Generalized T2 Test for Genome Association Studies

Abstract

Recent progress in the development of single-nucleotide polymorphism (SNP) maps within genes and across the genome provides a valuable tool for fine-mapping and has led to the suggestion of genomewide association studies to search for susceptibility loci for complex traits. Test statistics for genome association studies that consider a single marker at a time, ignoring the linkage disequilibrium between markers, are inefficient. In this study, we present a generalized T2 statistic for association studies of complex traits, which can utilize multiple SNP markers simultaneously and considers the effects of multiple disease-susceptibility loci. This generalized T2 statistic is a corollary to that originally developed for multivariate analysis and has a close relationship to discriminant analysis and common measure of genetic distance. We evaluate the power of the generalized T2 statistic and show that power to be greater than or equal to those of the traditional chi2 test of association and a similar haplotype-test statistic. Finally, examples are given to evaluate the performance of the proposed T2 statistic for association studies using simulated and real data.

An ensemble learning approach jointly modeling main and interaction effects in genetic association studies

“…The four methods include the FITF proposed by Millstein et al [2006], CSM proposed by Sha et al [2006], Hotelling's T 2 with the sequence-forward-selection (HT-SFS) proposed by Xiong et al [2002], and single-marker test (SMT). Originally, we planned to compare our method with MDR [Ritchie et al, 2001].…”

“…The results of Sha et al [2006] showed that the CSM and MDR have similar power. The Hotelling's T 2 statistic proposed by Xiong et al [2002] is equivalent to the T 2 given by equation (2) by coding the m-marker genotype as X = (x 1 , …, x m ), where x k = 0,1, or 2 corresponding to genotype aa, Aa, or AA at the kth marker. The HT-SFS works as follows.…”

“…The reasons we consider only up to two-locus combinations are that, for a large M, the computation is infeasible and the genotype data will be very sparse for higherorder locus combinations. To measure the effect of a locus combination, we use a generalized Hotelling's T 2 test that was recently proposed for the analysis of quantitative and qualitative traits with the use of multiple markers [Xiong et al, 2002;Chapman et al, 2003;Wallace et al, 2006]. Let individual i have trait value y i and genotype scores X i , respectively.…”

“…Denote the three genotypes aa, aA and AA in each single marker by 0, 1, and 2. For a l-locus combination, Xiong et al [2002] coded the genotype of individual i by X i = (x i1 , …, x il ), where x ij = 0, 1, or 2 according to the genotype at the jth marker. This kind of coding can only model additive effects of the markers, and the corresponding test will lose power on non-additive interactions and will have no power at all on pure interactions (no marginal effects).…”

“…Forming haplotypes over multiple neighboring loci in one gene can increase the power of gene mapping studies [Zhao et al, 2000;Fallin et al, 2001;Schaid et al, 2002;Zhang et al, 2003], but these methods only work locally in a given genomic region. Although various authors have postulated the need for methods that investigate multiple interacting genes jointly [Tiwari and Elston, 1998;Cox et al, 1999;Templeton, 2000;Wilson, 2001;Cordell et al, 2001;Cordell, 2002;Culverhouse et al, 2002;Moore and Williams, 2002;Moore, 2003], only a few viable approaches in this direction exist [Hoh et al, 2001;Xiong et al, 2002;Potter, 2006;Dudbridge et al, 2006;Ritchie et al, 2001Ritchie et al, , 2003Moore, 2004;Nelson et al, 2001;Culverhouse et al, 2004;Sha et al, 2006;Millstein et al, 2006].…”