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Genome-wide association studies of brain imaging data via weighted distance correlation.

Authors
  • Wen, Canhong1
  • Yang, Yuhui1
  • Xiao, Quan1
  • Huang, Meiyan2
  • Pan, Wenliang3
  • 1 Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei 230026, China. , (China)
  • 2 Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. , (China)
  • 3 Department of Statistical Science, School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China. , (China)
Type
Published Article
Journal
Bioinformatics (Oxford, England)
Publication Date
Dec 08, 2020
Volume
36
Issue
19
Pages
4942–4950
Identifiers
DOI: 10.1093/bioinformatics/btaa612
PMID: 32619001
Source
Medline
Language
English
License
Unknown

Abstract

Imaging genetics is mainly used to reveal the pathogenesis of neuropsychiatric risk genes and understand the relationship between human brain structure, functional and individual differences. Increasingly, the brain-wide imaging phenotypes in voxels are available to test the association with genetic markers. A challenge with analyzing such data is their high dimensionality and complex relationships. To tackle this challenge, we introduce a weighed distance correlation (wdCor) that can assess the association between genetic markers and voxel-based imaging data. Importantly, the wdCor test takes the voxel-based data as a whole multivariate phenotype, which preserves the spatial continuity and might enhance the power. Besides, an adaptive permutation procedure is introduced to determine the P-values of the wdCor test and also alleviate the computational burden in GWAS. In extensive simulation studies, wdCor achieves much better performances compared to the original distance correlation. We also successfully apply wdCor to conduct a large-scale analysis on data from the Alzheimer's disease neuroimaging project (ADNI). Our wdCor method provides new research directions and ideas for multivariate analysis of high-dimensional data, it can also be used as a tool for scientific analysis of imaging genetics research in practical applications. The R package wdcor, and the code for reproducing all results in this article is available in Github: https://github.com/yangyuhui0129/wdcor. Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

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