1. FGWAS: Functional genome wide association analysis
- Author
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Jingwen Zhang, Hongtu Zhu, Yang Yu, Paul M. Thompson, Chao Huang, Dehan Kong, Yalin Wang, Rebecca C. Knickmeyer, and Rivka R. Colen
- Subjects
0301 basic medicine ,Functional principal component analysis ,Multivariate statistics ,Genotype ,Cognitive Neuroscience ,Genome-wide association study ,Single-nucleotide polymorphism ,Computational biology ,Article ,Correlation ,03 medical and health sciences ,030104 developmental biology ,0302 clinical medicine ,Phenotype ,Neurology ,Neuroimaging ,Statistics ,Data analysis ,Humans ,030217 neurology & neurosurgery ,Algorithms ,Mathematics ,Genetic association ,Genome-Wide Association Study - Abstract
Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants and the gene-environmental interactions influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimers Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.
- Published
- 2017