SNPing away at candidate genes

We develop regression methodology to identify subsets of single nucleotide polymorphisms (SNPs) within candidate genes related to quantitative traits and apply our methods to the simulated Genetic Analysis Workshop (GAW) 12 d ata set. In the data set we find 694 SNP loci with minimum allele frequenci es of at least 0.01. We assume an additive casual model between these SNPs and all five quantitative traits. After initial screening using one-way ana lysis of variance, we employ a computationally efficient, simulated anneali ng algorithm to select among all possible subsets of SNP loci, using a gene ralization of Mallows' C, as our optimality criterion. The simple transitio n kernel we develop evaluates new subsets in O(1), by requiring just three arithmetic operations to calculate the proposed RSS based on the Gauss-Jord an pivot. We identify an SNP loci located at 6-5782 related to traits 2 and 3 and several sites on gene 2 related to trait 5 using a subsample of 1,00 0 individuals and the full data set (n = 8,250) for comparison. (C) 2001 Wi ley-Liss, Inc.