UNASSIGNED: It\'s well-documented that most economic traits have a complex genetic structure that is controlled by additive and non-additive gene actions. Hence, knowledge of the underlying genetic architecture of such complex traits could aid in understanding how these traits respond to the selection in breeding and mating programs. Computing and having estimates of the non-additive effect for economic traits in sheep using genome-wide information can be important because; non-additive genes play an important role in the prediction accuracy of genomic breeding values and the genetic response to the selection.
UNASSIGNED: This study aimed to assess the impact of non-additive effects (dominance and epistasis) on the estimation of genetic parameters for body weight traits in sheep.
UNASSIGNED: This study used phenotypic and genotypic belonging to 752 Scottish Blackface lambs. Three live weight traits considered in this study were included in body weight at 16, 20, and 24 weeks). Three genetic models including additive (AM), additive + dominance (ADM), and additive + dominance + epistasis (ADEM), were used.
UNASSIGNED: The narrow sense heritability for weight at 16 weeks of age (BW16) were 0.39, 0.35, and 0.23, for 20 weeks of age (BW20) were 0.55, 0.54, and 0.42, and finally for 24 weeks of age (BW24) were 0.16, 0.12, and 0.02, using the AM, ADM, and ADEM models, respectively. The additive genetic model significantly outperformed the non-additive genetic model (p < 0.01). The dominance variance of the BW16, BW20, and BW24 accounted for 38, 6, and 30% of the total phenotypic, respectively. Moreover, the epistatic variance accounted for 39, 0.39, and 47% of the total phenotypic variances of these traits, respectively. In addition, our results indicated that the most important SNPs for live weight traits are on chromosomes 3 (three SNPS including s12606.1, OAR3_221188082.1, and OAR3_4106875.1), 8 (OAR8_16468019.1, OAR8_18067475.1, and OAR8_18043643.1), and 19 (OAR19_18010247.1), according to the genome-wide association analysis using additive and non-additive genetic model.
UNASSIGNED: The results emphasized that the non-additive genetic effects play an important role in controlling body weight variation at the age of 16-24 weeks in Scottish Blackface lambs.
UNASSIGNED: It is expected that using a high-density SNP panel and the joint modeling of both additive and non-additive effects can lead to better estimation and prediction of genetic parameters.

Sweet cherry is consumed widely across the world and provides substantial economic benefits in regions where it is grown. While cherry breeding has been conducted in the Pacific Northwest for over half a century, little is known about the genetic architecture of important traits. We used a genome-enabled mixed model to predict the genetic performance of 505 individuals for 32 phenological, disease response and fruit quality traits evaluated in the RosBREED sweet cherry crop data set. Genome-wide predictions were estimated using a repeated measures model for phenotypic data across 3 years, incorporating additive, dominance and epistatic variance components. Genomic relationship matrices were constructed with high-density SNP data and were used to estimate relatedness and account for incomplete replication across years.
High broad-sense heritabilities of 0.83, 0.77, and 0.76 were observed for days to maturity, firmness, and fruit weight, respectively. Epistatic variance exceeded 40% of the total genetic variance for maturing timing, firmness and powdery mildew response. Dominance variance was the largest for fruit weight and fruit size at 34% and 27%, respectively. Omission of non-additive sources of genetic variance from the genetic model resulted in inflation of narrow-sense heritability but minimally influenced prediction accuracy of genetic values in validation. Predicted genetic rankings of individuals from single-year models were inconsistent across years, likely due to incomplete sampling of the population genetic variance.
Predicted breeding values and genetic values revealed many high-performing individuals for use as parents and the most promising selections to advance for cultivar release consideration, respectively. This study highlights the importance of using the appropriate genetic model for calculating breeding values to avoid inflation of expected parental contribution to genetic gain. The genomic predictions obtained will enable breeders to efficiently leverage the genetic potential of North American sweet cherry germplasm by identifying high quality individuals more rapidly than with phenotypic data alone.

Genome-wide association mapping and genomic predictions of phenotype of individuals in livestock are predominately based on the detection and estimation of additive genetic effects. Non-additive genetic effects are largely ignored. Studies in animals, plants, and humans to assess the impact of non-additive genetic effects in genetic analyses have led to differing conclusions. In this paper, we examined the consequences of including non-additive genetic effects in genome-wide association mapping and genomic prediction of total genetic values in a commercial population of 5,658 broiler chickens genotyped for 45,176 single nucleotide polymorphism (SNP) markers. We employed mixed-model equations and restricted maximum likelihood to analyze 7 feed related traits (TRT1 - TRT7). Dominance variance accounted for a significant proportion of the total genetic variance in all 7 traits, ranging from 29.5% for TRT1 to 58.4% for TRT7. Using a 5-fold cross-validation schema, we found that in spite of the large dominance component, including the estimated dominance effects in the prediction of total genetic values did not improve the accuracy of the predictions for any of the phenotypes. We offer some possible explanations for this counter-intuitive result including the possible confounding of dominance deviations with common environmental effects such as hatch, different directional effects of SNP additive and dominance variations, and the gene-gene interactions\' failure to contribute to the level of variance.