APPLYING LOW COVERAGE WHOLE GENOME SEQUENCING FOR INTELLIGENT GENOMIC ROUTING

• US 20200135300A1
• Filed: 10/28/2019
• Published: 04/30/2020
• Est. Priority Date: 10/26/2018
• Status: Active Application

• Current Assignee: Color Health Incorporated
• Original Assignee: Color Genomics, Inc.
• Inventors: Mishne, Gilad, Ginsberg, Jeremiah, Hon, Lawrence Sean, Stedden, William Michael, Homburger, Julian
• Application Number:
  US16/665,508
• Publication Number:
  US 20200135300A1
• Time in Patent Office: Days
• CPC Class Codes: C12Q 1/6869 Methods for sequencing
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 40/20 Supervised data analysis

Abstract

Techniques for next generation sequencing (NGS), and more particularly, to techniques for applying low coverage whole genome sequencing (lcWGS) in genome wide association studies (GWAS). One aspect includes performing a lcWGS of a biological sample from a subject to obtain a set of reads, determining an inference of a phenotype from the set of reads, obtaining self-reported data from the subject, executing a first query on eligibility criteria for a plurality of genomic routes to obtain a set of genomic routes that satisfy the first query, executing a second query on routing criteria for the set of genomic routes to obtain a subset of ranked genomic routes that satisfy the second query, and selecting one or more genomic routes from the subset of ranked genomic routes based on the ranking of each of the one or more genomic routes.

Claims

1. A method, comprising:

performing, at a processing system, a low coverage whole genome sequencing of a biological sample from a subject to obtain a set of reads;

evaluating, at a processing system, coverage of the set of reads;

performing, by the processing system, statistical association for loci of the set of reads having an observation;

determining, by the processing system, an inference of a phenotype from the set of reads based on the statistical association;

obtaining, by the processing system, self-reported data from the subject;

executing, by the processing system, a first query on eligibility criteria for a plurality of genomic routes to obtain a set of genomic routes that satisfy the first query, wherein the first query includes the phenotype;

executing, by the processing system, a second query on routing criteria for the set of genomic routes to obtain a subset of ranked genomic routes that satisfy the second query, wherein the second query includes the phenotype and at least one piece of information from the self-reported data; and

selecting, by the processing system, one or more genomic routes from the subset of ranked genomic routes based on the ranking of each of the one or more genomic routes.

2. The method of claim 1, further comprising providing, by the processing system, insight and/or information supporting a product, service, event, or benefit associated with each of the selected one or more genomic routes to the subject.

3. The method of claim 1, the first query is executed on a query dependent model configured to obtain each genomic route of the plurality of genomic routes that satisfies the first query, and the second query is executed on a learning to rank model trained to rank or prioritize each genomic route of the set of genomic routes that satisfies the query.

4. The method of claim 1, further comprising:

identifying, by the processing system, within the set of reads one or more off-target reads;

performing, by the processing system, a statistical association for loci of the one or more off-target reads having an observation; and

determining, by the processing system, an inference of another phenotype from the one or more off-target reads based on the statistical association,

wherein the second query includes the phenotype, the another phenotype, and at least one piece of information from the self-reported data.

5. The method of claim 1, further comprising:

performing, at the processing system, a high coverage whole genome sequencing of the sample to obtain another set of reads; and

performing, by the processing system, statistical association for loci of the another set of reads having an observation,

wherein the inference of the phenotype is determined from the set of reads and the another set of reads based on the statistical association of both the set of reads and the another set of reads.

6. The method of claim 2, wherein the insight and/or the information are provided to the subject without the phenotype.

7. The method of claim 2, wherein the insight and/or the information are provided to the subject with the phenotype.

8. A system comprising:

one or more processors; and

memory coupled to the one or more processors, the memory encoded with a set of instructions configured to perform a process comprising;

performing a low coverage whole genome sequencing of a biological sample from a subject to obtain a set of reads;

evaluating coverage of the set of reads;

performing statistical association for loci of the set of reads having an observation;

determining an inference of a phenotype from the set of reads based on the statistical association;

obtaining self-reported data from the subject;

executing a first query on eligibility criteria for a plurality of genomic routes to obtain a set of genomic routes that satisfy the first query, wherein the first query includes the phenotype;

executing a second query on routing criteria for the set of genomic routes to obtain a subset of ranked genomic routes that satisfy the second query, wherein the second query includes the phenotype and at least one piece of information from the self-reported data; and

selecting one or more genomic routes from the subset of ranked genomic routes based on the ranking of each of the one or more genomic routes.

9. The system of claim 8, wherein the process further comprises providing insight and/or information supporting a product, service, event, or benefit associated with each of the selected one or more genomic routes to the subject.

10. The system of claim 8, the first query is executed on a query dependent model configured to obtain each genomic route of the plurality of genomic routes that satisfies the first query, and the second query is executed on a learning to rank model trained to rank or prioritize each genomic route of the set of genomic routes that satisfies the query.

11. The system of claim 8, wherein the process further comprises:

identifying within the set of reads one or more off-target reads;

performing a statistical association for loci of the one or more off-target reads having an observation; and

determining an inference of another phenotype from the one or more off-target reads based on the statistical association,

wherein the second query includes the phenotype, the another phenotype, and at least one piece of information from the self-reported data.

12. The system of claim 8, wherein the process further comprises:

performing a high coverage whole genome sequencing of the sample to obtain another set of reads; and

performing statistical association for loci of the another set of reads having an observation,

wherein the inference of the phenotype is determined from the set of reads and the another set of reads based on the statistical association of both the set of reads and the another set of reads.

13. The system of claim 9, wherein the insight and/or the information are provided to the subject without the phenotype.

14. The system of claim 9, wherein the insight and/or the information are provided to the subject with the phenotype.

15. A non-transitory computer readable storage medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform a process comprising:

performing a low coverage whole genome sequencing of a biological sample from a subject to obtain a set of reads;

evaluating coverage of the set of reads;

performing statistical association for loci of the set of reads having an observation;

determining an inference of a phenotype from the set of reads based on the statistical association;

obtaining self-reported data from the subject;

executing a first query on eligibility criteria for a plurality of genomic routes to obtain a set of genomic routes that satisfy the first query, wherein the first query includes the phenotype;

executing a second query on routing criteria for the set of genomic routes to obtain a subset of ranked genomic routes that satisfy the second query, wherein the second query includes the phenotype and at least one piece of information from the self-reported data; and

selecting one or more genomic routes from the subset of ranked genomic routes based on the ranking of each of the one or more genomic routes.

16. The non-transitory computer readable storage medium of claim 15, wherein the process further comprises providing insight and/or information supporting a product, service, event, or benefit associated with each of the selected one or more genomic routes to the subject.

17. The non-transitory computer readable storage medium of claim 15, the first query is executed on a query dependent model configured to obtain each genomic route of the plurality of genomic routes that satisfies the first query, and the second query is executed on a learning to rank model trained to rank or prioritize each genomic route of the set of genomic routes that satisfies the query.

18. The non-transitory computer readable storage medium of claim 15, wherein the process further comprises:

identifying within the set of reads one or more off-target reads;

performing a statistical association for loci of the one or more off-target reads having an observation; and

determining an inference of another phenotype from the one or more off-target reads based on the statistical association,

wherein the second query includes the phenotype, the another phenotype, and at least one piece of information from the self-reported data.

19. The non-transitory computer readable storage medium of claim 15, wherein the process further comprises:

performing a high coverage whole genome sequencing of the sample to obtain another set of reads; and

performing statistical association for loci of the another set of reads having an observation,

wherein the inference of the phenotype is determined from the set of reads and the another set of reads based on the statistical association of both the set of reads and the another set of reads.

20. The non-transitory computer readable storage medium of claim 16, wherein the insight and/or the information are provided to the subject without the phenotype.