Fast eQTL Analysis for Twin Studies

• Activity Number: 77
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Zhaoyu Yin*+ and Fei Zou
• Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
• Keywords: score statistic ; mixed effects ; matrix representation ; high correlation

Abstract

Twin pairs are commonly used for studying complex mental disorders and mixed effects models are typically employed for modeling the dependence between twin pairs. However, the mixed effects models are very computationally intensive for eQTL (expression quantitative trait loci) analysis where associations between thousands of transcripts and millions of single nucleotide polymorphisms (SNPs) are tested. To overcome the computational challenge, we propose to first randomly split twin pairs into two groups so that within each group, the samples are unrelated. We then apply simple linear regression separately to each group. To combine analysis results from the two groups, we derive a score statistic which automatically adjusts the correlation between twin pairs. Simulation studies will be used to demonstrate the computational advantage of the proposed method. We will further show that the proposed method has well controlled type I errors and is almost as powerful as random effects models. The proposed method will be applied to a large depression study with over 1500 twin pairs.