Fast Association Tests for Genes with FAST

Pritam Chanda, Hailiang Huang, Dan E. Arking, Joel S. Bader

Research output: Contribution to journalArticle

Abstract

Gene-based tests of association can increase the power of a genome-wide association study by aggregating multiple independent effects across a gene or locus into a single stronger signal. Recent gene-based tests have distinct approaches to selecting which variants to aggregate within a locus, modeling the effects of linkage disequilibrium, representing fractional allele counts from imputation, and managing permutation tests for p-values. Implementing these tests in a single, efficient framework has great practical value. Fast ASsociation Tests (F<sc>ast</sc>) addresses this need by implementing leading gene-based association tests together with conventional SNP-based univariate tests and providing a consolidated, easily interpreted report. Fast scales readily to genome-wide SNP data with millions of SNPs and tens of thousands of individuals, provides implementations that are orders of magnitude faster than original literature reports, and provides a unified framework for performing several gene based association tests concurrently and efficiently on the same data. Availability: https://bitbucket.org/baderlab/fast/downloads/FAST.tar.gz, with documentation at https://bitbucket.org/baderlab/fast/wiki/Home.

Original languageEnglish (US)
Article numbere68585
JournalPloS one
Volume8
Issue number7
DOIs
StatePublished - Jul 23 2013

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ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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