Unknown

Dataset Information

0

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.

SUBMITTER: Huang M 

PROVIDER: S-EPMC4554832 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

altmetric image

Publications

FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data.

Huang Meiyan M   Nichols Thomas T   Huang Chao C   Yu Yang Y   Lu Zhaohua Z   Knickmeyer Rebecca C RC   Feng Qianjin Q   Zhu Hongtu H  

NeuroImage 20150527


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 G  ...[more]

Similar Datasets

| S-EPMC3981753 | biostudies-literature
| S-EPMC2900429 | biostudies-literature
| S-EPMC6092439 | biostudies-literature
| S-EPMC2795912 | biostudies-literature
| S-EPMC9915807 | biostudies-literature
| S-EPMC7261120 | biostudies-literature
| S-EPMC8144016 | biostudies-literature
| S-EPMC5340631 | biostudies-literature
| S-EPMC6527781 | biostudies-literature
| S-EPMC7207078 | biostudies-literature