High throughput resequencing and variation detection using high density microarrays

• US 20030124539A1
• Filed: 12/21/2001
• Published: 07/03/2003
• Est. Priority Date: 12/21/2001
• Status: Abandoned Application

• Current Assignee: Clinton Meade Lender Respresentative
• Original Assignee: Affymetrix, Inc. (Thermo Fisher Scientific Incorporated)
• Inventors: Warrington, Janet A., Shah, Nila A.
• Application Number:
  US10/028,482
• Publication Number:
  US 20030124539A1
• Time in Patent Office: Days
• US Class Current: 435/6
• CPC Class Codes: G01N 2035/00158 Elements containing microarrays, i.e. "biochip"
• CPC Class Codes: G01N 2035/00752 bar codes
• CPC Class Codes: G01N 35/0099 comprising robots or similar manipulators robots per se B25J
• 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 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Abstract

In one embodiment of the invention, methods and systems are provided for high thoughput genotyping. The system includes an automated sample preparation system, a sample tracking system, automated array processing and system for data analysis.

Claims

1. A system for high throughput detection of genotypes comprising

a sample preparation automation system;

a sample tracking system;

an automated high density probe array loader;

a computer system for managing hybridization data and for analyzing hybridization data to make genotype calls.

2. The system of claim 1 wherein the sample preparation automation system is a robotic device for handling multwell plates.

3. The system of claim 1 wherein the sample tracking system is a bar code system.

4. The system of claim 1 wherein computer system comprises a processor;

and a memory being coupled with the processor, the memory storing a plurality of machine instructions that cause the processor to perform the method step of analyzing the hybridization to determine the genotype, wherein the analyzing comprises calling a genotype by calculating the likelihood of a set of models for the hybridization and the base is called based upon the likelihood of the models;

wherein the distribution of hybridization intensities are assumed to be Gaussein and forward and reverse strand are treated as independent replicates.

5. The method of claim 4 wherein the models are five homozygote Models (Null, A, C, G, T) for a haploid and and 11 models (Null, A, C, G, T, A-C, A-G, A-T, C-G, C-T, G-T) for a diploid.

6. The method of claim 5 wherein likelihood of a model is calculated independently for both the forward and reverse strands and is combined for the overall likelihood of the model.

7. The method of claim 6 wherein a genotype is called if one model fits the hybridization data better than all other models.

8. A method for determining the genotype of a polymorphism comprising:

preparing a nucleic acid sample;

determining the hybridization of the nucleic acid sample with a high density oligonucleotide probe array;

wherein the high density oligonucleotide probe array having probes interrogating the polymorphism; and

analyzing the hybridization to determine the genotype, wherein the analyzing comprises calling a genotype by calculating the likelihood of a set of models for the hybridization and the base is called based upon the likelihood of the models.

9. The method of claim 8 wherein the models are five homozygote Models (Null, A, C, G, T) for a haploid and and 11 models (Null, A, C, G, T, A-C, A-G, A-T, C-G, C-T, G-T) for a diploid.

10. The method of claim 9 wherein likelihood of a model is calculated independently for both the forward and reverse strands and is combined for the overall likelihood of the model.

11. The method of claim 10 wherein a genotype is called if one model fits the hybridization data better than all other models.

12. The method of claim 11 wherein the likelihood of a set of hybridization intensity as measured by pixel intensities is:

13. The method of claim 12 wherein the mean and variance for a Null Model are estimated according to:

14. The method of claim 10 wherein the mean and variance for a hymozygous model are estimated according to: