A robust association test for detecting genetic variants with heterogeneous effects

Kai Yu, Han Zhang, William Wheeler, Hisani N Horne, Jinbo Chen, Jonine D Figueroa

Research output: Contribution to journalArticlepeer-review

Abstract

One common strategy for detecting disease-associated genetic markers is to compare the genotype distributions between cases and controls, where cases have been diagnosed as having the disease condition. In a study of a complex disease with a heterogeneous etiology, the sampled case group most likely consists of people having different disease subtypes. If we conduct an association test by treating all cases as a single group, we maximize our chance of finding genetic risk factors with a homogeneous effect, regardless of the underlying disease etiology. However, this strategy might diminish the power for detecting risk factors whose effect size varies by disease subtype. We propose a robust statistical procedure to identify genetic risk factors that have either a uniform effect for all disease subtypes or heterogeneous effects across different subtypes, in situations where the subtypes are not predefined but can be characterized roughly by a set of clinical and/or pathologic markers. We demonstrate the advantage of the new procedure through numeric simulation studies and an application to a breast cancer study.

Original languageEnglish
Pages (from-to)5-16
Number of pages12
JournalBiostatistics
Volume16
Issue number1
DOIs
Publication statusPublished - 23 Jul 2014

Keywords

  • Breast Neoplasms
  • Data Interpretation, Statistical
  • Female
  • Genetic Markers
  • Genetic Variation
  • Genome-Wide Association Study
  • Humans
  • Models, Genetic
  • Risk Factors

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