Voxelwise genome-wide association study (vGWAS)

Jason L. Stein, Xue Hua, Suh Lee, April J. Ho, Alex D. Leow, Arthur W. Toga, Andrew J. Saykin, Li Shen, Tatiana Foroud, Nathan Pankratz, Matthew J. Huentelman, David W. Craig, Jill D. Gerber, April N. Allen, Jason J. Corneveaux, Bryan M. DeChairo, Steven G. Potkin, Michael W. Weiner, Paul M. Thompson

Research output: Contribution to journalArticlepeer-review

200 Scopus citations

Abstract

The structure of the human brain is highly heritable, and is thought to be influenced by many common genetic variants, many of which are currently unknown. Recent advances in neuroimaging and genetics have allowed collection of both highly detailed structural brain scans and genome-wide genotype information. This wealth of information presents a new opportunity to find the genes influencing brain structure. Here we explore the relation between 448,293 single nucleotide polymorphisms in each of 31,622 voxels of the entire brain across 740 elderly subjects (mean age ± s.d.: 75.52 ± 6.82 years; 438 male) including subjects with Alzheimer's disease, Mild Cognitive Impairment, and healthy elderly controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used tensor-based morphometry to measure individual differences in brain structure at the voxel level relative to a study-specific template based on healthy elderly subjects. We then conducted a genome-wide association at each voxel to identify genetic variants of interest. By studying only the most associated variant at each voxel, we developed a novel method to address the multiple comparisons problem and computational burden associated with the unprecedented amount of data. No variant survived the strict significance criterion, but several genes worthy of further exploration were identified, including CSMD2 and CADPS2. These genes have high relevance to brain structure. This is the first voxelwise genome wide association study to our knowledge, and offers a novel method to discover genetic influences on brain structure.

Original languageEnglish (US)
Pages (from-to)1160-1174
Number of pages15
JournalNeuroImage
Volume53
Issue number3
DOIs
StatePublished - Nov 2010

Bibliographical note

Funding Information:
Data used in preparing this article were obtained from the Alzheimer's Disease Neuroimaging Initiative database ( www.loni.ucla.edu/ADNI ). Consequently, many ADNI investigators contributed to the design and implementation of ADNI or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators is available at http://www.loni.ucla.edu/ADNI/Collaboration/ADNI_Citation.shtml . This work was primarily funded by the ADNI (Principal Investigator: Michael Weiner; NIH grant number U01 AG024904 ). ADNI is funded by the National Institute of Aging, the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and the Foundation for the National Institutes of Health, through generous contributions from the following companies and organizations: Pfizer Inc., Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Merck and Co. Inc., AstraZeneca AB, Novartis Pharmaceuticals Corporation, the Alzheimer's Association, Eisai Global Clinical Development, Elan Corporation plc, Forest Laboratories, and the Institute for the Study of Aging (ISOA), with participation from the U.S. Food and Drug Administration. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. This study was supported by the National Institutes of Health through the NIH Roadmap for Medical Research , Grant U54 RR021813 entitled Center for Computational Biology (CCB). Information on the National Centers for Biomedical Computing can be obtained from ( http://nihroadmap.nih.gov/bioinformatics ). Additional support was provided by grants P41 RR013642 and M01 RR000865 from the National Center for Research Resources (NCRR) , a component of the National Institutes of Health (NIH). Algorithm development for this study was also funded by the NIBIB ( R01 EB007813 , R01 EB008281 , R01 EB008432 ), NICHHD ( R01 HD050735 ), and NIA ( R01 AG020098 ). Addition funding included R01-NS059873 from NIH/NINDS to MH. JS was also funded by NIH/NIDA (1-T90-DA022768:02), the ARCS foundation, and the NIMH ( 1F31MH087061 ).

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