System and method for cleaning noisy genetic data from target individuals using genetic data from genetically related individuals

• US 10,711,309 B2
• Filed: 04/30/2019
• Issued: 07/14/2020
• Est. Priority Date: 11/26/2005
• Status: Active Grant

• Current Assignee: Natera Incorporated (Natera)
• Original Assignee: Natera Incorporated (Natera)
• Inventors: Rabinowitz, Matthew, Banjevic, Milena, Demko, Zachary, Johnson, David
• Primary Examiner(s): Lin, Jerry
• Application Number:
  US16/399,931
• Publication Number:
  US 20190256918A1
• Time in Patent Office: 441 Days
• Field of Search: 702 19
• CPC Class Codes: C12Q 1/6827 for detection of mutation or polymorphism
• CPC Class Codes: C12Q 1/6876 Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
• CPC Class Codes: C12Q 1/6883 for diseases caused by alterations of genetic material
• 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: C12Q 2600/118 Prognosis of disease development
• CPC Class Codes: C12Q 2600/156 Polymorphic or mutational markers
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 20/40 Population genetics; Linkage disequilibrium
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 30/00 ICT specially adapted for sequence analysis involving nucleotides or amino acids
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Abstract

A system and method for determining the genetic data for one or a small set of cells, or from fragmentary DNA, where a limited quantity of genetic data is available, are disclosed. Genetic data for the target individual is acquired and amplified using known methods, and poorly measured base pairs, missing alleles and missing regions are reconstructed using expected similarities between the target genome and the genome of genetically related subjects. In accordance with one embodiment of the invention, incomplete genetic data is acquired from embryonic cells, fetal cells, or cell-free fetal DNA isolated from the mother'"'"'s blood, and the incomplete genetic data is reconstructed using the more complete genetic data from a larger sample diploid cells from one or both parents, with or without genetic data from haploid cells from one or both parents, and/or genetic data taken from other related individuals.

Claims

1. A method for determining genetic data for DNA from cancer cells, the method comprising:

isolating cell-free DNA from a biological sample and amplifying a plurality of target loci from the isolated cell-free DNA to obtain amplification products;

using high throughput DNA sequencing to detect genetic material from the amplification products and produce genetic data for the plurality of target loci on the chromosome or chromosome segment of interest;

creating a set of one or more hypotheses specifying genetic data for DNA from cancer cells;

determining the probability of each of the hypotheses given the produced genetic data; and

using the probabilities associated with each hypothesis to determine the most likely genetic data for DNA from cancer cells.

2. The method of claim 1, wherein the genetic data produced by high throughput DNA sequence is noisy and comprises allele drop out errors.

3. The method of claim 1, wherein the genetic data produced by high throughput DNA sequence is noisy and comprises measurement bias.

4. The method of claim 1, wherein the genetic data produced by high throughput DNA sequence is noisy and comprises incorrect measurements.

5. The method of claim 1, wherein the plurality of target loci comprise SNPs.

6. The method of claim 5, wherein the confidence that each SNP is correctly called is at least 95%.

7. The method of claim 5, wherein the confidence that each SNP is correctly called is at least 99%.

8. The method of claim 1, wherein the plurality of target loci comprise at least 70 SNPs.

9. The method of claim 1, further comprising normalizing the genetic data for differences in amplification and/or measurement efficiency between the loci.

10. The method of claim 1, wherein the probability of each of the hypotheses is determined without use of a reference sample.

11. The method of claim 1, wherein the biological sample is a blood sample.