Department of Mathematics
Analyzing association mapping in pedigree-based GWAS using a penalized multitrait mixed model
Genome-wide association studies (GWAS) have led to the identification of many genetic variants associated with complex diseases in the past 10 years. Penalization methods, with significant numerical and statistical advantages, have been extensively adopted in analyzing GWAS. This study has been partly motivated by the analysis of Genetic Analysis Workshop (GAW) 18 data, which have two notable characteristics. First, the subjects are from a small number of pedigrees and hence related. Second, for each subject, multiple correlated traits have been measured. Most of the existing penalization methods assume independence between subjects and traits and can be suboptimal. There are a few methods in the literature based on mixed modeling that can accommodate correlations. However, they cannot fully accommodate the two types of correlations while conducting effective marker selection. In this study, we develop a penalized multitrait mixed modeling approach. It accommodates the two different types of correlations and includes several existing methods as special cases. Effective penalization is adopted for marker selection. Simulation demonstrates its satisfactory performance. The GAW 18 data are analyzed using the proposed method.
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National Natural Science Foundation of China (NSFC). Grant Number: 61501389; Hong Kong Research Grant Council. Grant Number: HKBU_22302815, HKBU_12202114; Hong Kong Baptist University. Grant Number: FRG2/14-15/069, FRG2/14-15/077
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GWAS, multitrait analysis, pedigree, mixed modeling, penalization
Liu, Jin, Can Yang, Xingjie Shi, Cong Li, Jian Huang, Hongyu Zhao, and Shuangge Ma. "Analyzing association mapping in pedigree-based GWAS using a penalized multitrait mixed model." Genetic Epidemiology 40.5 (2016): 382-393.