Abstract
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.
Highlights
In genetics, the phenomenon that a single gene or locus influences more than one trait is known as pleiotropy
On the other hand, when a genetic variant is associated with multiple traits, an individual test of each trait may ignore the extra information that is available from combining multiple traits in the analysis, leading to lower power
The null rejection rates are almost identical between the score test and the likelihood ratio test (LRT), so we only report the results based on the LRT
Summary
The phenomenon that a single gene or locus influences more than one trait is known as pleiotropy. The residual for each subject from this regression model was used as a phenotype for the heritability and linkage analysis in the second step This single time point measurement approach does not fully utilize the information provided in the data and can decrease the power of detecting the associated SNPs or underlying genes. Wang et al (2014) incorporated an optimally weighted combination of variants in a mixed effects model for detecting rare and common variants associated with a longitudinal trait Results from these studies confirm an improved power when all time points are jointly analyzed.
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