Methods and processes for non-invasive assessment of genetic variations

• US 10,497,461 B2
• Filed: 03/12/2013
• Issued: 12/03/2019
• Est. Priority Date: 06/22/2012
• Status: Active Grant

• Current Assignee: Sequenom Incorporated (Laboratory Corporation Of America Holdings)
• Original Assignee: Sequenom Incorporated (Laboratory Corporation Of America Holdings)
• Inventors: Dzakula, Zeljko, Mazloom, Amin R., Deciu, Cosmin, Wang, Huiquan
• Primary Examiner(s): Brusca, John S
• Application Number:
  US13/797,508
• Publication Number:
  US 20130261983A1
• Time in Patent Office: 2,457 Days
• Field of Search: None
• CPC Class Codes: C12Q 1/6809 Methods for determination or identification of nucleic acids involving differential detection
• CPC Class Codes: C12Q 2535/122 Massive parallel sequencing
• CPC Class Codes: C12Q 2537/16 Assays for determining copy number or wherein the copy number is of special importance
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/10 Ploidy or copy number detection
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection

Abstract

Provided herein are methods, processes and apparatuses for non-invasive assessment of genetic variations.

Claims

1. A computer-implemented method for sequencing test sample nucleic acid from a pregnant female, generating a classification of presence of a sex chromosome aneuploidy for the test sample nucleic acid, and performing a medical procedure comprising amniocentesis or chorionic villus sampling, comprising:

(a) isolating circulating cell-free nucleic acid from blood of the pregnant female bearing a fetus;

(b) sequencing the test sample nucleic acid by a genome-wide massively parallel sequencer that generates sequence reads, which test sample nucleic acid comprises the isolated circulating cell-free nucleic acid;

(c) mapping, using a microprocessor, the sequence reads to portions of a reference genome;

(d) counting the sequence reads mapped to the portions, which counting generates counts of sequence reads mapped to the portions;

(e) calculating, using a microprocessor, a guanine and cytosine (GC) bias coefficient for the test sample nucleic acid based on a fitted relation between (i) the counts of the sequence reads mapped to each of the portions and (ii) GC content for each of the portions, wherein the GC bias coefficient is a slope for a fitted linear relation or a curvature estimation for a non-linear fitted relation;

(f) calculating, using a microprocessor, a genomic section level for each of the portions based on the counts of (d), the GC bias coefficient of (e) and a fitted relation, for each of the portions, between (i) the GC bias coefficient for each of multiple samples and (ii) the counts of the sequence reads mapped to each of the portions for the multiple samples, which calculating generates normalized genomic section levels;

(g) calculating, using a microprocessor, a Z-score from the normalized genomic section levels;

(h) generating, using a microprocessor, the classification of the presence of the sex chromosome aneuploidy for the test sample nucleic acid according to the Z-score calculated in (g); and

(i) performing a medical procedure comprising amniocentesis or chorionic villus sampling for the pregnant female after (h).

2. A computer-implemented method for sequencing test sample nucleic acid from a pregnant female and generating a classification of presence or absence of a sex chromosome aneuploidy for the test sample nucleic acid, comprising:

(a) isolating circulating cell-free nucleic acid from blood of the pregnant female bearing a fetus;

(b) sequencing the test sample nucleic acid by a genome-wide massively parallel sequencer that generates sequence reads, which test sample nucleic acid comprises the isolated circulating cell-free nucleic acid;

(c) mapping, using a microprocessor, the sequence reads to portions of a reference genome;

(d) counting, using a microprocessor, the sequence reads mapped to the portions, which counting generates counts of sequence reads mapped to the portions;

(e) calculating, using a microprocessor, a guanine and cytosine (GC) bias coefficient for the test sample nucleic acid based on a fitted relation between (i) the counts of the sequence reads mapped to each of the portions and (ii) GC content for each of the portions, wherein the GC bias coefficient is a slope for a fitted linear relation or a curvature estimation for a non-linear fitted relation;

(f) calculating, using a microprocessor, a genomic section level for each of the portions based on the counts of (d), the GC bias coefficient of (e) and a fitted relation, for each of the portions, between (i) the GC bias coefficient for each of multiple samples and (ii) the counts of the sequence reads mapped to each of the portions for the multiple samples, which calculating generates normalized genomic section levels;

(g) calculating, using a microprocessor, a Z-score from the normalized genomic section levels; and

(h) generating, using a microprocessor, the classification of the presence or absence of the sex chromosome aneuploidy for the test sample according to the Z-score calculated in (g).

3. The method of claim 2, wherein the sequencing is at about 1-fold coverage or less.

4. The method of claim 2, wherein the nucleic acid is from blood plasma or blood serum.

5. The method of claim 2, comprising generating and transmitting a laboratory prenatal test report comprising the classification of the presence or absence of the sex chromosome aneuploidy for the test sample.

6. The method of claim 5, wherein the report comprises one or more of a sensitivity, specificity and confidence interval for the classification.

7. The method of claim 6, wherein the sensitivity is 95% or greater and the specificity is 99% or greater.

8. The method of claim 2, comprising monitoring health of the fetus and pregnant female from whom the test sample was obtained based on the classification for the test sample.

9. The method of claim 2, wherein the sex chromosome aneuploidy is chosen from XXX, X, XXY and XYY.

10. The method of claim 2, wherein the GC bias coefficient for each of the multiple samples is the slope of a linear relation between (i) the counts of the sequence reads mapped to each of the portions of the reference genome for each of the samples, and (ii) the GC content for each of the portions for each of the samples.

11. The method of claim 10, wherein the fitted relation in (d) is linear and the normalized genomic section level Li is determined for each of the portions of the reference genome according to Equation α

;

L_i=(m_i−

G_iS) I<sup>−

1</sup>  

 

Equation α

wherein G₁, is the GC bias coefficient, I is the intercept of the fitted relation in (f), S is the slope of the relation in (f), m_i, is measured counts mapped to each portion of the reference genome and i is a sample.

12. The method of claim 2, comprising applying a secondary normalization to the normalized genomic section level calculated in (f).

13. The method of claim 12, wherein the secondary normalization comprises GC normalization.

14. The method of claim 2, wherein:

a subset of portions in chromosome Y in the reference genome are utilized for generating the classification; and

the subset comprises about 20 or more portions for chromosome Y.

15. The method of claim 14, wherein the portions for chromosome Y are chosen from among chrY_125, chrY_169, chrY_170, chrY_171, chrY_172, chrY_182, chrY_183, chrY_184, chrY_186, chrY_187, chrY_192, chrY_417, chrY_448, chrY_449, chrY_473, chrY_480, chrY_481, chrY_485, chrY_491, chrY_502, chrY_519, chrY_535, chrY_559, chrY_1176, chrY_1177, and chrY_1178.

16. The method of claim 14, wherein the portions for chromosome Y comprise one or more of chrY_1176, chrY_1177, and chrY_1176.

17. The method of claim 2, wherein:

a subset of portions in chromosome X in the reference genome are utilized for generating the classification; and

the subset comprises about 2350 or more portions for chromosome X.

18. The method of claim 2, wherein each portion of the reference genome comprises a nucleotide sequence of a predetermined length.

19. The method of claim 18, wherein the predetermined length is about 50 kilobases.

20. The method of claim 2, comprising prior to (e) removing or weighting the counts of sequence reads for certain portions of the reference genome according to a measure of error, mappability, repeatability, genomic portion-specific t-statistic or combination thereof.

21. The method of claim 20, comprising calculating a measure of error for the counts of sequence reads mapped to some or all of the portions of the reference genome and removing or weighting the counts of sequence reads for certain portions of the reference genome according to a threshold of the measure of error.

22. The method of claim 21, wherein the threshold is selected according to a standard deviation gap between a first genomic section level and a second genomic section level of 3.5 or greater.

23. The method of claim 21, wherein the measure of error is an R factor and the sequence read count for a portion of the reference genome having an R factor of about 7% or greater is removed prior to (e).