MARS: leveraging allelic heterogeneity to increase power of association testing

Farhad Hormozdiari, Junghyun Jung, Eleazar Eskin, Jong Wha Jong

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

Original languageEnglish
Article number128
JournalGenome Biology
Volume22
Issue number1
DOIs
StatePublished - Dec 2021

Keywords

  • Association studies
  • Causal variants
  • Set-based association analysis

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