Download Accounting for correlations among individuals for testing SNP single

Accounting for correlations among individuals for testing SNP single-locus and epistasis effects
in genome-wide association analysis
Li Ma1, Chris I. Amos2, Yang Da1
1
Department of Animal Science, University of Minnesota, St. Paul, MN 55108, USA
Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center, Houston,
TX 77030, USA
2
The existence of a large number of single nucleotide polymorphisms (SNPs) provides
opportunities to screen DNA variations affecting complex traits using an association analysis.
Family data are commonly used data structure in genetic analysis. To account for correlations
among individuals within the same family, a generalized least squares (GLS) method was
developed for testing SNP single-locus and epistasis effects of a quantitative trait based on an
extended Kempthorne model that allows Hardy-Weinberg disequilibrium and linkage
disequilibrium. Within each family, each individual is assumed to have a common phenotypic
variance, and a common phenotypic covariance is assumed for each pair of individuals.
Simplified formulations were derived for the inverse of the phenotypic variance-covariance
matrix so that matrix inversion, the most computationally intensive operation of the GLS
method, is no longer needed. Based on this GLS method, statistical tests were developed to test
three single-locus effects for each SNP and five pairwise effects for each pair of SNPs. The three
single-locus effects are SNP marker effect, additive and dominance effects, and the five pairwise
effects are two-locus interaction, additive × additive, additive × dominance, dominance ×
additive, and dominance × dominance effects. The method for epistasis testing can be a useful
tool to understand the exact mode of epistasis, to assemble genome-wide SNPs into an epistasis
network, and to assemble all SNP effects affecting a phenotype using pairwise epistasis tests.