Methods and processes for non-invasive assessment of genetic variations

• US 9,367,663 B2
• Filed: 11/05/2012
• Issued: 06/14/2016
• Est. Priority Date: 10/06/2011
• Status: Active Grant

• Current Assignee: Sequenom Incorporated (Laboratory Corporation Of America Holdings)
• Original Assignee: Sequenom Incorporated (Laboratory Corporation Of America Holdings)
• Inventors: Deciu, Cosmin, Dzakula, Zeljko, Ehrich, Mathias, Kim, Sung Kyun
• Primary Examiner(s): Brusca, John S
• Application Number:
  US13/669,136
• Publication Number:
  US 20130085681A1
• Time in Patent Office: 1,317 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: C12Q 1/6827 for detection of mutation or polymorphism
• CPC Class Codes: C12Q 2537/16 Assays for determining copy number or wherein the copy number is of special importance
• CPC Class Codes: C12Q 2537/165 Mathematical modelling, e.g. logarithm, ratio
• 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
• CPC Class Codes: G16B 30/10 Sequence alignment; Homology search
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

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

Claims

1. A method for determining presence or absence of a chromosome trisomy for a test sample, comprising:

(a) obtaining counts of sequence reads mapped to portions of a reference genome, which sequence reads are of circulating cell-free nucleic acid from a test sample from a pregnant female;

(b) determining a guanine and cytosine (GC) bias coefficient for the test sample 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 linear fitted relation;

(c) determining a genomic section level for each of the portions based on the counts of (a), the GC bias coefficient of (b) 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, thereby providing calculated genomic section levels; and

(d) determining presence or absence of a chromosome trisomy for the test sample according to the calculated genomic section levels.

2. The method of claim 1, wherein the fitted relation of (c) is linear.

3. The method of claim 2, wherein each of the fitted relation of (b) and the fitted relation of (c) independently are fitted by a linear regression.

4. The method of claim 3, wherein the GC bias coefficient for each of the multiple samples in (c)(i) is the slope of a fitted linear relation, for each of the multiple samples, between (i) the counts of the sequence reads mapped to each of the portions and (ii) GC content for each of the portions.

5. The method of claim 4, wherein a calculated genomic section levei L is determined for the test sample for each portion of the reference genome according to Equation B:

L=(M−

GS)/I 

 

Equation B

wherein M is the counts of the sequence reads mapped to the portion for the test sample, G is the GC bias coefficient for the test sample, I is an intercept of the fitted linear relation of (c) for the portion, S is a slope of the fitted linear relationship of (c) for the portion.

6. The method of claim 1, wherein the chromosome trisomy is a chromosome 21 trisomy.

7. The method of claim 1, which comprises filtering one or more portions and removing counts associated with the one or more portions for the determination of the presence or absence of the chromosome trisomy in part (d).

8. The method of claim 7, wherein the one or more portions filtered were selected according to one or more criteria chosen from measure of error or mappability, or measure of error and mappability.

9. The method of claim 8, wherein the one or more portions filtered were selected according to one or more criteria chosen from portions having no guanosine and cytosine (GC) content, portions consistently receiving no counts, and repeat masking.

10. The method of claim 8, wherein the measure of error is an R factor.

11. The method of claim 10, wherein portions of the reference genome having an R factor of about 7% or greater were selected as filtered portions.

12. The method of claim 10, wherein portions of the reference genome having an R factor of about 7% to about 10% were selected as filtered portions.

13. The method of claim 1, wherein (b) or (c), or (b) and (c), are implemented by a computer.

14. The method of claim 1, comprising:

generating the sequence reads of the circulating, cell-free nucleic acid from the test sample by a sequencing apparatus.

15. The method of claim 14, wherein the sequence reads are generated by massively parallel sequencing.

16. The method of claim 14, comprising:

mapping the sequence reads to the portions of the reference genome, and counting the mapped sequence reads.

17. A method for determining presence or absence of a chromosome trisomy for a test sample, comprising:

(a) obtaining counts of sequence reads mapped to portions of a reference genome;

which sequence reads are of circulating cell-free nucleic acid from a test sample from a pregnant female;

(b) (i) normalizing the counts in (a), thereby generating normalized counts, and removing normalized counts associated with one or more filtered portions, thereby yielding filtered normalized counts;

or (b) (ii) removing counts associated with one or more filtered portions, and normalizing the counts in portions that were not removed, thereby yielding filtered normalized counts;

wherein;

(1) the one or more filtered portions were selected according to one or more criteria chosen from measure of error or mappability, or measure of error and mappability; and

(2) the normalizing comprises;

determining a guanine and cytosine (GC) bias coefficient for the test sample 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 linear fitted relation;

determining a genomic section level for each of the portions based on the counts of (a), the GC bias coefficient 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, thereby providing calculated genomic section levels; and

(c) determining presence or absence of a chromosome trisomy for the test sample according to the calculated genomic section levels.

18. The method of claim 17, wherein the fitted relation of (c) is linear.

19. The method of claim 17, wherein the GC bias coefficient for each of the multiple samples is the slope of a fitted linear relation, for each of the multiple samples, between (i) the counts of the sequence reads mapped to each of the portions and (ii) GC content for each of the portions.

20. The method of claim 19, wherein a calculated genomic section level L is determined for the test sample for each portion according to Equation B:

L=(M−

GS)/I 

 

Equation B

wherein M1 is the counts of the sequence reads mapped to the portion for the test sample, G is the GC bias coefficient for the test sample, I is an intercept of the fitted linear relation for the portion, S is a slope of the fitted linear relationship for the portion.

21. The method of claim 17, wherein the one or more filtered portions were selected according to one or more criteria chosen from portions having no guanosine and cytosine (GC) content, portions consistently receiving no counts, and repeat masking.

22. The method of claim 17, wherein the measure of error is an R factor.

23. The method of claim 22, wherein portions of the reference genome having an R factor of about 7% or greater were selected as filtered portions.

24. The method of claim 22, wherein portions of the reference genome having an R factor of about 7% to about 10% were selected as filtered portions.

25. The method of claim 17, wherein the chromosome trisomy is a chromosome 21 trisomy.

26. The method of claim 17, comprising performing a secondary normalization of the filtered normalized counts or the levels.

27. The method of claim 26, wherein the secondary normalization is a LOESS normalization.

28. The method of claim 27, comprising (i) identifying a copy number variation in the calculated genomic section levels, and (ii) adjusting the calculated genomic section levels associated with the copy number variation.

29. The method of claim 28, comprising (1) identifying a first elevation in the calculated genomic section levels significantly different than a second elevation in the calculated genomic section levels, which first elevation is for a first set of genomic section levels, and which second elevation is for a second set of genomic section levels, (2) determining an expected elevation range for a homozygous and heterozygous copy number variation according to an uncertainty value for a segment of the genome, (3) adjusting the first elevation by a predetermined value when the first elevation is within one of the expected elevation ranges, thereby providing adjusted genomic section levels, and (4) determining the presence or absence of the chromosome trisomy according to the adjusted genomic section levels.

30. The method of claim 17, wherein (b) is implemented by a computer.

31. The method of claim 17, comprising:

generating the sequence reads of the circulating, cell-free nucleic acid from the test sample by a sequencing apparatus.

32. The method of claim 31, wherein the sequence reads are generated by massively parallel sequencing.

33. The method of claim 31, comprising:

mapping the sequence reads to the portions of the reference genome, and counting the mapped sequence reads.