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

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14 Pages
English

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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.

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Published 01 January 2010
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Language English
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Lee et al.  Genetics Selection Evolution 2010, 42 :22 http://www.gsejournal.org/content/42/1/22
G e n e t i c s S e l e c t i o n E v o l u t i o n R E S E A R C H Open Access R U es s e i ar n ch g the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits Sang Hong Lee* 1 , Michael E Goddard 2,3 , Peter M Visscher 1 and Julius HJ van der Werf 4
Background otide polymorphisms (SNP) across the whole genome, Complex traits are important in evolution, human medi- and genome-wide association studies have been per-cine, forensics and artificial selection programs [1-4]. formed in a number of species [12,13]. It is expected that Most complex traits show a mode of inheritance that may SNP and causal genes will be in linkage disequilibrium be caused by many functional genes with additive and (LD), making it possible to genetically dissect variation in dominance effects, and possibly epistatic interactions, complex traits in a more effective way [14]. Indeed, it has and environmental effects [5,6]. been shown that whole-genome dense SNP analyses can Traditionally, pedigree information has been used to provide extra benefits compared to classical approaches estimate heritabilities and genetic effects for complex based on pedigree information only [15]. traits [7-10]. In many family studies, non-genetic factors In this study, we propose novel strategies that utilize such as familial or shared environmental effects can be dense SNP data for the genetic dissection of complex confounded with genetic factors [11]. In particular for traits. First, we estimate a realized relationship matrix full-sibs there is confounding between shared environ- based on aggregate SNP information [16-18]. The real-mental effects, additive genetic effects and non-additive ized relationship matrix in a classical mixed linear model genetic effects. makes it possible to obtain more accurate and reliable Recently, it has become feasible to generate individual estimates for the narrow sense heritability, compared to genotype information on large numbers of single nucle- traditional pedigree-based analysis [19,20]. Second, we explicitly search for additional additive and dominance * Correspondence: hong.lee@qimr.edu.au b 1 Queensland Statistical Genetics, Quee nsland Institute of Medical Research, ae ffBeacytse stihaant  mmaoyd enl ost ehleacvtei obne eanp aplrroeaacdhy.  cIanp ttuhree dp, royc eusssi, nag Brisbane, Australia Full list of author information is available at the end of the article stochastic model selection of random SNP effects is car-© 2010 Lee et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commo ns At- tribution License (http://creativecommons.org/licenses/by/2.0), wh ich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.