A modified forward multiple regression in high-density genome-wide association studies for complex traits

Xiangjun Gu, Ralph F. Frankowski, Gary L. Rosner, Mary Relling, Bo Peng, Christopher I. Amos

Research output: Contribution to journalArticle

Abstract

Genome-wide association studies (GWAS) have been widely used to identify genetic effects on complex diseases or traits. Most currently used methods are based on separate single-nucleotide polymorphism (SNP) analyses. Because this approach requires correction for multiple testing to avoid excessive false-positive results, it suffers from reduced power to detect weak genetic effects under limited sample size. To increase the power to detect multiple weak genetic factors and reduce false-positive results caused by multiple tests and dependence among test statistics, a modified forward multiple regression (MFMR) approach is proposed. Simulation studies show that MFMR has higher power than the Bonferroni and false discovery rate procedures for detecting moderate and weak genetic effects, and MFMR retains an acceptable-false positive rate even if causal SNPs are correlated with many SNPs due to population stratification or other unknown reasons.

Original languageEnglish (US)
Pages (from-to)518-525
Number of pages8
JournalGenetic epidemiology
Volume33
Issue number6
DOIs
StatePublished - 2009
Externally publishedYes

Keywords

  • GWAS
  • MFMR
  • Multiple regression analysis
  • SNP
  • Separate SNP analysis

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

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