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An application of Random Forests to a genome-wide association dataset: Methodological considerations & new findings by Benjamin A Goldstein; Alan E Hubbard; Adele Cutler; Lisa F Barcellos is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Background: As computational power improves, the application of more advanced machine learning techniques to the analysis of large genome-wide association (gwa) datasets becomes possible. while most traditional statistical methods can only elucidate main effects of genetic variants on risk for disease, certain machine learning approaches are particularly suited to discover higher order and non-linear effects. one such approach is the random forests (rf) algorithm. the use of rf for snp discovery related to human disease has grown in recent years; however, most work has focused on small datasets or simulation studies which are limited. ## Results: Using a multiple sclerosis (ms) case-control dataset comprised of 300 k snp genotypes across the genome, we outline an approach and some considerations for optimally tuning the rf algorithm based on the empirical dataset. importantly, results show that typical default parameter values are not appropriate for large gwa datasets. furthermore, gains can be made by sub-sampling the data, pruning based on linkage disequilibrium (ld), and removing strong effects from rf analyses. the new rf results are compared to findings from the original m

Who reads An application of Random Forests to a genome-wide association dataset: Methodological considerations & new findings?

Author

Benjamin A Goldstein; Alan E Hubbard; Adele Cutler; Lisa F Barcellos

Publisher

BioMed Central; Springer (Biomed Central Ltd.); London: BioMed Central, 2000-; Springer Science and Business Media LLC (ISSN 1471-2156)

Published

2010

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)