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Báo cáo sinh học: "Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits | Lee et al. Genetics Selection Evolution 2010 42 22 http www.gsejournal.Org content 42 1 22 Ge n et i cs Selection Evolution RESEARCH Open Access Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits Sang Hong Lee 1 Michael E Goddard2 3 Peter M Visscher1 and Julius HJ van der Werf4 Abstract 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 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. Results and 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. Background 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 .