Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits

In this study, we constructed genetic covariance structures from whole-genome marker data, and thus used realized relationship matrices to estimate variance components in a heterogenous population of ~ 2200 mice for which four complex traits were investigated. These mice were genotyped for more than 10,000 single nucleotide polymorphisms (SNP) and the variances due to family, cage and genetic effects were estimated by models based on pedigree information only, aggregate SNP information, and model selection for specific SNP effects.We show that the use of genome-wide SNP information can disentangle confounding factors to estimate genetic variances by separating genetic and non-genetic effects. The estimated variance components using realized relationship were more accurate and less biased, compared to those based on pedigree information only. Models that allow the selection of individual SNP in addition to fitting a relationship matrix are more efficient for traits with a significant dominance variance.Complex traits are important in evolution, human medicine, forensics and artificial selection programs [1-4]. Most complex traits show a mode of inheritance that may be caused by many functional genes with additive and dominance effects, and possibly epistatic interactions, and environmental effects [5,6].Traditionally, pedigree information has been used to estimate heritabilities and genetic effects for complex traits [7-10]. In many family studies, non-genetic factors such as familial or shared environmental effects can be confounded with genetic factors [11]. In particular for full-sibs there is confounding between shared environmental effects, additive genetic effects and non-additive genetic effects.Recently, it has become feasible to generate individual genotype information on large numbers of single nucleotide polymorphisms (SNP) across the whole genome, and genome-wide association studies have been performed in a number of species [12,13]. It is expected that S