Implementation of the Realized Genomic Relationship Matrix to Open-Pollinated White Spruce Family Testing for Disentangling Additive from Nonadditive Genetic Effects
Language English Country Great Britain, England Media electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
26801647
PubMed Central
PMC4777135
DOI
10.1534/g3.115.025957
PII: g3.115.025957
Knihovny.cz E-resources
- Keywords
- GenPred, Mendelian sampling term, genetic variance decomposition, genomic selection, open-pollinated families, pedigree- and marker-based relationships, shared data resource,
- MeSH
- Algorithms MeSH
- Phenotype MeSH
- Genetic Variation MeSH
- Genome, Plant * MeSH
- Genomics * methods MeSH
- Genotype MeSH
- Genotyping Techniques MeSH
- Quantitative Trait, Heritable MeSH
- Models, Genetic MeSH
- Pollination genetics MeSH
- Picea classification genetics MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
The open-pollinated (OP) family testing combines the simplest known progeny evaluation and quantitative genetics analyses as candidates' offspring are assumed to represent independent half-sib families. The accuracy of genetic parameter estimates is often questioned as the assumption of "half-sibling" in OP families may often be violated. We compared the pedigree- vs. marker-based genetic models by analysing 22-yr height and 30-yr wood density for 214 white spruce [Picea glauca (Moench) Voss] OP families represented by 1694 individuals growing on one site in Quebec, Canada. Assuming half-sibling, the pedigree-based model was limited to estimating the additive genetic variances which, in turn, were grossly overestimated as they were confounded by very minor dominance and major additive-by-additive epistatic genetic variances. In contrast, the implemented genomic pairwise realized relationship models allowed the disentanglement of additive from all nonadditive factors through genetic variance decomposition. The marker-based models produced more realistic narrow-sense heritability estimates and, for the first time, allowed estimating the dominance and epistatic genetic variances from OP testing. In addition, the genomic models showed better prediction accuracies compared to pedigree models and were able to predict individual breeding values for new individuals from untested families, which was not possible using the pedigree-based model. Clearly, the use of marker-based relationship approach is effective in estimating the quantitative genetic parameters of complex traits even under simple and shallow pedigree structure.
Department of Biochemistry and Molecular Biology Oklahoma State University Stillwater Oklahoma 74078
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