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

Lee, Sang Hong, Goddard, Michael E., Visscher, Peter M. and van der Werf, Julius H. J. (2010) Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits. Genetics Selection Evolution, 42 22.1-22.14. doi:10.1186/1297-9686-42-22


Author Lee, Sang Hong
Goddard, Michael E.
Visscher, Peter M.
van der Werf, Julius H. J.
Title Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits
Journal name Genetics Selection Evolution   Check publisher's open access policy
ISSN 0999-193X
1297-9686
Publication date 2010-06
Sub-type Article (original research)
DOI 10.1186/1297-9686-42-22
Open Access Status DOI
Volume 42
Start page 22.1
End page 22.14
Total pages 14
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted 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.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Article # 22

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Brain Institute Publications
 
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