Secondary Analysis of Longitudinal Trait in Genetic Association Studies

• Activity Number: 410
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Huilin Li*+
• Companies: New York University
• Keywords: secondary analysis ; longitudinal trait

Abstract

Relating genome wide association study (GWAS) data to longitudinal phenotype data can provide special advantages, but also represents certain challenges. This is particularly challenging in secondary data analyses from case-control studies, as commonly found for GWAS investigations. Secondary data analyses using existing case-control GWAS data yield an effective and practical solution for genetic investigations of longitudinal traits that afford the opportunity to examine disease heterogeneity over time and early disease detection. In the paper, we developed the statistical inference approaches for secondary analysis of longitudinal traits in GWAS. We proposed to integrate the mixed-effects model, commonly used for longitudinal analysis, with weighted likelihood and retrospective likelihood methods, two theoretically justified secondary analysis methods for a single time trait, to develop robust and efficient approaches for secondary data analysis of longitudinal traits.