FGWAS: Functional genome wide association analysis

Chao Huang, Paul Thompson, Yalin Wang, Yang Yu, Jingwen Zhang, Dehan Kong, Rivka R. Colen, Rebecca C. Knickmeyer, Hongtu Zhu
2017 NeuroImage  
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
more » ... 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 The readers are welcome to request reprints from Dr. Hongtu Zhu. hzhu5@mdanderson.org; Phone: 346-814-0191. * Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: 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. Keywords Computational complexity; Functional genome wide association analysis; Multivariate varying coefficient model; Wild bootstrap Huang et al.
doi:10.1016/j.neuroimage.2017.07.030 pmid:28735012 pmcid:PMC5984052 fatcat:r5oyvirwg5gipl77ganlr73gpe