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Biostatistics. 2015 Jan;16(1):17-30. doi: 10.1093/biostatistics/kxu026. Epub 2014 Jun 23.

Multiple comparison procedures for neuroimaging genomewide association studies.

Author information

  • 1Department of Statistics, Penn State University, State College, PA 16802, USA wxh182@psu.edu.
  • 2Department of Statistics, University of Warwick, Conventry, CV4 7AL, UK.
  • 3Department of Statistics, Penn State University, State College, PA 16802, USA.

Abstract

Recent research in neuroimaging has focused on assessing associations between genetic variants that are measured on a genomewide scale and brain imaging phenotypes. A large number of works in the area apply massively univariate analyses on a genomewide basis to find single nucleotide polymorphisms that influence brain structure. In this paper, we propose using various dimensionality reduction methods on both brain structural MRI scans and genomic data, motivated by the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We also consider a new multiple testing adjustment method and compare it with two existing false discovery rate (FDR) adjustment methods. The simulation results suggest an increase in power for the proposed method. The real-data analysis suggests that the proposed procedure is able to find associations between genetic variants and brain volume differences that offer potentially new biological insights.

© The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

KEYWORDS:

Distance covariance; Genomewide association studies; Local false discovery rate; Multivariate analysis; Neuroimaging analysis; Positive false discovery rate

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