Conventional Breeding Values Research Articles

Including genotypic information in genetic evaluations increases the accuracy of sheep breeding values.

The impact of inclusion of genome-wide genotypes into breeding value predictions for UK Texel sheep is addressed in this article. The main aim was to investigate the level of change in the accuracy values for EBVs when information from animal genotypes is incorporated into the genetic evaluations. New genetic parameters for a range of lamb growth, carcass composition and health traits are described and applied in the estimation of conventional breeding values (EBVs) for almost 822,000 animals as well as genomic breeding values (gEBVs) after adding 10,143 genotypes. Principal component analyses showed that there are no major distinct groups; hence, the population is mainly homogenous and genetically well-linked. Results suggested that the highest change in accuracy was observed for the animals that are not phenotyped but have good links to the reference population. This was seen especially for the lowly heritable health traits thereby proving that the use of genotypes in breeding values estimation may accelerate the genetic gain by producing more accurate values especially for young, un-phenotyped animals.

Genomic selection in Gir cattle using female reference population

When a sizeable reference population of proven bulls is not available for implementing Genomic selection for a particular trait, and when a recording of certain traits on large scale is difficult, the use of a female reference population is recommended. Gir, one of the important milk purpose cattle breeds of India falls under this category. There is no large scale Progeny Testing (PT) programme in Gir, so proven bulls based on daughter performance in large numbers are not available. Considering the constraints, a genomic BLUP (GBLUP) model was implemented based on recorded cow reference population in Gir breed. Cows (3491) and 23 bulls were genotyped using INDUSCHIP for this purpose. Due to non-availability of pedigreed data, conventional breeding values (BV) of bulls and their reliabilities were not known. For comparison, assumed theoretical reliability of BV of a bull selected based on its dam's yield was compared with reliability obtained for genomic breeding value (GBV) using a GBLUP model. The reliability estimates for GBVs were 4 times higher than that for BVs. The predictive ability of the model was demonstrated by measuring the correlation between corrected phenotypes and GBVs for animals whose records were masked in a five-fold cross-validation study. The correlation was around 0.45 showing reasonable predictability of the GBLUP model. The GBVs were not biased. The regression coefficient between the corrected phenotype and GBV was 1.045. The present study demonstrates that it is feasible to implement genomic selection in Gir cattle in Indian conditions using a female reference population. It is expected that the bulls can be selected with around 4 fold more accuracy than the current method of selecting based on their dams' yield accelerating expected genetic growth in Gir cattle.

Genetic parameters for clinical mastitis in Czech Holstein cattle

Genetic parameters were estimated for clinical mastitis in Czech Holstein cattle. The datasets included 14 329 cows with 28 626 lactations. Clinical mastitis was defined as 0/1 occurrence per lactation. Single- or multi-trait repeatability linear animal models were employed for estimation of (co)variances and prediction of conventional or genomic breeding values. The inclusion of the random herd-year-month effect in the model was analysed. The estimated heritability for clinical mastitis ranged from 2.10% to 2.72%, while permanent environmental variance ratios or random herd-year-month effect ratios were twice higher than heritability. In the multi-trait models, udder type traits, such as fore udder attachment, rear udder attachment, rear udder width, front teat placement and udder depth, were employed. The highest genetic correlations of clinical mastitis occurred with rear udder width (0.41) and the lowest with front teat placement (–0.10). Both the multi-trait model and the genomic model provided breeding value estimates with higher reliability. In contrast, the model with random herd-year-season effects provided breeding values with lower accuracy.

Evaluation of gestation length in Czech Holstein cattle

An objective of our study was to evaluate gestation length and its genetic variability in the Czech Holstein population. Data set consisted of 770 865 records of gestation length in 375 574 Holstein cows and covered the period from 2012 to 2018. Mean gestation length was 277 ± 4.9 days, and it was 1.4 days longer in male calves compared to females, and 1.1 days longer in cows compared to heifers. Animal repeatability model with maternal effect was employed for variance component estimation. The direct genetic effect explained the highest proportion of variability, and it corresponded with moderate direct heritability (0.48), while maternal heritability was much lower (0.06). We estimated conventional and genomic breeding values with the genomic matrix based on 39 145 single nucleotide polymorphisms in 13 844 animals. Genomic breeding values were weakly (< 0.25) but significantly correlated with breeding values for type, production and fitness traits. Pearson correlations between breeding values indicated a negative association of direct gestation length with milk production, longevity and fertility of bulls, and a positive association of maternal gestation length with most of the type traits related to the body composition. Genetic trends for male and female parts of the population showed a tendency to the shortening of gestation, which should be of concern, as short gestation could be reflected in a negative indirect response in other correlated traits, such as the incidence of stillbirth, the health status of cows after calving, culling, or conception rate.

Genomic Breeding Values for Claw Diseases/Disorders in Czech Holstein Cows

Genomic breeding values (GEBV) were predicted for claw diseases/disorders in Holstein cows. The data sets included 6,498, 6,641 and 16,208 cows for the three groups of analysed disorders. The analysed traits were infectious diseases (ID), including digital and interdigital dermatitis and interdigital phlegmon, and non-infectious diseases (NID), including ulcers, white line disease, horn fissures, and double sole and overall claw disease (OCD), comprising all recorded disorders. Claw diseases/disorders were defined as 0/1 occurrence per lactation. Linear animal models were employed for prediction of conventional breeding values (BV) and genomic breeding values (GEBV), including the random additive genetic effect of animal and the permanent environmental effect of cow and fixed effects of parity, herd, year and month of calving. Both high and intermediate weights (80% and 50%, respectively) of genomic information were employed for GEBV50 and GEBV80 prediction. The estimated heritability for ID was 3.47%, whereas that for NID 4.61% and for OCD was 2.29%. Approximate genetic correlations among claw diseases/disorders traits ranged from 19% (ID x NID) to 81% (NID x OCD). The correlations between predicted BV and GEBV50 (84–99%) were higher than those between BV and GEBV80 (70–98%). Reliability of breeding values was low for each claw disease/disorder (on average, 3.7 to 14.8%) and increased with the weight of genomic information employed.

Genetic diversity and historic introgression in German Angler and Red Dual Purpose cattle and possibilities to reverse introgression

ABSTRACTRecovering native uniqueness has major importance for breeds with historic introgression. The aim of the study was to estimate population genetic parameters for two local red cattle breeds from Northern Germany and to study possibilities to reverse introgression. Genealogical information consisted of 90,783 individuals for German Angler and 187,255 individuals for Red Dual-Purpose cattle breed, with additional information on sex, born, breed, status, and conventional breeding values. It is concluded that the native genetic contribution could be included as an additional trait in the total merit index in order to recover a part of the native genetic background. Native contributions should be estimated in the long term from marker data in order to account for Mendelian sampling. The maintenance of a sufficient genetic diversity of native alleles can be achieved by an advanced OCS with appropriate constraints.

Genomic selection in the German Landrace population of the Bavarian herdbook.

The aim of our study was to compare different validation methods with respect to their impact on validation results and to evaluate the feasibility of genomic selection in the German Landrace population of the Bavarian herdbook. For this purpose, a sample of 337 boars and 1,676 sows was genotyped with the Illumina PorcineSNP60 BeadChip. Conventional BLUP breeding values for fertility, growth, carcass, and quality traits were deregressed and used as phenotypes in genomic BLUP. The resulting genomic breeding values were also blended with information from the full conventional breeding value estimation to include information from nongenotyped parents. Subsequent validation used forward prediction, realized reliabilities, and theoretical reliabilities. The results indicate that the validation methods showed a relatively large effect on in the displayed reliability levels in our study: forward prediction reliabilities were found to be much lower than the conventional parent-average reliabilities whereas corresponding realized and theoretical reliabilities were found substantially greater. Theoretical reliabilities appear to be the most consistent validation approach tested in our study, because they avoid the use of proxy variables. Generally, our results suggest a substantial potential for a genomic selection implementation for the Bavarian herdbook by using both sows and boars. Theoretical genomic reliabilities of direct genomic values of selection candidates were, on average, 31 to 36% greater than the conventional parent average reliabilities. However, the inclusion of residual information from conventional breeding values had only a marginal effect on reliabilities.

Genomic and conventional breeding values for clinical mastitis

Conventional and genomic breeding values for clinical mastitis were estimated,based on a dataset of 15,905 cows (36,250 lactations) from 10 farms, calving years from 1996 to 2014. The number of genotyped bulls was 2647 and 799 of them had offspring among the surveyed cows. Pedigree consisted of 64,306 individuals. Analyzed trait was defined as the number of incidence of clinical mastitis per standard lactation. Parity was limited from 1 to 7. The highest proportion of mastitis disease was observed in early lactation and decreased with month of lactation, while there was an upswing of diseases with parity. Genetic trend for cows showed a deteriorating tendency for clinical mastitis. From 1992 to 2010 there was a rise in the relative values of breeding values from 88% to 112% (worsening). Relative breeding values of these bulls were on average above 100%. The correlations between classical genomic breeding values and the values were high: 93% for the whole sample, 81% to 48 young genomic bulls without daughters born in years 2011 and 2012.

Multi-generational imputation of single nucleotide polymorphism marker genotypes and accuracy of genomic selection

Availability of high-density single nucleotide polymorphism (SNP) genotyping platforms provided unprecedented opportunities to enhance breeding programmes in livestock, poultry and plant species, and to better understand the genetic basis of complex traits. Using this genomic information, genomic breeding values (GEBVs), which are more accurate than conventional breeding values. The superiority of genomic selection is possible only when high-density SNP panels are used to track genes and QTLs affecting the trait. Unfortunately, even with the continuous decrease in genotyping costs, only a small fraction of the population has been genotyped with these high-density panels. It is often the case that a larger portion of the population is genotyped with low-density and low-cost SNP panels and then imputed to a higher density. Accuracy of SNP genotype imputation tends to be high when minimum requirements are met. Nevertheless, a certain rate of genotype imputation errors is unavoidable. Thus, it is reasonable to assume that the accuracy of GEBVs will be affected by imputation errors; especially, their cumulative effects over time. To evaluate the impact of multi-generational selection on the accuracy of SNP genotypes imputation and the reliability of resulting GEBVs, a simulation was carried out under varying updating of the reference population, distance between the reference and testing sets, and the approach used for the estimation of GEBVs. Using fixed reference populations, imputation accuracy decayed by about 0.5% per generation. In fact, after 25 generations, the accuracy was only 7% lower than the first generation. When the reference population was updated by either 1% or 5% of the top animals in the previous generations, decay of imputation accuracy was substantially reduced. These results indicate that low-density panels are useful, especially when the generational interval between reference and testing population is small. As the generational interval increases, the imputation accuracies decay, although not at an alarming rate. In absence of updating of the reference population, accuracy of GEBVs decays substantially in one or two generations at the rate of 20% to 25% per generation. When the reference population is updated by 1% or 5% every generation, the decay in accuracy was 8% to 11% after seven generations using true and imputed genotypes. These results indicate that imputed genotypes provide a viable alternative, even after several generations, as long the reference and training populations are appropriately updated to reflect the genetic change in the population.

Estimation and comparison of conventional and genomic breeding values in Holstein cattle of Antioquia, Colombia

ABSTRACT Objetive. To estimate and compare breeding values (EBV) using the conventional method (BLUP) and genomic breeding values (MEBV and GEBV) estimated through bayes C method for milk yield and milk quality traits in dairy cattle in Antioquia, Colombia. Materials and methods. Two methods were used to estimate breeding values: BLUP to estimate conventional breeding value (EBV) and bayes C to estimate genomic values (MEBV and GEBV). The traits evaluated were: milk yield (PL), protein percentage (PPRO), fat percentage (PGRA) and score somatic cell (SCS). The methods (BLUP and bayes C) were compared using Person correlation (rp), Spearman rank correlation (rs) and linear regression coefficient (b). Results. The Pearson and Spearman correlations among EBVs and genomic values (MEBV and GEBV) (rpMEBV;EBV and rsGEBV;EBV) were greater than 0.93 and the linear regression coefficients of EBVs on genomic values (MEBV and GEBV) (bMEBV;EBV, and bGEBV;EBV) ranged between 0.954 and 1.051 in all traits evaluated. Conclusions. The predictions of genomic values (MEBV and GEBV), using bayes C method were consistent with the predictions of the EBVs estimate through the conventional method (BLUP) in conditions of high Colombian tropic, allowing to obtain high associations between the breeding values.

Accuracy of direct genomic values for functional traits in Brown Swiss cattle

In this study, direct genomic values for the functional traits general temperament, milking temperament, aggressiveness, rank order in herd, milking speed, udder depth, position of labia, and days to first heat in Brown Swiss dairy cattle were estimated based on ~777,000 (777K) single nucleotide polymorphism (SNP) information from 1,126 animals. Accuracy of direct genomic values was assessed by a 5-fold cross-validation with 10 replicates. Correlations between deregressed proofs and direct genomic values were 0.63 for general temperament, 0.73 for milking temperament, 0.69 for aggressiveness, 0.65 for rank order in herd, 0.69 for milking speed, 0.71 for udder depth, 0.66 for position of labia, and 0.74 for days to first heat. Using the information of ~54,000 (54K) SNP led to only marginal deviations in the observed accuracy. Trying to predict the 20% youngest bulls led to correlations of 0.55, 0.77, 0.73, 0.55, 0.64, 0.59, 0.67, and 0.77, respectively, for the traits listed above. Using a novel method to estimate the accuracy of a direct genomic value (defined as correlation between direct genomic value and true breeding value and accounting for the correlation between direct genomic values and conventional breeding values) revealed accuracies of 0.37, 0.20, 0.19, 0.27, 0.48, 0.45, 0.36, and 0.12, respectively, for the traits listed above. These values are much smaller but probably also more realistic than accuracies based on correlations, given the heritabilities and samples sizes in this study. Annotation of the largest estimated SNP effects revealed 2 candidate genes affecting the traits general temperament and days to first heat.

A low-marker density implementation of genomic selection in aquaculture using within-family genomic breeding values

BackgroundGenomic selection can increase genetic gain within aquaculture breeding programs, but the high costs related to high-density genotyping of a large number of individuals would make the breeding program expensive. In this study, a low-cost method using low-density genotyping of pre-selected candidates and their sibs was evaluated by stochastic simulation.MethodsA breeding scheme with selection for two traits, one measured on candidates and one on sibs was simulated. Genomic breeding values were estimated within families and combined with conventional family breeding values for candidates that were pre-selected based on conventional BLUP breeding values. This strategy was compared with a conventional breeding scheme and a full genomic selection program for which genomic breeding values were estimated across the whole population. The effects of marker density, level of pre-selection and number of sibs tested and genotyped for the sib-trait were studied.ResultsWithin-family genomic breeding values increased genetic gain by 15% and reduced rate of inbreeding by 15%. Genetic gain was robust to a reduction in marker density, with only moderate reductions, even for very low densities. Pre-selection of candidates down to approximately 10% of the candidates before genotyping also had minor effects on genetic gain, but depended somewhat on marker density. The number of test-individuals, i.e. individuals tested for the sib-trait, affected genetic gain, but the fraction of the test-individuals genotyped only affected the relative contribution of each trait to genetic gain.ConclusionsA combination of genomic within-family breeding values, based on low-density genotyping, and conventional BLUP family breeding values was shown to be a possible low marker density implementation of genomic selection for species with large full-sib families for which the costs of genotyping must be kept low without compromising the effect of genomic selection on genetic gain.

Genomic Selection: Status in Different Species and Challenges for Breeding

Technical advances and development in the market for genomic tools have facilitated access to whole-genome data across species. Building-up on the acquired knowledge of the genome sequences, large-scale genotyping has been optimized for broad use, so genotype information can be routinely used to predict genetic merit. Genomic selection (GS) refers to the use of aggregates of estimated marker effects as predictors which allow improved individual differentiation at young age. Realizable benefits of GS are influenced by several factors and vary in quantity and quality between species. General characteristics and challenges of GS in implementation and routine application are described, followed by an overview over the current status of its use, prospects and challenges in important animal species. Genetic gain for a particular trait can be enhanced by shortening of the generation interval, increased selection accuracy and increased selection intensity, with species- and breed-specific relevance of the determinants. Reliable predictions based on genetic marker effects require assembly of a reference for linking of phenotype and genotype data to allow estimation and regular re-estimation. Experiences from dairy breeding have shown that international collaboration can set the course for fast and successful implementation of innovative selection tools, so genomics may significantly impact the structures of future breeding and breeding programmes. Traits of great and increasing importance, which were difficult to improve in the conventional systems, could be emphasized, if continuous availability of high-quality phenotype data can be assured. Equally elaborate strategies for genotyping and phenotyping will allow tailored approaches to balance efficient animal production, sustainability, animal health and welfare in future.

Testing different single nucleotide polymorphism selection strategies for prediction of genomic breeding values in dairy cattle based on low density panels

In human and animal genetics dense single nucleotide polymorphism (SNP) panels are widely used to describe genetic variation. In particular genomic selection in dairy cattle has become a routinely applied tool for prediction of additive genetic values of animals, especially of young selection candidates. The aim of the study was to investigate how well an additive genetic value can be predicted using various sets of approximately 3000 SNPs selected out of the 54 001 SNPs in an Illumina BovineSNP50 BeadChip high density panel. Effects of SNPs from the nine subsets of the 54 001 panel were estimated using a model with a random uncorrelated SNPs effect based on a training data set of 1216 Polish Holstein-Friesian bulls whose phenotypic records were approximated by deregressed estimated breeding values for milk, protein, and fat yields. Predictive ability of the low density panels was assessed using a validation data set of 622 bulls. Correlations between direct and conventional breeding values routinely estimated for the Polish population were similar across traits and clearly across sets of SNPs. For the training data set correlations varied between 0.94 and 0.98, for the validation data set between 0.25 and 0.46. The corresponding correlations estimated using the 54 001 panel were: 0.98 for the three traits (training), 0.98 (milk and fat yields, validation), and 0.97 (protein yield, validation). The optimal subset consisted of SNPs selected based on their highest effects for milk yield obtained from the evaluation of all 54 001 SNPs. A low density SNP panel allows for reasonably good prediction of future breeding values. Even though correlations between direct and conventional breeding values were moderate, for young selection candidates a low density panel is a better predictor than a commonly used average of parental breeding values.

Marker-assisted breeding value estimation for mastitis resistance in Finnish Ayrshire cattle

Marker-assisted breeding value estimation is expected to increase the accuracy of estimated breeding values, especially for traits with low heritability. Several quantitative trait loci (QTL) have been found for somatic cell score and clinical mastitis. The objective of this study was to demonstrate marker-assisted breeding value estimation, combining data of genotyped and ungenotyped animals in a large pedigree population using either identical-by-descent (IBD) or identical-by-state (IBS) haplotypes for some previously identified QTL regions for somatic cell score and clinical mastitis in Finnish Ayrshire cattle. For both methods, QTL variances were estimated based on daughter yield deviations of genotyped bulls. The QTL explained only a small proportion of genetic variance, especially with IBS haplotypes. Using IBD haplotypes gave more reranking of bulls and cows than using IBS haplotypes. Cross-validation showed no increase in predictive ability when using IBS haplotypes compared with conventional breeding value estimation, whereas a decrease in predictive ability was observed with IBD haplotypes. Furthermore, computing time was lower and convergence was better with IBS haplotypes than with IBD haplotypes. In this study on mastitis resistance in Finnish Ayrshire, conventional breeding value estimation would be advocated because of the lack in improvement of accuracy and predictive ability when using marker-assisted breeding value estimation. However, in situations where IBS haplotypes would explain 10 to 20% or more of the genetic variance, marker-assisted breeding value estimation with IBS haplotypes may yield greater accuracy and predictive ability than conventional breeding value estimation.

Using eigenvalues as variance priors in the prediction of genomic breeding values by principal component analysis

Genome-wide selection aims to predict genetic merit of individuals by estimating the effect of chromosome segments on phenotypes using dense single nucleotide polymorphism (SNP) marker maps. In the present paper, principal component analysis was used to reduce the number of predictors in the estimation of genomic breeding values for a simulated population. Principal component extraction was carried out either using all markers available or separately for each chromosome. Priors of predictor variance were based on their contribution to the total SNP correlation structure. The principal component approach yielded the same accuracy of predicted genomic breeding values obtained with the regression using SNP genotypes directly, with a reduction in the number of predictors of about 96% and computation time of 99%. Although these accuracies are lower than those currently achieved with Bayesian methods, at least for simulated data, the improved calculation speed together with the possibility of extracting principal components directly on individual chromosomes may represent an interesting option for predicting genomic breeding values in real data with a large number of SNP. The use of phenotypes as dependent variable instead of conventional breeding values resulted in more reliable estimates, thus supporting the current strategies adopted in research programs of genomic selection in livestock.

Prediction of haplotypes for ungenotyped animals and its effect on marker-assisted breeding value estimation

BackgroundIn livestock populations, missing genotypes on a large proportion of animals are a major problem to implement the estimation of marker-assisted breeding values using haplotypes. The objective of this article is to develop a method to predict haplotypes of animals that are not genotyped using mixed model equations and to investigate the effect of using these predicted haplotypes on the accuracy of marker-assisted breeding value estimation.MethodsFor genotyped animals, haplotypes were determined and for each animal the number of haplotype copies (nhc) was counted, i.e. 0, 1 or 2 copies. In a mixed model framework, nhc for each haplotype were predicted for ungenotyped animals as well as for genotyped animals using the additive genetic relationship matrix. The heritability of nhc was assumed to be 0.99, allowing for minor genotyping and haplotyping errors. The predicted nhc were subsequently used in marker-assisted breeding value estimation by applying random regression on these covariables. To evaluate the method, a population was simulated with one additive QTL and an additive polygenic genetic effect. The QTL was located in the middle of a haplotype based on SNP-markers.ResultsThe accuracy of predicted haplotype copies for ungenotyped animals ranged between 0.59 and 0.64 depending on haplotype length. Because powerful BLUP-software was used, the method was computationally very efficient. The accuracy of total EBV increased for genotyped animals when marker-assisted breeding value estimation was compared with conventional breeding value estimation, but for ungenotyped animals the increase was marginal unless the heritability was smaller than 0.1. Haplotypes based on four markers yielded the highest accuracies and when only the nearest left marker was used, it yielded the lowest accuracy. The accuracy increased with increasing marker density. Accuracy of the total EBV approached that of gene-assisted BLUP when 4-marker haplotypes were used with a distance of 0.1 cM between the markers.ConclusionsThe proposed method is computationally very efficient and suitable for marker-assisted breeding value estimation in large livestock populations including effects of a number of known QTL. Marker-assisted breeding value estimation using predicted haplotypes increases accuracy especially for traits with low heritability.

The effect of missing marker genotypes on the accuracy of gene-assisted breeding value estimation: a comparison of methods

In livestock populations, missing genotypes on a large proportion of the animals is a major problem when implementing gene-assisted breeding value estimation for genes with known effect. The objective of this study was to compare different methods to deal with missing genotypes on accuracy of gene-assisted breeding value estimation for identified bi-allelic genes using Monte Carlo simulation. A nested full-sib half-sib structure was simulated with a mixed inheritance model with one bi-allelic quantitative trait loci (QTL) and a polygenic effect due to infinite number of polygenes. The effect of the QTL was included in gene-assisted BLUP either by random regression on predicted gene content, i.e. the number of positive alleles, or including haplotype effects in the model with an inverse IBD matrix to account for identity-by-descent relationships between haplotypes using linkage analysis information (IBD–LA). The inverse IBD matrix was constructed using segregation indicator probabilities obtained from multiple marker iterative peeling. Gene contents for unknown genotypes were predicted using either multiple marker iterative peeling or mixed model methodology. For both methods, gene-assisted breeding value estimation increased accuracies of total estimated breeding value (EBV) with 0% to 22% for genotyped animals in comparison to conventional breeding value estimation. For animals that were not genotyped, the increase in accuracy was much lower (0% to 5%), but still substantial when the heritability was 0.1 and when the QTL explained at least 15% of the genetic variance. Regression on predicted gene content yielded higher accuracies than IBD–LA. Allele substitution effects were, however, overestimated, especially when only sires and males in the last generation were genotyped. For juveniles without phenotypic records and traits measured only on females, the superiority of regression on gene content over IBD–LA was larger than when all animals had phenotypes. Missing gene contents were predicted with higher accuracy using multiple-marker iterative peeling than with using mixed model methodology, but the difference in accuracy of total EBV was negligible and mixed model methodology was computationally much faster than multiple iterative peeling. For large livestock populations it can be concluded that gene-assisted breeding value estimation can be practically best performed by regression on gene contents, using mixed model methodology to predict missing marker genotypes, combining phenotypic information of genotyped and ungenotyped animals in one evaluation. This technique would be, in principle, also feasible for genomic selection. It is expected that genomic selection for ungenotyped animals using predicted single nucleotide polymorphism gene contents might be beneficial especially for low heritable traits.

GxE interactions for growth and carcass leanness: Re-ranking of boars in organic and conventional pig production

The objective of this study was to evaluate genotype by environment interactions for growth rate and carcass leanness in organic and conventional pig production environments. Organic breeding values for fattening traits were estimated for 37 Hampshire AI-boars based on slaughter records registered for 1805 crossbred offspring raised in an organic environment. The offspring were born and raised in herds certified for organic production. The statistical model included the fixed effects of sex, litter size at 2 weeks and herd. It also included the random effects of herd-year-season, birth litter and animal. Conventional breeding values for the same boars were captured from the breeding organization’s genetic evaluation. In the organic environment h 2 was estimated to 0.30 and 0.37 for growth rate and carcass leanness, respectively ( r g = − 0.11). Spearman rank correlations between organic and conventional breeding values, based on 29 boars with ≥ 20 progenies, were 0.48 for growth rate and 0.42 for carcass leanness. Both correlations were significantly different from 0 and 1. In conclusion, the results of the present study indicate weak genotype by environment interactions for both growth rate and carcass leanness in organic and conventional pig production environments, and there is some re-ranking of boars’ breeding values between environments.

Marker assisted selection of heifers improved milk somatic cell count compared to selection on conventional pedigree breeding values

Abstract. Although mastitis in cattle is an important factor for dairy economy and animal welfare and although udder health parameters have a substantial genetic variability, in many countries there is little or no improvement of udder health in the conventional commercial breeding programs. Strategies implementing information about Quantitative trait loci (QTL) via genetic marker information seem to offer new prospects to improve this situation. In a proof-of-principle approach, we show that selection of German Holstein heifers prior to first calving based on marker information regarding a confirmed QTL affecting somatic cell score (SCS) on bovine chromosome 18 (BTA18) (MAS strategy) indeed enabled prediction of halfsibs with a high (q) or a low (Q) number of somatic cells in milk already early in the first lactation. Compared to a strategy relying on conventional breeding values only (CON strategy), selection including marker information resulted in a stronger discrimination between and a higher uniformity within the MAS-Q and -q groups compared to the CON-Q and – q groups selected by conventional selection strategies. The selected heifers, which are clinically unaffected, however genetically different in their capacity of mammary gland protection against pathogens, will serve as tools for a comprehensive expression analysis to highlight molecular processes underlying a different genetic predisposition to mastitis susceptibility. Thus, functional mechanisms resulting in a decreased susceptibility of the Q individuals can be identified to further improve selection on udder health in cattle.