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

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationNext Generation Microarray Bioinformatics
Subtitle of host publicationMethods and Protocols
EditorsJunbai Wang, Tianhai Tian, Aik Choon Tan
Pages337-344
Number of pages8
DOIs
StatePublished - Jan 2 2012

Publication series

NameMethods in Molecular Biology
Volume802
ISSN (Print)1064-3745

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics

Keywords

  • EM algorithm
  • False discovery rate
  • Genome-wide association
  • Hidden Markov model
  • Multiple tests
  • SNP

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