Abstract
Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP) markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1) a simple additive genetic model (MA), 2) a model including both additive and additive by additive epistatic genetic effects (MAE), 3) a model including both additive and dominance genetic effects (MAD), and 4) a full model including all three genetic components (MAED). Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions.
Highlights
Non-additive genetic variation results from interactions between genes
Unlike association analysis which aims at identifying quantitative trait loci (QTL) or chromosome regions with significant effect on the trait of interest, genomic selection focuses on predicting breeding values
A linear mixed model including additive and non-additive genetic effects can be written as: y~XbzZaazZiizZddze where y is the vector of observations, b is the vector of non-genetic effects, a is the vector of additive genetic effects, i is the vector of epistatic effects, d is the vector of dominance effects, and e is the vector of random residuals
Summary
Non-additive genetic variation results from interactions between genes. Interactions between genes at the same locus are called dominance, and interactions between genes at different loci are called epistasis. Many studies have shown that nonadditive effects have a substantial contribution to variation of complex traits [1,2,3], this source of variation is generally ignored in the genetic evaluation of complex traits. Unlike association analysis which aims at identifying quantitative trait loci (QTL) or chromosome regions with significant effect on the trait of interest, genomic selection focuses on predicting breeding values (total additive genetic effects). When considering non-additive genetic effects, association analysis tries to find interactions among the specific genes that have a large effect on the trait of interest, while genomic selection pays attention to total non-additive genetic variations
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