Gang XuFollow

Award Date


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Mathematical Sciences

First Committee Member

Amei Amei

Second Committee Member

Malwane Ananda

Third Committee Member

Kaushik Ghosh

Fourth Committee Member

Edwin Oh


Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fails to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time.We developed several tests to fill the gap of analyzing time-varying genetic effects in longitudinal GWAS for binary traits. First, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect for common genetic variants. Second, we propose a group of retrospective variant set varying coefficient mixed model association tests, RSVMMATs, to detect time-varying effects of a set of rare genetic variants on a binary trait measured repeatedly over time. Through simulations, we illustrated that the retrospective varying-coefficient tests were robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT and RSVMMATs to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis (MESA). Our results demonstrated that the proposed methods could detect biologically relevant genetic variants and pathways in a genome-wide scan and provided insight into the genetic architecture of hypertension.


Generalized linear mixed model; Genome-wide association study; Model misspecification; Time-varying genetic effect; Varying coefficient model


Genetics | Statistics and Probability

File Format


File Size

948 KB

Degree Grantor

University of Nevada, Las Vegas




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