A 2-step strategy for detecting pleiotropic effects on multiple longitudinal traits

• Published in: Frontiers in genetics Vol. 5; p. 357
• Main Authors: Wang, Weiqiang, Feng, Zeny, Bull, Shelley B., Wang, Zuoheng
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 20.10.2014
• Subjects: Genetic association; Genetics; longitudinal data; mixed effects model; multiple traits; Pleiotropic effect; Single nucleotide polymorphisms (SNPs); genetic association; pleiotropic effect; multiple traits; single nucleotide polymorphisms (SNPs); longitudinal data; mixed effects model
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2014.00357

Genetic pleiotropy refers to the situation in which a single gene influences multiple traits and so it is considered as a major factor that underlies genetic correlation among traits. To identify pleiotropy, an important focus in genome-wide association studies (GWAS) is on finding genetic variants that are simultaneously associated with multiple traits. On the other hand, longitudinal designs are often employed in many complex disease studies, such that, traits are measured repeatedly over time within the same subject. Performing genetic association analysis simultaneously on multiple longitudinal traits for detecting pleiotropic effects is interesting but challenging. In this paper, we propose a 2-step method for simultaneously testing the genetic association with multiple longitudinal traits. In the first step, a mixed effects model is used to analyze each longitudinal trait. We focus on estimation of the random effect that accounts for the subject-specific genetic contribution to the trait; fixed effects of other confounding covariates are also estimated. This first step enables separation of the genetic effect from other confounding effects for each subject and for each longitudinal trait. Then in the second step, we perform a simultaneous association test on multiple estimated random effects arising from multiple longitudinal traits. The proposed method can efficiently detect pleiotropic effects on multiple longitudinal traits and can flexibly handle traits of different data types such as quantitative, binary, or count data. We apply this method to analyze the 16th Genetic Analysis Workshop (GAW16) Framingham Heart Study (FHS) data. A simulation study is also conducted to validate this 2-step method and evaluate its performance.