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Statistical Applications in Genetics and Molecular Biology

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Volume 12, Issue 6

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Random forests on distance matrices for imaging genetics studies

Aaron Sim
  • Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, UK
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Dimosthenis Tsagkrasoulis / Giovanni Montana
  • Corresponding author
  • Statistics Section, Department of Mathematics, Imperial College London, UK
  • Department of Biomedical Engineering, King’s College London, UK
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2013-11-19 | DOI: https://doi.org/10.1515/sagmb-2013-0040

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

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About the article

Corresponding author: Giovanni Montana, Statistics Section, Department of Mathematics, Imperial College London, UK; and Department of Biomedical Engineering, King’s College London, UK, e-mail:


Published Online: 2013-11-19

Published in Print: 2013-12-01


The overall scaling factor and constant additive term arising from the omitted variance terms in (6) can be ignored as only the relative values of Gnαs are of interest.

Exceptions include responses with infinite degrees of freedom, where the forced vectorial representations are infinite-dimensional; e.g., functions represented by its infinite vector of Fourier modes.

For example, identical measurements taken at different time points.

This value is chosen to ensure a measurable but weak signal of causal SNPs in the case-control set-up

We expect that by considering manifolds of dimensions >2, we will observe similar correspondences between the clustering and partitioning of weaker marginal SNP according to their maf.

These additional effects may have possible links to other non-dementia related neurological pathologies. However, for the purposes of the detection of disease-linked SNPs or SNP-SNP pairs, these associations are irrelevant.

http://hapmap.ncbi.nlm.nih.gov/

http://adni.loni.ucla.edu/data-samples/

ftp://ftp.ncbi.nlm.nih.gov/hapmap//phasing/2009-02_phaseIII/HapMap3_r2/

http://simupop.sourceforge.net/cookbook/pmwiki.php/Cookbook/SimuGWAS

The specific ranges selected are 0.195<maf<0.205 and 0.22<maf<0.24 respectively. The choice of 0.2 was made solely to identify loci with a clear difference between their major and minor allele frequency and is otherwise arbitrary.


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 12, Issue 6, Pages 757–786, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2013-0040.

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