Jeremie Vandenplas

Fertility is known to be negatively affected by heat stress. Our aim was to evaluate the impact of heat stress on estimated breeding values for conception rate (CR) for Holstein cattle in the Netherlands, by modeling sire breeding values for CR within each parity as a function of THI, using a broken stick random regression model. Our results showed a clear reduction in average CR at THI levels of 70 of up to 15% for parities 1 through 3, starting after a THI threshold of 60, while no clear reduction in CR was observed for parity 0. The broken stick model estimated 2 breeding values for each sire, based on information of daughters experiencing different THI levels at the time of insemination. The first breeding value modeled the intercept, i.e., the baseline breeding value at THI values below the identified threshold, while the second breeding value modeled the slope, which reflects the change in breeding value with changes in THI above the threshold. The results revealed a clear pattern of increased genetic variance and heritability at higher THI values for parities 1 through 3, while the random regression was not significant for parity 0. Across parities 1 through 3, estimated genetic correlations between CR at THI values of 60 and 65 ranged from 0.93 to 0.97, and between THI values of 60 and 70 ranged from 0.79 to 0.88. This suggested some meaningful re-ranking between THI 60 and 70, while selection at THI values below 60 should still improve CR at high THI. In line with previously reported genetic correlations, CR in maiden heifers is clearly different from CR in lactating cows. For lactating cows, a high genetic correlation (between 0.88 and 0.96) was found between the intercepts in the different parities. Genetic correlations between the slopes suggested that the genetic component of heat stress for CR is most similar between parities 2 and 3, and between parities 1 and 2, but quite different between parities 1 and 3.

During the last decade, there has been a growing interest for local and endangered breeds as they are often seen as more resilient and healthier than mainstream breeds. They can also provide high added‐value products like meat or dairy products. To better preserve these breeds, it is of main importance to characterize their genetic diversity and have an insight of historical gene flow with more mainstream breeds. In this study, we focused on the genetic relationships of five red‐pied cattle breeds: the east Belgian red and white (EBRW); the red‐pied of the Ösling (RPO), from Luxembourg; the deep red (DR) and the Meuse‐Rhine‐Yssel (MRY), both from the Netherlands; and the German red and white dual purpose (RDN). The EBRW, RPO and DR breeds have an official European endangered status. We first investigated the pedigree completeness of available genotyped animals as well as their complex historical relationships through the analysis of common ancestors. We also compared pedigree and SNP‐based inbreeding coefficients, defined as the sum of homozygosity‐by‐descent segments (HBD). We then dived into genomic relationships through a classical multi‐dimensional scaling (MDS) of genotyped animals and their admixture. The pedigree analyses showed the complex gene flow between all breeds and that the RPO breed was the most connected to other breeds. Results also showed that the level of inbreeding was so far not an issue in all five breeds even if some animals, for example, in EBRW and MRY breeds, showed higher inbreeding levels than the average of their breeds. Finally, the MDS and admixture analysis also highlighted complex gene flow between the studied breeds and that they may be considered as a genetic continuum. This genomic proximity has the potential to improve genetic evaluations of local breeds by the inclusion of information from more mainstream breeds like MRY and RDN.

Genomic selection (GS) has revolutionized animal breeding and accelerated genetic gains in breeding programs. Although GS has become common in cosmopolitan dairy cattle breeds, its implementation in local breeds has begun only more recently, or is still in progress. However, the introduction of GS in some cosmopolitan breeds has also been associated with increased inbreeding rates, raising concerns about the potential effects of GS on genetic diversity in smaller or local breeds. Our aim was to investigate the impact of GS on genetic diversity in 5 (small) local cattle breeds from 3 European countries: Meuse Rhine Yssel (MRY; from the Netherlands); Norwegian Red (Norsk Rødt Fe, NRF; from Norway); and Abondance (ABO; from France), Tarentaise (TAR; from France), and Vosgienne (VOS; from France). We investigated changes in population demographic structure, as well as trends and rates of kinship and inbreeding, using both pedigree- and genomic-based measures. The population size varied depending on the breed, with Vosgienne being the smallest and NRF being the largest. Single nucleotide polymorphism genotypes were available for 4,645 MRY, 193,489 NRF, 16,387 ABO, 8,578 TAR, and 4,472 VOS animals. Animals were genotyped with more than 40,000 SNPs. Overall, following the implementation of GS in these breeds, we observed a reduction of up to 4 years in generation intervals for sires, fewer calves that later became sires, and, for the French breeds, a broader sire usage. Such changes were likely due to GS enabling the preselection and screening of more young bulls. Additionally, the contributions of the top 10 sires were more evenly distributed after the introduction of GS. Although changes in inbreeding and kinship rates occurred after the introduction of GS, we found no consistent pattern across breeds: pedigree (and genomic runs of homozygosity [ROH]-based) inbreeding rates per generation increased in MRY from -0.67 before GS to 0.51 after GS (from -1.12 to 0.93) and TAR from 0.35 to 0.93 (from 0.68 to 0.86), but decreased in NRF from 0.26 to 0.05 (from 0.10 to 0.06), Abondance from 1.19 to 0.99 (from 2.39 to 0.58), and Vosgienne from 0.53 to 0.23 (from 0.88 to 0.19). Moreover, analysis of genomic ROH-based inbreeding by length class showed that after the implementation of GS, the largest changes in inbreeding level and inbreeding rates per generation occurred for shorter ROH segments. Our study suggests that changes and increases in inbreeding rates may occur after the introduction of GS, although they may not be directly due to the introduction of GS per se, but rather due to population management strategies, such as optimal contribution selection. Our findings emphasize the importance of monitoring changes in both genetic diversity and population demographic structure after implementing GS in local breeds, as well as adjusting breeding strategies when needed to ensure long-term sustainability.

Heat stress is a major environmental challenge affecting dairy cattle, leading to behavioral changes, production losses, and welfare concerns. As heat stress events intensify and become more frequent due to climate change, identifying animals that are able to maintain high production levels during heat stress events, commonly referred to as heat tolerance, is crucial for sustainable dairy production. In this study, we develop a pipeline to quantify the population-wise impact of heat stress on a dairy cattle population and subsequently define individual-based heat tolerance traits. Data from 677,318 Dutch Holstein cows were analyzed, including 15.6 million mid-infrared spectra and 762 million records from automated milking systems. An iterative approach using kernel regression was employed to estimate the population-wise effects of heat stress. Results indicate that fat and protein percentages decrease approximately linearly with increasing temperature-humidity index (THI), with an absolute reduction of 0.3% as THI increases from 30 to 70. In contrast, milk yield remains stable until a THI of 60, after which production losses increase quadratically, reaching a loss of 5.0% at a THI of 75. We subsequently define the heat tolerance phenotype of an animal as the slope from a linear regression model of the residuals of the population-wise models against THI for milk yield, concentration of fat, protein, lactose, and specific fatty acids. Compared with reaction-norm models, individual records per cow are combined into one joint record before model fitting, thus reducing computing times and allowing more flexibility in the design of the model. Heritabilities for heat tolerance traits ranged from 0.05 to 0.12, and genetic variances indicate substantial potential for breeding as an improvement of the population by 1 genetic standard deviation would already offset 69% of the losses in fat percentage, 65% in protein percentage, and 11% in milk yield. Heat tolerance based on milk yield showed favorable correlations with most commercial traits, whereas heat tolerance based on fat and protein percentage showed negative correlations with health and resilience. A GWAS using both SNPs and haplotype blocks from the software HaploBlocker identified potential QTL across the genome, with particularly strong signals on BTA5, 14, and 20. These findings support the long-term potential of genetic improvement through breeding for heat tolerance but highlight the need for complementary management strategies to mitigate heat stress impacts in the short term.

Heat stress is a major environmental challenge affecting dairy cattle, leading to behavioral changes, production losses, and welfare concerns. As heat stress events intensify and become more frequent due to climate change, identifying heat tolerant animals is crucial for sustainable dairy production. This study develops a pipeline to quantify the population-wise impact of heat stress on a dairy cattle population and subsequently defines individual-based heat tolerance traits. Data from 677,318 Dutch Holstein cows, including 15.6 million mid-infrared spectra and 762 million records from automated milking systems, were analyzed. An iterative approach using kernel regression was employed to estimate the population-wise effects of heat stress. Results indicate that fat and protein percentages decrease approximately linearly with increasing temperature humidity index (THI) with an absolute reduction of 0.3% from THI = 30 to THI = 70. In contrast, milk yield remains stable until a THI of 60, after which production losses increase quadratically reaching 5.0% at a THI of 75. The phenotype of an animal is subsequently defined as the slope in a linear regression model of the residuals of the population-wise models against THI for milk yield and concentration of fat, protein, lactose, and specific fatty acids. Compared to reaction-norm models, individual records per cow are combined before model fitting, thereby reducing computation times and allowing more flexibility in the design of the model. Heritabilities for heat tolerance traits ranged from 0.05 to 0.12, and genetic variances indicate substantial potential for breeding as an improvement of the population by one genetic standard deviation would already offset 69% of the losses in fat percentage, 65% in protein percentage, and 11% in milk yield. Heat tolerance based on milk yield showed favorable correlations with most commercial traits, whereas heat tolerance based on fat and protein percentage showed negative correlations to health and resilience. A genome-wide association study using both SNPs and haplotype blocks from the software HaploBlocker identified potential QTLs across the genome, with particularly strong signals on BTA5, 14, and 20. These findings support the potential of breeding for heat tolerance but highlight the need for complementary management strategies to mitigate heat stress impacts. Interpretive summary This study introduced a novel, computationally efficient method to quantify the impact of heat stress in dairy cattle and define novel heat tolerance traits based on milk production data from automated milking systems. Our results indicate quadratically increasing losses in milk yield with increasing heat load. The identified heat tolerance traits show substantial genetic variance, moderate heritabilities, and favorable correlation to key production traits. These findings highlight the potential for incorporating heat tolerance into dairy breeding goals to mitigate climate change impacts, improve animal welfare, and enhance sustainable milk production.

Background: Breeding programs select for multiple commercial traits, aiming to achieve genetic progress for all. Often, selection is based on a selection index, i . e . a linear combination of traits with weights defined by, among other information, the genetic correlation between traits. These correlations are typically estimated as a static parameter, and assumed equal to all individuals and generations. While research on the consequences of selection to genetic variances (Bulmer effect) is widely available, only a few studies focused on the consequences of selection to genetic correlations. Our study extended the already existing inferences about how selection affects genetic variances, to how multi-trait selection affects genetic correlations. In order to further our understanding of genetic correlations, we also proposed an alternative method to calculate genetic correlations between traits at the individual level, called by us as individualized sire genetic correlation (iSGC), obtained through the estimated breeding values (EBV) from evaluated daughters. Lastly, a case-study was performed on thirty years of data from the French Holstein dairy cattle population, for five traits studied pairwise: milk and protein yield, milking speed, somatic cell score, and cow conception rate. Results: Theory revealed that multi-trait selection leads to an attenuation (decrease) of positive genetic correlations, with potential to revert them to negative values, if initially low. Uncorrelated traits will become negatively correlated, and negative genetic correlations will be either intensified or attenuated (decrease or increase, respectively), depending on selection intensity, weights applied to the selection index, and the initial genetic correlation. Conclusion: Both theory and empirical results on real data confirm that selection does change the genetic correlation between traits in a population under selection. Moreover, empirical trajectories of the iSGC were in better agreement with the theory, than trajectories of populational genetic correlations. The iSGC searches for individual-specific patterns of correlations, and since it is measured on sires through the EBV of their daughters, it also considers the recombination of the genetic background. Along with the fact that trajectories of iSGC were in better agreement with theory, we believe it to be a potentially less biased measure of genetic correlations between traits.

This study aimed to use temperature-humidity index (THI) as an indicator for assessing heat stress conditions for fertility traits in the Holstein dairy cattle breed in the Netherlands. Data from AI and calving events of 416,814 first-parity cows from the Netherlands were used, considering different THI definitions based on different numbers of days before and after artificial insemination events. To achieve our aim, we investigated first, at population level, the relationship between different THI definitions and 4 fertility traits: conception rate, interval calving to first insemination, interval first to last insemination, and calving interval. Second, to investigate individual variation in the relationship between THI and fertility, variance components were estimated for each trait using the so-called broken stick model. This model assumes that breeding values are dependent on THI above but not below a THI threshold identified at the population level and explores the presence of genetic variation associated with fertility decline during heat stress. This study revealed considerable changes in fertility traits during periods of heat stress, with a THI threshold of 60 for conception rate and interval first to last insemination and 50 for interval calving to first insemination and calving interval. Interestingly, as THI levels increased, genetic variance and heritability also increased, indicating that at higher THI levels associated with reduced fertility, the genetic variation of fertility traits is greater. Furthermore, significant genotype-by-environment interactions were observed for all 4 fertility traits, suggesting changes in sire rankings between THI levels below and above the threshold. This study provides insights that may help breeding programs and farmers breed animals resilient to heat stress conditions.

Background: International evaluations combine data from different countries allowing breeders to have access to larger panels of elite bulls and to increase the accuracy of estimated breeding values (EBV). However, international and national evaluations can use different sources of information to compute EBV (EBV _INT and EBV _NAT , respectively), leading to differences between them. Choosing one of these EBV results in losing the information that is contained only in the discarded EBV. Our objectives were to define and validate a procedure to integrate publishable sires’ EBV _INT and their associated reliabilities computed from pedigree-based or single-step international beef cattle evaluations into national evaluations to obtain “blended” EBV. The Italian (ITA) pedigree-based national evaluation was used as a case study to validate the integration procedure. Methods: Publishable sires’ international information, i.e. EBV _INT and their associated reliabilities, was included in the national evaluation as pseudo-records. Data were available for 444,199 individual age-adjusted weaning weights of Limousin cattle from eight countries and 17,607 genotypes from four countries (ITA excluded). To mimic differences between international and national evaluations, international evaluations included phenotypes (and genotypes) of animals born prior to January 2019, while national evaluations included ITA phenotypes of animals born until April 2019. International evaluations using all available information were considered as reference scenarios. Publishable sires were divided into three groups: sires with ≥ 15, < 15 and no recorded offspring in ITA. Results: Overall, for these three groups, integrating either pedigree-based or single-step international information into national pedigree-based evaluations improved the similarity of the blended EBV with the reference EBV compared to national evaluations without integration. For instance, the correlation with the reference EBV for direct (maternal) EBV went from 0.61 (0.79) for a national evaluation without integration to 0.97 (0.88) when integrating single-step international information, on average across all groups of publishable sires. Conclusions: Our proposed one-animal-at-a-time integration procedure yields blended EBV that are in close agreement with full international EBV for all groups of animals analysed. The procedure can be directly applied by countries since it does not rely on specific software and is computationally inexpensive, allowing straightforward integration of publishable sires’ EBV _INT from pedigree-based or single-step based international beef cattle evaluations into national evaluations.

Background: Single-step genomic best linear unbiased prediction (ssGBLUP) models allow the combination of genomic, pedigree, and phenotypic data into a single model, which is computationally challenging for large genotyped populations. In practice, genotypes of animals without their own phenotype and progeny, so-called genotyped selection candidates, can become available after genomic breeding values have been estimated by ssGBLUP. In some breeding programmes, genomic estimated breeding values (GEBV) for these animals should be known shortly after obtaining genotype information but recomputing GEBV using the full ssGBLUP takes too much time. In this study, first we compare two equivalent formulations of ssGBLUP models, i.e. one that is based on the Woodbury matrix identity applied to the inverse of the genomic relationship matrix, and one that is based on marker equations. Second, we present computationally-fast approaches to indirectly compute GEBV for genotyped selection candidates, without the need to do the full ssGBLUP evaluation. Results: The indirect approaches use information from the latest ssGBLUP evaluation and rely on the decomposition of GEBV into its components. The two equivalent ssGBLUP models and indirect approaches were tested on a six-trait calving difficulty model using Irish dairy and beef cattle data that include 2.6 million genotyped animals of which about 500,000 were considered as genotyped selection candidates. When using the same computational approaches, the solving phase of the two equivalent ssGBLUP models showed similar requirements for memory and time per iteration. The computational differences between them were due to the preprocessing phase of the genomic information. Regarding the indirect approaches, compared to GEBV obtained from single-step evaluations including all genotypes, indirect GEBV had correlations higher than 0.99 for all traits while showing little dispersion and level bias. Conclusions: In conclusion, ssGBLUP predictions for the genotyped selection candidates were accurately approximated using the presented indirect approaches, which are more memory efficient and computationally fast, compared to solving a full ssGBLUP evaluation. Thus, indirect approaches can be used even on a weekly basis to estimate GEBV for newly genotyped animals, while the full single-step evaluation is done only a few times within a year.