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Activity Number: 382 - Biomedical Data Analysis in Genetics and Genomics
Type: Invited
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: ENAR
Abstract #308031
Title: Mixed Models for Gene-Based Association Analysis of Complex Traits
Author(s): Ruzong Fan* and Yingda Jiang and Daniel E. Weeks and Chi-yang Chiu and Ana I. Vazquez and Ao Yuan and Alexander F. Wilson and Joan E. Bailey-Wilson and Momaio Xiong and Richard J. Cook and Lajmi Lakhal Chaieb and Christopher I. Amos and Qi Yan and Wei Chen and Michael B. Gorin and Yvette Conley
Companies: Georgetown University Medical Center (GUMC) and University of Pittsburgh and Graduate School of Public Health, University of Pittsburgh and University of Tennessee, Health Science Center and Michigan State University and Georgetown University Medical Center and NIH and NIH and University of Texas - Houston and University of Waterloo and University of Laval and Baylor College of Medicine and University of Pittsburgh and University of Pittsburgh and UCLA and University of Pittsburgh
Keywords: Gene-based Association Analysis ; functional regression models ; mixed models ; complex traits; family-based or cryptically related data ; whole exome association studies
Abstract:

Functional regression models have been developed for unrelated samples to test for association between a quantitative or a dichotomous or a survival trait and genetic variants in a gene region. In major gene association analysis, the models have higher power than sequence kernel association tests (SKAT), its optimal unified tests (SKAT-O), and a combined sum test of rare and common variant effect (SKAT-C). Here we extend this approach to accommodate family-based or cryptically related data using functional mixed models. The mixed models treat the effect of major gene as fixed mean via functional data analysis techniques, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix. The association between trait and major gene is tested by likelihood ratio statistics. The proposed mixed models provide a new tool for conducting family-based research studies in public health for complex or multifactorial diseases. The models and related test statistics can be applied to whole genome and whole exome association studies. We apply the methods to a study of age-related macular degeneration.


Authors who are presenting talks have a * after their name.

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