Method and System for Accurate Construction Of Long Range Haplotype

Abstract

In an embodiment of the present invention, a modified version of the PHASE model is implemented that is substantially more accurate than the FastPHASE model. Modifications in an embodiment of the present invention include using a parameterization EM algorithm similar to that of the FastPHASE model, and to perform optimization on haplotypes rather than MCMC sampling. In an embodiment, the imputed haplotypes themselves are used as hidden states in the HMM because this is believed to be important for the PHASE model's accuracy. This increase in accuracy becomes more pronounced with increasing sample size. This difference is attributed to the PHASE model's likelihood which produces long, shared haplotypes between pairs of individuals.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority to U.S. Provisional Application No. 61 / 492,365 filed Jun. 1, 2011, which is hereby incorporated by reference in its entirety for all purposes.STATEMENT OF GOVERNMENT SPONSORED SUPPORT[0002]This invention was made with Government support under contracts GM073059 and HG002357 awarded by the National Institutes of Health. The Government has certain rights in this invention.FIELD OF THE INVENTION[0003]The present invention generally relates to the field of computcr diagnostics. More particularly, the present invention relates to methods for analyzing a genome.BACKGROUND OF THE INVENTION[0004]The term haplotype refers to the combination of alleles at multiple loci along a chromosome. In linkage disequilibrium (LD) mapping, haplotype-based tests are thought to improve power for detecting untyped variants. In population genetics studies of evolutionary histories, haplotype data have been used to detect recombinat...

AI Technical Summary

Benefits of technology

[0009]In an embodiment of the present invention, a modified version of the PHASE model is implemented that is substantially more accurate than the FastPHASE model. Modifications in an embodiment of the present invention include using a parameterization EM algorithm similar to that of the FastPHASE model, and to perform optimization on haplotypes rather than MCMC sampling. In an embodiment, the imputed haplotypes themselves are used as hidden states in the HMM because this is believed to be important for the PHASE model's accuracy. This increase in accuracy becomes more pronounced with increasing sample size. This difference is attributed to the PHASE model's likelihood which produces long, shared haplotypes between pairs of individuals.

Problems solved by technology

When implemented via Markov Chain Monte Carlo algorithms (MCMC), PHASE is computationally intensive where it can be difficult to perform analysis of large-scale datasets, such as those generated in genome-wide association studies (GWAS).

Images