Screening large-scale association study data: exploiting interactions using random forests |
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Authors: | Email author" target="_blank">Kathryn?L?LunettaEmail author L?Brooke?Hayward Jonathan?Segal Paul?Van Eerdewegh |
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Institution: | (1) Oscient Pharmaceuticals, Inc. (formerly Genome Therapeutics Corporation), Waltham, Massachusetts, USA;(2) Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA;(3) Genizon BioSciences Inc., Montreal, Quebec, Canada;(4) Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA |
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Abstract: | Background Genome-wide association studies for complex diseases will produce genotypes on hundreds of thousands of single nucleotide
polymorphisms (SNPs). A logical first approach to dealing with massive numbers of SNPs is to use some test to screen the SNPs,
retaining only those that meet some criterion for futher study. For example, SNPs can be ranked by p-value, and those with
the lowest p-values retained. When SNPs have large interaction effects but small marginal effects in a population, they are
unlikely to be retained when univariate tests are used for screening. However, model-based screens that pre-specify interactions
are impractical for data sets with thousands of SNPs. Random forest analysis is an alternative method that produces a single
measure of importance for each predictor variable that takes into account interactions among variables without requiring model
specification. Interactions increase the importance for the individual interacting variables, making them more likely to be
given high importance relative to other variables. We test the performance of random forests as a screening procedure to identify
small numbers of risk-associated SNPs from among large numbers of unassociated SNPs using complex disease models with up to
32 loci, incorporating both genetic heterogeneity and multi-locus interaction. |
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