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Genome-Wide Association Studies and Genomic Prediction

Volume 1019 of the series Methods in Molecular Biology pp 465-477

Date:

Epistasis, Complexity, and Multifactor Dimensionality Reduction

  • Qinxin PanAffiliated withComputational Genetics Laboratory, Dartmouth Medical School, Dartmouth College
  • , Ting HuAffiliated withComputational Genetics Laboratory, Dartmouth Medical School, Dartmouth College
  • , Jason H. MooreAffiliated withDepartment of Genetics, Dartmouth Medical School, Dartmouth College

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Abstract

Genome-wide association studies (GWASs) and other high-throughput initiatives have led to an information explosion in human genetics and genetic epidemiology. Conversion of this wealth of new information about genomic variation to knowledge about public health and human biology will depend critically on the complexity of the genotype to phenotype mapping relationship. We review here computational approaches to genetic analysis that embrace, rather than ignore, the complexity of human health. We focus on multifactor dimensionality reduction (MDR) as an approach for modeling one of these complexities: epistasis or gene–gene interaction.

Key words

GWAS MDR Filter approach Stochastic search GPUMDR Network approach