Inclusion Of Genomic Information Research Articles

Genomic prediction of fertility and calving traits in Holstein cattle based on models including epistatic genetic effects.

The goal of this study was to investigate whether the inclusion of genomic information and epistatic (additive by additive) genetic effects would increase the accuracy of predicting phenotypes adjusted for known environmental effects, reduce prediction bias and minimize the confounding between additive and additive by additive epistatic effects on fertility and calving traits in Holstein cattle. Phenotypic and genotypic records were available for 6090 cows. Eight cow traits were assessed including 56-day nonreturn rate (NRR), number of services (NS), calving to first insemination (CTFS), first insemination to conception (FSTC), gestation length (GL), calving ease (CE), stillbirth (SB) and calf size (CZ). Four scenarios were assessed for their ability to predict adjusted phenotypes, which included: (1) traditional pedigree-based Best Linear Unbiased Prediction (P-BLUP) for additive genetic effects (PA); (2) P-BLUP for additive and epistatic (additive by additive) genetic effects (PAE); (3) genomic BLUP (G-BLUP) for additive genetic effects (GA); and (4) G-BLUP for additive and epistatic genetic effects (GAEn, where n = 1-3 depending on the alternative ways to construct the epistatic genomic matrix used). Constructing epistatic relationship matrix as the Hadamard product of the additive genomic relationship matrix (GAE1), which is the usual method and implicitly assumes a model that fits all pairwise interactions between markers twice and includes the interactions of the markers with themselves (dominance). Two additional constructions of the epistatic genomic relationship matrix were compared to test whether removing the double counting of interactions and the interaction of the markers with themselves (GAE2), and removing double counting of interactions between markers, but including the interaction of the markers with themselves (GAE3) would had an impact on the prediction and estimation error correlation (i.e. confounding) between additive and epistatic genetic effects. Fitting epistatic genetic effects explained up to 5.7% of the variance for NRR (GAE3), 7.7% for NS (GAE1), 11.9% for CTFS (GAE3), 11.1% for FSTC (GAE2), 25.7% for GL (GAE1), 2.3% for CE (GAE1), 14.3% for SB (GAE3) and 15.2% for CZ (GAE1). Despite a substantial proportion of variance being explained by epistatic effects for some traits, the prediction accuracies were similar or lower for GAE models compared with pedigree models and genomic models without epistatic effects. Although the prediction accuracy of direct genomic values did not change significantly between the three variations of the epistatic genetic relationship matrix used, removing the interaction of the markers with themselves reduced the confounding between additive and additive by additive epistatic effects. These results suggest that epistatic genetic effects contribute to the variance of some fertility and calving traits in Holstein cattle. However, the inclusion of epistatic genetic effects in the genomic prediction of these traits is complex and warrant further investigation.

Moving towards sustainable breeding objectives and cow welfare in dairy production: a South African perspective.

Genetic advancements have resulted in improved dairy production over many decades, due to the focus of breeding objectives on production as the driving force for genetic progress and overall farm profitability. Major advancements were made in the easy-to-measure traits with moderate to high heritability, which resulted in unintended consequences on herd fertility, health, and welfare of cows. In addition, climate change and animal welfare concerns demanded balanced breeding objectives and selection approaches for sustainable production-including health and longevity. The inclusion of genomic information into genetic evaluations has been proved to benefit traits associated with welfare and sustainable production. Cow welfare traits remain complex and suitable phenotypes are not always easy to measure or readily available for genetic evaluations. The challenge for improvement of cow welfare often lies within implementation of sensitive and measurable parameters. The aim of this review was to explore the reconsideration of breeding objectives in the dairy industry towards sustainable dairy production and cow welfare with reference to selection of dairy animals in South Africa.

Changes in genetic parameters for milk yield and heat tolerance in the Thai Holstein crossbred dairy population under different heat stress levels and over time

The objectives of this study were to investigate changes in genetic parameters for milk yield (MY) and heat tolerance of the crossbred Thai Holstein Friesian population under different heat stress levels over time, and to investigate the threshold point of heat stress manifestation on milk production. Genetic parameters were estimated using single-step genomic REML (ssGREML) and traditional REML models. Data included 58,965 test-day MY records from 1999 to 2008 (old data) and 105,485 test-day MY records from 2009 to 2018 (recent data) from the first parity of 24,520 cows. The pedigree included 55,168 animals, of which 882 animals had genotypes. Variance components were estimated with the REMLF90 program using a repeatability model with random regressions on a function of temperature-humidity index (THI) for additive genetic and permanent environmental effects. Fixed effects included farm-calving season combination, breed group-months in milk combination, and age at first calving. Random effects included additive genetic (intercept and slope) effects, permanent environmental (intercept and slope) effects, and herd-month-year of test. The phenotypic mean for MY was 13.33 ± 4.39 kg/d in the old data, and 14.48 ± 4.40 kg/d in the recent data. Estimates over different THI levels for the intercept additive genetic variance using old data ranged from 2.61 to 2.77 and from 5.02 to 5.38 using recent data with the REML method. In ssGREML analyses (performed with recent data only) the estimates for the intercept additive genetic variance ranged from 4.71 to 5.05. Estimates for the slope additive genetic variance were close to zero in all cases, with the largest values (0.024-0.030) at the most extreme THI value (80). Using REML, the covariance between the intercept and the slope additive genetic effects (THI from 72 to 80) ranged from -0.001 to 0.019 with old data and from 0.027 to 0.060 with recent data. The same covariance ranged from 0.026 to 0.057 in ssGREML analyses. The covariance between the intercept and the slope permanent environmental effects ranged from -0.42 to -0.67 for all data and THI levels. Across THI levels, the genetic correlation between MY and heat tolerance varied from -0.06 to 0.13 with old data, from 0.16 to 0.30 with recent data in REML analyses, and from 0.15 to 0.30 in ssGREML analyses, suggesting that in the current population the top animals for MY are more resistant to heat stress. This was expected, because of the introduction of Bos indicus genes in the last years. Heritability estimates for MY ranged from 0.19 to 0.21 (old data) and from 0.33 to 0.40 (recent data) for REML analyses. Heritability estimates for MY using ssGREML ranged from 0.31 to 0.38. A decline in MY was found when the animals' breed composition had more than 97.3% of Holstein genetics, and it was greatest at THI 80. The heritability and genetic correlations observed in this study show that selection for MY is possible without a negative correlated response for heat tolerance. Although the inclusion of genomic information is expected to increase the accuracy of selection, more genotypes must be collected for successful application. Future research should address other production and fitness traits within the Thai Holstein population.

Inclusion of genomic information in estimation of genetic parameters for body weights and visual scores in Nelore cattle

ABSTRACT The aim of this study was to evaluate genomic information inclusion in genetic parameter estimation of standardized body weight at birth and at 240, 365, and 450 days of age, and visual scores for body structure, precocity, and body muscularity, measured as yearlings in Nelore cattle. We compared genetic parameters, (co)variance components (estimated from Bayesian inference and Gibbs sampling), breeding value accuracies, genetic trends, and principal component analysis (PCA) obtained through traditional GBLUP and ssGBLUP methods. For all traits [...]

Genetic Parameters of Workability Traits in the Population of Polish Holstein-Friesian Cows Based on Conventional and Genomic Data.

Simple SummaryWorkability traits are a group of functional traits that affect the economics of dairy production and are increasingly included in selection indexes. The most important of them include milking speed and temperament. The aim of this study was to estimate genetic and phenotypic parameters of workability traits. The estimation was carried out by considering two approaches: the first using pedigree data and the second using pedigree and genomic data. The obtained results indicate that workability traits belong to low heritable traits and are positively correlated genetically and phenotypically, which means a possibility of their effective improvement in the population, taking into account that the genomic information of sires did not have a significant effect on the estimated genetic parameters.Heritabilities of workability (WT) traits—milking speed (MS) and temperament (MT)—as well as genetic and phenotypic correlations between these traits in the population of Polish Holstein-Friesian (PHF) cows were estimated. The estimation of genetic parameters was performed twice: first with the use of pedigree data; and second with the use of pedigree and genomic data. Phenotypic data from routinely conducted MS and MT evaluations for 1,045,511 cows born from 2004 to 2013 were available; the cows were evaluated from 2011 to 2015. The main dataset was reduced based on imposed restrictions (e.g., on age of calving, stage of lactation and day of first trial milking). The dataset prepared in this manner comprised 391,615 cows. It was then reduced to daughters of 10% randomly selected sires for computational reasons. Finally, for genetic parameter estimation, 13,280 records of cows were used. The linear observation model included additive random effects of animal, fixed effects of herd-year-season of calving subclass (HYS) and lactation phase, fixed regressions on cow age at calving and the percent of HF breed genes in the cow genotype. Heritabilities estimated based on pedigree data were 0.12 (±0.0067) for MS and 0.08 (±0.0063) for MT, the genetic correlation between MS and MT was estimated at 0.05 (±0.0002) and the phenotypic correlation coefficient was estimated at 0.14 (±0.0004). The inclusion of genomic information of sire bulls had no clear effect on the size of the estimated WT genetic parameters. The heritabilities of MS and MT were 0.11 (±0.0065) and 0.09 (±0.0012), respectively. The genetic and phenotypic correlation coefficients were 0.07 (±0.0003) and 0.12 (±0.0005), respectively. The sizes of the obtained heritabilities of WT and of the genetic and phenotypic correlation between these traits indicate the possibility of effective population improvement for both WT traits.

Genomic Prediction in Local Breeds: The Rendena Cattle as a Case Study.

Simple SummaryAlthough genomic selection is being used in many livestock species, it has not yet been considered in local breeds due to the lower population size and the potential less effective impact on the genetic evaluation of these breeds. The current research aims to investigate how genomic data can impact the accuracy of genetic predictions for beef traits in Rendena, a small local cattle breed of the North-East of Italy selected for a dual purpose. Classical animal models using only phenotypic information were compared with two models that integrated genomic data with pedigree information. The genomic models presented better accuracy in estimated breeding values of the animals than the ‘classical’ animal model, especially the ‘simpler’ one assuming homogeneous variances of single nucleotide polymorphisms. Our results show that the inclusion of genomic information can be successfully applied to breeding selection scenarios even in small local cattle breeds such as Rendena.The maintenance of local cattle breeds is key to selecting for efficient food production, landscape protection, and conservation of biodiversity and local cultural heritage. Rendena is an indigenous cattle breed from the alpine North-East of Italy, selected for dual purpose, but with lesser emphasis given to beef traits. In this situation, increasing accuracy for beef traits could prevent detrimental effects due to the antagonism with milk production. Our study assessed the impact of genomic information on estimated breeding values (EBVs) in Rendena performance-tested bulls. Traits considered were average daily gain, in vivo EUROP score, and in vivo estimate of dressing percentage. The final dataset contained 1691 individuals with phenotypes and 8372 animals in pedigree, 1743 of which were genotyped. Using the cross-validation method, three models were compared: (i) Pedigree-BLUP (PBLUP); (ii) single-step GBLUP (ssGBLUP), and (iii) weighted single-step GBLUP (WssGBLUP). Models including genomic information presented higher accuracy, especially WssGBLUP. However, the model with the best overall properties was the ssGBLUP, showing higher accuracy than PBLUP and optimal values of bias and dispersion parameters. Our study demonstrated that integrating phenotypes for beef traits with genomic data can be helpful to estimate EBVs, even in a small local breed.

Comparison of genetic parameters and estimated breeding values for worm resistance in meat sheep obtained using traditional and genomic models.

Gastrointestinal nematode infections have caused expressive losses in sheep production worldwide. The improvement of host genetic resistance to worms has been used as a strategy to mitigate this problem. In this sense, the inclusion of genomic information has shown potential to increase the accuracy of prediction of breeding values and speed up selection. In this study, we aimed to compare estimates of genetic parameters and breeding values for traits that indicate the resistance to gastrointestinal nematode infection in Santa Inês sheep using the pedigree-based BLUP or including genomic information. There were 1478 animals in the pedigree, of which 271 were genotyped using the OvineSNP50 BeadChip (Illumina, Inc.). The host resistance was assessed using the following traits: fecal nematode egg counts (FEC); FAMACHA score (FAMACHA); and resistance to gastrointestinal nematode infection (RGNI) as a combination of FEC, FAMACHA, body condition score, and hematocrit. The genetic parameters and breeding values were estimated using single- and multi-trait analyses. For RGNI, the heritability estimates ranged from 0.25 using the single-trait genomic model (S-H) to 0.54 using the traditional multi-trait model (M-A). The heritability estimates for FEC ranged from 0.06 to 0.36, using the single-trait pedigree-based model (S-A) and the multi-trait genomic model (M-H), respectively. For FAMACHA, the heritability estimates ranged from 0.46 (M-H) to 0.54 (M-A). Estimates of genetic correlation ranged from 0.22 to 0.69. The inclusion of genomic information provided gain in accuracy for all traits. All estimates of predictive ability obtained using genomic data in a multi-trait setting were higher than those obtained using single-trait models. The estimates of predictive ability ranged from 0.03 (S-A) to 0.46 (M-H). The heritability estimates obtained using genomic information showed that all traits evaluated are suitable for genomic selection. Despite the low accuracies obtained, the use of the genomic model provided more accurate estimates of breeding values in comparison to the pedigree-based model.

Application of a novel 50K SNP genotyping array to assess the genetic diversity and linkage disequilibrium in a farmed Pacific white shrimp (Litopenaeus vannamei) population

The inclusion of genomic information became a reality in shrimp breeding and it is expected to accelerate the genetic gain over time. The decay of linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) is an important measure to evaluate the feasibility of implementing genomic selection. The aim of this study was to evaluate the use of a novel 50 K SNP array tool to characterize the genomic diversity, LD and effective population size (Ne) in a farmed shrimp population. A total of 96 animals (40 sires and 56 dams) were genotyped using the novel Illumina AquaArray HD (50 K) vannamei®. Quality control (QC) of genomic data was performed and three different minor allele frequency (MAF) exclusion thresholds were applied: < 0.10 (QC1), < 0.05 (QC2) and < 0.01 (QC3). After QC, 34,425, 39,091 and 42,789 SNPs were retained for QC1, QC2 and QC3, respectively, validating the high informativeness of this SNP array to this particular shrimp breeding population. The population showed a considerable high overall heterozygosity in comparison to other aquaculture species meaning that genetic diversity is stable despite selection. The principal component analysis revealed three genetically distant groups with the first two principal components explaining 27.7 % of total variation. LD decayed rapidly in the first 30Kb of distance between markers from 0.20 to 0.07 and then decreased to 0.02 in the long-range distance. These results suggest a relatively recent incorporation of animals from different populations in the broodstock. Ne size reduced from 7,871 to 301 animals in 827 generations for QC1, 8,253 to 305 animals for QC2 and 8,957 to 315 animals for QC3, both in 899 generations. Contemporary Ne was close to 86 for all QCs. The level of LD estimated suggests that genomic selection and genome-wide association studies are feasible in shrimp by using this SNP array.

Single-step genomic evaluation of lambing ease in Australian terminal sire breed sheep

Context Australian sheep breeding values (ASBVs) for the categorical trait of lambing ease are routinely estimated by Sheep Genetics via a threshold model. This has been pedigree-only, and has not utilised genomic information. Aim The present study aimed to update the genetic evaluation model and parameters for lambing ease for terminal sire sheep (dominated by White Suffolk and Poll Dorset breeds). The model includes correlations with birthweight and gestation length. Cross-validation was used to determine the value of the improved models and the inclusion of genomic information. Methods New data-preparation pipelines were developed to accommodate improved data-filtering methods and contemporary group construction. Genetic parameters, including correlations among traits, were estimated using continuous and threshold models, with various combinations of effects in mixed animal models. Cross-validation of breeding values was performed against progeny performance, by using forward prediction. Key results The increased volume of data, improved data preparation steps and enhanced evaluation software now allow a more complex model to be fitted, including maternal, sire by flock-year and genetic group effects, which were significant for all traits, along with the inclusion of multiple sire groups in the pedigree. However, the inclusion of the direct-maternal covariance and sire by flock-year terms resulted in unrealistically inflated estimates of some components, and thus the final covariance matrices required some adjustments. Cross-validation of breeding values was performed against progeny performance using forward prediction. For all traits, the phenotype accuracies and estimated breeding value correlations were higher from the new model without genomics than were those from the current routine evaluation. The benefit from including genomic information based on cross-validation is minimal currently but is expected to improve as the size of the reference population grows. Further work is required to define acceptable data-quality thresholds for the construction of datasets for routine breeding value estimation. Conclusions The new model and parameters resulted in ASBVs with an improved predictive ability, with increased accuracy and reduced bias compared with the current analysis. Furthermore, a small increase in accuracy was observed for all traits from utilising genomic information in the model. Implications The new genetic evaluation procedures and models will be used to update those being applied in the routine Sheep Genetics evaluation system and also support further index development for the terminal sire breeds in Australia.

17 Investigating pig survival in different productive stages using genomic models

Abstract Pig survival is an economically important trait with animal welfare implications. Although survival is highly affected by the environment, previous studies reported genetic variability of this trait, indicating the opportunity for improvement through selection. Genomic information is currently included in the major commercial pig breeding programs, which can be incorporated by single-step genomic BLUP (ssGBLUP). The objectives of this study were to: 1) estimate (co)variance components for farrowing, lactation, nursery, and finishing survival; and 2) compare the individual breeding value accuracies obtained using traditional pedigree-based BLUP (BLUP) and ssGBLUP methods. Individual survival records were collected for a crossbred pig population, and two-trait threshold models, which included maternal effects, were used for (co)variance components estimation. Direct and maternal breeding values were estimated using BLUP and ssGBLUP methods, and individual accuracies were obtained based on posterior standard deviation. Heritabilities for the four survival phases were low, ranging from 0.04 to 0.12. Pre-weaning survival was controlled by dam and piglet additive gene effects. The additive direct and maternal components were equally important at farrowing, whereas the piglet’s own genetic merit was the most expressive during lactation. Common environment estimates were higher than maternal genetic effects, indicating early life experiences related to the sow, but independent of the maternal genetic component. Nursery and finishing survival showed the same or higher heritabilities compared to pre-weaning stages. The genetic correlation between the pre-weaning phases was high (0.68), whereas the correlation between the post-weaning measurements was moderate (0.42). The incorporation of genomic information through ssGBLUP increased the individual accuracy, on average, from 0.36 to 0.41 for direct and from 0.29 to 0.37 for maternal effects compared to BLUP. Although the heritabilities for survival in different productive stages are low, genetic gains can be obtained, given that breeding values benefit from the inclusion of genomic information.

PSXII-37 Validation of single-step GBLUP genomic predictions from threshold models using the linear regression method: an application in chicken mortality

Abstract The objective of this study was to determine whether the linear regression (LR) method could be used to validate genomic threshold models used for analysis of categorical data. Statistics for the LR method were computed from estimated breeding values (EBVs) using the whole and truncated datasets. The method was tested using simulated and real chicken datasets. The simulated dataset included ten generations of 4,500 birds each; genotypes were available for the last three generations. Each animal was assigned a continuous trait, which was converted to a binary score assuming an incidence of failure of 7%. The real dataset included the survival status of 186,596 broilers (mortality rate equal to 7.2%) and genotypes of 18,047 birds. Both datasets were analyzed using Best Linear Unbiased Predictor (BLUP) or single-step GBLUP (ssGBLUP). The whole dataset included all phenotypes available, whereas in the partial dataset, phenotypes of the most recent generation were removed. In the simulated dataset, the accuracies based on the LR formulas were 0.45 for BLUP and 0.76 for ssGBLUP, whereas the correlations between true breeding values and EBVs were 0.37 and 0.65, respectively. The gain in accuracy by adding genomic information was overestimated by 0.09 when using the LR method compared to the true increase in accuracy. However, when the estimated ratio between the additive variance computed based on pedigree only and on pedigree and genomic information was considered, the difference between true and estimated gain was less than 0.02. Accuracies of BLUP and ssGBLUP with the real dataset were 0.41 and 0.47, respectively. Smaller improvements in accuracy when using ssGBLUP with the real dataset were due to population structure and lower heritability. The LR method is a useful tool for estimating improvements in accuracy of EBVs due to the inclusion of genomic information when traditional validation methods are not applicable.

Genetic evaluation of sheep for resistance to gastrointestinal nematodes and body size including genomic information.

ObjectiveThe genetic evaluation of Santa Inês sheep was performed for resistance to gastrointestinal nematode infection (RGNI) and body size using different relationship matrices to assess the efficiency of including genomic information in the analyses.MethodsThere were 1,637 animals in the pedigree and 500, 980, and 980 records of RGNI, thoracic depth (TD), and rump height (RH), respectively. The genomic data consisted of 42,748 SNPs and 388 samples genotyped with the OvineSNP50 BeadChip. The (co)variance components were estimated in single- and multi-trait analyses using the numerator relationship matrix (A) and the hybrid matrix H, which blends A with the genomic relationship matrix (G). The BLUP and single-step genomic BLUP methods were used. The accuracies of estimated breeding values and Spearman rank correlation were also used to assess the feasibility of incorporating genomic information in the analyses.ResultsThe heritability estimates ranged from 0.11±0.07, for TD (in single-trait analysis using the A matrix), to 0.38±0.08, for RH (using the H matrix in multi-trait analysis). The estimates of genetic correlation ranged from −0.65±0.31 to 0.59±0.19, using A, and from −0.42±0.30 to 0.57±0.16 using H. The gains in accuracy of estimated breeding values ranged from 2.22% to 75.00% with the inclusion of genomic information in the analyses.ConclusionThe inclusion of genomic information will benefit the direct selection for the traits in this study, especially RGNI and TD. More information is necessary to improve the understanding on the genetic relationship between resistance to nematode infection and body size in Santa Inês sheep. The genetic evaluation for the evaluated traits was more efficient when genomic information was included in the analyses.

Evaluaciones genéticas usando el mejor predictor lineal insesgado genómico en una etapa en bovinos

Las evaluaciones genéticas convencionales han estado enmarcadas en la estimación de valores genéticos a partir de los sistemas de ecuaciones de modelos mixtos que consideran efectos aleatorios y fijos simultáneamente. En los últimos años, el desarrollo en tecnologías de secuenciación del genoma ha permitido obtener información genómica que puede ser incluida en las evaluaciones genéticas para incrementar las confiabilidades, el progreso genético y disminuir el intervalo generacional. El mejor predictor lineal insesgado en una etapa es una metodología que incluye información genómica reemplazando la matriz de parentesco por una matriz que combina el parentesco por pedigrí y genómico de una población genotipada, permitiendo la estimación de valores genéticos para animales no genotipados. El objetivo de este artículo de revisión fue la descripción de la metodología, sus recientes avances, y conocer algunas de las estrategias que podrían ser llevadas a cabo cuando el número de animales genotipados es bajo.

212 Genomic prediction of carcass average daily gain, fat and loin depth in three-way crossbred pigs including information collected on purebreds

Abstract The purpose of this study was to predict three-way crossbred performance for carcass traits using different crossbred/purebred reference populations. Carcass measures (average daily gain, back-fat and loin depths) were collected in 4,893 three-way-cross individuals (CB individuals, 1,252 being genotyped). Live measures of body weight and tissue deposition were collected on 3,050 purebred Duroc individuals (PB individuals, 941 being genotyped), paternal-half-sibs (PHS) of the CB individuals. Models’ predictive performance was tested via 4-fold cross-validation. The basic model included CB phenotypes from the training set without inclusion of genomic information (i.e. pedigree BLUP). We also sequentially included: 1) CB genotypes; 2) PB phenotypes and genotypes for the training families (PBt); 3) PB phenotypes and genotypes for the validation families (PBv). Variance components (heritabilities and genetic correlations between CB and PB traits) were not estimated but fixed at different values within a plausible interval, the combination of such parameters that gave the best predictive ability was considered for that model. Results reported pedigree prediction of CB traits to show about 0.25 accuracy (correlation between breeding value and adjusted phenotype) for the three traits. The inclusion of CB genotypes was beneficial, with an increase ranging from 25 to 50% (depending on the trait) compared to pedigree prediction. When PBt genotypes and phenotypes were included, prediction accuracy dropped to almost null accuracy. When PBv genotypes and phenotypes were included, predictive performance was better than models that included CB information only. Results suggest that PB information can improve selection accuracy for CB traits, with the condition PB are PHS of the CB in validation. Otherwise, inclusion of PB information from the training set can be detrimental. CB genotypes, on the other hand, always improve prediction accuracy. We can conclude that reference populations aimed at improving CB performance should include phenotypes and genotypes from these individuals.

Reaction norm for yearling weight in beef cattle using single-step genomic evaluation.

When the environment on which the animals are raised is very diverse, selecting the best sires for different environments may require the use of models that account for genotype by environment interaction (G × E). The main objective of this study was to evaluate the existence of G × E for yearling weight (YW) in Nellore cattle using reaction norm models with only pedigree and pedigree combined with genomic relationships. Additionally, genomic regions associated with each environment gradient were identified. A total of 67,996 YW records were used in reaction norm models to calculate EBV and genomic EBV. The method of choice for genomic evaluations was single-step genomic BLUP (ssGBLUP). Traditional and genomic models were tested on the ability to predict future animal performance. Genetic parameters for YW were obtained with the average information restricted maximum likelihood method, with and without adding genomic information for 5,091 animals. Additive genetic variances explained by windows of 200 adjacent SNP were used to identify genomic regions associated with the environmental gradient. Estimated variance components for the intercept and the slope in traditional and genomic models were similar. In both models, the observed changes in heritabilities and genetic correlations for YW across environments indicate the occurrence of genotype by environment interactions. Both traditional and genomic models were capable of identifying the genotype by environment interaction; however, the inclusion of genomic information in reaction norm models improved the ability to predict animals' future performance by 7.9% on average. The proportion of genetic variance explained by the top SNP window was 0.77% for the regression intercept (BTA5) and 0.82% for the slope (BTA14). Single-step GBLUP seems to be a suitable model to predict genetic values for YW in different production environments.

The impact of training strategies on the accuracy of genomic predictors in United States Red Angus cattle.

Genomic selection (GS) has become an integral part of genetic evaluation methodology and has been applied to all major livestock species, including beef and dairy cattle, pigs, and chickens. Significant contributions in increased accuracy of selection decisions have been clearly illustrated in dairy cattle after practical application of GS. In the majority of U.S. beef cattle breeds, similar efforts have also been made to increase the accuracy of genetic merit estimates through the inclusion of genomic information into routine genetic evaluations using a variety of methods. However, prediction accuracies can vary relative to panel density, the number of folds used for folds cross-validation, and the choice of dependent variables (e.g., EBV, deregressed EBV, adjusted phenotypes). The aim of this study was to evaluate the accuracy of genomic predictors for Red Angus beef cattle with different strategies used in training and evaluation. The reference population consisted of 9,776 Red Angus animals whose genotypes were imputed to 2 medium-density panels consisting of over 50,000 (50K) and approximately 80,000 (80K) SNP. Using the imputed panels, we determined the influence of marker density, exclusion (deregressed EPD adjusting for parental information [DEPD-PA]) or inclusion (deregressed EPD without adjusting for parental information [DEPD]) of parental information in the deregressed EPD used as the dependent variable, and the number of clusters used to partition training animals (3, 5, or 10). A BayesC model with π set to 0.99 was used to predict molecular breeding values (MBV) for 13 traits for which EPD existed. The prediction accuracies were measured as genetic correlations between MBV and weighted deregressed EPD. The average accuracies across all traits were 0.540 and 0.552 when using the 50K and 80K SNP panels, respectively, and 0.538, 0.541, and 0.561 when using 3, 5, and 10 folds, respectively, for cross-validation. Using DEPD-PA as the response variable resulted in higher accuracies of MBV than those obtained by DEPD for growth and carcass traits. When DEPD were used as the response variable, accuracies were greater for threshold traits and those that are sex limited, likely due to the fact that these traits suffer from a lack of information content and excluding animals in training with only parental information substantially decreases the training population size. It is recommended that the contribution of parental average to deregressed EPD should be removed in the construction of genomic prediction equations. The difference in terms of prediction accuracies between the 2 SNP panels or the number of folds compared herein was negligible.

Sexual dimorphism in livestock species selected for economically important traits.

Most breeding companies evaluate economically important traits in males and females as a single trait, assuming genetic correlation of 1 between phenotypes measured in both sexes. This assumption may not be true because genes may be differently expressed in males and females. We estimated genetic correlations between males and females for growth and efficiency traits in broiler chickens, growth traits in American Angus beef cattle, and birth weight and preweaning mortality in purebred pigs; therefore, each trait was treated differently in males and females. Variance components were estimated in single- and multiple-trait models, jointly or separated into both sexes. Furthermore, we calculated traditional and genomic evaluations, and we correlated EBV or genomic EBV (GEBV) from joint and separate evaluations for males and females. For broiler chickens, genetic correlations ranged from 0.86 to 0.94. For Angus cattle, genetic correlations ranged from 0.86 to 0.98 for early growth traits and were less, ranging from 0.68 to 0.84, for postweaning gain. In pigs, genetic correlations ranged from 0.98 to 0.99 for birth weight and from 0.71 to 0.73 for preweaning mortality. For some models in all 3 animal species, the joint and separate analyses had different heritabilities. Despite differences in heritability, the correlations within the sex-specific trait EBV and between the sex-specific and the joint trait EBV were very strong, regardless of the model or inclusion of genomic information. Males and females differed for traits measured late in the animal's life; however, strong traditional EBV correlations and also GEBV correlations indicate that considering the traits equal in males and females may have no negative impact on selection.

The effect of including genomic relationships in the estimation of genetic parameters of functional traits in pigs.

The term functionality in animal breeding is used for traits that increase the efficiency of production by lowering the input cost, such as animal health and leg weakness related to longevity. The main objective of the study was to investigate the impact of genomic information, in a multivariate variance component analysis, on some of these traits. In addition, the effect of the inclusion was studied by testing the model's prediction ability based on best linear unbiased estimates for fixed and random effects. The material in this study consists of phenotypes from 76,683 animals, of which 4933 animals are genotyped. The heritabilities for front leg conformation, stayability, osteochondrosis and arched back, estimated using the traditional pedigree, were found to be between 0.12 and 0.29. When using the combined genomic and pedigree relationship matrix, the heritabilities were between 0.14 and 0.36. The results show that the combined relationship matrix can be used for the estimation of (co)variance components, and that the predictive ability of the model in this study marginally increases with the inclusion of genomic information.

Breeding the dairy cow of the future: what do we need?

Genetics is responsible for approximately half the observed changes in animal performance in well structured breeding programs. Key characteristics of the dairy cow of the future include (1) production of a large quantity of high-value output (i.e. milk and meat), (2) good reproductive performance, (3) good health status, (4) good longevity, (5) no requirement for a large quantity of feed, yet being able to eat sufficient feed to meet its requirements, (6) easy to manage (i.e. easy calving, docile), (7) good conformation (over and above reflective of health, reproductive performance and longevity), (8) low environmental footprint, and (9) resilience to external perturbations. Pertinent and balanced breeding goals must be developed and implemented to achieve this type of animal; excluding any characteristic from the breeding goal could be detrimental for genetic gain in this characteristic. Attributes currently not explicitly considered in most dairy-cow breeding objectives include product quality, feed intake and efficiency, and environmental footprint; animal health is poorly represented in most breeding objectives. Lessons from the past deterioration in reproductive performance in the global Holstein population remind us of the consequences of ignoring or failing to monitor certain animal characteristics. More importantly, however, current knowledge clearly demonstrates that once unfavourable trends have been identified and the appropriate breeding strategy implemented, the reversal of genetic trends is achievable, even for low-heritability traits such as reproductive performance. Genetic variation exists in all the characteristics described. In the genomics era, the relevance of heritability statistics for most traits is less; the exception is traits not amenable to routine measurement in large populations. Phenotyping strategies (e.g. more detailed phenotypes, larger population) will remain a key component of an animal breeding strategy to achieve the cow of the future as well as providing the necessary tools and information to monitor performance. The inclusion of genomic information in genetic evaluations is, and will continue, to improve the accuracy of genetic evaluations, which, in turn, will augment genetic gain; genomics, however, can also contribute to gains in performance over and above support of increased genetic gain. Nonetheless, the faster genetic gain and thus reduced ability to purge out unfavourable alleles necessitates the appropriate breeding goal and breeding scheme and very close monitoring of performance, in particular for traits not included in the breeding goals. Developments in other disciplines (e.g. reproductive technologies), coupled with commercial struggle for increased market share of the breeding industry, imply a possible change in the landscape of dairy-cow breeding in the future.

Genomic information in pig breeding: Science meets industry needs

The inclusion of genomic information on prediction of breeding values has been explored by the pig breeding industry as molecular techniques evolved. Except for a few successful cases, for example the HAL-1843®, the use of genomic information for pig breeding has been limited. The development of SNP chips with high density markers across the genome coupled with new statistical methods allowed genomic selection (GS) to become a common practice. GS application entailed important advantages for pig breeding as it increased the accuracy of the breeding values for selection candidates and offered an opportunity for the practice of new selection strategies. Nevertheless, the application of GS is not straightforward for every target trait and breeding scheme. Many efforts have been done to evaluate the new methods and strategies to efficiently implement GS in pig breeding. Several aspects of the population and breeding objectives must be considered before GS is applied. This paper reviews the current status and challenges faced by pig breeders in the implementation of GS and the future opportunities that may arise as molecular technologies advance.