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Abstract Details

Activity Number: 62
Type: Topic Contributed
Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract - #306783
Title: Bayesian Methods for Genetics and Brain Imaging
Author(s): Wesley Thompson*+
Companies: University of California at San Diego
Address: , San Diego, CA, 92093,
Keywords: Bayesian ; Neuroimaging ; Genetics ; Biomedical ; High-Dimensional

Brain imaging and genetics are typical of high-dimensional datasets currently being acquired in biomedical research. The high-dimensionality has rendered traditional hypothesis testing paradigms insufficient. For example, most single-nucleotide polymorphisms (SNPs) in a genome wide significance study (GWAS) will have a tiny effect, even for non-null SNPs. An overly-stringent Bonferroni correction will zero out almost all of these SNPs. When associating genetic information with brain imaging phenotypes, the problem seems even more hopeless. We describe Bayesian methods for intelligently reducing the dimensionality in both genetic and neuroimaging domains that leverage scientific information about structure in both domains. We demonstrate that these methods greatly increase the power to detect relationships, and also enable interpretation about mechanisms that are more accessible to scientists.

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