Validation of single-step genomic BLUP random regression test-day models and SNP effects analysis on milk yield in French Saanen goats.

The shape of the lactation curve is linked to an animal's health, feed requirements, and milk production throughout the year. Random regression models (RRM) are widely used for genetic evaluation of total milk production throughout the lactation and for milk yield persistency. Genomic information used with the single-step genomic BLUP method (ssGBLUP) substantially improves the accuracy of genomic prediction of breeding values in the main dairy cattle breeds. The aim of this study was to implement an RRM using ssGBLUP for milk yield in Saanen dairy goats in France. The data set consisted of 7,904,246 test-day records from 1,308,307 lactations of Saanen goats collected in France between 2000 and 2017. The performance of this type of evaluation was assessed by applying a validation step with data targeting candidate bucks. The model was compared with a nongenomic evaluation and a traditional evaluation that use cumulated performance throughout the lactation model (LM). The incorporation of genomic information increased correlations between daughter yield deviations (DYD) and estimated breeding values (EBV) obtained with a partial data set for candidate bucks. The LM and the RRM had similar correlation between DYD and EBV. However, the RRM reduced overestimation of EBV and improved the slope of the regression of DYD on EBV obtained at birth. This study shows that a genomic evaluation from a ssGBLUP RRM is possible in dairy goats in France and that RRM performance is comparable to a LM but with the additional benefit of a genomic evaluation of persistency. Variance of adjacent SNPs was studied with LM and RRM following the ssGBLUP. Both approaches converged on approximately the same regions explaining more than 1% of total variance. Regions associated with persistency were also found.

Developing flexible models for genetic evaluations in smallholder crossbred dairy farms.

The productivity of smallholder dairy farms is very low in developing countries. Important genetic gains could be realized using genomic selection, but genetic evaluations need to be tailored for lack of pedigree information and very small farm sizes. To accommodate this situation, we propose a flexible Bayesian model for the genetic evaluation of milk yield, which allows us to simultaneously account for nongenetic random effects for farms and varying SNP variance (BayesR model). First, we used simulations based on real genotype data from Indian crossbred dairy cattle to demonstrate that the proposed model can separate the true genetic and nongenetic parameters even for small farm sizes (2 cows on average) although with high standard errors in scenarios with low heritability. The accuracy of genomic genetic evaluation increased until farm size was approximately 5. We then applied the model to real data from 4,655 crossbred cows with 106,109 monthly test day milk records and 689,750 autosomal SNPs. We estimated a heritability of 0.16 (0.04) for milk yield and using cross-validation, a genomic estimated breeding value (GEBV) accuracy of 0.45 and bias (regression of phenotype on GEBV) of 1.04 (0.26). Estimated genetic parameters were very similar using BayesR, BayesC, and genomic BLUP approaches. Candidate genes near the top variants, IMMP2L and ARHGEF2, have been previously associated with milk protein composition, mastitis resistance, and milk cholesterol content. The estimated heritability and GEBV accuracy for milk yield are much lower than those from intensive or pasture-based systems in many countries. Further increases in the number of phenotyped and genotyped animals in farms with at least 2 cows (preferably 3-5, to allow for dropout of cows) are needed to improve the estimation of genetic effects in these smallholder dairy farms.

Genetic relationships between weight loss in early lactation and daily milk production throughout lactation in Holstein cows.

After calving, high-yielding dairy cows mobilize body reserves for energy, sometimes to the detriment of health and fertility. This study aimed to estimate the genetic correlation between body weight loss until nadir and daily milk production (MY24) in first- (L1) and second-lactation (L2) Holstein cows. The data set included 859,020 MY24 records and 570,651 daily raw body weight (BWr) phenotypes from 3,989 L1 cows, and 665,361 MY24 records and 449,449 BWr phenotypes from 3,060 L2 cows, recorded on 36 French commercial farms equipped with milking robots that included an automatic weighing platform. To avoid any bias due to change in digestive content, BWr was adjusted for variations in feed intake, estimated from milk production and BWr. Adjusted body weight was denoted BW. The genetic parameters of BW and MY24 in L1 and L2 cows were estimated using a 4-trait random regression model. In this model, the random effects were fitted by second-order Legendre polynomials on a weekly basis from wk 1 to 44. Nadir of BW was found to be earlier than reported in the literature, at 29 d in milk, and BW loss from calving to nadir was also lower than generally assumed, close to 29 kg. To estimate genetic correlations between body weight loss and production, we defined BWL5 as the loss of weight between wk 1 and 5 after calving. Genetic correlations between BWL5 and MY24 ranged from -0.26 to 0.05 in L1 and from -0.11 to 0.10 in L2, according to days in milk. These moderate to low values suggest that it may be possible to select for milk production without increasing early body mobilization.

Exploring underlying drivers of genotype by environment interactions in feed efficiency traits for dairy cattle with a mechanistic model involving energy acquisition and allocation.

Feed efficiency (FE) is a complex phenotype made up of multiple traits for which there is potential for substantial genotype by environment interaction (G × E). The objective of this study is to evaluate the extent of G × E for FE traits with a simulation approach. We used a mechanistic model of the dairy cow that simulates trajectories of phenotypes throughout lifetime, depending on trajectories of resource acquisition and allocation, driven by 4 genetic scaling parameters, and depending on the nutritional environment (quantity and quality of feed resources). The cow model, calibrated for a grass-based farming system and seasonal calving, was combined with a genetic module. This simulated genetic variation in the 4 genetic scaling parameters related to resource acquisition and allocation, based on a simple balanced pedigree structure (200 paternal half-sib groups each of 100 daughters). The population of 20,000 cows generated was simulated in 4 nutritional environment scenarios, representing a gradient of feeding constraints. In each scenario, 6 traits derived from the model outputs were analyzed to obtain population genetic parameters. Genetic correlations between second-lactation production and FE were positive and high in all scenarios and increased as the nutritional environment became more constraining. A measure of lifetime FE was positively correlated with second-lactation production under a less constrained environment, but these correlations decreased as the environment became more constraining. The genetic correlation between body reserves at second calving, and lifetime FE was positive and low in the least constraining scenario and increased as the environment became more constraining. In addition to genetic parameters, we looked at the distributions of acquisition and allocation parameters among the best performing cows for lactation and life FE, in the 2 most contrasted scenarios. The 4 subpopulations of best cows had acquisition and allocation strategies different from the whole population. In conclusion, this simulation study identifies the potential underlying biological basis for important G × E in FE traits. This highlights the importance of having a balanced breeding goal when undertaking selection that should also be based on phenotypes relevant to the target performance environment.

Influence of production, reproduction, morphology, and health traits on true and functional longevity in French Holstein cows.

In the long term, resilient animals are able to maintain their normal biological processes when confronted with environmental perturbations, reducing their risk of being culled. Therefore, longevity can be proposed as an indicator of long-term resilience. Decisions to remove a given dairy cow from the herd are mainly related to low milk production (i.e., voluntary culling) or to reasons other than production (i.e., involuntary culling). The aptitude of animals to delay any culling is defined as true longevity (TL), whereas functional longevity (FL) is the ability to avoid involuntary culling. The aim of the study was to investigate the influence of production, reproduction, morphology, and health traits on TL and FL, to identify risk factors for culling. Data included 278,217 lactations from 122,461 Holstein Friesian cows reared in 640 herds. The length of productive life, calculated as the time between first calving and culling, or censoring, was used as the measure of longevity. Survival analysis was performed using proportional hazards models assuming a piecewise Weibull distribution of the baseline hazard function, with or without adjustment for milk production to evaluate FL and TL. Insemination status, calving ease, mastitis, somatic cell count, displaced abomasum, and udder depth had significant relationships with TL and FL. Differences in estimates of relative risk between TL and FL showed that milk production often influenced culling decisions: farmers are more prone to cull animals with low production even when they had good other characteristics. The culling risk factors identified in the present study can be used to study resilience in dairy cattle and to improve genetic evaluations of functional or total longevity.

Exploring simultaneous perturbation profiles in milk yield and body weight reveals a diversity of animal responses and new opportunities to identify resilience proxies.

Livestock husbandry aims to manage the environment in which animals are reared to enable them to express their production potential. However, animals are often confronted with perturbations that affect their performance. Evaluating effects of these perturbations on animal performance could provide metrics to quantify and understand how animals cope with their environment, and therefore to better manage them. Body weight (BW) and milk yield (MY) dynamics over lactation may be used for this purpose. The goal of this study was to estimate an unperturbed performance trajectory using a differential smoothing approach on both MY and BW time series, and then to identify the perturbations and extract their phenotypic features. Daily MY and BW records from 490 primiparous Holstein cows from 33 commercial French herds were used. From the fitting procedure, estimated unperturbed performance trajectories of BW and MY were clustered into 3 groups. After the fitting procedure, 1,754 deviations were detected in the MY time series and 964 were detected in the BW time series across all cows. Overall, 425 of these deviations were detected during the same period (±10 d) in both MY and BW time series, 76 of which started at the same time. Results suggest that combining various individual dynamic measures and revealing the relationship that exists between them could be of great value in obtaining reliable estimates of resilience components in large populations.

Estimation of genetic parameters for dairy traits and somatic cell score in the first 3 parities using a random regression test-day model in French Alpine goats.

Lactation curve shape can affect an animal's health, feed requirements, and milk production throughout the year. We implemented a random regression model for the genetic evaluation of lactation curve shapes of dairy traits in French Alpine goats for their first 3 parities. Milk, fat, and protein yields, fat and protein contents, somatic cell score, and fat/protein ratio were considered. The data consisted of test-day records from 49,849 first lactation Alpine goats during their first 3 lactations. The reference model used a Legendre polynomial of order 2 for each parity to describe the genetic and permanent environmental effects, and was compared with a model that combined the second and third parities. A rank reduction of the variance-covariance matrix was also performed using an eigenvalue decomposition for each parity from the 2 models. Genetic parameters were consistent between the models tested. With a reduction to rank 2 and combining the second and third parities, the first 2 principal components correctly summarized the genetic variability of milk yield level and persistency, with a near-nil correlation between the 2, and with a much shorter computation time than the reference model. A favorable correlation of +0.43 between milk yield persistency and fat/protein ratio persistency at the beginning of the lactation was found from buck estimated breeding values.

Ability of dairy cows to ensure pregnancy according to breed and genetic merit for production traits under contrasted pasture-based systems.

The present study aimed to assess and measure the effects of breed, genetic merit for production traits, and feeding systems (FS) on the ability of dairy cows to ensure pregnancy through its components (fertilization, embryonic losses, recalving). An experiment was conducted over 9 yr on Normande and Holstein cows assigned to contrasted FS. Diets were based on maize silage in winter and grazing plus concentrate in spring in the high FS group, and on grass silage in winter and grazing with no concentrate during spring in the low FS group. Within breeds, cows were classified into 2 groups with similar estimated breeding values (EBV) for milk solids: cows with high EBV for milk yield were included in a milk group and those with high EBV for fat and protein contents were included in a content group. Holstein cows produced more milk throughout lactation than Normande cows (the differential was greater in the high FS group, +2,294 kg, compared with +1,280 kg in the low FS group) and lost more body condition to nadir (the differential was greater in the high FS group, -1.00 point, compared with -0.80 point in the low FS group). Within breeds, milk solids production was similar between genetic groups. Cows in the high FS group produced more milk (+2,495 kg for Holstein and +1,481 kg for Normande cows) and had a higher body condition score at nadir (+0.40 point for Holstein and +0.60 point for Normande) than cows in the low FS group. Holstein cows had a lower recalving rate than Normande cows (-19 percentage units). We found no effect of genetic group and FS on fertility of Normande cows. However, according to FS, Holstein cows in the content group exhibited different fertility failure patterns. In the low FS group, Holstein cows in the content group had more nonfertilizations or early embryo mortality (+26 percentage units at first and second services) than Holstein cows in the milk group. In the high FS group, Holstein cows in the content group had a higher proportion of late embryo mortality than in the milk group (+10 percentage units at first and second services). We observed no effect of FS on recalving rate; however, indicators of energy balance (protein content or body condition score) were positively associated with successful conception and pregnancy. This suggested a link between genetic merit for fat and protein content and lower ability of dairy cows to ensure pregnancy because of more nonfertilizations and early or late embryo mortality.

Genomic and single-step evaluations of carcass traits of young bulls in dual-purpose cattle.

Genetic evaluations for carcass traits of young bulls in Normande and Montbeliarde breeds are currently being developed in France. In order to determine a suitable genomic evaluation for three carcass traits of young bulls, genomic breeding values were estimated for young candidates to selection using different approaches. Records of 111,789 Normande and 118,183 Montbeliarde were used. Average progeny pre-adjusted performances (DYD) were calculated for sires. Evaluation approaches were compared based on an assessment of their accuracy (correlation between DYD and estimated breeding values [EBVs]) and bias (regression coefficient of DYD on EBVs) on the 20% youngest AI sires. All genomic approaches were generally more accurate than BLUP (+.045 to +.116 correlation points), except for age at slaughter where single-step GBLUP (SSGBLUP) was the only genomic method leading to a greater accuracy (+.038 to +.126 points). The best setting of the SSGBLUP relationship matrix was characterized by a weight of 30% for pedigree information in the genomic relationship matrix. SSGBLUP was the most valuable evaluation approach for the evaluation of carcass traits of Normande and Montbeliarde young bulls.

Genomic evaluation of regional dairy cattle breeds in single-breed and multibreed contexts.

An important prerequisite for high prediction accuracy in genomic prediction is the availability of a large training population, which allows accurate marker effect estimation. This requirement is not fulfilled in case of regional breeds with a limited number of breeding animals. We assessed the efficiency of the current French routine genomic evaluation procedure in four regional breeds (Abondance, Tarentaise, French Simmental and Vosgienne) as well as the potential benefits when the training populations consisting of males and females of these breeds are merged to form a multibreed training population. Genomic evaluation was 5-11% more accurate than a pedigree-based BLUP in three of the four breeds, while the numerically smallest breed showed a < 1% increase in accuracy. Multibreed genomic evaluation was beneficial for two breeds (Abondance and French Simmental) with maximum gains of 5 and 8% in correlation coefficients between yield deviations and genomic estimated breeding values, when compared to the single-breed genomic evaluation results. Inflation of genomic evaluation of young candidates was also reduced. Our results indicate that genomic selection can be effective in regional breeds as well. Here, we provide empirical evidence proving that genetic distance between breeds is only one of the factors affecting the efficiency of multibreed genomic evaluation.

Estimation of genetic parameters for functional longevity in the South African Holstein cattle using a piecewise Weibull proportional hazards model.

Non-genetic factors influencing functional longevity and the heritability of the trait were estimated in South African Holsteins using a piecewise Weibull proportional hazards model. Data consisted of records of 161,222 of daughters of 2,051 sires calving between 1995 and 2013. The reference model included fixed time-independent age at first calving and time-dependent interactions involving lactation number, region, season and age of calving, within-herd class of milk production, fat and protein content, class of annual variation in herd size and the random herd-year effect. Random sire and maternal grandsire effects were added to the model to estimate genetic parameters. The within-lactation Weibull baseline hazards were assumed to change at 0, 270, 380 days and at drying date. Within-herd milk production class had the largest contribution to the relative risk of culling. Relative culling risk increased with lower protein and fat per cent production classes and late age at first calving. Cows in large shrinking herds also had high relative risk of culling. The estimate of the sire genetic variance was 0.0472 ± 0.0017 giving a theoretical heritability estimate of 0.11 in the complete absence of censoring. Genetic trends indicated an overall decrease in functional longevity of 0.014 standard deviation from 1995 to 2007. There are opportunities for including the trait in the breeding objective for South African Holstein cattle.

Toward improved postpartum cyclicity of primiparous dairy cows: Effects of genetic merit for production traits under contrasting feeding systems.

Milk genetic merit is known to affect commencement of luteal activity (C-LA) in dairy cows. This effect is considered to be due to energy exported in milk production. The present study aimed to identify and quantify the effects of genetic characteristics [breed and estimated breeding value (EBV) for milk yield and fat and protein contents] and feeding system on C-LA of primiparous cows. From 2006 to 2013, an experiment was conducted on 97 primiparous dairy (Holstein) and 97 primiparous dual-purpose (Normande) cows. Within breed, cows were classified into 2 groups: cows with high EBV for milk yield were included in a "milk group" and those with high EBV for fat and protein contents were included in a "content group." Within breed, exported energy in milk and body weight (BW) loss were similar for both genetic groups. Two grazing-based strategies were used, a high feeding system (maize silage in winter and grazing plus concentrate) and a low feeding system (grass silage in winter and grazing with no concentrate). Interval from calving to C-LA was studied performing survival analyses. Milk progesterone profile, milk yield, and body condition were analyzed using χ(2)-test and analysis of covariance. Holstein cows produced more milk (+1,810 kg in the high feeding system and +1,120 kg in the low feeding system) and lost more BW from wk 1 to 14 of lactation (-1.4 kg/wk) than Normande cows, whereas Normande cows had earlier C-LA than Holstein cows. Within breed, cows in the content group had earlier C-LA (associated hazard ratio=2.0) than cows in the milk group. Body weight at calving and loss from wk 1 to 14 of lactation tended to be associated with later C-LA. Cows in the high feeding system produced more milk (+2,040 kg for the Holstein cows and +1,350 kg for Normande cows) and lost less BW from wk 1 to 14 of lactation (+3.8 kg/wk) than cows in the low feeding system. No effect of feeding system or milk yield was observed on C-LA. Prolonged luteal phases were frequent (18% of cows) and were not associated with either breed or genetic group. Ovarian cycles were longer for Holstein than for Normande cows (+1.7d) because of a longer luteal phase and a longer interluteal interval. Results of the study could be useful to establish strategies to manage declining reproductive performances at genetic and environmental levels. This study showed that cows with a genetic predisposition to export milk energy through fat and protein contents had earlier C-LA than predisposed to export milk energy through yield.

Genetic changes of survival traits over the past 25 yr in Dutch dairy cattle.

Genetic correlations and heritabilities for survival were investigated over a period of 25 yr to evaluate if survival in first lactation has become a different trait and if this is affected by adjusting for production level. Survival after first calving until 12mo after calving (surv_12mo) and survival of first lactation (surv_1st_lac) were analyzed in Dutch black-and-white cows. The data set contained 1,108,745 animals for surv_12mo and 1,062,276 animals for surv_1st_lac, with first calving between 1989 and 2013. The trait survival as recorded over 25 yr was split in five 5-yr intervals to enable a multitrait analysis. Bivariate models using subsets of the full data set and multitrait and autoregressive models using the full data set were used. Survival and functional survival were analyzed. Functional survival was defined as survival adjusted for within-herd production level for 305-d yield of combined kilograms of fat and protein. Mean survival increased over time, whereas genetic variances and heritability decreased. Bivariate models yielded large standard errors on genetic correlations due to poor connectedness between the extreme 5-yr intervals. The more parsimonious models using the full data set gave nonunity genetic correlations. Genetic correlations for survival were below 0.90 between intervals separated by 1 or more 5-yr intervals. Genetic correlations for functional survival did not indicate that definition of survival changed (≥0.90). The difference in genetic correlations between survival and functional survival is likely explained by lower emphasis of dairy farmers on culling in first lactation for low yield in more recent years. This suggests that genetic evaluation for longevity using historical data should analyze functional survival rather than survival.

Short communication: Improving accuracy of predicting breeding values in Brazilian Holstein population by adding data from Nordic and French Holstein populations.

The present study investigated the improvement of prediction reliabilities for 3 production traits in Brazilian Holsteins that had no genotype information by adding information from Nordic and French Holstein bulls that had genotypes. The estimated across-country genetic correlations (ranging from 0.604 to 0.726) indicated that an important genotype by environment interaction exists between Brazilian and Nordic (or Nordic and French) populations. Prediction reliabilities for Brazilian genotyped bulls were greatly increased by including data of Nordic and French bulls, and a 2-trait single-step genomic BLUP performed much better than the corresponding pedigree-based BLUP. However, only a minor improvement in prediction reliabilities was observed in nongenotyped Brazilian cows. The results indicate that although there is a large genotype by environment interaction, inclusion of a foreign reference population can improve accuracy of genetic evaluation for the Brazilian Holstein population. However, a Brazilian reference population is necessary to obtain a more accurate genomic evaluation.

Short communication: Genetic relationships between functional longevity and direct health traits in Austrian Fleckvieh cattle.

The aim of this study was to conduct a multitrait 2-step approach applied to yield deviations and deregressed breeding values to get genetic parameters of functional longevity, clinical mastitis, early fertility disorders, cystic ovaries, and milk fever of Austrian Fleckvieh cattle. An approximate multitrait approach allows the combination of information from pseudo-phenotypes derived from different statistical models in routine genetic evaluation, which cannot be estimated easily in a full multitrait model. A total of 66,890 Fleckvieh cows were included in this study. For estimating genetic parameters, a simple linear animal model with year of birth as a fixed effect and animal as a random genetic effect was fitted. The joint analysis of yield deviations and deregressed breeding values was feasible. As expected, heritabilities were low, ranging from 0.03 (early fertility disorders) to 0.15 (functional longevity). Genetic correlations between functional longevity and clinical mastitis, early fertility disorders, cystic ovaries, and milk fever were 0.63, 0.29, 0.20, and 0.20, respectively. Within direct health traits genetic correlations were between 0.14 and 0.45. Results suggest that selecting for more robust disease-resistant cows would imply an improvement of functional longevity.

Inbreeding and crossbreeding parameters for production and fertility traits in Holstein, Montbéliarde, and Normande cows.

Breed differences and nonadditive genetic effects for milk production traits, somatic cell score (SCS), conception rate (CR), and days to first service (DFS) were estimated for Holstein × Montbéliarde and Holstein × Normande crossbreds, using an animal model adapted from the French genetic evaluation and extended to across-breed analysis. Inbreeding and breed differences were estimated from all purebred recorded cows. Only records from 1,137 herds with Holstein × Montbéliarde crossbred cows and from 1,033 herds with Holstein × Normande crossbred cows were used to estimate crossbreeding parameters. In these herds, crossbred cows represented about 13% of the total number of recorded animals compared with <1% when all herds were considered. Compared with the Montbéliarde and Normande breeds, the Holstein breed was genetically superior for production [+951kg and +2,444kg for 305-d mature-equivalent (305ME) milk, +40kg and +102kg for 305ME fat, +17kg and +54kg for 305ME protein, respectively] and inferior for fertility traits (-12 and -9% for CR, respectively). Inbreeding depression caused loss of yield for production traits (from -32 to -41kg of 305ME milk, -1.4 to -1.7kg of 305ME fat, and -1.1 to -1.3kg of 305ME protein per inbreeding percentage), a small increase in SCS (+0.001 to 0.006) and DFS (+0.12d), and a decrease in CR (-0.27 to -0.44%). Favorable heterosis effects were found for all traits (+494 to 524kg of 305ME milk, +21 to 22kg of 305ME fat, +15 to 16kg of 305ME protein, -0.05 to -0.04 SCS, +2 to 3% for CR, and -3 to 6d of DFS), to such a point that F1 crossbreds could compete with Holstein cows for milk production while having a better fertility. However, recombination losses suggested that some F1 heterosis was lost for backcross cows.

Sustainable dairy cattle selection in the genomic era.

Genomic selection offers considerable flexibility to increase genetic trends in dairy cattle breeding. It is also an opportunity for more sustainable breeding, in terms of breeding goal and genetic variability. With a shorter generation interval, there is a big risk of increasing inbreeding if semen dissemination policy of elite bulls is not changed. However, using a large number of young bulls as service bulls and bull sires is a solution for both maximizing genetic trend while reducing inbreeding trend. Female genotyping is a key challenge for within-herd selection but, more importantly, for selection of new traits and for replacement of current reference populations based upon progeny-tested bulls. Genomic selection also opens new avenues for more customized breeding across herds or production systems. A big challenge is to reduce the dependency of genomic predictions on relationship between candidates and the reference population. A strong effort is presently dedicated to integrating genome sequence information into predictions, to improve their accuracy and persistency. In the longer term, further customization of selection will be possible by accounting for G × E interactions. Important developments are also necessary to decrease loss of favourable alleles through genetic drift.

Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population.

Single-breed genomic selection (GS) based on medium single nucleotide polymorphism (SNP) density (~50,000; 50K) is now routinely implemented in several large cattle breeds. However, building large enough reference populations remains a challenge for many medium or small breeds. The high-density BovineHD BeadChip (HD chip; Illumina Inc., San Diego, CA) containing 777,609 SNP developed in 2010 is characterized by short-distance linkage disequilibrium expected to be maintained across breeds. Therefore, combining reference populations can be envisioned. A population of 1,869 influential ancestors from 3 dairy breeds (Holstein, Montbéliarde, and Normande) was genotyped with the HD chip. Using this sample, 50K genotypes were imputed within breed to high-density genotypes, leading to a large HD reference population. This population was used to develop a multi-breed genomic evaluation. The goal of this paper was to investigate the gain of multi-breed genomic evaluation for a small breed. The advantage of using a large breed (Normande in the present study) to mimic a small breed is the large potential validation population to compare alternative genomic selection approaches more reliably. In the Normande breed, 3 training sets were defined with 1,597, 404, and 198 bulls, and a unique validation set included the 394 youngest bulls. For each training set, estimated breeding values (EBV) were computed using pedigree-based BLUP, single-breed BayesC, or multi-breed BayesC for which the reference population was formed by any of the Normande training data sets and 4,989 Holstein and 1,788 Montbéliarde bulls. Phenotypes were standardized by within-breed genetic standard deviation, the proportion of polygenic variance was set to 30%, and the estimated number of SNP with a nonzero effect was about 7,000. The 2 genomic selection (GS) approaches were performed using either the 50K or HD genotypes. The correlations between EBV and observed daughter yield deviations (DYD) were computed for 6 traits and using the different prediction approaches. Compared with pedigree-based BLUP, the average gain in accuracy with GS in small populations was 0.057 for the single-breed and 0.086 for multi-breed approach. This gain was up to 0.193 and 0.209, respectively, with the large reference population. Improvement of EBV prediction due to the multi-breed evaluation was higher for animals not closely related to the reference population. In the case of a breed with a small reference population size, the increase in correlation due to multi-breed GS was 0.141 for bulls without their sire in reference population compared with 0.016 for bulls with their sire in reference population. These results demonstrate that multi-breed GS can contribute to increase genomic evaluation accuracy in small breeds.

Review: Improving residual feed intake modelling in the context of nutritional- and genetic studies for dairy cattle.

The residual feed intake (RFI) model has recently gained popularity for ranking dairy cows for feed efficiency. The RFI model ranks the cows based on their expected feed intake compared to the observed feed intake, where a negative phenotype (eating less than expected) is favourable. Yet interpreting the biological implications of the regression coefficients derived from RFI models has proven challenging. In addition, multitrait modelling of RFI has been proposed as an alternative to the least square RFI in nutrition and genetic studies. To solve the challenge with the biological interpretation of RFI regression coefficients and suggest ways to improve the modelling of RFI, an interdisciplinary effort was required between nutritionists and geneticists. Therefore, this paper aimed to explore the challenges with the traditional least square RFI model and propose solutions to improve the modelling of RFI. In the traditional least square RFI model, one set of fixed effects is used to solve systematic effects (e.g., seasonal effects and age at calving) for traits with different means and variances. Thereby, measurement and model fitting errors can accumulate in the phenotype, resulting in undesirable effects. A multivariate RFI model will likely reduce this problem, as trait-specific fixed effects are used. In addition, regression coefficients for DM intake on milk energy tend to have more biologically meaningful estimates in multitrait RFI models, which indicates a confounding effect between the fixed effects and regression coefficients in the least square RFI model. However, defining precise expectations for regression coefficients from RFI models or sourcing for accurate feed norm coefficients seems difficult, especially if the coefficients are applied to a wide cattle population with varying diets or management systems, for example. To improve multitrait modelling of RFI, we suggest improving the modelling of changes in energy status. Furthermore, a novel method to derive the energy density of the diet and individual digestive efficiency is proposed. Digestive efficiency is defined as the part of the efficiency associated with digestive processes, which primarily reflects the conversion from gross energy to metabolisable energy. We show the model was insensitive to prior values of energy density in feed and that there was individual variation in digestive efficiency. The proposed method needs further development and validation. In summary, using multitrait RFI can improve the accuracy of the ranking of dairy cows' feed efficiency, consequently improving economic and environmental sustainability on dairy farms.

Comparison between sire-maternal grandsire and animal models for genetic evaluation of longevity in a dairy cattle population with small herds.

Survival analysis techniques for sire-maternal grandsire (MGS) and animal models were used to test the genetic evaluation of longevity in a Slovenian Brown cattle population characterized by small herds. Three genetic models were compared: a sire-MGS model for bulls and an approximate animal model based on estimated breeding values (EBV) from the sire-MGS model for cows, an animal model, and an animal model based on the estimated variance components from the sire-MGS model. In addition, modeling the contemporary group effect was defined as either a herd or a herd-year (HY) effect. With various restrictions on the minimum HY group size (from 1 to 10 cows per HY), changes in estimates of variance components, and consequently also in EBV, were observed for the sire-MGS and animal models. Variance of contemporary group effects decreased when an HY effect was fitted instead of a herd effect. In the case of a sire-MGS model, estimates of additive genetic variance were mostly robust to changes in minimum HY group size or fitting herd or HY effect, whereas they increased in the animal model when HY instead of herd effects was fitted, possibly revealing some confounding between cow EBV and contemporary group effect. Estimated heritabilities from sire-MGS models were between 0.091 and 0.119 and were mainly influenced by the restriction on the HY group size. Estimated heritabilities from animal models were higher: between 0.125 and 0.160 when herd effect was fitted and between 0.171 and 0.210 when HY effect was fitted. Rank correlations between the animal model and the approximate animal model based on EBV from the sire-MGS model were high: 0.94 for cows and 0.93 for sires when a herd effect was fitted and 0.90 for cows and 0.93 for sires when an HY effect was fitted. Validation performed on the independent validation data set revealed that the correlation between sire EBV and daughter survival were slightly higher with the approximate animal model based on EBV from the sire-MGS model compared with the animal model. The correlations between the sire EBV and daughter survival were higher when the model included an HY effect instead of a herd effect. To avoid confounding and reduce computational requirements, it is suggested that the approximate animal model based on EBV from the sire-MGS model and HY as a contemporary group effect is an interesting compromise for practical applications of genetic evaluation of longevity in cattle populations.