Including dominance effects in the prediction model through locus-specific weights on heterozygous genotypes can greatly improve genomic predictive abilities

Tianfei Liu,Yan Wang,Dingming Shu,Chenglong Luo,Hao Qu,Guosheng Su,Jie Ma

doi:10.1038/s41437-022-00504-6

Tianfei Liu, Yan Wang + Show 5 more

Open Access

https://doi.org/10.1038/s41437-022-00504-6

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Abstract

The dominance effect is considered to be a key factor affecting complex traits. However, previous studies have shown that the improvement of the model, including the dominance effect, is usually less than 1%. This study proposes a novel genomic prediction method called CADM, which combines additive and dominance genetic effects through locus-specific weights on heterozygous genotypes. To the best of our knowledge, this is the first study of weighting dominance effects for genomic prediction. This method was applied to the analysis of chicken (511 birds) and pig (3534 animals) datasets. A 5-fold cross-validation method was used to evaluate the genomic predictive ability. The CADM model was compared with typical models considering additive and dominance genetic effects (ADM) and the model considering only additive genetic effects (AM). Based on the chicken data, using the CADM model, the genomic predictive abilities were improved for all three traits (body weight at 12th week, eviscerating percentage, and breast muscle percentage), and the average improvement in prediction accuracy was 27.1% compared with the AM model, while the ADM model was not better than the AM model. Based on the pig data, the CADM model increased the genomic predictive ability for all the three pig traits (trait names are masked, here designated as T1, T2, and T3), with an average increase of 26.3%, and the ADM model did not improve, or even slightly decreased, compared with the AM model. The results indicate that dominant genetic variation is one of the important sources of phenotypic variation, and the novel prediction model significantly improves the accuracy of genomic prediction.

Highlights

(1) Additive genetic effects are the additive effects of a number of genes that can be accumulative and stably inherited, while nonadditive genetic effects are the effects of gene interactions that can change by allele recombination and cannot be stably inherited
MATERIALS AND METHODS The novel prediction model The model developed in this study is based on the assumption that the degree of dominance is different at different loci, and the model accounts for the difference in degrees of dominance by locus-specific weights on heterozygous genotypes
Genetic variances Table 1 shows the variances in proportion to the phenotypic variance for the three quantitative traits (BMP, eviscerating percentage (EP), and W12) from the chicken dataset

Summary

Introduction

With the continuous growth of the global population, human demand for food and security continues to increase. Hybridization is an important tool for increasing meat and grain production. The performance of hybrid individuals is affected by genetic factors such as additive, dominance, and epistatic genetic effects. Traditional genetic evaluation methods only consider additive genetic effects but not nonadditive genetic effects (dominance and epistatic effects). There are three main reasons why traditional genetic evaluation methods only consider additive effects. (2) The traditional pedigree-based genetic evaluation method has difficulty accurately estimating nonadditive genetic effects since pedigree-based nonadditive genetic relationships between individuals are usually weak, and it is difficult to distinguish nonadditive genetic effects (such as dominance effects) and common environmental effects on siblings. (3) There is a high computational demand for computing the inverses of relationship matrices for non-additive genetic effects for large data There are three main reasons why traditional genetic evaluation methods only consider additive effects. (1) Additive genetic effects are the additive effects of a number of genes that can be accumulative and stably inherited, while nonadditive genetic effects are the effects of gene interactions that can change by allele recombination and cannot be stably inherited. (2) The traditional pedigree-based genetic evaluation method has difficulty accurately estimating nonadditive genetic effects since pedigree-based nonadditive genetic relationships between individuals are usually weak, and it is difficult to distinguish nonadditive genetic effects (such as dominance effects) and common environmental effects on siblings. (3) There is a high computational demand for computing the inverses of relationship matrices for non-additive genetic effects for large data

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Conclusion