A fast computational framework for genome-wide association studies with neuroimaging data
Abstract
In the last few years, it has become possible to acquire high-dimensional neuroimaging and genetic data on relatively large cohorts of subjects, which provides novel means to understand the large between-subject variability observed in brain organization. Genetic association studies aim at unveiling correlations between the genetic variants and the numerous phenotypes extracted from brain images and thus face a dire multiple comparisons issue. While these statistics can be accumulated across the brain volume for the sake of sensitivity, the significance of the resulting summary statistics can only be assessed through permutations. Fortunately, the increase of computational power can be exploited, but this requires designing new parallel algorithms. The MapReduce framework coupled with efficient algorithms permits to deliver a scalable analysis tool that deals with high-dimensional data and thousands of permutations in a few hours. On a real functional MRI dataset, this tool shows promising results with a genetic variant that survives the very strict correction for multiple testing.
Domains
Computer Science [cs] Distributed, Parallel, and Cluster Computing [cs.DC] Mathematics [math] Statistics [math.ST] Statistics [stat] Statistics Theory [stat.TH] Computer Science [cs] Bioinformatics [q-bio.QM] Life Sciences [q-bio] Quantitative Methods [q-bio.QM] Cognitive science Neuroscience Life Sciences [q-bio] Genetics
Origin : Files produced by the author(s)
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