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Comparing Simultaneous Multi-slice Diffusion Acquisitions Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Power Estimates for Voxel-Based Genetic Association Studies Using Diffusion Imaging

Authors : Neda Jahanshad, Peter Kochunov, David C. Glahn, John Blangero, Thomas E. Nichols, Katie L. McMahon, Greig I. de Zubicaray, Nicholas G. Martin, Margaret J. Wright, Clifford R. Jack Jr., Matt A. Bernstein, Michael W. Weiner, Arthur W. Toga, Paul M. Thompson

Published in: Computational Diffusion MRI and Brain Connectivity

Publisher: Springer International Publishing

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Abstract

The quest to discover genetic variants that affect the human brain will be accelerated by screening brain images from large populations. Even so, the wealth of information in medical images is often reduced to a single numeric summary, such as a regional volume or an average signal, which is then analyzed in a genome wide association study (GWAS). The high cost and penalty for multiple comparisons often constrains us from searching over the entire image space. Here, we developed a method to compute and boost power to detect genetic associations in brain images. We computed voxel-wise heritability estimates for fractional anisotropy in over 1,100 DTI scans, and used the results to threshold FA images from new studies. We describe voxel selection criteria to optimally boost power, as a function of the sample size and allele frequency cut-off. We illustrate our methods by analyzing publicly-available data from the ADNI2 project.

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Footnotes
1
At each location on the genome, a person has a specific nucleotide or base-pair combination; SNPs are common variants in the genetic code, carried by at least 1 % of the population.
 
2
A phenotype is a biological measure that is subjected to genetic analysis, such as the size of a brain region, or a diffusion imaging measure in a specific region.
 
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Metadata
Title
Power Estimates for Voxel-Based Genetic Association Studies Using Diffusion Imaging
Authors
Neda Jahanshad
Peter Kochunov
David C. Glahn
John Blangero
Thomas E. Nichols
Katie L. McMahon
Greig I. de Zubicaray
Nicholas G. Martin
Margaret J. Wright
Clifford R. Jack Jr.
Matt A. Bernstein
Michael W. Weiner
Arthur W. Toga
Paul M. Thompson
Copyright Year
2014
Book
Computational Diffusion MRI and Brain Connectivity

Print ISBN: 978-3-319-02474-5

Electronic ISBN: 978-3-319-02475-2

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-02475-2_21

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