IP Library › Granted Patent US 12,706,175
Granted Patent B2
US 12,706,175 · App. 16/698,678 · Granted Aug 11, 2026

Methods and processes for non-invasive assessment of genetic variations

Inventors: Zeljko Dzakula (San Diego, CA); Amin R. Mazloom (San Diego, CA); Cosmin Deciu (San Diego, CA); Huiquan Wang (San Diego, CA)
Assignee: SEQUENOM, INC.
G16B20/10C12Q1/6809G16B20/00G16B20/20C12Q2535/122C12Q2537/16
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Quick Facts
Patent No.
US 12,706,175
App. No.
16/698,678
Granted
Aug 11, 2026
Kind
B2
Abstract

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

Claims (44)

1 . A method for determining sex chromosome karyotype for a fetus, comprising:

(I) sequencing circulating cell-free nucleic acid in a test sample from a pregnant subject bearing a fetus by genome-wide massively parallel sequencing, thereby generating sequence reads;

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

(III) counting, using a microprocessor, the sequence reads mapped to the portions of the reference genome, thereby generating counts of the sequence reads;

(IV) normalizing the counts of the sequence reads according to a process comprising:

(a) determining, using a microprocessor, a guanine and cytosine (GC) bias coefficient for the test sample based on a linear regression of

(i) the counts of the sequence reads mapped to each of the portions, and

(ii) a GC content for each of the portions, wherein the GC bias coefficient is a slope of the linear regression;

(b) for each portion, receiving onto memory model parameters comprising a slope and an intercept from a linear regression of

(i) a GC bias coefficient for each of multiple samples in a dataset, wherein the GC bias coefficient for each of the multiple samples in the dataset is the slope of a linear regression of (1) counts of sequence reads mapped to each of the portions of the reference genome for each of the multiple samples, and (2) GC content for each of the portions for each of the multiple samples, and

(ii) counts of sequence reads mapped to the portion of the reference genome for each of the multiple samples, wherein the dataset comprises samples comprising circulating cell-free nucleic acid from multiple pregnant women with known fetal karyotype; and

(c) generating, using a microprocessor, genomic section level (L) for each of the portions according to L i =(m i −G i S)I −1 ,

wherein G is the GC bias coefficient determined in (a), I is the intercept provided in (b), S is the slope provided in (b), m is the counts of the sequence reads generated in (III), and i is the test sample;

(V) determining a sex chromosome karyotype for the fetus according to the genomic section levels generated in (c); and

(VI) generating a report for the sex chromosome karyotype for the fetus based on the determination in (V).

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

3 . The method of claim 1 , further comprising applying a secondary normalization to the genomic section level generated in (c).

4 . The method of claim 3 , wherein the secondary normalization comprises GC normalization.

5 . The method of claim 1 , further comprising determining a chromosome X elevation and a chromosome Y elevation from a plurality of genomic section levels generated in (e).

6 . The method of claim 5 , further comprising plotting the chromosome X elevation, or derivative thereof, versus the chromosome Y elevation, or derivative thereof, on a two-dimensional graph, thereby generating a plot position.

7 . The method of claim 6 , further comprising determining a sex chromosome karyotype for the fetus according to the plot position.

8 . The method of claim 1 , further comprising prior to (IV):

determining a measure of error for the counts of the 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, mappability, repeatability, genomic portion-specific t-statistic, or combination thereof.

9 . The method of claim 8 , 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.

10 . The method of claim 8 , 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 7% or greater is removed.

11 . The method of claim 1 , wherein the portions of the reference genome are in one or more sex chromosomes.

12 . The method of claim 11 , wherein the number of portions of the reference genome is 20 or more portions for chromosome Y.

13 . The method of claim 12 , 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.

14 . The method of claim 13 , wherein the portions for chromosome Y comprise one or more of chrY_1176, chrY_1177, and chrY_1178.

15 . The method of claim 13 , wherein the portions for chromosome Y do not comprise one or more of chrY_1176, chrY_1177, and chrY_1178.

16 . The method of claim 13 , further comprising: comparing genomic section levels, or derivatives thereof, for one or more of chrY_1176, chrY_1177, and chrY_1178, to genomic section levels, or derivatives thereof, for one or more of 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 and chrY_559, thereby generating a comparison.

17 . The method of claim 16 , wherein sequence read counts for one or more of chrY_1176, chrY_1177, and chrY_1178 are removed or replaced according to the comparison.

18 . The method of claim 1 , wherein a subset of portions in chromosome X in the reference genome is utilized for determining sex chromosome karyotype, wherein the subset comprises about 2350 or more portions for chromosome X.

19 . The method of claim 1 , wherein the reference genome is from a male subject.

20 . The method of claim 1 , wherein the reference genome is from a female subject.

21 . The method of claim 1 , wherein each portion of the reference genome comprises a nucleotide sequence of a predetermined length.

22 . The method of claim 21 , wherein the predetermined length is 50 kilobases.

23 . The method of claim 1 , wherein the sex chromosome karyotype is chosen from XX, XY, XXX, X, XXY and XYY.

24 . The method of claim 1 , wherein the nucleic acid is from blood plasma or blood serum.

25 . The method of claim 1 , further comprising after (c), generating a Z-score from the genomic section levels.

26 . The method of claim 1 , wherein (V) is performed using a microprocessor.

27 . The method of claim 1 , wherein the sequencing in (I) comprises simultaneous analysis of up to 96 samples in an 8-lane flow cell.

28 . The method of claim 1 , wherein the sequencing in (I) comprises simultaneous analysis of up to 384 samples in an 8-lane flow cell.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 17, 2020
From: DZAKULA, ZELJKO; MAZLOOM, AMIN; DECIU, COSMIN; WANG, HUIQUAN
To: SEQUENOM, INC.
Reel/Frame 053243/0036 →
Continuity (3)
Continuation 13797508 · Mar 12, 2013
Provisional Application 61663482 · Jun 22, 2012
Related Publication 20200160934A1 · May 21, 2020
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