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

Lee SH, Goddard ME, Visscher PM, van der Werf JH - Genet. Sel. Evol. (2010)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Queensland Statistical Genetics, Queensland Institute of Medical Research, Brisbane, Australia. hong.lee@qimr.edu.au

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 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.

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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Variation of estimated genetic values within families. Estimated genetic values plotted against family mean for model 1 (A) and model 2 (B)
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Figure 3: Variation of estimated genetic values within families. Estimated genetic values plotted against family mean for model 1 (A) and model 2 (B)

Mentions: The better performance of the realized relationship matrix based on SNP information, compared to the numerator relationship matrix based on pedigree, is probably due to the fact that SNP-based analysis can better predict some of the variation within a family [16]. In Figure 3, a validation set for REP was used as an example to show variation in estimated genetic values within families. As shown in Figure 3, individual genetic values estimated from model 1 based on pedigree information are the same for all the members of the same family whereas those from model 2 based on SNP vary within families (Figure 3B). Part of the variation within families could be captured by SNP information, resulting in consistent improvement on the estimation of phenotypes (Table 7). Similar results were observed for other traits.


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

Lee SH, Goddard ME, Visscher PM, van der Werf JH - Genet. Sel. Evol. (2010)

Variation of estimated genetic values within families. Estimated genetic values plotted against family mean for model 1 (A) and model 2 (B)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2903499&req=5

Figure 3: Variation of estimated genetic values within families. Estimated genetic values plotted against family mean for model 1 (A) and model 2 (B)
Mentions: The better performance of the realized relationship matrix based on SNP information, compared to the numerator relationship matrix based on pedigree, is probably due to the fact that SNP-based analysis can better predict some of the variation within a family [16]. In Figure 3, a validation set for REP was used as an example to show variation in estimated genetic values within families. As shown in Figure 3, individual genetic values estimated from model 1 based on pedigree information are the same for all the members of the same family whereas those from model 2 based on SNP vary within families (Figure 3B). Part of the variation within families could be captured by SNP information, resulting in consistent improvement on the estimation of phenotypes (Table 7). Similar results were observed for other traits.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Queensland Statistical Genetics, Queensland Institute of Medical Research, Brisbane, Australia. hong.lee@qimr.edu.au

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 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.

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.

Show MeSH