Journal of Animal Breeding and Genetics最新文献

We estimated the desired genetic gains for important traits in Beluga sturgeon (Huso huso) using an analytic hierarchy process (AHP) and weighted goal programming (WGP). Two questionnaires were distributed to 122 private Beluga sturgeon farmers in Iran in two stages. The initial stage involved the first questionnaire, which aimed to gather information on the environmental and management aspects of the farms. Additionally, farmers were requested to rate 4 important traits from a list of 12 traits for genetic improvement in the Beluga breeding program. Based on the results, four traits, that is, ovarian fat lobe weight (OFW), total caviar weight (CW), body weight of broodstock (BW) and larval body area at hatching (LBA) were rated highest. In the second stage, the second questionnaire asked farmers to make six pairwise comparisons between the above traits to determine preferences of these traits. The analytical hierarchy process was used to estimate individual preference (Ind-P) values using Super Decisions software. The medians of the Ind-P values were OFW and CW (0.30), BW (0.27) and LBA (0.10). Social group preference (Soc-P) values were estimated for five categories, with 13 social groups using the WGP model in LINGO software. Most disagreements in Soc-P values were found between the commercial products and water temperature categories. Consensus preference values (Con-P) for the above two categories were obtained by the extended WGP model with the range of λ between 0 and 1 in LINGO software. The average best Con-P values were OFW = BW (0.29), LBA (0.21) and CW (0.19). Desired genetic gains (%) were calculated using the percentage genetic improvement (G%) multiplied by the mean of Con-P, yielding values of 2.09, 1.88, 1.44 and 0.62 for BW, CW, LBA and OFW, respectively. The most important achievements of this research are the determination of the economic traits importance and multi-traits selection in the Beluga breeding program.

Gait visual scores are widely applied to horse breeding because they are a fast and easy phenotyping strategy, allowing the numeric interpretation of a complex biological process such as gait quality. However, they may suffer from subjectivity or high environmental influence. We aimed to investigate potential causal relationships among six visual gait scores in Campolina horses. The data included 5475 horses with records for at least one of the following traits: Dissociation (Di), Comfort (C), Style (S), Regularity (R), Development (De), and Gait total Scores (GtS). The pedigree comprised three generations with 14,079 horses in the additive relationship matrix. Under a Bayesian framework, (co)variance components were estimated through a multitrait animal model (MTM). Then, the inductive causation algorithm (IC) was applied to the residual (co)variance matrix samples. The resulting undirected graph from IC was directed in 6 possible causal structures, each fitted by a structural equation model. The final causal structure was chosen based on deviance information criteria (DIC). It was found that S significantly impacts the causal network of gait, directly and indirectly affecting C. The indirect causal effect of S on C was through the direct effect of S on De, then the direct effect of De on R, and finally, the direct effect of R on C. Di was caused by S, which is the reason for the genetic correlation between Di and GtS, due to causal effects being added to the model, they absorb the genetic correlation between Di and GtS. Those paths have biological meaning to horse movements and can help breeders and researchers better understand horses' complex causal network of gait.

Genetic trends are a valuable tool for analysing the efficiency of breeding programs. They are calculated by averaging the predicted breeding values for all individuals born within a specific time period. Moreover, partitioned genetic trends allow dissecting the contributions of several selection paths to overall genetic progress. These trends are based on the linear relationship between breeding values and the Mendelian sampling terms of ancestors, enabling genetic trends to be split into contributions from different categories of individuals. However, (1) the use of predicted breeding values in calculating partitioned genetic trends depends on the variance components used and (2) a multiple trait analysis allows accounting for selection on correlated traits. These points are often not considered. To overcome these limitations, we present a software called "TM_TRENDS." This software performs a Bayesian analysis of partitioned genetic trends in a multiple trait model, accounting for uncertainty in the variance components. To illustrate the capabilities of this tool, we analysed the partitioned genetic trends for five traits (Birth Weight, Weight at 210 days, Cold Carcass Weight, Carcass Conformation, and Fatness Conformation) in two Spanish beef cattle breeds, Pirenaica and Rubia Gallega. The global genetic trends showed an increase in Carcass Conformation and a decrease in Birth Weight, Weight at 210 days, Cold Carcass Weight, and Fatness Conformation. These trends were partitioned into six categories: non-reproductive individuals, dams of females and non-reproductive individuals, dams of sires, sires with fewer than 20 progeny, sires between 20 and 50 progeny, and sires with more than 50 progeny. The results showed that the main source of genetic progress comes from sires with more than 50 progenies, followed by dams of males. Additionally, two additional features of the Bayesian analysis are presented: the calculation of the posterior probability of the global and partitioned genetic response between two time points, and the calculation of the posterior probability of positive (or negative) genetic progress.

Both selection and mating systems are essential tools for breeders to conserve the genetic variance and improve the performance of livestock animals. How to effectively balance the genetic gain and inbreeding has always been an important issue in quantitative genetics research. In this study, a total of 11 selection methods, including random and truncation selection, six conventional selection methods, three different optimal contribution selection (OCS) methods and three mating strategies including random mating, minimum-coancestry mating based on pedigree (MCPed) and genomic information (MCmarker), were performed using stochastic simulations. The long-term effects of different combinations of selection and mating strategies on the genetic gain, the rate of inbreeding and genetic diversity in the small-scale pig conservation populations were investigated. The results showed that different strategies of selection and mating methods had different effects on genetic gain and inbreeding rate. For maintaining additive genetic variance, the optimal strategy was random selection with random mating, followed by SIREhalf-DAMfullRandom selection (which means selecting dams randomly from each full-sib family) and random mating. For mainting the number of common ancestors, the optimal strategy was SIREhalf-DAMfull selection (which means selecting dams with the highest estimated breeding value within each full-sib family) and random mating, followed by SIREhalf-DAMfullRandom selection and random mating, OCS and MCPed mating. For genetic diversity metrics, taking He and Ho as an example, the optimal strategy was GOCS (optimal contribution selection based on genomic information) with MCmarker mating. For genetic gain, the optimal strategy was truncation selection and MCmarker mating, followed by POCS (optimal contribution selection based on pedigree information) and MCmarker mating, truncation selection and MCPed mating. For the rate of inbreeding, the optimal strategy was SIREhalf-DAMfull selection and MCPed mating. Our findings can help breeding managers and farmers choose a more suitable and sustainable strategy for maintaining the genetic diversity and improving the genetic gain of local pig breeds.

The aim was to estimate the heritabilities for accumulated weight gain between 60 and 90 days (WG1), 90 and 120 days (WG2) and 120 and 150 days of age (WG3), pre-slaughter body weight (PRE), meat-to-shell ratio (MS), carcass yield (CY) and age at first oviposition (AFO) in a population of snails Cornu aspersum maximum. Single (for heritabilities) and bi-trait (for genetic correlations) analyses were performed using Bayesian inference. The animal model included the random effect of animal and systematic effects of contemporary groups and covariates. The heritability estimates for WG1, WG2 and WG3 were 0.59, 0.60 and 0.32, respectively. Heritabilities for PRE, MS, CY and AFO ranged from 0.22 to 0.51. Environmental factors mostly influenced PRE among the studied traits. However, for carcass traits and age at first oviposition, the 95% HPD intervals of estimates were large. Only the genetic correlations between weight gains reached chain convergence. The correlation between WG1 and WG2 was 0.74, between WG2 and WG3 was 0.57, and between WG1 and WG3 was 0.22 (not statistically significant). In this sense, WG1 appears to be the optimal period for evaluating the body performance of snails. Genetic improvement in WG2 may be obtained by direct selection for WG1 in this population of Cornu aspersum maximum.

The purpose of this work was to test the application of selection criteria that consider the genetic variances of future generations. This has not been done previously in numerically large livestock breeding programs based on estimated rather than assumed known marker effects. A generic pure-line pig breeding program was simulated in which 40 males and 400 females were selected every generation. Daily gain was used as an example trait. Three variance-considering criteria as well as different reference population sizes were compared to investigate the effect of differences in genomic prediction accuracy on selection decisions. All variance-considering criteria retained more genetic variance than if selection was based on estimated breeding values (max. 20%). This effect was more pronounced for higher prediction accuracies and criteria assessing the variance more generations ahead. After 20 generations, the criterion with the longest planning horizon combined with the largest reference population resulted in a 2% higher genetic level of boars selected to produce finisher pigs. While the advantage of accounting for future generations diminished with lower accuracy or shorter planning horizons, the variance-considering criteria never performed worse than selection based on genomic estimated breeding values (GEBV) with respect to commercial genetic gain. We are reporting various accuracy metrics to help judge the effectiveness of using one of our tested criteria in real breeding programs. While we did not find large benefits for genetic gain when considering future variances in selection decisions, we also did not find negative side effects, while considerably more genetic variance was maintained. This means that using variance-considering criteria results in either equally good or better performance than truncation selection based on estimated breeding values. Our criteria can be applied to any genomic breeding program as long as phased genotypes, estimated marker effects and a genetic map are available.

In the late 1950s, Katahdin hair sheep were developed as a composite breed of medium size and moderate prolificacy, with potential to express resistance to gastrointestinal nematodes. With increasing popularity and the recent adoption of genomic prediction in their genetic evaluation, there is a risk of decreasing variation with selection based on genomically enhanced estimated breeding values. While Katahdin pedigrees are readily available for monitoring diversity, they may not capture the entirety of genetic relationships. We aimed to characterise the genomic population structure and diversity present in the breed, and how these impact the size of a reference population necessary to achieve accurate genomic predictions. Genotypes of Katahdin sheep from 81 member flocks in the National Sheep Improvement Program (NSIP) were used. After quality control, there were 9704 animals and 31,984 autosomal single nucleotide polymorphisms analysed. Population structure was minimal as a single ancestral population explained 99.9% of the genetic variation among animals. The current N_e was estimated to be 150, and despite differences in trait heritabilities, the effect of N_e on the accuracy of genomic predictions suggested the breed should aim for a reference population size of 15,000 individuals. The average degree of inbreeding estimated from runs of homozygosity (ROH) was 16.6% ± 4.7. Four genomic regions of interest, previously associated with production traits, contained ROH shared among > 50% of the breed. Based on four additional methods, average genomic inbreeding coefficients ranged from 0.011 to 0.012. The current population structure and diversity of the breed reflects genetic connectedness across flocks due to the sharing of animals. Shared regions of ROH should be further explored for incorporation of functional effects into genomic predictions to increase selection gains. Negative impacts on genetic diversity due to genomic selection are not of immediate concern for Katahdin sheep engaged in NSIP.

Many breeding programmes have to perform preselection, as genotyping and phenotyping all potential breeder candidates is often not a feasible option. There is need to understand how preselection affects the quality of the genomic estimated breeding values (EBVs) at final selection and thereby can affect genetic progress. This simulation study evaluated nine different preselection strategies in a broiler breeder programme and their effect on the quality of the (genomic) EBVs and genetic progress for three different traits: body weight (Body Weight), residual feed intake (RFI) and body weight gain (Gain). All birds have Body Weight recorded at preselection, but only the preselected birds were phenotyped for RFI and Gain and genotyped. The following criteria and intensities were studied: preselection based on phenotypic Body Weight (P), on a BLUP index (B) or on an ssGBLUP Index (G). Additionally, all criteria were studied with three different selection intensities, 10% of the males and 30% of the females (P10, B10, G10), 15% of the males and 45% of the females (P15, B15, G15) and 20% of the males and 60% of the females (P20, B20, G20). The accuracy at preselection with G10 was more accurate than B10 for both RFI and Gain (0.71 vs. 0.58 and 0.65 vs. 0.55 respectively), and also G15 was more accurate than B15 for both RFI and Gain (0.72 vs. 0.63 and 0.67 vs. 0.64 respectively); thus, the difference in accuracy reduces with an increasing number of birds being preselected. Differences in accuracy at final selection were mostly notable in the RFI trait between P10, B10 and G10, where G10 showed the highest accuracy (0.82 vs. 0.84 vs. 0.86 respectively). This difference in accuracy for RFI disappeared when more animals were preselected. For Body Weight and Gain, the BLUP preselection resulted in the highest accuracy at final selection when selection intensity decreased. The dispersion bias of EBVs at final selection was most pronounced in the P10 and P15 for Body Weight (0.81 and 0.92) but disappeared at P20 (0.97). The dispersion bias for all other criteria and traits was relatively small. Genetic progress was mostly affected when P10 or P15 was used at preselection, where the progress in Body Weight was noticeably higher, but prominently lower for RFI and Gain. The BLUP and ssGBLUP preselection had very similar genetic progress across traits and showed comparable improvements in the selection index. In conclusion, with high preselection intensity, the use of ssGBLUP at preselection might be favoured as there is an improvement in genetic progress across traits in all scenarios, which is due to the increased preselection accuracy. When preselection intensity decreases, the benefit of using ssGBLUP over BLUP at preselection disappears.

Whole-genome sequence (WGS) data was used to estimate genomic breeding values for growth and carcass traits in Australian Angus cattle. The study aimed to compare the accuracy and bias of genomic predictions with three marker densities, including 50K, high-density (HD) and WGS. The dataset used in this study consisted of animals born between 2013 and 2022. Body weight traits included birthweight, weight at 400 days and weight at 600 days of age. The carcass traits were carcass weight, carcass intramuscular fat and carcass marbling score. The accuracy and bias of prediction were assessed using the cross-validation. Further, for the growth traits, animals in the validation group were subdivided into two subgroups, which were moderately or highly related to the reference. Genomic best linear unbiased prediction (GBLUP) was used to compare genomic predictions with the three marker densities. The prediction accuracies were generally similar across the marker densities, ranging between 0.61 and 0.68 for the body weight traits and between 0.40 and 0.52 for the carcass traits. However, the accuracies marginally decreased as the marker density increased for all the traits studied. A similar lack of difference was found when considering the accuracy by the relatedness subgroups. The results indicated that no meaningful difference in prediction accuracy was estimated when comparing the three marker densities due to the population structure. In conclusion, there was no substantial improvement in genomic prediction when using the WGS in this study.

Feeding behaviour traits, such as number, duration or intake per feeder visit, have been associated with feed efficiency in dairy cattle. Those traits, however, do not fully capture cows' feeding patterns throughout the day. The goal of this study was to propose a new phenotype for characterising within-day feeding patterns and estimate its heritability and genetic correlations with dry matter intake (DMI), secreted milk energy, metabolic body weight and residual feed intake. Feeding patterns were evaluated using 4.8 million bunk visits from 1684 midlactation Holstein cows collected from 2009 to 2023 with an Insentec system. Feed efficiency traits were available from 6099 lactating Holstein cows at six research stations across the United States. Daily bunk visits were ordered, with Time 0 designated as the time of first feed delivery. Intake proportions were calculated by visit for each cow by dividing feed intake per visit by the total intake of the cow for that day. Feeding patterns were characterised by the area under the curve of cumulative feed intake proportions for each cow throughout the day. The feeding pattern phenotype per cow was defined as the average of areas under the curve across days, whereas consistency of feeding pattern was calculated as the natural logarithm of variance of daily area under the curve values. Estimates of heritability and genetic correlations were performed using Bayesian inference with an animal model, considering lactation, days in milk and cohort (trial-treatment) as fixed effects and animal as a random effect. Heritability estimates for average area under the curve and variance of daily area under the curve were 0.35 ± 0.05 and 0.16 ± 0.05, respectively. The genetic correlation between average area under the curve and secreted milk energy was -0.30 ± 0.14. Genetic correlations between average area under the curve and DMI, metabolic body weight and residual feed intake were not statistically significant. Variance of daily area under the curve was genetically correlated with DMI (0.47 ± 0.15), secreted milk energy (0.40 ± 0.17) and metabolic body weight (0.28 ± 0.13). The genetic correlation between variance of daily area under the curve and residual feed intake was not significant. Overall, we provided a reliable method to truly characterise feeding patterns in midlactation dairy cows. Feeding pattern and its consistency were heritable, indicating that a significant proportion of phenotypic variation is explained by additive genetic effects. Genetic correlation estimates indicate that cows with more consistent daily feeding patterns have lower DMI, lower secreted milk energy and lower metabolic body weight.