponding GC content of a sample from a pregnant woman with a female fetus, Refers to the fitted coverage depth calculated from the relationship between the chromosome X coverage depth of the male subject and the corresponding GC content. Furthermore, the fetal fraction can be calculated according to the following formula: among them Is the fitted coverage depth calculated from the relationship between the chromosome X coverage depth and the corresponding GC content of a sample from a pregnant woman with a female fetus, Refers to the fitted coverage depth calculated from the relationship between the chromosome Y coverage depth and the corresponding GC content of a sample from a pregnant woman with a female fetus. Refers to the fitted coverage depth calculated from the relationship between the chromosome X coverage depth of the male subject and the corresponding GC content, Refers to the fitted coverage depth calculated from the relationship between the chromosome Y coverage depth of the male subject and the corresponding GC content.

In one embodiment, the genetic abnormality is a chromosomal abnormality. In another embodiment, the genetic abnormality is aneuploidy. In yet another embodiment, the fetal aneuploidy is an autosomal disorder selected from the group consisting of trisomy 13, 18 and 21. In still another embodiment, the fetal aneuploidy is a sex chromosome disorder selected from the group consisting of XO, XXX, XXY, and XYY.

In some embodiments, comparing the fitted coverage depth of the chromosome to the depth of coverage is performed by a statistical hypothesis test, wherein one hypothesis is that the fetus is euploid (H0) and the other hypothesis is that the fetus is non-holistic Ploid (H1). Statistics can be calculated for these two assumptions. In some embodiments, the student t statistic is calculated for H0 and H1, respectively, according to the following formula: with , where fxy is the fetal fraction. In some embodiments, the log likelihood ratios of t1 and t2 are calculated according to the following formula: L _{i , j} =log( p (t1 _{i , j} ,degree | D))/log( p ( t 2 _{i , j} ,degree |T)), where degree is the degree of t distribution, D is diploid, T is trisomy, p (t1 _{i , j} ,degree | ^* ), ^* =D, and T is the given degree of t distribution. Conditional probability density.

In one embodiment, the fetal gender is female and the student t statistic is calculated according to the following formula: ,among them It is the fitted coverage depth calculated from the relationship between the chromosome X coverage depth and the corresponding GC content of a sample from a pregnant woman with a female fetus. In some embodiments, |t1|>3.13 means that the fetus may be XXX or XO. In some embodiments, |t1|>5 indicates that the fetus is XXX or XO.

In another embodiment, the fetal gender is male and the student t statistic is calculated according to the following formula: ,among them It is the fitted coverage depth calculated from the relationship between the chromosome X coverage depth and the corresponding GC content of a sample from a pregnant woman with a female fetus. In some embodiments, |t2|>3.13 indicates that the fetus may be XXY or XYY. In some embodiments, |t2|>5 indicates that the fetus is XXY or XYY.

Also provided herein is a method of determining a genetic abnormality in a fetus comprising: a) obtaining a sequence message covering a plurality of polynucleotide fragments of a chromosome of interest from more than one normal sample; b) said fragment based on said sequence message Assigning to the chromosome; c) calculating the depth of coverage and GC content of the chromosome based on the sequence information of the normal sample; d) determining the relationship between the depth of coverage of the chromosome and the GC content; e) obtaining a plurality of multinuclei from the biological sample Sequence information of the nucleotide fragment; f) based on the sequence from the biological sample a column message assigns the fragment to a chromosome; g) calculating a depth of coverage and a GC content of the chromosome based on a sequence message of the biological sample; h) using a GC content of the chromosome and a depth of coverage and GC content of the chromosome The relationship between the calculated coverage depths of the chromosomes; and i) comparing the fitted coverage depth of the chromosomes to the depth of coverage, wherein the difference between them indicates a fetal genetic abnormality.

In another aspect, provided herein is a computer readable medium comprising a plurality of instructions for performing prenatal diagnosis of a fetal genetic abnormality, the process comprising the steps of: a) receiving a sequence of a plurality of polynucleotide fragments from a sample a message; b) assigning the polynucleotide fragment to a chromosome based on the sequence message; c) calculating a depth of coverage and a GC content of the chromosome based on the sequence message; d) using a GC content of the chromosome and establishing the The relationship between the depth of coverage of the chromosome and the GC content calculates the fitted coverage depth of the chromosome; and e) compares the fitted coverage depth of the chromosome to the depth of coverage, wherein the difference between them indicates a fetal genetic abnormality.

In yet another aspect, provided herein is a system for detecting a genetic abnormality in a fetus comprising: a) means for obtaining a sequence message of a plurality of polynucleotide fragments from a sample; and b) comprising for performing a genetic abnormality of the fetus A computer readable medium for pre-diagnosing multiple instructions. In some embodiments, the system further comprises a biological sample obtained from a pregnant female subject, wherein the biological sample comprises a plurality of polynucleotide fragments.

The present invention relates to a method for non-invasive detection of fetal genetic abnormalities by large-scale sequencing of polynucleotide fragments from maternal biological samples. It also provides a relationship between the depth of chromosome-based coverage and the corresponding GC content to remove the difference in GC content due to chromosomes. The method of sequencing the GC deviation. Accordingly, a method is provided herein to fit the depth of coverage of the chromosome content of each sample relative to the GC content of the polynucleotide fragment by locally weighted polynomial regression, thereby calculating the Student t calculation for correction of GC content by calculation. Reference parameters.

This paper also provides a method for determining fetal genetic abnormalities by using statistical analysis of statistical hypothesis testing. In addition, methods are provided to calculate data quality control (DQC) criteria that can be used to determine the amount of clinical sample required for a particular statistical significance level.

I. Definition

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art. All patents, patent applications, published patent applications, and other publications are hereby incorporated by reference in their entirety in their entirety in their entirety in their entirety. If the definitions listed in this section are contrary or otherwise inconsistent with the definitions listed in the patents, patent applications, published patent applications and other publications incorporated by reference, the definitions listed in this section take precedence over The references are incorporated into the definitions in this article.

The singular forms "a", "an" and "the" For example, a "one" two body includes one or plural two bodies.

The term "chromosomal abnormality" refers to the deviation between the structure of a subject's chromosome and a normal homologous chromosome. The term "normal" refers to the predominant karyotype or banding pattern that occurs in a normal individual of a particular species. Chromosomal abnormalities can be number or structural, including but not limited to aneuploidy, polyploidy, inversion, trisomy, haplotype, repeat, deletion, partial staining Loss, increase, partial chromosome increase, insertion, chromosome fragmentation, chromosomal region, chromosomal rearrangement, and translocation. Chromosomal abnormalities may be associated with the presence of pathological conditions or with the predisposition to pathological conditions. Single nucleotide polymorphisms ("SNPs") as defined herein are not chromosomal abnormalities.

Monosomy X (XO, missing the entire X chromosome) is the most common type of Turner syndrome, with 1 in every 2,500 to 3,000 newborn girls (Sybert and McCauley N Engl J Med (2004) 351:1227- 1238). XXY syndrome is a condition in which men have an extra X chromosome, and approximately 1 out of every 1,000 males (Bock, Understanding Klinefelter Syndrome: A Guide for XXY Males and Their Families. NIH Pub. No. 93-3202 (1993)). XYY syndrome is a sex chromosome aneuploidy in men with an extra Y chromosome, with 47 chromosomes instead of normal 46, and 1 in 1000 males, and may lead to male infertility (Aksglaede, et al. , J Clin Endocrinol Metab (2008) 93: 169-176).

Turner syndrome includes several conditions in which monomeric X (XO, lacking the entire sex chromosome, Pap corpuscle) is most common. Women usually have two X chromosomes, but one of these sex chromosomes is missing in Turner syndrome. One case occurred in 2,000 to 5,000 phenotype women, and the syndrome appeared in a variety of ways. Cranfield syndrome is a condition in which men have an extra X chromosome. In humans, Kranfeldt syndrome is the most common sex chromosome disorder and is the second most common condition caused by the presence of extra chromosomes. The condition occurs in approximately 1 in every 1,000 males. XYY syndrome is a sex chromosome aneuploidy in men with an extra Y chromosome, with 47 chromosomes instead of the normal 46. This produces a 47, XYY karyotype. The condition is usually asymptomatic, with 1 in 1000 males, which may lead to male infertility.

Trisomy 13 (Pata Syndrome), Trisomy 18 (Edward Syndrome), and Trisomy 21 (Down Syndrome) are the most important autosomal trisomy in the clinic. How to detect them has always been a hot spot. Detection of the above fetal chromosomal aberrations is important in prenatal diagnosis (Ostler, Diseases of the eye and skin: a color atlas. Lippincott Williams & Wilkins.pp. 72. ISBN 9780781749992 (2004); Driscoll and Gross N Engl J Med (2009) ) 360: 2556-2562; Kagan, et al. , Human Reproduction (2008) 23: 1968-1975).

The term "reference unique read" refers to a chromosomal segment with a unique sequence. Therefore, such fragments can be clearly assigned to single chromosome localization. Reference unique reads of chromosomes can be constructed based on published reference genome sequences such as hg18 or hg19.

The terms "polynucleotide", "oligonucleotide", "nucleic acid" and "nucleic acid molecule" are used interchangeably herein to refer to a polynucleotide of any length in the form of a multimer, which may include ribonucleotides, Deoxyribonucleotides, analogs thereof or mixtures thereof. This term refers only to the primary structure of the molecule. Thus, the terms include triple-stranded, double-stranded, and single-stranded deoxyribonucleic acids ("DNA") as well as three-stranded, double-stranded, and single-stranded ribonucleic acids ("RNAs"). It also includes modified (eg, by alkylation and/or by capping) polynucleotides and non-modified forms of polynucleotides. More specifically, the terms "polynucleotide", "oligonucleotide", "nucleic acid" and "nucleic acid molecule" include polydeoxyribonucleotides (including 2-deoxy-D-ribose), polyribonucleotides ( Containing D-ribose), including tRNA, rRNA, hRNA and spliced or unspliced mRNA, any other type of polynucleotide that is an N-glycoside or C-glycoside of a purine or pyrimidine base, and a non-nucleotide-containing backbone other polymers, such as polyamides (such as a peptide nucleic acid ( "PNA")) and multi-morpholino (commercially available from Anti-Virals, Inc., Corvallis, OR., for example NeuGene ^®) and other polymer Synthetic sequence-specific nucleic acid multimers, provided that the multimer comprises nucleobases in a configuration that allows for base pairing and base stacking, for example, found in DNA and RNA. Thus, these terms include, for example, 3'-deoxy-2', 5'-DNA, oligodeoxyribonucleotide N3'-P5' phosphite, 2'-O-alkyl substituted RNA, DNA and RNA Intermixes between PNA and DNA or RNA, including known types of modifications, such as labels, alkylates, "caps", one or multiple nucleotide substitutions, analogs, internucleotide modifications (eg, those having uncharged linkers (eg, methyl phosphate, phosphotriester, phosphite, carbamate, etc.), those having a negatively charged linker (eg, phosphonium sulfonate, dithiophosphate) And those having a positively charged linker (eg, aminoalkylphosphonium, aminoalkyl phosphate), including pendant moieties such as proteins (including enzymes (eg, nucleases), toxins, antibodies, signal peptides, Those of poly L-lysine, etc., having an intercalating agent (for example, acridine, psoralen, etc.), containing a chelate (for example, a chelate of a metal, a radioactive metal, boron, a metal oxide, etc.) Those containing alkylates, those having modified linkers (eg, alpha anomeric nucleic acids); and unrepaired The form of a polynucleotide or oligonucleotide.

"Large-scale parallel sequencing" refers to techniques for sequencing millions of nucleic acid fragments, for example by attaching randomly fragmented genomic DNA to a light-transmissive plane and performing solid-phase amplification to form millions of clusters. High density sequencing flow cells, each cluster containing approximately 1000 copies of the template per square centimeter. These templates were sequenced using 4-color DNA synthesis and sequencing techniques. See Illumina, Inc., San Diego, Calif for products. The sequencing used in the present invention is preferably carried out without a pre-amplification or cloning step, but can be combined with an amplification-based method in a microfluidic wafer having a reaction chamber that can be used for both PCR and microtemplate-based sequencing. Combine. Only a random sequence message of approximately 30 bp is required to determine the sequence as belonging to a particular human chromosome. Longer sequences can uniquely identify more specific targets. In this example, a large number of 35 bp reads are obtained. Further description of large scale parallel sequencing methods can be found in Rogers and Ventner, Nature (2005) 437:326-327.

As used herein, "biological sample" refers to any sample obtained from macromolecules and biomolecules of a living or viral source or other source, including any cell type or tissue of a subject from which a nucleic acid, protein or other macromolecule can be obtained. The biological sample can be a sample obtained directly from a biological source, or a processed sample. For example, an amplifiable isolated nucleic acid constitutes a biological sample. Biological samples include, but are not limited to, body fluids such as blood, plasma, serum, cerebrospinal fluid, synovial fluid, urine, and perspiration; tissue and organ samples of automated plants and processed samples obtained therefrom.

The phrase "covering the chromosome" means that the sequence information of the plurality of nucleic acid fragments can cover at least a part of the chromosome, that is, for example, the sequence information of the plurality of nucleic acid fragments can cover a part of the chromosome, and the sequence information of the plurality of nucleic acid fragments can be covered. A sequence message to a portion of the chromosome and a portion of at least one other chromosome, the plurality of nucleic acid fragments can cover a portion of the chromosome and all of the at least one other chromosome, and the sequence information of the plurality of nucleic acid fragments can cover all of the chromosome The sequence information of the plurality of nucleic acid fragments can cover all of the chromosomes and a portion of at least one other chromosome, or the sequence information of the plurality of nucleic acid fragments can cover all of the chromosomes and all of the at least one other chromosome.

It will be understood that aspects and embodiments of the invention described herein include aspects and embodiments of "consisting of" and/or "consisting essentially of."

Other objects, advantages and features of the present invention will become apparent from the following detailed description. Will become clear.

II. Establish the relationship between depth of coverage and GC content

Provided herein is a method for establishing a relationship between a depth of coverage of a chromosome and a GC content, the method comprising: obtaining a sequence message covering a plurality of polynucleotide fragments of the chromosome from more than one sample; based on the sequence A message assigns the fragment to a chromosome; calculating a depth of coverage and a GC content of the chromosome based on the sequence message for each sample; and determining a relationship between a depth of coverage of the chromosome and a GC content. The operational steps can be performed in a non-specific order. In some embodiments, the method can be performed in the following order: a) obtaining a sequence message covering a plurality of polynucleotide fragments of the chromosome from more than one sample; b) assigning the fragment to the sequence based on the sequence message Chromosome; c) calculating the depth of coverage and GC content of the chromosome based on the sequence message for each sample; and d) determining the relationship between the depth of coverage of the chromosome and the GC content.

To calculate the depth of coverage and GC content of the chromosomal region, the sequence information of the polynucleotide fragment is obtained by sequencing the template DNA obtained from the sample. In one embodiment, the template DNA comprises both maternal DNA and fetal DNA. In another embodiment, the template DNA is obtained from the blood of a pregnant woman. Blood can be collected using any conventional technique for blood collection, including but not limited to venipuncture. For example, blood can be taken from the inside of the elbow or the vein on the back of the hand. Blood samples can be collected from pregnant women at any time during pregnancy. For example, a blood sample can be pregnant at 1 □ 4, 4 □ 8, 8 □ 12, 12 □ 16, 16 □ 20, 20 □ 24, 24 □ 28, 28 □ 32, 32 □ 36, 36 □ 40 or 40 □ 44 Week, preferably 8-28 weeks of gestation, collected from pregnant women.

The polynucleotide fragment is assigned to a chromosomal region based on the sequence message. Reference The genomic sequence is used to obtain the reference unique reads. The term "reference unique read" as used herein refers to all unique polynucleotide fragments that are assigned to a specific genomic region based on a reference genomic sequence. In some embodiments, the reference unique reads have the same length, such as about 10, 12, 15, 20, 25, 30, 35, 40, 50, 100, 200, 300, 500, or 1000 bp. In other embodiments, the human genome version hg18 or hg19 can be used as the reference genomic sequence. Chromosome localization is about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000 on the chromosome. A continuous window of 4000, 5000, 6000, 7000, 8000, 9000, 10,000 or more KB. The chromosomal location can also be a single chromosome.

The term "coverage depth" as used herein refers to the ratio between the number of segments assigned to a chromosomal region and the number of reference unique reads for that chromosomal region, calculated using the following formula: C _{i , j} = n _{i , j} / N _j , j =1,2,...,22, X , Y (1)

Where n _{i , j} is the number of unique sequence reads mapped to chromosome _j in sample i; C _{i , j} is the depth of coverage of chromosome j in sample i; N _j is the number of reference unique reads in chromosome j.

In some embodiments, polynucleotide fragments that are not assigned to a single chromosomal region or that are assigned to a plurality of chromosomal regions are ignored. In some embodiments, the depth of coverage is based on the depth of coverage of another chromosomal region, the depth of coverage of another chromosome, the average coverage depth of all other autosomes, the average coverage depth of all other chromosomes, or the average coverage depth of all chromosomes. And standardized. In some embodiments, the average coverage depth of 22 autosomes is used as a normalization constant to calculate the difference in the total number of sequence reads obtained for different samples:

Where cri,j represents the relative depth of coverage of chromosome j in sample i. From now on, the "relative coverage depth" of each chromosome refers to a standardized value that is used to compare different samples and for subsequent analysis.

The GC content of the chromosomal location can be calculated based on a unique reference read in the chromosomal location or based on the average GC percentage of the chromosomal location of the sequenced polynucleotide fragments assigned to the chromosomal location. The CC content of a chromosome can be calculated using the following formula: GC _{i , j} = NGC _{i , j} / BASE _{i , j} (3)

Where i represents sample i, j represents chromosome j, NGC _i,j represents the number of G and CDNA bases on chromosome j in sample i, and BASE _i,j represents the number of DNA bases on chromosome j in sample i.

The depth of coverage and GC content can be based on sequence information of polynucleotide fragments obtained from a single sample or from a plurality of samples. To establish the relationship between the depth of coverage of the chromosomal region and the GC content, the calculations can be based on polynucleotide fragments obtained from at least 1, 2, 5, 10, 20, 50, 100, 200, 500 or 1000 samples. Sequence message.

In some embodiments, the relationship between depth of coverage and GC content is a non-linear relationship.

The Loess algorithm or local weighted polynomial regression can be used to estimate the non-linear relationship (correlation) between pairs of values, such as coverage depth and GC content.

III. Determining fetal genetic abnormalities

Also provided herein is a method of determining a genetic abnormality in a fetus comprising: a) obtaining a sequence message of a plurality of polynucleotide fragments from a sample; b) assigning the fragment to a chromosome based on the sequence message; c) The sequence message calculates the depth of coverage and GC content of the chromosome; d) calculates the fitted coverage depth of the chromosome using the GC content of the chromosome and establishes the relationship between the depth of coverage of the chromosome and the GC content; and e) The fitting coverage depth and coverage depth of the chromosome are compared, wherein the difference between them indicates fetal genetic abnormality.

The method can be used for detecting fetal chromosomal abnormalities, and can be particularly used for detecting aneuploidy, polyploidy, monosomy, trisomy, trisomy 21, trisomy 13, trisomy 14, trisomy. 15. Trisomy 16, trisomy 18, trisomy 22, triploid, tetraploid and sex chromosome abnormalities, including XO, XXY, XYY and XXX. It is also possible to focus on certain regions of the human genome in accordance with the methods of the invention, with the aim of identifying partial and partial trisomies. For example, the method can involve analyzing sequence data in a determined chromosome sliding "window", such as a continuous, non-overlapping 50 Kb region distributed over the entire chromosome. Partial trisomy 13q, 8p (8p23.1), 7q, distal 6p, 5p, 3q (3q25.1), 2q, 1q (1q42.1 and 1q21-qter), part, have been reported, among others Xpand monomeric 4q35.1. In the case of 18q21.1-qter repeats, partial duplication of the long arm of chromosome 18 can lead to Edwards syndrome (Mewar, et al. , Am J Hum Genet. (1993) 53:1269-78).

In some embodiments, the fetal fraction is estimated based on sequence information obtained for a polynucleotide fragment from a sample. The depth of coverage and GC content of chromosomes X and Y can be used to estimate the fetal fraction. In some embodiments, the fetal The sequence information obtained from the polynucleotide fragment from the sample is not determined based on the sequence information. The depth of coverage and GC content of chromosomes X and Y can be used to determine the sex of the fetus.

In some embodiments, the fitted coverage depth of the chromosome is compared to the depth of coverage by statistical hypothesis testing, wherein one hypothesis is that the fetus is euploid (H0) and the other hypothesis is that the fetus is aneuploid Body (H1). In some embodiments, the student t statistic is calculated for the two hypotheses, respectively, as t1 and t2. In some embodiments, the log likelihood ratios of t1 and t2 are calculated. In some embodiments, a log likelihood ratio > 1 indicates the fetal trisomy.

IV. Computer readable medium and system for diagnosing fetal genetic abnormalities

In another aspect, provided herein is a computer readable medium comprising a plurality of instructions for performing prenatal diagnosis of a fetal genetic abnormality, the working process comprising the steps of: a) receiving said sequence message; b) based on said The sequence message assigns the polynucleotide fragment to the chromosome; c) calculates the depth of coverage and GC content of the chromosome based on the sequence message; d) uses the GC content of the chromosome and the established depth of coverage and GC content of the chromosome The relationship between the calculated coverage depths of the chromosomes; and e) comparing the fitted coverage depth of the chromosomes to the depth of coverage, wherein the difference between them indicates a genetic abnormality.

In yet another aspect, provided herein is a system for detecting fetal aneuploidy comprising: a) means for obtaining a sequence message of said plurality of polynucleotide fragments; and b) comprising for performing a fetus A computer readable medium of multiple instructions for prenatal diagnosis of genetic abnormalities. In some embodiments, the system further comprises a biological sample obtained from a pregnant female subject, wherein the biological sample comprises a plurality of polynucleotide fragments.

It will be apparent to those of ordinary skill in the art that several different sequencing methods and variants can be used. In one embodiment, the sequencing is performed using massively parallel sequencing. Massively parallel sequencing, for example, the platform 454 (Roche) (Margulies, et al , Nature (2005) 437:. 376-380), Illumina Genome Analyzer ( or Solexa ^TM platform) or SOLiD System on (Applied Biosystems) or using Helicos True single molecule DNA sequencing technology (Harris, et al, Science ( 2008) 320:. 106-109), Pacific Biosciences single molecule, real-time (SMRT ^TM) technology, and nanopore sequencing (Soni and Meller, Clin Chem ( 2007) 53: 1996-2001) those achieved such that many nucleic acid molecules isolated from the sample can be sequenced in a high-order multiplex in a parallel manner (Dear, Brief Funct Genomic Proteomic (2003) 1: 397-416). Each of these platforms can sequence clonal amplified or even unamplified nucleic acid fragment single molecules. Commercially available sequencing equipment can be used to obtain sequence information for polynucleotide fragments.

V. Example

The following examples are presented to illustrate the invention but not to limit it.

Example 1 Analysis of factors affecting detection sensitivity: GC bias and gender

The principle step framework for calculating the depth of coverage and GC content is shown in Figure 1. The inventors used software to cut the hg18 reference sequence into l-mers (where the l-mer is a read segment that is manually decomposed from the human sequence reference by the same length "l" as the sample sequencing read). Collect these "unique" l-mers as instructor's reference unique reads. Second, the inventors mapped their sequencing sample reads to a reference unique read of each chromosome. Third, the inventor removes outliers by applying a one-fifth outlier cutoff to obtain a clean data set. Finally, the inventor calculates the depth of coverage for each chromosome for each sample and calculates a mapping to each stain for each sample. The GC content of the unique reads of the sequencing of the body.

To investigate how GC content affects the inventor's data, the inventors selected 300 euploid cases with karyotype results and spread their sequencing read coverage depth and associated GC content to the graph, which shows The strong correlation between them has not been reported before (Figure 2). In Fig. 2, the depth of coverage is strongly correlated with the GC-content, showing a clear downward trend in some chromosomes such as 4, 13, etc., and an upward trend in other chromosomes such as 19, 22, and the like. All chromosomes were arranged in ascending order of their intrinsic GC-content. As shown in Figure 3, the downward trend was present in the lower GC-content group chromosomes, while the upward trend was present in the higher GC-content group chromosomes. This can be explained by the fact that if a polynucleotide fragment sequenced for one sample has a higher GC-content than other samples, the sample exhibits a depth of coverage compared to the depth of coverage of other samples in the lower GC-content group chromosome. The lieutenant will decline and will rise in the higher GC-content group chromosomes.

A possible explanation for this different trend in different GC-content chromosomes is the difference in GC-content composition in the different chromosomes shown in Figure 4, combined with the GC bias introduced during sequencing. The GC content for each 35-mer reference unique read for each chromosome was used to classify the GC content into 36 levels. Calculate the percentage of each level of GC composition as each chromosome and then use it to draw a heat map with the Heatmap2 software. Taking chromosome 13 as an example, most of it consists of lower GC-content sequence segments, but a small portion consists of higher GC-content sequence segments. If the conditions in the sequencing or PCR process favor the sequencing of these higher GC-content segments, then a larger portion of chromosome 13 with a low GC-content will be difficult to sequence, resulting in a change in the depth of coverage of chromosome 13 in the sample. Got lower. In contrast, in higher GC-content groups such as chromosome 19 In this sample, the depth of coverage of chromosome 19 becomes higher because most of chromosome 19 has a higher GC-content than that preferred by the sequencer. Regardless of the chromosome, the less GC-containing segments and the GC-rich segments are difficult to sequence, but the effects of GC bias are different for different chromosomes with different GC-content compositions. Each reference chromosome was divided into 1 KB segments and the GC content of each unique reference read in the segment was calculated. The GC content of each segment present in the appropriate spacing structure [0.3, 0.6] was divided by the step size of 0.001, and then the relative coverage of each interval was calculated. Figure 5 shows a plot of relative coverage and GC content for each chromosome.

The t-test of two independent samples was used to analyze the effect of fetal gender on the data. For GC content, no significant differences were found between autosomes other than sex chromosomes, but there was a significant difference in UR% between women and men (Chiu et al., (2008) Proc Natl Acad Sci USA 105:20458- 20463), suggesting that there is no need to distinguish fetal sex when detecting autosomal aneuploidy, but it is necessary to first distinguish fetal gender when detecting sex chromosome aneuploidy such as XO, XYY, and the like.

Example 2 Statistical Model

Using this phenomenon discussed above, the inventors attempted to use a local polynomial to fit the relationship between the depth of coverage and the corresponding GC content. The depth of coverage consists of the following functions of the GC and the residuals of the normal distribution: cr _{i , j} = f ( GC _{i , j} ) + ε _{i , j} , j =1, 2,...,22, X , Y (4)

Where f (GC _i,j ) represents the relationship between sample i, the depth of coverage of chromosome j and the corresponding GC content, ε _i,j represents the residual of sample i, chromosome j. There is a strong linear relationship between the depth of coverage and the corresponding GC content, so the inventors applied the loess algorithm to fit the coverage depth to the corresponding GC content, and the inventors calculated the model for the inventor. Say the important value, that is, the fit coverage depth:

The depth is covered with the fit, and the standard deviation and student t are calculated according to Equation 6 and Equation 7 below:

Example 3 Fetal Score Estimation

Since the fetal score is very important for the inventor's detection, the inventors estimated the fetal score before the test procedure. As pointed out earlier, the inventors sequenced 19 adult males. When comparing their coverage depth with the case of a female fetus, the inventors found that the chromosomal X coverage depth of males is close to 1/2 of that of females. Y coverage depth is nearly 0.5 times larger than that of women. Therefore, the inventors can rely on the depth of coverage of chromosomes X and Y as in Equation 8, Equation 9, and Equation 10 and consider the GC correlation to estimate the fetal fraction:

among them Refers to the fitting coverage depth obtained by regression correlation of the chromosome X coverage depth and the corresponding GC content in the case of a female fetus. Refers to the fitting coverage depth obtained by regression correlation of the chromosome Y coverage depth and the corresponding GC content in the case of a female fetus. Refers to the fitting coverage depth obtained by regression correlation of male adult chromosome X coverage depth and corresponding GC content. It refers to the fitting coverage depth obtained by regression correlation of male adult chromosome Y coverage depth and corresponding GC content. In order to simplify the calculation, set with equal, with equal.

Example 4 Calculating the residual of each chromosome

Figure 6 shows that the standard deviation of each chromosome (see Equation 3) is affected by the number of reference cases involved in the total number of unique reads. Under the condition that the total number of unique reads of 1.7 million is sequenced for each case, when the number of selected cases exceeds 150, the standard deviation hardly increases. However, the standard deviation is different for different chromosomes. After considering the GC bias, the inventors' method has a modest standard deviation for the following chromosomes: chromosome 13 (0.0063), chromosome 18 (0.0066), and chromosome 21 (0.0072). The standard deviation of chromosome X is higher than the chromosomes mentioned above, and it requires more strategies for accurate anomaly detection.

Figure 7 shows the Q-Q plot where the residuals are compiled into a normal distribution and the normal distribution indicates that the student t calculation is reasonable.

Example 5 Distinguishing the sex of the fetus

In order to discover a sex chromosome disorder, it is best to distinguish the sex of the fetus. When the inventors studied the frequency distribution of chromosome Y coverage depth in 300 cases, there were two distinct peaks, suggesting that gender can be distinguished by the depth of coverage of chromosome Y. Cases with a depth of less than 0.04 can be considered to have a female fetus, while greater than 0.051 is considered to have a male fetus, and between 0.04 and 0.051 is considered gender uncertainty, as shown in Figure 8. For these cases of gender uncertainty and aneuploidy, logistic regression is used to predict their gender, as in Equation 11 (Fan, et al. , Proc Natl Acad Sci USA (2008) 42: 16266-16271):

Where cr.a _{i , x} and cr.a _{i , y} are the normalized relative coverage of X and Y _, respectively.

Compared with the karyotype results, the inventor's method of distinguishing the sex of the fetus performed very well in its 300 reference cases with 100% accuracy, while only one case was mistaken when it was carried out in its 901 case group, and The chromosome Y coverage depth of the wrong case is between 0.04 and 0.051.

Example 6 Diagnostic performance of GC-correlated t-test method

Sample collection

903 participants were expected to be recruited from Shenzhen People's Hospital and Shenzhen Maternal and Child Health Center, with their karyotype results. Licenses were obtained from the public review department of each of the recruiting units, and all participants signed informed consent. Record the mother's age and gestational age when taking blood. The 903 cases include 2 trisomy 13 cases, 15 trisomy 18 cases, 16 trisomy 21 cases, 3 XO cases, 2 XXY cases and 1 XYY case. The distribution of its karyotype results is shown in Figure 9.

Maternal plasma DNA sequencing

Peripheral venous blood (5 ml) was collected from each participating pregnant woman into an EDTA tube and centrifuged at 1,600 g for 10 minutes in 4 hours. The plasma was transferred to a microcentrifuge tube and centrifuged at 16,000 g for 10 minutes to remove residual cells. Cell-free plasma at 80 ° C Save to DNA extraction. Each plasma sample is only frozen and thawed once.

For large-scale parallel genome sequencing, DNA library construction was performed using DNA extracted from 600 μl of maternal plasma according to a modified Illumina-based protocol. Briefly, T4 DNA polymerase, Klenow ^TM polymerase and T4 polynucleotide kinase maternal plasma DNA of paired end fragments. The terminal A residue was added, and then a commercially available aptamer (Illumina) was ligated to the DNA fragment. Then, the aptamer-ligated DNA was additionally amplified using a conventional multiplexer using 17 cycles of PCR. PCR products were purified using ^{Agencourt AMPure TM 60 ml Kit (Beckman} ). DNA 1000 Kit size in the analysis of sequencing libraries distributed over the 2100 Bioanalyzer ^TM (Agilent), and real-time quantitative PCR. Then, an equal amount of the sequenced library having a different index (index) is combined into one, then cluster station (one terminal sequencing) on Illumina GA II ^TM.

Nineteen male euploid samples were sequenced for subsequent analysis of fetal DNA fraction estimates. The inventors have developed a new GC-related t-test for the diagnosis of trisomy 13, trisomy 18, trisomy 21 and sex chromosome abnormalities. The inventors have compared this new method with the other two mentioned below. Methods were compared in terms of diagnostic performance.

Example 7 Detection of fetal aneuploidy such as trisomy 13, 18 and 21

To determine if the chromosome copy number in the patient case deviated from normal, the depth of coverage of the chromosome was compared to all other reference cases. All previous studies have only one null hypothesis. The inventor introduced the binary hypothesis for the first time by using two null hypotheses. A null hypothesis (H0: the fetus is euploid) assumes that the average coverage depth of the patient case distribution is equal to the average coverage depth of all normal reference distributions, which means that if the null hypothesis is accepted then the patient case is Euploid. Using the Student t test, t1 can be calculated as Equation 12:

Another null hypothesis (H1: the fetus is aneuploidy) is that the average coverage depth of the patient case distribution with a bad fetal fraction is equal to the average coverage depth of the aneuploidy case distribution with the same fetal fraction, which means if The patient case is aneuploidy if the null hypothesis is accepted. Student t statistic, t2 is calculated as Equation 13:

|t1|>3 and |t2|<3 will indicate aneuploid cases in most cases, especially when the distribution between the euploid case and the aneuploid case is completely distinguished, while in other conditions For example, if the precision is insufficient or the fetal score is insufficient, etc., |t1| may be less than 3, but the fetus is abnormal. The combination of t1 and t2 can help the inventor make a more correct decision, and then the inventors apply the log likelihood ratios of t1 and t2 of Equation 14: L _{i , j} =log( p (t1 _{i , j} ,degree | D))/ Log( p ( t 2 _{i , j} ,degree|T)) (14)

Where L _i,j is a log likelihood ratio. If the ratio is greater than 1, the inventors will conclude that the fetus may be trisomy.

However, in the case of a female fetus, it is difficult for the inventor to estimate the fetal fraction, so calculation is impossible. However, based on the empirical distribution of fetal fractions, the inventors can obtain a score reference value (RV) of 7%.

A total of 903 cases were studied, of which 866 carried a euploid fetus, and 300 cases were randomly selected to develop the GC-related student t method. In addition, 2 trisomy 13, 12 trisomy 18, 16 trisomy 21, 4 XO (by 3 XO cases and 1 chimera 45, xo/46, xx (27:23) case Composition), 2 XXY and 1 The XYY case participated in the research of the inventor. After the comparison, the inventors obtained an average of 1.7 million data (SD = 306185) unique matching reads (no mismatches) for each case. All T13 cases (two of 2) were successfully identified by using the inventor's newly developed CG-related student t test, while 901 of the 901 non-trisomy 13 cases were correctly classified (Figure 10A). . The sensitivity and specificity of this method are 100% and 100% (Table 1).

For the trisomy 18, 12 of the 12 trisomy 18 cases and 888 of the 891 non-trisomy 18 cases can be correctly identified (Fig. 10A). The sensitivity and specificity of this method are 100% and 99.66%, respectively. For the trisomy 21, 16 of the 16 trisomy 21 cases and 16 of the 16 non-trisomy 21 cases can also be correctly detected (Fig. 10A). The sensitivity and specificity of this method are 100% and 100%, respectively.

Example 8 Detection of XO, XXX, XXY, XYY

In the above, the inventors considered the detection of autosomal trisomies, and it is also possible to detect sexual chromosomal disorders such as XO, XXX, XXY and XYY by the inventors' methods.

First, gender is identified through gender differentiation. If the test case is confirmed to have a female fetus, then the student t value t1 needs to be calculated. For use in XXX or XO testing, where And std _Xf is the same as Equation 10; if t1 is greater than 3.13 or less than -3.13, the case may be XXX or XO. However, considering that the accuracy is limited by the large deviation of the depth of coverage of chromosome X, the inventors again sampled the plasma and repeated the experiment to make a more reliable decision at |t1|<5 (even if |t1|>3.13). In this case, |t1|>5 was confirmed to be aneuploidy. All inventors' detection methods are based on the premise that the data meets the standard quality control.

If the test sample is confirmed to harbor a male fetus, the fetal DNA fraction is first estimated by Y and X. At the same time, the inventors were able to extrapolate the depth of the fitting of the chromosome X by the fetal DNA fraction estimated only by the chromosome Y coverage depth, and can calculate t2. . If t2 is too large (greater than 5) or too small (less than -5), the fetus may be XXY or XYY. In addition, the difference between fetal scores independently estimated by X and Y will provide a message for detecting conditions related to the sex chromosome.

In the XO test, 3 of the 4 XO cases were detected, and the case that could not be identified was the chimera case (Fig. 10B). The sensitivity and specificity of the method were 75% (100% if the inventor ignored the chimeric case) and 99.55%. For the XXY case, all 2 cases were successfully identified, and 901 of the 901 non-XXY cases were correctly classified (Figure 10B) with 100% sensitivity and 100% specificity. For the XYY case, the inventors correctly identified it (Fig. 10B) with sensitivity and specificity of 100% and 100%, respectively.

In order to evaluate whether the new method of the present invention has any advantages when compared with the other two reported methods: z value and GC corrected z value, the inventors used all three methods to analyze the inventor's 900 cases, the same 300 Each case is used as a reference group for all of these methods. The accuracy of the measurement is always reflected in the confidence value (CV). In the inventors' study, the CV of the standard z-value method was greater in chromosomes 18 and 21 of clinical interest than in other methods (Fig. 11), resulting in lower sensitivity to trisomy 18 and 21 (Table 1). .

Table 1 Comparison of sensitivity and specificity of different methods

For the GC corrected z-value method, the CV value for chromosome 13 is 0.0066, 100% sensitivity rate and 100% specific rate. For the new GC-related student t method discussed herein, the CV value for chromosome 13 is 0.0063, 100% sensitivity and 100% specificity. In chromosome 18, the CV of the two methods were 0.0062 and 0.0066, respectively, both of which were 100% sensitive and their specific rates were 99.89% and 99.96%, respectively. For chromosome 21, the performance was similar when comparing the CV of the two methods: 0.0088 and 0.0072, respectively. Both in the inventor's small case study led to 100% identical sensitivity rates and reached the same 100% specificity rate. Moreover, the performance of both methods is superior to the standard z-value method. The newly developed GC correlation method of the inventors has not only good performance compared to the GC calibration method, but it has another advantage in detecting sex chromosome abnormalities such as XO, XXY and XYY. The inventor's data show that it is difficult to distinguish the sex of a fetus by using the data bias presented by the sex chromosome introduced by multiplying the weighting factor to repair the number of sequence tags when performing the GC calibration method, so that it seems difficult to detect the sex chromosome disorder. .

Example 9 Theoretical Performance of GC Correlation T-Test Method Considering Data Size, Gestational Week, and Fetal DNA Fraction

Measurement of aneuploidy is still difficult because of the high background of maternal DNA up to today (Fan, et al. , Proc Natl Acad Sci USA (2008) 42: 16266-16271) and random small amounts of fetal DNA scores through large scale The Parallel Genome Sequencing (MPGS) method is the most important limiting factor for aneuploidy detection. However, there was no major breakthrough in determining the minimum fetal DNA score clinically before the MPGS test specifically for female fetuses, and the only clinical clue associated with fetal DNA scores was gestational age. A statistically significant correlation between fetal DNA fraction and gestational age has previously been reported (Lo, et al. , Am. J. Human Genet. (1998) 62:768-775). In the inventor's study, in order to study the relationship between the estimated fetal DNA fraction and gestational age, the inventor in Fig. 12 plots all the cases involving male fetuses obtained by estimating formula 10 (a total of 427 Case) fetal DNA score. The estimated fetal DNA score for each sample was correlated with gestational age (P less than 0.0001). It has also been shown that even in 20 weeks of gestational age, 4 of the 65 cases have a fetal DNA score of less than 5%, which will adversely affect the accuracy of the test. To evaluate the fetal fraction estimation method, the inventors selected some cases of the hierarchical distribution of estimated fetal scores and then used Q-PCR to help calculate another relevant fetal fraction. Then, the inventors obtained a correlation standard curve showing a strong correlation between them, which proves that the estimation of the fetal fraction by the inventor's method is credible.

At the same time, the depth of sequencing (the total number of unique reads) is another important factor affecting the accuracy of aneuploidy detection as reflected by the standard deviation. When the number of reference cases reaches 150, the standard deviation of each chromosome used in the inventors' GC-related methods can be fixed at a certain depth of sequencing level (Fig. 13). To investigate how the depth of sequencing affects the standard deviation of each chromosome, the inventors sequenced 150 not only at the 1.7 million level of the present invention, but also at another sequencing depth level of 5 million (SD = 1.7 million) total unique reads. Cases. Depending on these two sets, the inventors found that the standard deviation is linearly related to the reciprocal of the square root of the total number of unique reads, as shown in Figure 6.

For a given fetal DNA fraction, the inventors can estimate the total number of unique reads used in the methods of the invention to detect deviations from normal chromosome copy number when t1 is equal to 3 (Fig. 14). It has been shown that the fewer fetal DNA fractions, the greater the depth of sequencing required. In the 1.7 million unique reads of the present invention, the method of the present invention is capable of detecting aneuploid fetuses of chromosomes 13 and X having a fetal DNA fraction of more than 4.5%, and staining more than 4%. The aneuploidy fetus of bodies 21 and 18; and in the 5 million reference set of the invention, the method of the invention is capable of detecting trisomy 18 and trisomy 21 of even about 3% of the fetal DNA fraction. If the inventors want to identify a fetus with a fetal fraction of about 4% of chromosomal X abnormalities such as XXX or XO, the total unique number required in these cases and corresponding reference cases should be 5 million. If the fetal DNA is less than 3.5%, the sequencing depth requirement will exceed 20M. Moreover, if the DNA fetal fraction is lower, the detection will become unreliable and difficult to perform, so the inventors suggest another strategy, that is, to re-sample the maternal plasma when the gestational age becomes larger, and then perform the experiment of the present invention and analyze the data again. Because there is a greater likelihood that the fetal DNA score will increase with gestational age as the gestational age becomes larger. Moreover, the strategy can also be applied to samples suspected of having a small fraction of fetal DNA.

Even though the method of the present invention works well, it is not convincing if there is no large set of abnormal cases. To estimate the sensitivity of the GC-related student t method to which the present invention is applied, the inventors have disclosed theoretical sensitivities that take into account different gestational ages and different sequencing depths.

The inventors calculated the theoretical sensitivity of aneuploidy by the following steps. First, the inventors applied regression analysis to fit fetal DNA scores with gestational age. ,among them Is the mean fetal DNA score fitted to the i-th gestational age gsa _i and estimates the approximate fetal DNA score by applying Gaussian function density estimates (Birke, (2008) Journal of Statistical Planning and Inference 139: 2851-2862). Refers to the estimated fetal DNA scores distributed between the 19th and 20th gestational weeks, and then based on the relationship between fetal DNA scores and gestational age. Extrapolating the distribution of fetal DNA scores in other weeks, among which Is the fitted probability density of the fetal DNA fraction in the i-th gestational age, where X is the data for 19 and 20 gestational weeks (Fig. 12). Second, the inventor estimates the standard deviation based on the total number of unique reads previously mentioned. , where tuqn is the total number of unique reads. Finally, in order to calculate the sensitivity of each gestational age at a certain depth of sequencing level based on the estimated fetal DNA fraction and standard deviation estimated at each sequencing depth, the inventors calculated the probability density of false negatives for each fetal DNA fraction. (In this article, the inventors hypothesized that fetal DNA fraction fluctuations are normally distributed) and then integrate them to obtain a false negative rate (FNR) of gestational age consisting of all fetal DNA fraction levels. Where j is chromosome j. Easily, the theoretical sensitivity of a certain sequencing depth of the gestational age is calculated as 1 □ FNR. Fig. 15 - Fig. 21 show the figures calculated by the inventors. Student t is set to be greater than 3 to identify female fetus aneuploidy, whereas for male fetus, when calculating the probability density of false negatives for each score, a log likelihood greater than 1 is used as the inventor in the binary hypothesis And the critical value, which helps achieve higher sensitivity than women.

However, the inventors' reasoning is relatively conservative because it is difficult to obtain a distribution of the true distribution of infinitely close fetal DNA scores with gestational age - especially small gestational age in small-scale sampling.

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The above are only the preferred embodiments of the present invention, and all changes and modifications made to the scope of the present invention should be within the scope of the present invention.

Figure 1 shows the principle of calculating the depth of coverage and GC content by using the sequence information of the polynucleotide fragment.

Figure 2 shows the normalized coverage depth-GC content correlation established by using data from 300 reference cases. The normalized coverage depth for each case is plotted against the GC content of the sequence. The cross indicates the case of a euploid female fetus, which indicates a case of a euploid male fetus. The solid line is the fitted line of depth and GC content. In addition, FIG. 2 includes a 2A diagram, a 2B diagram, a 2C diagram, and a 2D diagram.

Figure 3 shows the trend between normalized depth of coverage and corresponding GC content by arranging chromosomes with inherently elevated GC content of the chromosome. The intrinsic rising GC content of each chromosome here refers to the average GC content of the sequence tags of the chromosomes in the 300 reference cases.

Figure 4 shows the different components of the CG class for each chromosome. The GC content per 35 bp read of the reference unique reads was calculated for each chromosome, the GC content was graded to 36 levels, and the percentage of each level was calculated as the GC composition of each chromosome. The chromosomes are then mapped by heat mapping and hierarchically clustered.

Figure 5 shows that the sequencing bias can introduce the correlation shown in Figure 2 by artificially simulating the sequencer preferences. Here, FIG. 5 includes a 5A diagram, a 5B diagram, a 5C diagram, and a 5D diagram.

Figure 6 plots the standard deviation of the total number of polynucleotide fragments relative to the sequence. In 150 samples, the corrected standard deviation of each chromosome shows a linear relationship with the reciprocal of the square root of the number of unique reads. 6 shows FIG. 6A, FIG. 6B, FIG. 6C, and FIG. 6D.

Figure 7 shows the QQ for each chromosome residual calculated by Equation 3. The graph shows a linear relationship with a normal distribution. Among them, Fig. 7 includes Fig. 7A and Fig. 7B.

Figure 8 shows a histogram of the depth of chromosome Y coverage. There are two peaks indicating that the gender of the case can be distinguished by the depth of coverage of chromosome Y. The curve is the distribution of the relative depth of coverage of chromosome Y by kernel density estimation with a Gaussian function.

Figure 9 shows a simplified diagram of the process used to diagnose fetal chromosomal abnormalities in 903 test samples.

Figure 10 shows the results of aneuploidy: trisomy 13, 18 and 21 and XO, XXY, XYY cases and normal cases. Figure 10A shows a plot of normalized depth of coverage versus GC content for chromosomes 13, 18 and 21. Figure 10B shows a plot of chromosomes X and Y. The circle represents the relative coverage depth and GC content of normal female fetuses, and the points represent normal male fetuses. The solid line is the fitted line of relative coverage and GC content, the dashed line is the absolute value of the t value is 1, the dotted line is the absolute value of the t value is 2, the dotted line: the absolute value of the t value is 3.

Figure 11 compares the confidence values for different diagnostic methods.

Figure 12 shows the relationship between fetal DNA score and gestational age. The fraction of fetal DNA in maternal plasma correlates with gestational age. Fetal DNA scores are estimated by X and Y. There was a statistically significant correlation between mean fetal DNA scores and gestational age (P < 0.001). Note that the R2 value indicates that the square of the correlation coefficient is small. The minimum score is 3.49%.

Figure 13 shows the relationship between the standard deviation and the number of cases required for detection. The standard deviation of each chromosome calculated by Equation 5 varies with the number of different samples. when The standard deviation becomes stable when the number of samples exceeds 100.

Figure 14 shows the estimated number of unique reads used to detect fetal aneuploidy in acellular plasma as a function of fetal DNA fraction. For aneuploidy of chromosomes 13, 18, 21 and X and even Y (from the relationship between X and Y) each having a different length, the estimate is based on a confidence t value level of not less than 3. As the fetal DNA fraction decreases, the total number of required shotgun sequences increases. Using the sequencing throughput of 4 million sequence reads per channel on the flowcell, trisomy 21 can be detected if 3.5% of the cell-free DNA is fetal. When the score and the number of unique reads are small, for example, 4% and 5 million reads, the aneuploidy of chromosome X is not easily detected. Different chromosomes require different levels of fetal DNA fraction and unique reads, which may be due to the GC structure of the chromosome.

Figure 15 shows a constant value plot of the sensitivity of the trisomy of female fetus chromosome 13 for each gestational age and data volume as reflected by the amount of data and gestational age (weeks).

Figure 16 shows a constant value plot of the sensitivity of the trisomy of female fetus chromosome 18 for each gestational age and data volume as reflected by the amount of data and gestational age (weeks).

Figure 17 shows a constant value plot of the sensitivity of the trisomy of female fetus chromosome 21 for each gestational age and data volume as reflected by the amount of data and gestational age (weeks).

Figure 18 shows a constant value plot of the sensitivity of the trisomy of female fetus chromosome X for each gestational age and data volume as reflected by the amount of data and gestational age (weeks).

Fig. 19 shows a constant value map for detecting the sensitivity of the trisomy of the male chromosome 13 as reflected by the amount of data and the gestational age (week). For each gestational age and data point, the inventor first calculates the empirical distribution of fetal DNA scores and standard deviations for a given amount of data, and compares the scores estimated by XY or Y, and then the inventor calculates each sensitivity. Aneuploidy of sexual type. Figure 20 shows the sensitivity constant values of the trisomy of male chromosome 18 as reflected by the amount of data and gestational age (week).

Figure 21 shows the sensitivity constant value map for detecting the trisomy of male chromosome 21 as reflected by the amount of data and gestational age (week).

Claims (28)

A method for establishing a relationship between a depth of coverage of a chromosome and a GC content, the method comprising: obtaining a sequence message covering a plurality of polynucleotide fragments of the chromosome from more than one sample; assigning the fragment to the sequence based on the sequence message Chromosome; the coverage depth and GC content of the chromosome are calculated based on the sequence information for each sample; and the relationship between the depth of coverage of the chromosome and the GC content is determined. The method of claim 1, wherein the assigning is performed by comparing the sequence of the fragment to a reference human genome sequence. The method of claim 1, wherein the depth of coverage of the chromosome is a ratio between the number of fragments assigned to the chromosome and the number of reference unique reads of the chromosome. The method of claim 3, wherein the coverage depth is standardized. The method of claim 4, wherein the normalization is calculated relative to the coverage of another chromosome, all other autosomes, or all other chromosomes. The method of claim 1, wherein the GC content of the chromosome is the average GC content of all fragments assigned to the chromosome. The method of claim 1, wherein the chromosome is chromosome 1, 2, ..., 22, X or Y. The method of claim 4, wherein the relationship is the following formula: cr _{i , j} = f ( GC _{i , j} )+ ε _{i , j} , j =12,...,22, X , Y , wherein f ( GC _i,j ) represents a function of the relationship between the depth of coverage of the chromosome i of the sample i and the corresponding GC content, ε _i,j represents the residual of the chromosome i of the sample i. The method of claim 1, wherein the relationship between the depth of coverage and the GC content is calculated by local polynomial regression. The method of claim 9, wherein the relationship is a non-linear relationship. The method of claim 10, wherein the relationship is determined by a loess algorithm. The method of claim 8, further comprising: calculating the fitting coverage depth according to the following formula: The method of claim 12, further comprising: calculating the standard deviation according to the following formula: Where ns represents the number of reference samples. The method of claim 13, further comprising: calculating the student t statistic according to the following formula: A method for detecting a genetic abnormality in a fetus comprising: a) obtaining a sequence message of a plurality of polynucleotide fragments from a sample; b) assigning the fragment to the chromosome based on the sequence message; c) calculating the depth of coverage and GC content of the chromosome based on the sequence information; d) using the GC content of the chromosome and establishing the coverage depth of the chromosome and the GC content The relationship calculates the fit coverage depth of the chromosome; and e) compares the fitted coverage depth of the chromosome to the depth of coverage, wherein the difference between them indicates a fetal genetic abnormality. The method of claim 15, further comprising: f) determining the sex of the fetus. The method of claim 16, wherein the fetal gender is determined according to the following formula: Where cr.a _{i , x} and cr.a _{i , y} are the normalized relative coverage of the X and Y chromosomes _, respectively. The method of claim 16, further comprising: g) estimating the fetal fraction. The method of claim 15, wherein the fitting coverage depth of the chromosome is compared with the depth of coverage by statistical hypothesis testing. As for the method described in claim 19, one of the assumptions is that the fetus is euploid (H0), and the other hypothesis is that the fetus is aneuploid (H1). The method of claim 20, wherein the student t statistic is calculated for both hypotheses. The method of claim 21, wherein the student t statistic is calculated for H0 and H1 according to the following formula: with , where fxy is the fetal fraction. The method of claim 22, wherein the log likelihood ratio of t1 and t2 is calculated according to the following formula: L _{i , j} =log( p (t1 _{i , j} ,degree | D))/log( p ( t 2 _{i , j} ,degree|T)), where Li,j is a log-likelihood ratio, where degree is the degree of t distribution, D is diploid, and T is trisomies, p (t1 _{i , j} ,degree | ^* ), ^* =D,T represents the conditional probability density for a given t-distribution. A method for determining a genetic abnormality in a fetus comprising: a) obtaining a sequence message covering a plurality of polynucleotide fragments of a chromosome of interest from more than one normal sample; b) assigning the fragment to the chromosome based on the sequence message; c) Calculating the depth of coverage and GC content of the chromosome based on sequence information from the normal sample; d) determining the relationship between the depth of coverage of the chromosome and the GC content; e) obtaining sequence information of the plurality of polynucleotide fragments from the biological sample; Generating the fragment to the chromosome based on sequence information from the biological sample; g) calculating the depth of coverage and GC content of the chromosome based on the sequence information from the biological sample; h) using the GC content of the chromosome and the coverage of the chromosome The relationship between depth and GC content is used to calculate the fitted coverage depth of the chromosome; and i) the fitted coverage depth of the chromosome is compared to the depth of coverage, wherein the difference between them indicates a fetal genetic abnormality. A computer readable medium comprising a plurality of instructions for performing prenatal diagnosis of a fetal genetic abnormality, the working process comprising the steps of: a) receiving a sequence message of a plurality of polynucleotide fragments from a sample; b) based on the sequence of messages The polynucleotide fragment is assigned to the chromosome; c) calculating the depth of coverage and the GC content of the chromosome based on the sequence information; d) calculating the relationship between the GC content of the chromosome and the established depth of coverage of the chromosome and the GC content The fit of the chromosome covers the depth; and e) the fit coverage depth of the chromosome is compared to the depth of coverage, wherein the difference between them indicates fetal genetic abnormality. The computer readable medium of claim 25, wherein the working process further comprises: f) determining the sex of the fetus. The computer readable medium of claim 26, wherein the working process further comprises: g) estimating the fetal fraction. A system for detecting a genetic abnormality in a fetus comprising: a) means for obtaining a sequence message of a plurality of polynucleotide fragments from a sample; and b) the computer readable medium of claim 25.