title: FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data.
abstract: More and more large-scale imaging genetic studies are being widely conducted to collect a rich set of imaging, genetic, and clinical data to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. Several major big-data challenges arise from testing genome-wide (NC>12 million known variants) associations with signals at millions of locations (NV~10(6)) in the brain from thousands of subjects (n~10(3)). The aim of this paper is to develop a Fast Voxelwise Genome Wide Association analysiS (FVGWAS) framework to efficiently carry out whole-genome analyses of whole-brain data. FVGWAS consists of three components including a heteroscedastic linear model, a global sure independence screening (GSIS) procedure, and a detection procedure based on wild bootstrap methods. Specifically, for standard linear association, the computational complexity is O (nNVNC) for voxelwise genome wide association analysis (VGWAS) method compared with O ((NC+NV)n(2)) for FVGWAS. Simulation studies show that FVGWAS is an efficient method of searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. Finally, we have successfully applied FVGWAS to a large-scale imaging genetic data analysis of ADNI data with 708 subjects, 193,275voxels in RAVENS maps, and 501,584 SNPs, and the total processing time was 203,645s for a single CPU. Our FVGWAS may be a valuable statistical toolbox for large-scale imaging genetic analysis as the field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors.forename: Meiyan
authors.initials: M
authors.lastname: Huang
authors.affiliation: Department of Statistics, University of Warwick, Coventry, UK.
authors.forename: Thomas
authors.initials: T
authors.lastname: Nichols
authors.affiliation: Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Chao
authors.initials: C
authors.lastname: Huang
authors.affiliation: Department of Statistics and Operation Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Yang
authors.initials: Y
authors.lastname: Yu
authors.affiliation: Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Zhaohua
authors.initials: Z
authors.lastname: Lu
authors.affiliation: Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Rebecca C
authors.initials: RC
authors.lastname: Knickmeyer
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
authors.affiliation: Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Hongtu
authors.initials: H
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D012984:Software
keywords: Computational complexity; Family-wise error rate; Heteroscedastic linear model; Voxelwise genome wide association; Wild bootstrap