Genetic Association Analysis Under Complex Survey Sampling

• Activity Number: 291
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: International Chinese Statistical Association
• Author(s): Danyu Lin*
• Companies: The University of North Carolina
• Keywords: Cohort studies ; Generalized estimating equations ; Horvitz-Thompson estimator ; Muti-stage cluster sampling ; Rare variants ; Superpopulation inference

Abstract

The cohort design allows investigators to explore the genetic basis of a variety of diseases and traits in a single study while avoiding major weaknesses of the case-control design. Most cohort studies employ multi-stage cluster sampling with unequal probabilities to conveniently select participants with desired characteristics, and participants from different clusters may be genetically related. Analysis that ignores the complex sampling design may yield biased estimation of the genetic association and inflation of the type I error. We develop weighted estimators that reflect unequal selection probabilities and differential nonresponse rates, and we derive variance estimators that properly account for the sampling design and the potential relatedness of participants in different sampling units. We compare, both analytically and numerically, the proposed weighted estimators with unweighted estimators that disregard the sampling design. We demonstrate the usefulness of the proposed methods through analysis of MetaboChip data in the Hispanic Community Health Study/Study of Latinos, which is the largest health study of the Hispanic/Latino population in the United States.