Combined single-step evaluation of functional longevity of dairy cows including correlated traits.

BACKGROUND: For years, multiple trait genetic evaluations have been used to increase the accuracy of estimated breeding values (EBV) using information from correlated traits. In France, accurate approximations of multiple trait evaluations were implemented for traits that are described by different models by combining the results of univariate best linear unbiased prediction (BLUP) evaluations. Functional longevity (FL) is the trait that has most benefited from this approach. Currently, with many single-step (SS) evaluations, only univariate FL evaluations can be run. The aim of this study was to implement a "combined" SS (CSS) evaluation that extends the "combined" BLUP evaluation to obtain more accurate genomic (G) EBV for FL when information from five correlated traits (somatic cell score, clinical mastitis, conception rate for heifers and cows, and udder depth) is added. RESULTS: GEBV obtained from univariate SS (USS) evaluations and from a CSS evaluation were compared. The correlations between these GEBV showed the benefits of including information from correlated traits. Indeed, a CSS evaluation run without any performances on FL showed that the indirect information from correlated traits to evaluate FL was substantial. USS and CSS evaluations that mimic SS evaluations with data available in 2016 were compared. For each evaluation separately, the GEBV were sorted and then split into 10 consecutive groups (deciles). Survival curves were calculated for each group, based on the observed productive life of these cows as known in 2021. Regardless of their genotyping status, the worst group of heifers based on their GEBV in 2016 was well identified in the CSS evaluation and they had a substantially shorter herd life, while those in the best heifer group had a longer herd life. The gaps between groups were more important for the genotyped than the ungenotyped heifers, which indicates better prediction of future survival. CONCLUSIONS: A CSS evaluation is an efficient tool to improve FL. It allows a proper combination of information on functional traits that influence culling. In contrast, because of the strong selection intensity on young bulls for functional traits, the benefit of such a "combined" evaluation of functional traits is more modest for these males.

Two approaches to account for genotype-by-environment interactions for production traits and age at first calving in South African Holstein cattle.

BACKGROUND: If not accounted for, genotype x environment (G×E) interactions can decrease the accuracy of genetic evaluations and the efficiency of breeding schemes. These interactions are reflected by genetic correlations between countries lower than 1. In countries that are characterized by a heterogeneity of production systems, they are also likely to exist within country, especially when production systems are diverse, as is the case in South Africa. We illustrate several alternative approaches to assess the existence of G×E interactions for production traits and age at first calving in Holsteins in South Africa. Data from 257,836 first lactation cows were used. First, phenotypes that were collected in different regions were considered as separate traits and various multivariate animal models were fitted to calculate the estimates of heritability for each region and the genetic correlations between them. Second, a random regression approach using long-term averages of climatic variables at the herd level in a reaction norm model, was used as an alternative way to account for G×E interactions. Genetic parameter estimates and goodness-of-fit measures were compared. RESULTS: Genetic correlations between regions as low as 0.80 or even lower were found for production traits, which reflect strong G×E interactions within South Africa that can be linked to the production systems (pasture vs total mixed ration). A random regression model including average rainfall during several decades in the herd surroundings gave the best goodness-of-fit for production traits. This can be related to a preference for total mixed ration on farms with limited rainfall. For age at first calving, the best model was based on a random regression on maximum relative humidity and maximum temperature in summer. CONCLUSIONS: Our results indicate that G×E interactions can be accounted for when genetic evaluations of production traits are performed in South Africa, by either considering production records in different regions as different correlated traits or using a reaction norm model based on herd management characteristics. From a statistical point of view, climatic variables such as average rainfall over a long period can be included in a random regression model as proxies of herd production systems and climate.

Genomic evaluation of milk yield in a smallholder crossbred dairy production system in India.

BACKGROUND: India is the largest milk producer globally, with the largest proportion of cattle milk production coming from smallholder farms with an average herd size of less than two milking cows. These cows are mainly undefined multi-generation crosses between exotic dairy breeds and indigenous Indian cattle, with no performance or pedigree recording. Therefore, implementing genetic improvement based on genetic evaluation has not yet been possible. We present the first results from a large smallholder performance recording program in India, using single nucleotide polymorphism (SNP) genotypes to estimate genetic parameters for monthly test-day (TD) milk records and to obtain and validate genomic estimated breeding values (GEBV). RESULTS: The average TD milk yield under the high, medium, and low production environments were 9.64, 6.88, and 4.61 kg, respectively. In the high production environment, the usual profile of a lactation curve was evident, whereas it was less evident in low and medium production environments. There was a clear trend of an increasing milk yield with an increasing Holstein Friesian (HF) proportion in the high production environment, but no increase above intermediate grades in the medium and low production environments. Trends for Jersey were small but yield estimates had a higher standard error than HF. Heritability estimates for TD yield across the lactation ranged from 0.193 to 0.250, with an average of 0.230. The additive genetic correlations between TD yield at different times in lactation were high, ranging from 0.846 to 0.998. The accuracy of phenotypic validation of GEBV from the method that is believed to be the least biased was 0.420, which was very similar to the accuracy obtained from the average prediction error variance of the GEBV. CONCLUSIONS: The results indicate strong potential for genomic selection to improve milk production of smallholder crossbred cows in India. The performance of cows with different breed compositions can be determined in different Indian environments, which makes it possible to provide better advice to smallholder farmers on optimum breed composition for their environment.

Invited review: Disentangling residual feed intake-Insights and approaches to make it more fit for purpose in the modern context.

Residual feed intake (RFI) is an increasingly used trait to analyze feed efficiency in livestock, and in some sectors such as dairy cattle, it is one of the most frequently used traits. Although the principle for calculating RFI is always the same (i.e., using the residual of a regression of intake on performance predictors), a wide range of models are found in the literature, with different predictors, different ways of considering intake, and more recently, different statistical approaches. Consequently, the results are not easily comparable from one study to another as they reflect different biological variabilities, and the relationship between the residual (i.e., RFI) and the underlying true efficiency also differs. In this review, the components of the RFI equation are explored with respect to the underlying biological processes. The aim of this decomposition is to provide a better understanding of which of the processes in this complex trait contribute significantly to the individual variability in efficiency. The intricacies associated with the residual term, as well as the energy sinks and the intake term, are broken down and discussed. Based on this exploration as well as on some recent literature, new forms of the RFI equation are proposed to better separate the efficiency terms from errors and inaccuracies. The review also considers the time period of measurement of RFI. This is a key consideration for the accuracy of the RFI estimation itself, and also for understanding the relationships between short-term efficiency, animal resilience, and long-term efficiency. As livestock production moves toward sustainable efficiency, these considerations are increasingly important to bring to bear in RFI estimations.

Semen production and semen quality of indigenous buffalo breeds under hot semiarid climatic conditions in India.

Semen data comprising of 97,023 ejaculates of 197 bulls from 6 buffalo breeds were analyzed. The traits considered were as follows: ejaculate volume, sperm concentration, mass activity, initial and post-thaw motility, total sperm, and total motile sperm before and after thawing as well as a composite trait equal to the theoretical number of doses which can be produced from each given ejaculate. The objective was to measure the semen production potential of indigenous buffalo bulls and identify factors affecting these traits. A linear mixed model was used, including a random bull effect along with other fixed factors: the order of the ejaculate on a particular day, the interval between collections, the time of collection, the breed, the age at collection, the semen collector, and the year and month of collection. The study showed breed wise variation for all traits. The first ejaculate of a bull on a particular day was superior to the second for nearly all the traits. Longer collection intervals are better than shorter intervals for all the parameters, although short collection interval of 2 to 4 days produced higher yield in terms of total semen doses without hampering semen quality. The study also showed a slight decrease of semen quality with time of collection within a day. The Murrah breed showed comparatively consistent performance during their whole life compared with the other breeds. Repeatability estimates for semen traits were found to be low (0.09 for mass activity) to relatively large (> 0.4 for volume and concentration). A negative correlation was found between bull effects for semen volume and concentration while a high positive correlation was found between mass activity, initial motility, and post-thaw motility. Results of the study will help in suggesting suitable management and breeding plans for semen production traits.

Use of sexed semen and female genotyping affects genetic and economic outcomes of Montbéliarde dairy herds depending on the farming system considered.

Genomic evaluation of cows and the use of sexed semen have recently provided opportunities for commercial dairy farmers to accelerate genetic progress at the herd level by increasing both selection accuracy and selection intensity. Because implementing genomic tests or using sexed semen generate extra costs, a higher investment capacity of the farm is required. In this study, we compared the effect of female genotyping alone or combined with the use of sexed semen on genetic and economic performance of the herds. Three typical Montbéliarde herds with different farming systems were considered: a 77-cow herd producing milk at a high price sold to make cheese with a protected designation of origin, a 60-cow herd producing organic milk at a medium price sold for dairy, and a 120-cow herd producing standard milk at a lower price sold for dairy. Eight alternative scenarios were simulated over a 10-yr period for each herd, with combinations of the following: use (or not) of dairy sexed semen, use (or not) of beef breed semen, use (or not) of female genotyping at 15 d of age. A mechanistic, stochastic, and dynamic model was used to mimic the farmer's daily decisions and the individual cow's biology. Heifers (80%) and first-lactation cows (30%) that ranked highest on the French total merit index (France's national dairy index) were inseminated with sexed semen to ensure replacement and to maximize genetic gain, when sexed semen was used. During the 10 yr of simulation, scenarios that included sexed semen (whether female genotyping was used or not) gained, on average, one extra year of overall genetic gain over scenarios that did not include sexed semen. During the same period, scenarios that used female genotyping (whether sexed semen was used or not) gained, on average, 5 mo of overall genetic gain over scenarios using parent average only. The highest gains in net margin were always obtained when combining use of sexed semen with terminal crossbreeding. Maximum genotyping prices under which routine female genotyping is economically valuable (breakeven prices of genotyping) were under €37. Maximum genotyping prices, such that the female genotyping costs are refunded within 10 yr of investment (investor genotyping price), were under €26. However, they would be higher over a longer period of use because genetic gain is cumulative. Because genotyping price is expected to decrease in the future, female genotyping will be worthwhile if combined with the use of sexed semen and beef breed semen.

Genetic parameters for first lactation dairy traits in the Alpine and Saanen goat breeds using a random regression test-day model.

BACKGROUND: Random regression models (RRM) are widely used to analyze longitudinal data in genetic evaluation systems because they can better account for time-course changes in environmental effects and additive genetic values of animals by fitting the test-day (TD) specific effects. Our objective was to implement a random regression model for the evaluation of dairy production traits in French goats. RESULTS: The data consisted of milk TD records from 30,186 and 32,256 first lactations of Saanen and Alpine goats. Milk yield, fat yield, protein yield, fat content and protein content were considered. Splines were used to model the environmental factors. The genetic and permanent environmental effects were modeled by the same Legendre polynomials. The goodness-of-fit and the genetic parameters derived from functions of the polynomials of orders 0 to 4 were tested. Results were also compared to those from a lactation model with total milk yield calculated over 250 days and to those of a multiple-trait model that considers performance in six periods throughout lactation as different traits. Genetic parameters were consistent between models. Models with fourth-order Legendre polynomials led to the best fit of the data. In order to reduce complexity, computing time, and interpretation, a rank reduction of the variance covariance matrix was performed using eigenvalue decomposition. With a reduction to rank 2, the first two principal components correctly summarized the genetic variability of milk yield level and persistency, with a correlation close to 0 between them. CONCLUSIONS: A random regression model was implemented in France to evaluate and select goats for yield traits and persistency, which are independent i.e. no genetic correlation between them, in first lactation.

Genomic analysis of claw lesions in Holstein cows: Opportunities for genomic selection, quantitative trait locus detection, and gene identification.

Claw lesions are the third most important health issue in dairy cattle, after mastitis and reproductive disorders, and genomic selection is a key component for long-term improvement of claw health. The objectives of this study were to assess the feasibility of a genomic evaluation for claw health in French Holstein cows, explore possibilities to increase evaluation accuracy, and gain a better understanding of the genetic determinism of claw health traits. The data set consisted of 48,685 trimmed Holstein cows, including 9,646 that were genotyped; 478 genotyped sires were also used. Seven claw lesion traits were evaluated using BLUP, genomic BLUP, BayesC, and single-step genomic BLUP, and the accuracies obtained using these approaches were measured through a validation study. The BayesC approach was used to detect quantitative trait locus (QTL) regions associated with the 7 individual traits (digital dermatitis, heel horn erosion, interdigital hyperplasia, sole hemorrhage circumscribed, sole hemorrhage diffused, sole ulcer, and white line fissure) based on their Bayes factor. Annotated genes on these regions were reported. Genomic evaluation approaches generally did not allow for greater accuracies than BLUP, except for single-step genomic BLUP. Accuracies were moderate, but best and worst validation animals were correctly discriminated and showed significant differences in lesion frequencies. A total of 192 QTL regions were identified, including 13 with major evidence or involved for 2 of the traits. A high number of genes were present on these regions, and several had functions associated with the immune system. In particular, the EPYC gene is located close to a major evidence QTL for resistance to digital dermatitis that is also a QTL for interdigital hyperplasia (on chromosome 5, around 20.9 MB) and has been associated with Ehlers-Danlos syndrome in cattle. Genomic selection can be used to improve resistance to individual claw lesions, and several possibilities exist to improve accuracies of genomic evaluations.

Genomics for Ruminants in Developing Countries: From Principles to Practice.

Using genomic information, local ruminant populations can be better characterized and compared to selected ones. Genetic relationships between animals can be established even without systematic pedigree recording, provided a budget is available for genotyping. Genomic selection (GS) can rely on a subset of the total population and does not require a costly national infrastructure, e.g., based on progeny testing. Yet, the use of genomic tools for animal breeding in developing countries is still limited. We identify three main reasons for this: (i) the instruments for cheap recording of phenotypes and data management are still limiting. (ii) many developing countries are recurrently exposed to unfavorable conditions (heat, diseases, poor nutrition) requiring special attention to fitness traits, (iii) a high level of expertise in quantitative genetics, modeling, and data manipulation is needed to perform genomic analyses. Yet, the potential outcomes go much beyond genetic improvements and can improve the resilience of the whole farming system. They include a better management of genetic diversity of local populations, a more balanced genetic progress and the possibility to unravel the genetic basis of adaptation of local breeds through whole genome approaches. A GS program being developed by BAIF, a large Indian NGO, is analyzed as a pilot case. It relies on the creation of a female reference population of Bos indicus and crossbreds, recorded with modern technology (e.g., smartphones) to collect performances at low cost in tiny herds on production and fertility. Finally, recommendations for the implementation of GS in developing countries are proposed.

Genetic evaluation of claw health traits accounting for potential preselection of cows to be trimmed.

Claw lesions are one of the most important health issues in dairy cattle. Although the frequency of claw lesions depends greatly on herd management, the frequency can be lowered through genetic selection. A genetic evaluation could be developed based on trimming records collected by claw trimmers; however, not all cows present in a herd are usually selected by the breeder to be trimmed. The objectives of this study were to investigate the importance of the preselection of cows for trimming, to account for this preselection, and to estimate genetic parameters of claw health traits. The final data set contained 25,511 trimming records of French Holstein cows. Analyzed claw lesion traits were digital dermatitis, heel horn erosion, interdigital hyperplasia, sole hemorrhage circumscribed, sole hemorrhage diffused, sole ulcer, and white line fissure. All traits were analyzed as binary traits in a multitrait linear animal model. Three scenarios were considered: including only trimmed cows in a 7-trait model (scenario 1); or trimmed cows and contemporary cows not trimmed but present at the time of a visit (considering that nontrimmed cows were healthy) in a 7-trait model (scenario 2); or trimmed cows and contemporary cows not trimmed but present at the time of a visit (considering lesion records for trimmed cows only), in an 8-trait model, including a 0/1 trimming status trait (scenario 3). For scenario 3, heritability estimates ranged from 0.02 to 0.09 on the observed scale. Genetic correlations clearly revealed 2 groups of traits (digital dermatitis, heel horn erosion, and interdigital hyperplasia on the one hand, and sole hemorrhage circumscribed, sole hemorrhage diffused, sole ulcer, and white line fissure on the other hand). Heritabilities on the underlying scale did not vary much depending on the scenario: the effect of the preselection of cows for trimming on the estimation of heritabilities appeared to be negligible. However, including untrimmed cows as healthy caused bias in the estimation of genetic correlations. The use of a trimming status trait to account for preselection appears promising, as it allows consideration of the exhaustive population of cows present at the time a trimmer visited a farm without causing bias in genetic parameters.

Short communication: The combined use of linkage disequilibrium-based haploblocks and allele frequency-based haplotype selection methods enhances genomic evaluation accuracy in dairy cattle.

The construction and use of haploblocks [adjacent single nucleotide polymorphisms (SNP) in strong linkage disequilibrium] for genomic evaluation is advantageous, because the number of effects to be estimated can be reduced without discarding relevant genomic information. Furthermore, haplotypes (the combination of 2 or more SNP) can increase the probability of capturing the quantitative trait loci effect compared with individual SNP markers. With regards to haplotypes, the allele frequency parameter is also of interest, because as a selection criterion, it allows the number of rare alleles to be reduced, and the effects of those alleles are usually difficult to estimate. We have proposed a simple pipeline that simultaneously incorporates linkage disequilibrium and allele frequency information in genomic evaluation, and here we present the first results obtained with this procedure. We used a population of 2,235 progeny-tested bulls from the Montbéliarde breed for the tests. Phenotype data were available in the form of daughter yield deviations on 5 production traits, and genotype data were available from the 50K SNP chip. We conducted a classical validation study by splitting the population into training (80% oldest animals) and validation (20% youngest animals) sets to emulate a real-life scenario in which the selection candidates had no available phenotype data. We measured all reported parameters for the validation set. Our results proved that the proposed method was indeed advantageous, and that the accuracy of genomic evaluation could be improved. Compared with results from a genomic BLUP analysis, correlations between daughter yield deviations (a proxy for true) and genomic estimated breeding values increased by an average of 2.7 percentage points for the 5 traits. Inflation of the genomic evaluation of the selection candidates was also significantly reduced. The proposed method outperformed the other SNP and haplotype-based tests we had evaluated in a previous study. The combination of linkage disequilibrium-based haploblocks and allele frequency-based haplotype selection methods is a promising way to improve the efficiency of genomic evaluation. Further work is needed to optimize each step in the proposed analysis pipeline.

Genomic selection in domestic animals: Principles, applications and perspectives.

The principles of genomic selection are described, with the main factors affecting its efficiency and the assumptions underlying the different models proposed. The reasons of its fast adoption in dairy cattle are explained and the conditions of its application to other species are discussed. Perspectives of development include: selection for new traits and new breeding objectives; adoption of more robust approaches based on information on causal variants; predictions of genotype×environment interactions.

Alternative haplotype construction methods for genomic evaluation.

Genomic evaluation methods today use single nucleotide polymorphism (SNP) as genomic markers to trace quantitative trait loci (QTL). Today most genomic prediction procedures use biallelic SNP markers. However, SNP can be combined into short, multiallelic haplotypes that can improve genomic prediction due to higher linkage disequilibrium between the haplotypes and the linked QTL. The aim of this study was to develop a method to identify the haplotypes, which can be expected to be superior in genomic evaluation, as compared with either SNP or other haplotypes of the same size. We first identified the SNP (termed as QTL-SNP) from the bovine 50K SNP chip that had the largest effect on the analyzed trait. It was assumed that these SNP were not the causative mutations and they merely indicated the approximate location of the QTL. Haplotypes of 3, 4, or 5 SNP were selected from short genomic windows surrounding these markers to capture the effect of the QTL. Two methods described in this paper aim at selecting the most optimal haplotype for genomic evaluation. They assumed that if an allele has a high frequency, its allele effect can be accurately predicted. These methods were tested in a classical validation study using a dairy cattle population of 2,235 bulls with genotypes from the bovine 50K SNP chip and daughter yield deviations (DYD) on 5 dairy cattle production traits. Combining the SNP into haplotypes was beneficial with all tested haplotypes, leading to an average increase of 2% in terms of correlations between DYD and genomic breeding value estimates compared with the analysis when the same SNP were used individually. Compared with haplotypes built by merging the QTL-SNP with its flanking SNP, the haplotypes selected with the proposed criteria carried less under- and over-represented alleles: the proportion of alleles with frequencies <1 or >40% decreased, on average, by 17.4 and 43.4%, respectively. The correlations between DYD and genomic breeding value estimates increased by 0.7 to 0.9 percentage points when the haplotypes were selected using any of the proposed methods compared with using the haplotypes built from the QTL-SNP and its flanking markers. We showed that the efficiency of genomic prediction could be improved at no extra costs, only by selecting the proper markers or combinations of markers for genomic prediction. One of the presented approaches was implemented in the new genomic evaluation procedure applied in dairy cattle in France in April 2015.

Error rate for imputation from the Illumina BovineSNP50 chip to the Illumina BovineHD chip.

BACKGROUND: Imputation of genotypes from low-density to higher density chips is a cost-effective method to obtain high-density genotypes for many animals, based on genotypes of only a relatively small subset of animals (reference population) on the high-density chip. Several factors influence the accuracy of imputation and our objective was to investigate the effects of the size of the reference population used for imputation and of the imputation method used and its parameters. Imputation of genotypes was carried out from 50,000 (moderate-density) to 777,000 (high-density) SNPs (single nucleotide polymorphisms). METHODS: The effect of reference population size was studied in two datasets: one with 548 and one with 1289 Holstein animals, genotyped with the Illumina BovineHD chip (777 k SNPs). A third dataset included the 548 animals genotyped with the 777 k SNP chip and 2200 animals genotyped with the Illumina BovineSNP50 chip. In each dataset, 60 animals were chosen as validation animals, for which all high-density genotypes were masked, except for the Illumina BovineSNP50 markers. Imputation was studied in a subset of six chromosomes, using the imputation software programs Beagle and DAGPHASE. RESULTS: Imputation with DAGPHASE and Beagle resulted in 1.91% and 0.87% allelic imputation error rates in the dataset with 548 high-density genotypes, when scale and shift parameters were 2.0 and 0.1, and 1.0 and 0.0, respectively. When Beagle was used alone, the imputation error rate was 0.67%. If the information obtained by Beagle was subsequently used in DAGPHASE, imputation error rates were slightly higher (0.71%). When 2200 moderate-density genotypes were added and Beagle was used alone, imputation error rates were slightly lower (0.64%). The least imputation errors were obtained with Beagle in the reference set with 1289 high-density genotypes (0.41%). CONCLUSIONS: For imputation of genotypes from the 50 k to the 777 k SNP chip, Beagle gave the lowest allelic imputation error rates. Imputation error rates decreased with increasing size of the reference population. For applications for which computing time is limiting, DAGPHASE using information from Beagle can be considered as an alternative, since it reduces computation time and increases imputation error rates only slightly.

High-density marker imputation accuracy in sixteen French cattle breeds.

BACKGROUND: Genotyping with the medium-density Bovine SNP50 BeadChip® (50K) is now standard in cattle. The high-density BovineHD BeadChip®, which contains 777,609 single nucleotide polymorphisms (SNPs), was developed in 2010. Increasing marker density increases the level of linkage disequilibrium between quantitative trait loci (QTL) and SNPs and the accuracy of QTL localization and genomic selection. However, re-genotyping all animals with the high-density chip is not economically feasible. An alternative strategy is to genotype part of the animals with the high-density chip and to impute high-density genotypes for animals already genotyped with the 50K chip. Thus, it is necessary to investigate the error rate when imputing from the 50K to the high-density chip. METHODS: Five thousand one hundred and fifty three animals from 16 breeds (89 to 788 per breed) were genotyped with the high-density chip. Imputation error rates from the 50K to the high-density chip were computed for each breed with a validation set that included the 20% youngest animals. Marker genotypes were masked for animals in the validation population in order to mimic 50K genotypes. Imputation was carried out using the Beagle 3.3.0 software. RESULTS: Mean allele imputation error rates ranged from 0.31% to 2.41% depending on the breed. In total, 1980 SNPs had high imputation error rates in several breeds, which is probably due to genome assembly errors, and we recommend to discard these in future studies. Differences in imputation accuracy between breeds were related to the high-density-genotyped sample size and to the genetic relationship between reference and validation populations, whereas differences in effective population size and level of linkage disequilibrium showed limited effects. Accordingly, imputation accuracy was higher in breeds with large populations and in dairy breeds than in beef breeds. More than 99% of the alleles were correctly imputed if more than 300 animals were genotyped at high-density. No improvement was observed when multi-breed imputation was performed. CONCLUSION: In all breeds, imputation accuracy was higher than 97%, which indicates that imputation to the high-density chip was accurate. Imputation accuracy depends mainly on the size of the reference population and the relationship between reference and target populations.

Effects of a national genomic preselection on the international genetic evaluations.

Genomic preselection of young bulls is now widely implemented in dairy breeding schemes, especially in the Holstein breed. However, if this step is not accounted for in genetic evaluation models, the national breeding values of bulls retained by a genomic preselection and of their progeny are estimated with bias. It follows that countries participating in international genetic evaluations will provide a selected and possibly biased set of data to the Interbull Centre (Swedish University of Agricultural Sciences, Uppsala, Sweden). The objective of the study was to show evidence of bias at the international level due to a genomic preselection step in national breeding schemes. The consequence of a genomic preselection for the international evaluations (i.e., using selected and biased national estimated breeding values) was simulated using actual national estimated breeding values as a proxy for genomically enhanced breeding values. Data were provided for 3 countries with a large population of Holstein bulls. International breeding values from simulated scenarios were compared with international breeding values using all available data, assumed to be complete and unbiased. Bias was measured among young bulls retained by a genomic preselection and their contemporaries in other countries. The results were analyzed by traits measured within each country and by country of origin of the young bulls. It turned out that sending preselected data, though based on genomic information, created bias in international evaluations, penalizing young bulls from the country sending the incorrect data. It also had an effect on the young bulls from the other countries. Sending biased data further affected the quality of international evaluations. This study underlines the importance of accounting for genomic preselection at the national level first. Moreover, submitting all available data appeared essential to maintain the quality of the international genetic evaluations after implementation of a genomic preselection step.

Inclusion of cow records in genomic evaluations and impact on bias due to preferential treatment.

BACKGROUND: Today, genomic evaluations are an essential feature of dairy cattle breeding. Initially, genomic evaluation targeted young bulls but recently, a rapidly increasing number of females (both heifers and cows) are being genotyped. A rising issue is whether and how own performance of genotyped cows should be included in genomic evaluations. The purpose of this study was to assess the impact of including yield deviations, i.e. own performance of cows, in genomic evaluations. METHODS: Two different genomic evaluations were performed: one including only reliable daughter yield deviations of proven bulls based on their non-genotyped daughters, and one including both daughter yield deviations for males and own yield deviations for genotyped females. Milk yield, the trait most prone to preferential treatment, and somatic cell count, for which such a bias is very unlikely, were studied. Data consisted of two groups of animals from the three main dairy breeds in France: 11 884 elite females genotyped by breeding companies and 7032 cows genotyped for a research project (and considered as randomly selected from the commercial population). RESULTS: For several measures that could be related to preferential treatment bias, the elite group presented a different pattern of estimated breeding values for milk yield compared to the other combinations of trait and group: for instance, for milk yield, the average difference between estimated breeding values with or without own yield deviations was significantly different from 0 for this group. Correlations between estimated breeding values with or without yield deviations were lower for elite females than for randomly selected cows for milk yield but were very similar for somatic cell count. CONCLUSIONS: This study demonstrated that including own milk performance of elite females leads to biased (over-estimated) genomic evaluations. Thus, milk production records of elite cows require specific treatment in genomic evaluation.

Modelling and estimation of genotype by environment interactions for production traits in French dairy cattle.

BACKGROUND: Genotype by environment interactions are currently ignored in national genetic evaluations of dairy cattle. However, this is often questioned, especially when environment or herd management is wide-ranging. The aim of this study was to assess genotype by environment interactions for production traits (milk, protein, fat yields and fat and protein contents) in French dairy cattle using an original approach to characterize the environments. METHODS: Genetic parameters of production traits were estimated for three breeds (Holstein, Normande and Montbéliarde) using multiple-trait and reaction norm models. Variables derived from Herd Test Day profiles obtained after a test day model evaluation were used to define herd environment. RESULTS: Multiple-trait and reaction norm models gave similar results. Genetic correlations were very close to unity for all traits, except between some extreme environments. However, a relatively wide range of heritabilities by trait and breed was found across environments. This was more the case for milk, protein and fat yields than for protein and fat contents. CONCLUSIONS: No real reranking of animals was observed across environments. However, a significant scale effect exists: the more intensive the herd management for milk yield, the larger the heritability.

Genetic resistance to rhabdovirus infection in teleost fish is paralleled to the derived cell resistance status.

Genetic factors of resistance and predisposition to viral diseases explain a significant part of the clinical variability observed within host populations. Predisposition to viral diseases has been associated to MHC haplotypes and T cell immunity, but a growing repertoire of innate/intrinsic factors are implicated in the genetic determinism of the host susceptibility to viruses. In a long-term study of the genetics of host resistance to fish rhabdoviruses, we produced a collection of double-haploid rainbow trout clones showing a wide range of susceptibility to Viral Hemorrhagic Septicemia Virus (VHSV) waterborne infection. The susceptibility of fibroblastic cell lines derived from these clonal fish was fully consistent with the susceptibility of the parental fish clones. The mechanisms determining the host resistance therefore did not associate with specific host immunity, but rather with innate or intrinsic factors. One cell line was resistant to rhabdovirus infection due to the combination of an early interferon IFN induction--that was not observed in the susceptible cells--and of yet unknown factors that hamper the first steps of the viral cycle. The implication of IFN was well consistent with the wide range of resistance of this genetic background to VSHV and IHNV, to the birnavirus IPNV and the orthomyxovirus ISAV. Another cell line was even more refractory to the VHSV infection through different antiviral mechanisms. This collection of clonal fish and isogenic cell lines provides an interesting model to analyze the relative contribution of antiviral pathways to the resistance to different viruses.

A common reference population from four European Holstein populations increases reliability of genomic predictions.

BACKGROUND: Size of the reference population and reliability of phenotypes are crucial factors influencing the reliability of genomic predictions. It is therefore useful to combine closely related populations. Increased accuracies of genomic predictions depend on the number of individuals added to the reference population, the reliability of their phenotypes, and the relatedness of the populations that are combined. METHODS: This paper assesses the increase in reliability achieved when combining four Holstein reference populations of 4000 bulls each, from European breeding organizations, i.e. UNCEIA (France), VikingGenetics (Denmark, Sweden, Finland), DHV-VIT (Germany) and CRV (The Netherlands, Flanders). Each partner validated its own bulls using their national reference data and the combined data, respectively. RESULTS: Combining the data significantly increased the reliability of genomic predictions for bulls in all four populations. Reliabilities increased by 10%, compared to reliabilities obtained with national reference populations alone, when they were averaged over countries and the traits evaluated. For different traits and countries, the increase in reliability ranged from 2% to 19%. CONCLUSIONS: Genomic selection programs benefit greatly from combining data from several closely related populations into a single large reference population.