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Novel approach for estimating high-dimensional genetic influence on a longitudinal phenotype

Abstract Number:

3053 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Pei Zhang (1), Paul Albert (2), Hyokyoung Hong (3)

Institutions:

(1) University of Maryland, College Park, N/A, (2) National Cancer Institute, N/A, (3) NIH, N/A

Co-Author(s):

Paul Albert  
National Cancer Institute
Hyokyoung Hong  
NIH

First Author:

Pei Zhang  
University of Maryland, College Park

Presenting Author:

Pei Zhang  
University of Maryland, College Park

Abstract Text:

Although researchers have developed approaches for estimating high-dimensional genetic influence on cross-sectional data, there has been little work in generalizing these approaches in a mixed model for longitudinal settings. We develop a linear mixed model incorporating two separate genetic effects on the baseline and rate of change for a longitudinal response. Methodological challenges arise from the need to deal with the high-dimensional computation and to account for the crossed nature of the genetic and subject-specific random effects, which induce dependence between longitudinal measurements across all subjects. We propose a modified average information restricted maximum likelihood (AI-ReML) method to obtain the estimation for the variances of these two separate genetic effects. We illustrate our methodology through examining two separate genetic effects integrating approximately 7 million genetic variants on the trajectory of prostate-specific antigen (PSA) level in healthy males from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Our analysis uncovers a substantial genetic influence on the rate of change in PSA level over time.

Keywords:

longitudinal data|high dimension|genetic effects|ReML estimation|AI-ReML algorithm|PSA level

Sponsors:

Section on Statistics in Epidemiology

Tracks:

Disease Prediction

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