Abstract
The Fisher’s infinitesimal model is traditionally used in quantitative genetics and genomic selection, and it attributes most genetic variance to additive variance. Recently, the dominance maximization model was proposed and it prioritizes the dominance variance based on alternative parameterizations. In this model, the additive effects at the locus level are introduced into the model after the dominance variance is maximized. In this study, the new parameterizations of additive and dominance effects on quantitative genetics and genomic selection were evaluated and compared with the parameterizations traditionally applied using the genomic best linear unbiased prediction method. As the parametric relative magnitude of the additive and dominance effects vary with allelic frequencies of populations, we considered different minor allele frequencies to compare the relative magnitudes. We also proposed and evaluated two indices that combine the additive and dominance variances estimated by both models. The dominance maximization model, along with the two indices, offers alternatives to improve the estimates of additive and dominance variances and their respective proportions and can be successfully used in genetic evaluation.
Highlights
Genomic selection (GS), as proposed by Meuwissen et al (2001), enables the identification of genetically superior individuals before their phenotypic data is collected and increases selection accuracy, accelerating and boosting the efficiency of genetic improvement (Simeão et al, 2021)
GS emphasizes the simultaneous prediction of the genetic effects of thousands of genetic DNA markers dispersed throughout the genome of an individual in order to capture the effects of all loci and it explains all the genetic variance of a quantitative trait (Resende and Alves, 2020)
In the process of deriving biometric expressions, the additive variance is maximized while the dominance variance is the residue of the total genetic variance
Summary
Genomic selection (GS), as proposed by Meuwissen et al (2001), enables the identification of genetically superior individuals before their phenotypic data is collected and increases selection accuracy, accelerating and boosting the efficiency of genetic improvement (Simeão et al, 2021). GS emphasizes the simultaneous prediction of the genetic effects of thousands of genetic DNA markers dispersed throughout the genome of an individual in order to capture the effects of all loci and it explains all the genetic variance of a quantitative trait (Resende and Alves, 2020). In the process of deriving biometric expressions, the additive variance is maximized while the dominance variance is the residue of the total genetic variance. The additive effects are introduced into the model after the dominance variance has been maximized
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