A dynamic model for genome-wide association studies

Kiranmoy Das, Jiahan Li, Zhong Wang, Chunfa Tong, Guifang Fu, Yao Li, Meng Xu, Kwangmi Ahn, David Mauger, Runze Li, Rongling Wu

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)629-639
Number of pages11
JournalHuman genetics
Issue number6
StatePublished - Jun 2011

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)


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