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Abstract Background Accuracy of genomic prediction depends on the heritability of the trait, the size of the training set, the relationship of the candidates to the training set, and the $$\text {Min}(N_{\text {QTL}},M_e)$$ Min ( N QTL , M e ) , where $$N_{\text {QTL}}$$ N QTL is the number of QTL and $$M_e$$ M e is the number of independently segregating chromosomal segments. Due to LD, the number $$Q_e$$ Q e of independently segregating QTL (effective QTL) can be lower than $$\text {Min}(N_{\text {QTL}},M_e)$$ Min ( N QTL , M e ) . In this paper, we show that $$Q_e$$ Q e is inversely associated with the trait-specific genomic relationship of a candidate to the training set. This provides an explanation for the inverse association between $$Q_e$$ Q e and the accuracy of prediction. Methods To quantify the genomic relationship of a candidate to all members of the training set, we considered the $$k^2$$ k 2 statistic that has been previously used for this purpose. It quantifies how well the marker covariate vector of a candidate can be represented as a linear combination of the rows of the marker covariate matrix of the training set. In this paper, we used Bayesian regression to make this statistic trait specific and argue that the trait-specific genomic relationship of a candidate to the training set is inversely associated with $$Q_e$$ Q e . Simulation was used to demonstrate the dependence of the trait-specific $$k^2$$ k 2 statistic on $$Q_e$$ Q e , which is related to $$N_{\text {QTL}}$$ N QTL . Conclusions The posterior distributions of the trait-specific $$k^2$$ k 2 statistic showed that the trait-specific genomic relationship between a candidate and the training set is inversely associated to $$Q_e$$ Q e and $$N_{\text {QTL}}$$ N QTL . Further, we show that trait-specific genomic relationship between a candidate and the training set is directly related to the size of the training set.

Abstract Background Managing genetic diversity is critically important for maintaining species fitness. Excessive homozygosity caused by the loss of genetic diversity can have detrimental effects on the reproduction and production performance of a breed. Analysis of genetic diversity can facilitate the identification of signatures of selection which may contribute to the specific characteristics regarding the health, production and physical appearance of a breed or population. In this study, breeds with well-characterized traits such as fine wool production (Rambouillet, N = 745), parasite resistance (Katahdin, N = 581) and environmental hardiness (Dorper, N = 265) were evaluated for inbreeding, effective population size (N e ), runs of homozygosity (ROH) and Wright’s fixation index (FST) outlier approach to identify differential signatures of selection at 36,113 autosomal single nucleotide polymorphisms (SNPs). Results Katahdin sheep had the largest current N e at the most recent generation estimated with both the GONe and NeEstimator software. The most highly conserved ROH Island was identified in Rambouillet with a signature of selection on chromosome 6 containing 202 SNPs called in an ROH in 50 to 94% of the individuals. This region contained the DCAF16, LCORL and NCAPG genes that have been previously reported to be under selection and have biological roles related to milk production and growth traits. The outlier regions identified through the FST comparisons of Katahdin with Rambouillet and Dorper contained genes with known roles in milk production and mastitis resistance or susceptibility, and the FST comparisons of Rambouillet with Katahdin and Dorper identified genes related to wool growth, suggesting these traits have been under natural or artificial selection pressure in these populations. Genes involved in the cytokine-cytokine receptor interaction pathways were identified in all FST breed comparisons, which indicates the presence of allelic diversity between these breeds in genomic regions controlling cytokine signaling mechanisms. Conclusions In this paper, we describe signatures of selection within diverse and economically important U.S. sheep breeds. The genes contained within these signatures are proposed for further study to understand their relevance to biological traits and improve understanding of breed diversity.

Abstract Background Resilience is the capacity of an animal to be minimally affected by disturbances or to rapidly return to its initial state before exposure to a disturbance. Resilient livestock are desired because of their improved health and increased economic profit. Genetic improvement of resilience may also lead to trade-offs with production traits. Recently, resilience indicators based on longitudinal data have been suggested, but they need further evaluation to determine whether they are indeed predictive of improved resilience, such as disease resilience. This study investigated different resilience indicators based on deviations between expected and observed egg production (EP) by exploring their genetic parameters, their possible trade-offs with production traits, and their relationships with antibody traits in chickens. Methods Egg production in a nucleus breeding herd environment based on 1-week-, 2-week-, or 3-week-intervals of two purebred chicken lines, a white egg-laying (33,825 chickens) and a brown egg-laying line (34,397 chickens), were used to determine deviations between observed EP and expected average batch EP, and between observed EP and expected individual EP. These deviations were used to calculate three types of resilience indicators for two life periods of each individual: natural logarithm-transformed variance (ln(variance)), skewness, and lag-one autocorrelation (autocorrelation) of deviations from 25 to 83 weeks of age and from 83 weeks of age to end of life. Then, we estimated their genetic correlations with EP traits and with two antibody traits. Results The most promising resilience indicators were those based on 1-week-intervals, as they had the highest heritability estimates (0.02–0.12) and high genetic correlations (above 0.60) with the same resilience indicators based on longer intervals. The three types of resilience indicators differed genetically from each other, which indicates that they possibly capture different aspects of resilience. Genetic correlations of the resilience indicator traits based on 1-week-intervals with EP traits were favorable or zero, which means that trade-off effects were marginal. The resilience indicator traits based on 1-week-intervals also showed no genetic correlations with the antibody traits, which suggests that they are not informative for improved immunity or vice versa in the nucleus environment. Conclusions This paper gives direction towards the evaluation and implementation of resilience indicators, i.e. to further investigate resilience indicator traits based on 1-week-intervals, in breeding programs for selecting genetically more resilient layer chickens.

Abstract Background Genetic merit, or breeding values as referred to in livestock and crop breeding programs, is one of the keys to the successful selection of animals in commercial farming systems. The developments in statistical methods during the twentieth century and single nucleotide polymorphism (SNP) chip technologies in the twenty-first century have revolutionized agricultural production, by allowing highly accurate predictions of breeding values for selection candidates at a very early age. Nonetheless, for many breeding populations, realized accuracies of predicted breeding values (PBV) remain below the theoretical maximum, even when the reference population is sufficiently large, and SNPs included in the model are in sufficient linkage disequilibrium (LD) with the quantitative trait locus (QTL). This is particularly noticeable over generations, as we observe the so-called erosion of the effects of SNPs due to recombinations, accompanied by the erosion of the accuracy of prediction. While accurately quantifying the erosion at the individual SNP level is a difficult and unresolved task, quantifying the erosion of the accuracy of prediction is a more tractable problem. In this paper, we describe a method that uses the relationship between reference and target populations to calculate expected values for the accuracies of predicted breeding values for non-phenotyped individuals accounting for erosion. The accuracy of the expected values was evaluated through simulations, and a further evaluation was performed on real data. Results Using simulations, we empirically confirmed that our expected values for the accuracy of PBV accounting for erosion were able to correctly determine the prediction accuracy of breeding values for non-phenotyped individuals. When comparing the expected to the realized accuracies of PBV with real data, only one out of the four traits evaluated presented accuracies that were significantly higher than the expected, approaching $$\sqrt{{{\text{h}}}^{2}}$$ h 2 . Conclusions We defined an index of genetic correlation between reference and target populations, which summarizes the expected overall erosion due to differences in allele frequencies and LD patterns between populations. We used this correlation along with a trait’s heritability to derive expected values for the accuracy ( $${\text{R}}$$ R ) of PBV accounting for the erosion, and demonstrated that our derived $${\text{E}}\left[{\text{R}}|{\text{erosion}}\right]$$ E R | erosion is a reliable metric.