Implementation of the Realized Genomic Relationship Matrix to Open-Pollinated White Spruce Family Testing for Disentangling Additive from Nonadditive Genetic Effects - Gamal El-Dien, Omnia; Ratcliffe, Blaise; Klápště, Jaroslav; Porth, Ilga; Chen, Charles; El-Kassaby, Yousry A (G3 (Bethesda, Md.), 2016)
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.
Keywords: Quantitative Trait, Heritable; Models, Genetic; modely genetické; algoritmy; Genome, Plant; genotyp; Algorithms; genomika; Picea; Genotype; shared data resource; Mendelian sampling term; genotypizační techniky; genom rostlinný; open-pollinated families; genetická variace; fenotyp; Genotyping Techniques; pedigree- and marker-based relationships; Genetic Variation; Pollination; kvantitativní znak dědičný; Phenotype; GenPred; Genomics; genomic selection; smrk; opylení; genetic variance decomposition