title: A dynamic model for genome-wide association studies.
abstract: Although genome-wide association studies (GWAS) are widely used to identify the genetic and environmental etiology of a trait, several key issues related to their statistical power and biological relevance have remained unexplored. Here, we describe a novel statistical approach, called functional GWAS or fGWAS, to analyze the genetic control of traits by integrating biological principles of trait formation into the GWAS framework through mathematical and statistical bridges. fGWAS can address many fundamental questions, such as the patterns of genetic control over development, the duration of genetic effects, as well as what causes developmental trajectories to change or stop changing. In statistics, fGWAS displays increased power for gene detection by capitalizing on cumulative phenotypic variation in a longitudinal trait over time and increased robustness for manipulating sparse longitudinal data.
authors.affiliation: Department of Statistics, The Pennsylvania State University, University Park, PA, USA.
authors.forename: Kiranmoy
authors.initials: K
authors.lastname: Das
authors.forename: Jiahan
authors.initials: J
authors.lastname: Li
authors.forename: Zhong
authors.initials: Z
authors.lastname: Wang
authors.forename: Chunfa
authors.initials: C
authors.lastname: Tong
authors.forename: Guifang
authors.initials: G
authors.lastname: Fu
authors.forename: Yao
authors.initials: Y
authors.lastname: Li
authors.forename: Meng
authors.initials: M
authors.lastname: Xu
authors.forename: Kwangmi
authors.initials: K
authors.lastname: Ahn
authors.forename: David
authors.initials: D
authors.lastname: Mauger
authors.forename: Runze
authors.initials: R
authors.lastname: Li
authors.forename: Rongling
authors.initials: R
authors.lastname: Wu
mesh_terms: D005260:Female; D055106:Genome-Wide Association Study; D006801:Humans; D008297:Male; D008957:Models, Genetic; D010641:Phenotype