TY - JOUR
T1 - FVGWAS
T2 - Fast voxelwise genome wide association analysis of large-scale imaging genetic data
AU - Alzheimer’s disease neuroimaging initiative
AU - Huang, Meiyan
AU - Nichols, Thomas
AU - Huang, Chao
AU - Yu, Yang
AU - Lu, Zhaohua
AU - Knickmeyer, Rebecca C.
AU - Feng, Qianjin
AU - Zhu, Hongtu
N1 - Publisher Copyright:
© 2015 Elsevier Inc..
PY - 2015/9/1
Y1 - 2015/9/1
N2 - 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~106) in the brain from thousands of subjects (n~103). 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)n2) 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.
AB - 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~106) in the brain from thousands of subjects (n~103). 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)n2) 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.
KW - Computational complexity
KW - Family-wise error rate
KW - Heteroscedastic linear model
KW - Voxelwise genome wide association
KW - Wild bootstrap
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U2 - 10.1016/j.neuroimage.2015.05.043
DO - 10.1016/j.neuroimage.2015.05.043
M3 - Article
C2 - 26025292
AN - SCOPUS:84940437292
SN - 1053-8119
VL - 118
SP - 613
EP - 627
JO - NeuroImage
JF - NeuroImage
ER -