Abstract
Abstract. A computing simplification was applied to marker-assisted genetic evaluation of quantitative traits including additive and non-additive effects of QTL as well as residual polygenic effects. Different situations including QTL and the residual polygenic effect estimated as a sum or separately, and with or without non-additive effects integrated in models were evaluated. The computing simplification was used in combinations with different models and parameterizations. An example data was adopted to illustrate the simplified computing strategy and was compared with the computing method of direct inversion. Identical results were obtained from both computing strategies. The main advantage of the simplification is that it does not require inversion of non-additive relationship matrices and relationship matrices of QTL, and the number of random effects in mixed model equations is the same as any animal model with only additive effects.
Highlights
In linear model genetic evaluations, the independent variables are usually results in a probability distribution of QTL genotype rather than a specific genotype
After FERNANDO and GROSSMAN (1989) proposed the gametic model BLUP method for marker-assisted genetic evaluation, several methods have been developed for simplifying the computations of the marker-assisted genetic evaluation and reducing the number of mixed model equations for the evaluation
The simplifications of CANTET and SMITH (1991) and HOSCHELE (1993) are useful for establishing mixed model equations by either expressing the effects of non-parents with their parents or for eliminating the QTL effect equations of those animals that are either not genotyped or do not provide relationship ties
Summary
In linear model genetic evaluations, the independent variables are usually results in a probability distribution of QTL genotype rather than a specific genotype. (3) Gametic model: This model takes QTL covariates as random effects and uses marker information to quantify the similarities between the random allelic QTL effects of different individuals in the population. It was proposed by FERNANDO and GROSSMAN (1989) and has been used very widely. VAN ARENDONK et al (1994) developed a method to estimate directly the sum of the QTL allelic effects and residual polygenic effects for the purpose of genetic evaluation and reduced the number of mixed model equations to one for each animal. LIU; MATHUR: Simplifications of marker-assisted genetic evaluation and accounting for non-additive interaction effects allow more accurate estimation of QTL effects and positions. Example data were adopted to illustrate marker-assisted QTL effect estimations and polygenic effect evaluations combined with different models
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