Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits.
BACKGROUND
In the analysis of complex traits, genetic effects can be confounded with non-genetic effects, especially when using full-sib families. Dominance and epistatic effects are typically confounded with additive genetic and non-genetic effects. This confounding may cause the estimated genetic variance components to be inaccurate and biased.


METHODS
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 approximately 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.


CONCLUSIONS
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.
Lee
Sang Hong
SH
Queensland Statistical Genetics, Queensland Institute of Medical Research, Brisbane, Australia. hong.lee@qimr.edu.au
Goddard
Michael E
ME
Visscher
Peter M
PM
van der Werf
Julius Hj
JH
D000818:Animals; D015982:Bias; D014644:Genetic Variation / Q000235:genetics*; D016678:Genome; D005838:Genotype; D051379:Mice; D008954:Models, Biological; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D012044:Regression Analysis; D035781:Siblings