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Variable Ranking by Random Forests Model for Genome-Wide Association Study

An important step in the genome-wide association study (GWAS) is the ranking of single nucleotide polymorphisms (SNPs). We propose a method based on the variable importance measure from the random forests model. SNPs in the entire genome region are randomly divided into subsets. We then fit the random forests model to each subset to compute subranks for the SNPs. The ranks of the SNPs are defined based on these subranks and then iteratively improved. We study the impact of the parameters and show that our method performs well in comparison to popular existing methods. We apply our method to select SNPs in a real-data study of the link between SNPs and human fingerprint ridge counts.