ArticleGenetics, selection, evolution : GSE2010
Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits.
Article in Genetics, selection, evolution : GSE, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Improvement in genetic evaluation of quantitative traits in sheep by enriching genetic model with dominance effects.Scientific reports · 2025Article
- Comparison on genomic prediction using pedigree BLUP and single step GBLUP through the Hanwoo full-sib family.Animal bioscience · 2023Article
- Heritability Estimation Approaches Utilizing Genome-Wide Data.Current protocols · 2023Review
- Heritability and genetic correlation estimates of semen production traits with litter traits and pork production traits in purebred Duroc pigs.Journal of animal science · 2022Article
- Genomic prediction for testes weight of the tiger pufferfish, Takifugu rubripes, using medium to low density SNPs.Scientific reports · 2021Article
- Examining adaptive evolution of immune activity: opportunities provided by gastropods in the age of 'omics'.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Review
- Estimation of Additive and Dominance Genetic Effects on Body Weight, Carcass and Ham Quality Traits in Heavy Pigs.Animals : an open access journal from MDPI · 2021Article
- Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values inFrontiers in plant science · 2021Article
- Genetic Variation in Drought-Tolerance Traits and Their Relationships to Growth inFrontiers in plant science · 2021Article
- Modeling the Dependence Structure in Genome Wide Association Studies of Binary Phenotypes in Family Data.Behavior genetics · 2020Article
- Genomic selection on breeding time in a wild bird population.Evolution letters · 2019Article
- Estimating the purebred-crossbred genetic correlation of body weight in broiler chickens with pedigree or genomic relationships.Genetics, selection, evolution : GSE · 2019Article
- Genome-wide associations and detection of potential candidate genes for direct genetic and maternal genetic effects influencing dairy cattle body weight at different ages.Genetics, selection, evolution : GSE · 2019Article
- Detecting the dominance component of heritability in isolated and outbred human populations.Scientific reports · 2018Article
- Using Machine Learning to Discover Latent Social Phenotypes in Free-Ranging Macaques.Brain sciences · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundIn 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.
methodsIn 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
conclusionsWe 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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