Hidden markov models for controlling false discovery rate in genome-wide association analysis

Document Type

Article

Publication Date

1-2-2012

Abstract

Genome-wide association studies (GWAS) have shown notable success in identifying susceptibility genetic variants of common and complex diseases. To date, the analytical methods of published GWAS have largely been limited to single single nucleotide polymorphism (SNP) or SNP-SNP pair analysis, coupled with multiplicity control using the Bonferroni procedure to control family wise error rate (FWER). However, since SNPs in typical GWAS are in linkage disequilibrium, simple Bonferonni correction is usually over conservative and therefore leads to a loss of efficiency. In addition, controlling FWER may be too stringent for GWAS where the number of SNPs to be tested is enormous. It is more desirable to control the false discovery rate (FDR). We introduce here a hidden Markov model (HMM)-based PLIS testing procedure for GWAS. It captures SNP dependency by an HMM, and based which, provides precise FDR control for identifying susceptibility loci.

Identifier

84555190004 (Scopus)

ISBN

[9781617793998]

Publication Title

Methods in Molecular Biology

External Full Text Location

https://doi.org/10.1007/978-1-61779-400-1_22

ISSN

10643745

PubMed ID

22130891

First Page

337

Last Page

344

Volume

802

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