Artificial intelligence and global normalization methods for genotyping

• US 7,467,117 B2
• Filed: 11/08/2005
• Issued: 12/16/2008
• Est. Priority Date: 03/24/2004
• Status: Expired due to Term

• Current Assignee: Illumina Incorporated
• Original Assignee: Illumina Incorporated
• Inventors: Kermani, Bahram Ghaffarzadeh
• Primary Examiner(s): Hirl; Joseph P.
• Assistant Examiner(s): Kennedy; Adrian L
• Application Number:
  US11/270,227
• Publication Number:
  US 20060224529A1
• Time in Patent Office: 1,134 Days
• Field of Search: 706/20, 706/16, 706/15, 707/6
• US Class Current: 706/20
• 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 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
• CPC Class Codes: G16B 40/10 Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 40/30 Unsupervised data analysis

Abstract

Described herein are systems and methods for normalizing data without the use of external controls. Also described herein are systems and methods for analyzing cluster data, such as genotyping data, using an artificial neural network.

Claims

1. A method of genetic analysis, comprising carrying out instructions on a computer system for

(a) obtaining genetic data comprising n sets of first and second signal values related in a coordinate system, wherein said first and second signal values are indicative of the levels of a first and second allele, respectively, at n loci, wherein n is an integer greater than one;

(b) comparing fit of said genetic data to each of a plurality of cluster models using an artificial neural network, thereby determining a best fit cluster model;

(c) assigning said signal values to at least one cluster according to said best fit cluster model, wherein if said best fit cluster model contains at least one actual cluster and at least one missing cluster;

(d) using a second artificial neural network to create a proposed location for said at least one missing cluster; and

(e) analyzing genetic data using said cluster model, wherein said analyzing is performed for determining a genotype based on said genetic data.

2. The method of claim 1, wherein said plurality of cluster models comprises at least seven cluster models.

3. The method of claim 1, further comprising training said first or second artificial neural network with an algorithm selected from the group consisting of a genetic algorithm, back-propagation algorithm, Levenberg-Marquardt algorithm and Bayesian algorithm.

4. The method of claim 1, wherein said first or second artificial neural network comprises a 3 layer feed-forward artificial neural network or a two layer artificial neural network.

5. The method of claim 1, wherein said coordinate system comprises two dimensions.

13. The method of claim 1, wherein said genetic data is represented in a graphical format.

14. The method of claim 13, wherein said graphical format comprises Cartesian coordinates.

15. The method of claim 1, wherein said genetic data is provided in a tabular format.

16. The method of claim 1, further comprising reporting said best fit cluster model to a user.

17. The method of claim 1, wherein said genetic data comprises data from a genotyping assay.

18. The method of claim 17, further comprising generating a genotyping score from said best fit cluster model.

6. A method of genetic analysis, comprising carrying out instructions on a computer system for

(a) obtaining genetic data comprising n sets of first and second signal values related in a coordinate system, wherein said first and second signal values are indicative of the levels of a first and second allele, respectively, at n loci, wherein n is an integer greater than one;

(b) comparing fit of said genetic data to each of a plurality of cluster models using an artificial neural network, thereby determining a best fit cluster model;

(c) assigning said signal values to at least one cluster according to said best fit cluster model, wherein if said best fit cluster model contains at least one actual cluster and fewer than three actual clusters;

(d) using a second artificial neural network to create a proposed location for at least one missing cluster, wherein the sum of actual and missing clusters is three; and

(e) analyzing genetic data using said cluster model, wherein said analyzing is performed for determining a genotype based on said genetic data.

7. The method of claim 6, wherein if said best fit cluster model contains one actual cluster, then using said second artificial neural network to propose a location for two missing clusters.

8. The method of claim 7, further comprising separately training said artificial neural network for proposing locations for said two missing clusters.

9. The method of claim 6, wherein if said best fit cluster model contains two actual clusters, then using said second artificial neural network to propose a location for one missing clusters.

10. The method of claim 6, wherein said genetic data comprises n sets of first, second and third signal values related in a coordinate system, wherein said first, second and third signal values are indicative of the levels of a first, second and third allele, respectively, at n loci.

11. The method of claim 10, wherein said coordinate system comprises three dimensions.

12. The method of claim 11, wherein wherein if said best fit cluster model contains at least one actual cluster and fewer than six actual clusters, then using a second artificial neural network to create a proposed location for at least one missing cluster, wherein the sum of actual and missing clusters is six.

19. A method of genotyping, comprising carrying out instruction on a computer system for

(a) obtaining genetic data comprising n sets of first and second signal values related in a coordinate system, wherein said first and second signal values are indicative of the levels of a first and second allele, respectively, at n loci, wherein n is an integer greater than one;

(b) comparing fit of said genetic data to each of a plurality of cluster models using an artificial neural network, thereby determining a best fit cluster model;

(c) assigning said signal values to at least one cluster according to said best fit cluster model, wherein said best fit cluster model contains at least one actual cluster and at least one missing cluster;

(d) using a second artificial neural network to create a proposed location for said at least one missing cluster; and

(e) generating a genotyping score from a genetic analysis using said best fit cluster model, wherein said genotyping score is used to determine a genotype.

20. The method of claim 19, further comprising reporting said genotyping score to a user.