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; Alzheimer's Disease Neuroimaging Initiative

doi:10.1016/j.neuroimage.2017.07.030

FGWAS: Functional genome wide association analysis

Neuroimage. 2017 Oct 1:159:107-121. doi: 10.1016/j.neuroimage.2017.07.030. Epub 2017 Jul 20.

Authors

Chao Huang¹, Paul Thompson², Yalin Wang³, Yang Yu⁴, Jingwen Zhang¹, Dehan Kong⁵, Rivka R Colen⁶, Rebecca C Knickmeyer⁷, Hongtu Zhu⁸; Alzheimer's Disease Neuroimaging Initiative

Affiliations

¹ Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
² Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA.
³ School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
⁴ Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁵ Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.
⁶ Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
⁷ Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁸ Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: htzhu@email.unc.edu.

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 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 Alzheimer's 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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms*
Genome-Wide Association Study / methods*
Genotype
Humans
Phenotype

Abstract

Publication types

MeSH terms

Grants and funding