title: Analyzing Association Mapping in Pedigree-Based GWAS Using a Penalized Multitrait Mixed Model.
abstract: 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.
authors.affiliation: Center of Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
authors.forename: Jin
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
authors.forename: Can
authors.initials: C
authors.lastname: Yang
authors.affiliation: Department of Statistics, Nanjing University of Finance and Economics, Nanjing, Jiangsu, China.
authors.forename: Xingjie
authors.initials: X
authors.lastname: Shi
authors.affiliation: Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Cong
authors.initials: C
authors.lastname: Li
authors.affiliation: Department of Statistics & Actuarial Science, University of Iowa, Iowa City, Iowa, United States of America.
authors.forename: Jian
authors.initials: J
authors.lastname: Huang
authors.affiliation: Department of Biostatistics, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
authors.affiliation: Department of Biostatistics, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Shuangge
authors.initials: S
authors.lastname: Ma
mesh_terms: D019540:Area Under Curve; D003198:Computer Simulation; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D010375:Pedigree; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D019655:Quantitative Trait, Heritable; D012372:ROC Curve
keywords: GWAS; mixed modeling; multitrait analysis; pedigree; penalization