Random forests on distance matrices for imaging genetics studies

Sim Aaron, Tsagkrasoulis Dimosthenis and Giovanni Montana ()
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Sim Aaron: Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, UK
Tsagkrasoulis Dimosthenis: Statistics Section, Department of Mathematics, Imperial College London, UK

Statistical Applications in Genetics and Molecular Biology, 2013, vol. 12, issue 6, 757-786

Abstract: We propose a non-parametric regression methodology, Random Forests on Distance Matrices (RFDM), for detecting genetic variants associated to quantitative phenotypes, obtained using neuroimaging techniques, representing the human brain’s structure or function. RFDM, which is an extension of decision forests, requires a distance matrix as the response that encodes all pair-wise phenotypic distances in the random sample. We discuss ways to learn such distances directly from the data using manifold learning techniques, and how to define such distances when the phenotypes are non-vectorial objects such as brain connectivity networks. We also describe an extension of RFDM to detect espistatic effects while keeping the computational complexity low. Extensive simulation results and an application to an imaging genetics study of Alzheimer’s Disease are presented and discussed.

Keywords: genetic associations; random forests; quantitative traits; imaging genetics; Alzheimer’s Disease (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1515/sagmb-2013-0040

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