Breeding Values For Production Traits Research Articles

127. Sheep rumen volume as a methane proxy – terminal sire breed comparison and relationships with breeding values for production traits

127. Sheep rumen volume as a methane proxy – terminal sire breed comparison and relationships with breeding values for production traits

Impact of environmental disturbances on estimated genetic parameters and breeding values for growth traits in pigs

Due to the diversification of farming systems and climate change, farm animals are exposed to environmental disturbances to which they respond differently depending on their robustness. Disturbances such as heat stress or sanitary challenges (not always recorded, especially when they are of short duration and low intensity) have a transitory impact on animals, resulting in changes in phenotypes of production (feed intake, BW, etc.). The aim of this study was to evaluate the impact of such unknown disturbances on the estimated genetic parameters and breeding values (BV) for production traits. A population of 6 120 individuals over five generations divided into eight batches of 10 pens was generated, each individual underwent an ≃100-day test period. A longitudinal phenotype mimicking piglet weight during the fattening period was simulated for each individual in two situations: disturbed and non-disturbed. The disturbed phenotype was modified according to the robustness of the animal and the intensity and duration of the disturbance that the animal was subjected to. Various sets of simulations (1 000 replicates per set) were considered depending on the type of disturbance (at the level of the batch, pen, or individual), the genetic correlation (negative, neutral, or positive) between the two components of the robustness (resistance and resilience), the genetic correlation (negative, neutral, or positive) between growth and the components of robustness, and the heritability of the components of robustness (weak or moderate). An animal model was used to estimate the genetic parameters and BV for two production traits: the BW at 100 days of age (BW100) and average daily gain (ADG). The estimated heritability of the production traits was lower in the disturbed situation compared to the non-disturbed one (reduction of 0.08 and 0.05 points respectively for BW100 and ADG). The correlations between estimated breeding values of the observed phenotypes (EBV) and BV for production traits in absence of disturbance were lower in the disturbed situation (reduction of 0.04 and 0.06 points for BW100 and ADG respectively) while the partial correlation between EBV and BV for robustness was not significantly different from 0 in the two situations. These results suggest that selection in a well-controlled environment with random disturbances of low intensities does not allow to improve animal robustness while it is less effective for improving production traits than selection under no environmental disturbances.

Breeding value for stayability and productive traits of Saanen does obtained by survival analysis versus traditional genetic evaluation

Our objectives were (1) to evaluate how production traits (lactation length, milk production, and protein, lactose, fat and total dry extract yields) affect the permanence of Saanen goats in the herd for longer than 28 months; (2) to estimate genetic parameters for stayability (STAY28) using an Weibull proportional risk model; and (3) to evaluate the genetic associations between both stayability and production through genetic correlations and regression analysis. Two methodologies were utilized in the evaluation: (1) survival analysis using the Weibull animal model; and (2) threshold-linear model through Bayesian approach using Gibbs sampler in two-trait analysis. The data set had 3344 information. Animal effect was included as random in both methodologies. In analyses with Weibull distribution, the contemporary group effect was considered random and in the threshold-linear model was systematic. In the Weibull risk model, production traits were divided in five classes (class 0, class 1, class 2, class 3, and class 4) and the effect of parturition age was significant (P < 0.05) for all production variables. For milk production, the highest class (class 4) presented significant effect on STAY28, with reduced culling risk of does from this class in comparison with the other ones. Heritability values for STAY28 were low in both methodologies (between 0.05 and 0.09 across classes for the Weibull model and 0.07 for the threshold-linear model). Genetic correlation estimates between STAY28 and production traits were null in the threshold-linear model. Regression of breeding values of production traits as a function of STAY28 demonstrated a significant relationship between these traits. Even with slower favorable changes, the use of STAY28 for selecting animals with higher breeding values is a viable option to increase the productive lifespan of dairy goats, resulting in animals that are more productive. Therefore, the improvement of productive traits of does brings positive effects in the stay in the herd for longer than 28 months.

A variance component estimation approach to infer associations between Mendelian polledness and quantitative production and female fertility traits in German Simmental cattle

BackgroundManaging beneficial Mendelian characteristics in dairy cattle breeding programs implies that the correlated genetic effects are considered to avoid possible adverse effects in selection processes. The Mendelian trait polledness in cattle is traditionally associated with the belief that the polled locus has unfavorable effects on breeding goal traits. This may be due to the inferior breeding values of former polled bulls and cows in cattle breeds, such as German Simmental, or to pleiotropic or linkage effects of the polled locus.MethodsWe focused on a variance component estimation approach that uses a marker-based numerator relationship matrix reflecting gametic relationships at the polled locus to test for direct pleiotropic or linked quantitative trait loci (QTL) effects of the polled locus on relevant traits. We applied the approach to performance, health, and female fertility traits in German Simmental cattle.ResultsOur results showed no evidence for any pleiotropic QTL effects of the polled locus on test-day production traits milk yield and fat percentage, on the mastitis indicator ‘somatic cell score’, and on several female fertility traits, i.e. 56 days non return rate, days open and days to first service. We detected a significant and unfavorable QTL effect accounting for 6.6% of the genetic variance for protein percentage only.ConclusionsPleiotropy does not explain the lower breeding values and phenotypic inferiority of polled German Simmental sires and cows relative to the horned population in the breed. Thus, intensified selection in the polled population will contribute to increased selection response in breeding goal traits and genetic merit and will narrow the deficit in breeding values for production traits.

Accuracy of breeding values for production traits in turkeys (Meleagris gallopavo) using recursive models with or without genomics

BackgroundKnowledge about potential functional relationships among traits of interest offers a unique opportunity to understand causal mechanisms and to optimize breeding goals, management practices, and prediction accuracy. In this study, we inferred the phenotypic causal networks among five traits in a turkey population and assessed the effect of the use of such causal structures on the accuracy of predictions of breeding values.MethodsPhenotypic data on feed conversion ratio, residual feed intake, body weight, breast meat yield, and walking score in addition to genotype data from a commercial breeding population were used. Causal links between the traits were detected using the inductive causation algorithm based on the joint distribution of genetic effects obtained from a standard Bayesian multiple trait model. Then, a structural equation model was implemented to infer the magnitude of causal structure coefficients among the phenotypes. Accuracies of predictions of breeding values derived using pedigree- and blending-based multiple trait models were compared to those obtained with the pedigree- and blending-based structural equation models.ResultsIn contrast to the two unconditioned traits (i.e., feed conversion ratio and breast meat yield) in the causal structures, the three conditioned traits (i.e., residual feed intake, body weight, and walking score) showed noticeable changes in estimates of genetic and residual variances between the structural equation model and the multiple trait model. The analysis revealed interesting functional associations and indirect genetic effects. For example, the structural coefficient for the path from body weight to walking score indicated that a 1-unit genetic improvement in body weight is expected to result in a 0.27-unit decline in walking score. Both structural equation models outperformed their counterpart multiple trait models for the conditioned traits. Applying the causal structures led to an increase in accuracy of estimated breeding values of approximately 7, 6, and 20% for residual feed intake, body weight, and walking score, respectively, and different rankings of selection candidates for the conditioned traits.ConclusionsOur results suggest that structural equation models can improve genetic selection decisions and increase the prediction accuracy of breeding values of selection candidates. The identified causal relationships between the studied traits should be carefully considered in future turkey breeding programs.

A study of Genomic Prediction across Generations of Two Korean Pig Populations.

Simple SummaryCommercial genotyping has become accessible at a relatively low cost and nowadays it is widely used by breeders to predict production and economic traits. Many studies explored the benefits of using DNA information in breeding programs, and many methods have been established to optimize the use of such information. To date, however, very few studies have explored how prediction accuracies change across generations. Here we present a short evaluation across five generations in two pig breeds and evaluate the accuracy of the prediction of relevant production traits using different generational groups.Genomic models that incorporate dense marker information have been widely used for predicting genomic breeding values since they were first introduced, and it is known that the relationship between individuals in the reference population and selection candidates affects the prediction accuracy. When genomic evaluation is performed over generations of the same population, prediction accuracy is expected to decay if the reference population is not updated. Therefore, the reference population must be updated in each generation, but little is known about the optimal way to do it. This study presents an empirical assessment of the prediction accuracy of genomic breeding values of production traits, across five generations in two Korean pig breeds. We verified the decay in prediction accuracy over time when the reference population was not updated. Additionally we compared the prediction accuracy using only the previous generation as the reference population, as opposed to using all previous generations as the reference population. Overall, the results suggested that, although there is a clear need to continuously update the reference population, it may not be necessary to keep all ancestral genotypes. Finally, comprehending how the accuracy of genomic prediction evolves over generations within a population adds relevant information to improve the performance of genomic selection.

Association of SlC11A1 gene and production traits using logistic regression

SlC11A1 gene is a potential candidate gene related to susceptibility to a wide range of intracellular pathogens. In this research, we used logistic regression analysis in a case-control study to find the relationship between SLC11A1 gene and estimated breeding values of production traits (estimated breeding values of protein and fat yield, and protein and fat percentage) in Holstein dairy cattle in a selective genotyping method. Samples have been genotyped based on clinical mastitis data using PCR-SSCP method, previously. The contrast between heterozygous genotype and both homozygous genotypes showed that despite of significant relationship between clinical mastitis and SLC11A1, there was no negative effect of the gene of interest on production traits. Hence, considering the unfavorable relationship between clinical mastitis and production traits, selection of SLC11A1 gene will have no unfavorable effect on production traits, and it will not disorganize selection breeding programs based on health data.

Comparative assessment of sire evaluation by univariate and bivariate animal model for estimation of breeding values of first lactation traits in HF cross cattle

The aim of the present investigation was to study the superiority of bivariate over univariate sire evaluation. Data were collected on 1,988 first parity Karan Fries cows, spread over 31 years. The (co) variance components estimated by using average information restricted maximum likelihood (AIREML) were fitted into univariate and bivariate animal models for prediction of breeding values. Low heritability estimates were obtained for fertility traits ranging from 0.02 (FDPR) to 0.19 (AFC) indicating lesser role of additive gene action in fertility of dairy cattle. Comparative analysis revealed that the breeding values estimated using bivariate animal model had lower error variance and greater range in comparison to univariate animal models. The mean sire breeding values for production traits estimated by bivariate analysis ranged from 3055.50 to 3063.15 kg and were higher compared to the mean sire breeding values estimated by univariate animal model. The inclusion of fertility traits along with production traits improved the differentiating ability of bivariate animal model with respect to the production performance.

Congruence between selection on breeding values and farmers’ selection criteria in sheep breeding under conventional nucleus breeding schemes

Designing breeding schemes suitable for smallholder livestock production systems in developing regions has hitherto been a challenge. The suggested schemes either do not address farmers’ breeding goals (centralized station-based nucleus schemes) or yield slow genetic progress (village-based schemes). A new breeding scheme that integrates the merits of previously suggested schemes has been designed for Menz sheep improvement in Ethiopia. It involves selection based on breeding values in nucleus flocks to produce elite rams, a one-time only provision of improved rams to villagers and a follow-up village-based selection to sustain genetic progress in village flocks. Here, we assessed whether conventional selection of breeding rams based on breeding values for production traits, which is the practice in station-based nucleus flocks, meets farmers’ breeding objectives. We also elicited determinants of farmers’ ram choice. Low but significant correlations were found between rankings of rams based on farmers’ selection criteria, estimated breeding values (EBV) and body weight (BW). Appearance traits (such as color and horn) and meat production traits (BW and linear size traits) significantly determined farmers’ breeding ram choice. The results imply that conventional selection criteria based solely on EBV for production traits do not address farmers’ trait preferences fully, but only partially. Thus, a two-stage selection procedure involving selection on breeding values in nucleus centers followed by farmers’ selection among top- ranking candidate rams is recommended. This approach accommodates farmers’ preferences and speeds up genetic progress in village-based selection. The Menz sheep scheme could be applied elsewhere with similar situations to transform conventional station-based nucleus breeding activities into participatory breeding programs.

Polymorphism of the beta-casein gene and its associations with breeding value for production traits of Holstein–Friesian bulls

Beta-casein A1 protein variant might be one of the risk factors in the etiology of human disorders like diabetes and ischemic heart disease. However, from the practical perspective selecting for the A2 allele in dairy cattle requires knowledge of whether the A1/A2 polymorphism is associated with breeding values for production traits. A DNA fragment containing A1/A2 polymorphic region was amplified and genotyped using the PCR-ACRS (Amplification Created Restriction Site) technique in 478 Holstein–Friesian bulls yielding the allele frequencies of 0.35 and 0.65 for the A1 and the A2 variants, respectively. A linear regression model was used for testing the association between the polymorphism and breeding values for production traits and showed that the allele coding the A2 protein variant increases breeding values for milk and protein yield and decreases breeding values for fat percentage in milk. Genotyping A.I. bulls at beta-casein locus and preferring A2 allele may lead to two benefits: increasing breeding value for protein yield and decreasing frequency of A1 protein variant, being considered as risk factor for human health.

Accuracy of genomic breeding values in multi-breed dairy cattle populations

BackgroundTwo key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. Both findings are a problem for breeds where the number of individuals in the reference population is limited. A multi-breed reference population is a potential solution, and here we investigate the accuracies of GEBV in Holstein dairy cattle and Jersey dairy cattle when the reference population is single breed or multi-breed. The accuracies were obtained both as a function of elements of the inverse coefficient matrix and from the realised accuracies of GEBV.MethodsBest linear unbiased prediction with a multi-breed genomic relationship matrix (GBLUP) and two Bayesian methods (BAYESA and BAYES_SSVS) which estimate individual SNP effects were used to predict GEBV for 400 and 77 young Holstein and Jersey bulls respectively, from a reference population of 781 and 287 Holstein and Jersey bulls, respectively. Genotypes of 39,048 SNP markers were used. Phenotypes in the reference population were de-regressed breeding values for production traits. For the GBLUP method, expected accuracies calculated from the diagonal of the inverse of coefficient matrix were compared to realised accuracies.ResultsWhen GBLUP was used, expected accuracies from a function of elements of the inverse coefficient matrix agreed reasonably well with realised accuracies calculated from the correlation between GEBV and EBV in single breed populations, but not in multi-breed populations. When the Bayesian methods were used, realised accuracies of GEBV were up to 13% higher when the multi-breed reference population was used than when a pure breed reference was used. However no consistent increase in accuracy across traits was obtained.ConclusionPredicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived. However in multi-breed populations, Bayesian approaches give higher accuracies for some traits. Finally, multi-breed reference populations will be a valuable resource to fine map QTL.

Use of test-day records beyond three hundred five days for estimation of three hundred five-day breeding values for production traits and somatic cell score of Canadian Holsteins

The Canadian Test-Day Model includes test-day (TD) records from 5 to 305 d in milk (DIM). Because 60% of Canadian Holstein cows have at least one lactation longer than 305 d, a significant number of TD records beyond 305 DIM could be included in the genetic evaluation. The aim of this study was to investigate whether TD records beyond 305 DIM could be useful for estimation of 305-d estimated breeding value (EBV) for milk, fat, and protein yields and somatic cell score. Data were 48,638,184 TD milk, fat, and protein yields and somatic cell scores from the first 3 lactations of 2,826,456 Canadian Holstein cows. All production traits were preadjusted for the effect of pregnancy. Subsets of data were created for variance-component estimation by random sampling of 50 herds. Variance components were estimated using Gibbs sampling. Full data sets were used for estimation of breeding values. Three multiple-trait, multiple-lactation random regression models with TD records up to 305 DIM (M305), 335 DIM (M335), and 365 DIM (M365) were fitted. Two additional models (M305a and M305b) used TD records up to 305 DIM and variance components previously estimated by M335 and M365, respectively. The effects common to all models were fixed effects of herd × test-date and DIM class, fixed regression on DIM nested within region × age × season class, and random regressions for additive genetic and permanent environmental effects. Legendre polynomials of order 6 and 4 were fitted for fixed and random regressions, respectively. Rapid increase of additive genetic and permanent environmental variances at extremes of lactations was observed with all 3 models. The increase of additive genetic and permanent environmental variances was at earlier DIM with M305, resulting in greater variances at 305 DIM with M305 than with M335 and M365. Model M305 had the best ability to predict TD yields from 5 through 305 DIM and less error of prediction of 305-d EBV than M335 and M365. Model M335 had smaller change of 305-d EBV of bulls over the period of 7 yr than did M305 and M365. Model M305a had the least error of prediction and change of 305-d EBV from all models. Therefore, the use of TD records of Holstein cows from 5 through 305 DIM and variance components estimated using records up to 335 DIM is recommended for the Canadian Test-Day Model.

Test-day models for South African dairy cattle for participation in international evaluations

Variance components and breeding values of production traits and somatic cell score of South African Guernsey, Ayrshire, Holstein and Jersey breeds have been estimated using a multi-lactation repeatability test-day model, including tests of the first three lactations as repeated measures and fitting the permanent environmental effect across lactations. Multitrait evaluations were done for the production traits (milk, butterfat and protein) and single trait evaluations for somatic cell score. Heritability estimates were comparable with yield and somatic cell score estimates obtained by test-day models from other countries (17-24% for milk yield; 10-13% for butterfat yield; 14-19% for protein yield and 6-8% for somatic cell score). Proofs of qualifying sires were sent to the International Bull Evaluation Service (INTERBULL) for participation in the March 2005 test runs. Genetic correlations between South Africa and other participating countries, estimated by INTERBULL, compared well with those amongst the other participating countries. Trend validation tests were successful using this methodology for all traits and breeds except for somatic cell score of the Guernsey breed, due to insufficient data for this trait. South Africa can now participate in routine INTERBULL evaluations to obtain Multiple Across Country Evaluation (MACE) breeding values, using this methodology.

Implementation of a Routine Genetic Evaluation for Longevity Based on Survival Analysis Techniques in Dairy Cattle Populations in Switzerland

Genetic evaluation of sires for functional longevity of their daughters based on survival analysis has been implemented in the populations of Braunvieh, Simmental, and Holstein cattle in Switzerland. A Weibull mixed sire-maternal grandsire survival model was used to estimate breeding values of sires with data on cows that calved since April 1, 1980. Data on Braunvieh and Simmental cows included about 1.1million records, data on Holstein cows comprised about 220,000 records. Data contained approximately 20 to 24% right-censored records and 6 to 9% left-truncated records. Besides the random sire and maternal grandsire effects, the model included effects of herd-year-season, age at first calving, parity, stage of lactation, alpine pasturing (Braunvieh and Simmental), and relative milk yield and relative fat and protein percentage within herd to account for culling for production. Heritability of functional longevity, estimated on a subset of data including approximately 150,000 animals, were 0.181, 0.198, and 0.184 for Braunvieh, Simmental, and Holstein, respectively. Breeding values were estimated for all sires with at least six daughters or three granddaughters in the data. Breeding values of sires are expressed in months of functional productive life and published in sire catalogs along with breeding values for production traits.

Associations between blood groups, blood protein polymorphisms and breeding values for production traits in Swedish Red and White Dairy bulls

The relationships of nine blood group systems and two blood protein polymorphisms with breeding values for several production traits were examined in dairy cattle of the Swedish Red and White (SRB) breed. The material consisted of 2212 bulls; the bulls were performance tested for growth rate and their breeding values for milk yield, fat and protein content in milk were estimated from progeny tests. The direct effect of marker alleles or marker phenotypes was analysed in a multiple regression model. Several significant associations were found; many supported earlier findings. However, the contribution of the markers to the total variation of the breeding values was very small. Linkage between marker loci and production loci was studied in offspring from heterozygous sires by estimating the interactions between sire and marker alle using a model eliminating the direct effects of sire and marker alle. There were strong indications of linkage between some marker loci (e.g. B, J and Am-1 loci) and loci with large effects on production traits.