French Dairy Cattle Research Articles

Assessing and explaining trends in dairy cattle herd performance variables while using three-breed rotational crossbreeding: empirical evidence from commercial farms

Three-breed rotational crossbreeding can improve the functional traits of dairy cows, but few farmers in most Western countries use it. This could be due to a lack of knowledge of its medium- and long-term effects on animal performance at the herd level, regardless of the initial states of the farm and herd, the crossbreeding programme(s) used and changes in farm management and structure while transitioning to it. We aimed at assessing changes in animal performance of dairy cattle herds transitioning towards three-breed rotational crossbreeding and at identifying factors that explained them. We sampled 13 French dairy cattle farms for which animal performance data were available for a 9-year period (2009–2017) and described their trends in herd management and the farming system when introducing crossbreeding. We calculated five herd performance variables: mean milk productivity per cow-year (MilkCow), mean Milk solids content (FatProtCont), and the percentages in the herd of cows with no more than 117 days open (FertileCow), with mean somatic cell score less than 4 over the lactation period (HealthyLact) and with four lactations or more (L4+). We defined 15 variables related to herd and farm structure and management. We applied linear regression to each variable over the study period and used the intercept and slope to describe their initial values in 2009 and their trends, respectively. We performed partial least squares (PLS) regression to assess relationships between the trends in herd performance variables and potential explanatory variables, including the initial values of herd performance variables and the initial values of and trends in herd and farm structure and management variables. Linear regressions showed that within herds, median MilkCow decreased (–177 kg/cow per year), FertileCow increased (+1.5%/year) and FatProtCont either remained stable or increased (+0.1 g/kg per cow). Conversely, changes were less distinct for HealthyLact and L4+. From 2009 to 2017, herd size increased in most farms (med = +1.2 LU/year). The PLS regression highlighted that herds in which MilkCow decreased the most over time were those in which HealthyLact and L4+ decreased and increased the most, respectively. Moreover, for herds in which FertileCow increased the most, FatProtCont either decreased, or increased less. Based on association scores from the PLS, changes in the genetic composition of the herd influenced changes in herd performance variables more than did changes in overall farm management (|0.28|–|0.59| vs |0.26|–|0.50|). Our findings suggest that dairy crossbreeding is relevant to rebalance herd performance between production and functional traits.

Reintroducing genetic diversity in populations from cryopreserved material: the case of Abondance, a French local dairy cattle breed

BackgroundGenetic diversity is a necessary condition for populations to evolve under natural adaptation, artificial selection, or both. However, genetic diversity is often threatened, in particular in domestic animal populations where artificial selection, genetic drift and inbreeding are strong. In this context, cryopreserved genetic resources are a promising option to reintroduce lost variants and to limit inbreeding. However, while the use of ancient genetic resources is more common in plant breeding, it is less documented in animals due to a longer generation interval, making it difficult to fill the gap in performance due to continuous selection. This study investigates one of the only concrete cases available in animals, for which cryopreserved semen from a bull born in 1977 in a lost lineage was introduced into the breeding scheme of a French local dairy cattle breed, the Abondance breed, more than 20 years later.ResultsWe found that this re-introduced bull was genetically distinct with respect to the current population and thus allowed part of the genetic diversity lost over time to be restored. The expected negative gap in milk production due to continuous selection was absorbed in a few years by preferential mating with elite cows. Moreover, the re-use of this bull more than two decades later did not increase the level of inbreeding, and even tended to reduce it by avoiding mating with relatives. Finally, the reintroduction of a bull from a lost lineage in the breeding scheme allowed for improved performance for reproductive abilities, a trait that was less subject to selection in the past.ConclusionsThe use of cryopreserved material is an efficient way to manage the genetic diversity of an animal population, by mitigating the effects of both inbreeding and strong selection. However, attention should be paid to mating of animals to limit the disadvantages associated with incorporating original genetic material, notably a discrepancy in the breeding values for selected traits or an increase in inbreeding. Therefore, careful characterization of the genetic resources available in cryobanks could help to ensure the sustainable management of populations, in particular local or small populations. These results could also be transferred to the conservation of wild threatened populations.

Dairy crossbreeding challenges the French dairy cattle sociotechnical regime

Rotational crossbreeding can be faster and more effective than pure breeding in improving the functional traits of cows and developing robust dairy cattle systems. However, this practice remains uncommon worldwide. This is largely assumed to be due to a scarcity of knowledge on dairy crossbreeding schemes and their benefits for cows and herd performance. However, crucial questions remain: Why do so few technical references exist? Why and how do some farmers embrace this practice despite the lack of guidance? To answer these questions, we adopted a sociotechnical view of dairy crossbreeding to characterize the factors that drive, support, and impede its development in France. We qualitatively analyzed 73 articles from two French technical journals on dairy cattle and semi-structured interviews of 17 dairy cattle farmers who practice crossbreeding and 15 representatives of key stakeholders in the sector. Using all these data, we identified three drivers of using dairy crossbreeding: technical problems related to breeding highly specialized purebred cows, the shift towards more sustainable and resilient dairy cattle systems, and a desire to regain decision-making autonomy in farm management. Based on farmers’ interviews, it became evident that they developed know-how “on the job” and created new support networks to overcome impediments that were primarily cognitive/cultural but also technological and market-driven; with these resources, they progressively turned away from the mainstream sociotechnical regime. Based on stakeholders’ interviews, we characterized four groups that limited the development of dairy crossbreeding and one that supported it, and we identified organizational, cognitive/cultural, technological, and economic barriers—many of which were interrelated and self-reinforcing—to its use. These findings demonstrate that dairy crossbreeding is widely recognized as beneficial for the sustainability of dairy cattle farms but remains uncommon in France due to the sociotechnical lock-in of the dairy sector around both pure-breeding and high-specialization paradigms of herd management.

Resilience of French organic dairy cattle farms and supply chains to the Covid-19 pandemic

ContextIdentifying and developing resilient farming and food systems has emerged as a top priority during the Covid-19 pandemic. Many academics suggest that farming and food systems should move towards agroecological models to achieve better resilience. However, there was limited evidence to support this statement during the Covid-19 pandemic. ObjectiveOur objectives were to report evidence for the resilience of French organic dairy cattle farms and supply chains to the Covid-19 pandemic and to discuss the features of those farms and supply chains that promoted resilience. MethodsWe combined online surveys with farmers, semi-structured interviews with supply chain actors and a review of the gray and technical literature, and whenever possible, we compared this qualitative data against quantitative industry data. We also asked farmers to rank 19 pre-identified risks according to their likelihood and potential impacts. Results and conclusionsWe showed the pandemic had zero to moderate impacts on most farms. Among respondents, 38 farmers reported no impacts, another 43 experienced minor impacts on aspects such as their income and workload while only 5 faced major impacts, such as the closure of sales outlets. Most farms were family farms and were not greatly affected by worker availability issues. Moreover, the vast majority of these farms were nearly autonomous for livestock feeding and none reported input supply shortages or related impacts on farm functioning and productivity. The pandemic had moderate impacts on supply chains. Despite staff reductions, supply chains continued producing sufficient amounts of dairy products to meet consumer demand. To do so, they narrowed the scope of products manufactured to concentrate on a basic mix: milk, cream, butter and plain yogurt. Logistics were also adapted by hiring retired drivers to keep up with milk collection and reorganizing the delivery of products by shunting usual sub-level platforms that were saturated. Consequently, even after this pandemic, farmers remained more concerned with climate change-related risks on their farms than by sanitary risks. Several resilience factors were identified that promoted buffer and adaptive capacity at the farm level and that favored adaptive capacity at the supply chain level. SignificanceThese findings confirm the relevance of agroecological models in achieving resilience in farming and food systems against shocks such as the Covid-19 pandemic. This preliminary work carried out at the end of the first lock-down period needs to be pursued in order to understand the impacts of the Covid-19 pandemic over longer time horizons.

Intensified Use of Reproductive Technologies and Reduced Dimensions of Breeding Schemes Put Genetic Diversity at Risk in Dairy Cattle Breeds.

In the management of dairy cattle breeds, two recent trends have arisen that pose potential threats to genetic diversity: the use of reproductive technologies (RT) and a reduction in the number of bulls in breeding schemes. The expected outcome of these changes, in terms of both genetic gain and genetic diversity, is not trivial to predict. Here, we simulated 15 breeding schemes similar to those carried out in large French dairy cattle breeds; breeding schemes differed with respect to their dimensions, the intensity of RT use, and the type of RT involved. We found that intensive use of RT resulted in improved genetic gain, but deteriorated genetic diversity. Specifically, a reduction in the interval between generations through the use of ovum pick-up and in vitro fertilization (OPU-IVF) resulted in a large increase in the inbreeding rate both per year and per generation, suggesting that OPU-IVF could have severe adverse effects on genetic diversity. To achieve a given level of genetic gain, the scenarios that best maintained genetic diversity were those with a higher number of sires/bulls and a medium intensity of RT use or those with a higher number of female donors to compensate for the increased intensity of RT.

Confirmed effects of candidate variants for milk production, udder health, and udder morphology in dairy cattle

BackgroundOver the last years, genome-wide association studies (GWAS) based on imputed whole-genome sequences (WGS) have been used to detect quantitative trait loci (QTL) and highlight candidate genes for important traits. However, in general this approach does not allow to validate the effects of candidate mutations or determine if they are truly causative for the trait(s) in question. To address these questions, we applied a two-step, within-breed GWAS approach on 15 traits (5 linked with milk production, 2 with udder health, and 8 with udder morphology) in Montbéliarde (MON), Normande (NOR), and Holstein (HOL) cattle. We detected the most-promising candidate variants (CV) using imputed WGS of 2515 MON, 2203 NOR, and 6321 HOL bulls, and validated their effects in three younger populations of 23,926 MON, 9400 NOR, and 51,977 HOL cows.ResultsBull sequence-based GWAS detected 84 QTL: 13, 10, and 30 for milk production traits; 3, 0, and 2 for somatic cell score (SCS); and 8, 2 and 16 for udder morphology traits, in MON, NOR, and HOL respectively. Five genomic regions with effects on milk production traits were shared among the three breeds whereas six (2 for production and 4 for udder morphology and health traits) had effects in two breeds. In 80 of these QTL, 855 CV were highlighted based on the significance of their effects and functional annotation. The subsequent GWAS on MON, NOR, and HOL cows validated 8, 9, and 23 QTL for production traits; 0, 0, and 1 for SCS; and 4, 1, and 8 for udder morphology traits, respectively. In 47 of the 54 confirmed QTL, the CV identified in bulls had more significant effects than single nucleotide polymorphisms (SNPs) from the standard 50K chip. The best CV for each validated QTL was located in a gene that was functionally related to production (36 QTL) or udder (9 QTL) traits.ConclusionsUsing this two-step GWAS approach, we identified and validated 54 QTL that included CV mostly located within functional candidate genes and explained up to 6.3% (udder traits) and 37% (production traits) of the genetic variance of economically important dairy traits. These CV are now included in the chip used to evaluate French dairy cattle and can be integrated into routine genomic evaluation.

The impact of genomic selection on genetic diversity and genetic gain in three French dairy cattle breeds

BackgroundIn France, implementation of genomic evaluations in dairy cattle breeds started in 2009 and this has modified the breeding schemes drastically. In this context, the goal of our study was to understand the impact of genomic selection on the genetic diversity of bulls from three French dairy cattle breeds born between 2005 and 2015 (Montbéliarde, Normande and Holstein) and the factors that are involved.MethodsWe compared annual genetic gains, inbreeding rates based on runs of homozygosity (ROH) and pedigree data, and mean ROH length within breeds, before and after the implementation of genomic selection.ResultsGenomic selection induced an increase in mean annual genetic gains of 50, 71 and 33% for Montbéliarde, Normande and Holstein bulls, respectively, and in parallel, the generation intervals were reduced by a factor of 1.7, 1.9 and 2, respectively. We found no significant change in inbreeding rate for the two national breeds, Montbéliarde and Normande, and a significant increase in inbreeding rate for the Holstein international breed, which is now as high as 0.55% per year based on ROH and 0.49% per year based on pedigree data (equivalent to a rate of 1.36 and 1.39% per generation, respectively). The mean ROH length was longer for bulls from the Holstein breed than for those from the other two breeds.ConclusionsWith the implementation of genomic selection, the annual genetic gain increased for bulls from the three major French dairy cattle breeds. At the same time, the annual loss of genetic diversity increased for Holstein bulls, possibly because of the massive use of a few elite bulls in this breed, but not for Montbéliarde and Normande bulls. The increase in mean ROH length in Holstein may reflect the occurrence of recent inbreeding. New strategies in breeding schemes, such as female donor stations and embryo transfer, and recent implementation of genomic evaluations in small regional breeds should be studied carefully in order to ensure the sustainability of breeding schemes in the future.

A Meta-Analysis Including Pre-selected Sequence Variants Associated With Seven Traits in Three French Dairy Cattle Populations.

A within-breed genome-wide association study (GWAS) is useful when identifying the QTL that segregates in a breed. However, an across-breed meta-analysis can be used to increase the power of identification and precise localization of QTL that segregate in multiple breeds. Precise localization will allow including QTL information from other breeds in genomic prediction due to the persistence of the linkage phase between the causal variant and the marker. This study aimed to identify and confirm QTL detected in within-breed GWAS through a meta-analysis in three French dairy cattle breeds. A set of sequence variants selected based on their functional annotations were imputed into 50 k genotypes for 46,732 Holstein, 20,096 Montbeliarde, and 11,944 Normande cows to identify QTL for milk production, the success rate at insemination of cows (fertility) and stature. We conducted within-breed GWAS followed by across-breed meta-analysis using a weighted Z-scores model on the GWAS summary data (i.e., P-values, effect direction, and sample size). After Bonferroni correction, the GWAS result identified 21,956 significantly associated SNP (PFWER < 0.05), while meta-analysis result identified 9,604 significant SNP (PFWER < 0.05) associated with the phenotypes. The meta-analysis identified 36 QTL for milk yield, 48 QTL for fat yield and percentage, 29 QTL for protein yield and percentage, 13 QTL for fertility, and 16 QTL for stature. Some of these QTL were not significant in the within-breed GWAS. Some previously identified causal variants were confirmed, e.g., BTA14:1802265 (fat percentage, P = 1.5 × 10−760; protein percentage, P = 7.61 × 10−348) both mapping the DGAT1-K232A mutation and BTA14:25006125 (P = 8.58 × 10−140) mapping PLAG1 gene was confirmed for stature in Montbeliarde. New QTL lead SNP shared between breeds included the intronic variant rs109205829 (NFIB gene), and the intergenic variant rs41592357 (1.38 Mb upstream of the CNTN6 gene and 0.65 Mb downstream of the CNTN4 gene). Rs110425867 (ZFAT gene) was the top variant associated with fertility, and new QTL lead SNP included rs109483390 (0.1 Mb upstream of the TNFAIP3 gene and 0.07 Mb downstream of PERP gene), and rs42412333 (0.45 Mb downstream of the RPL10L gene). An across-breed meta-analysis had greater power to detect QTL as opposed to a within breed GWAS. The QTL detected here can be incorporated in routine genomic predictions.

Short communication: Confirmation of candidate causative variants on milk composition and cheesemaking properties in Montbéliarde cows

In a previous study, we identified candidate causative variants located in 24 functional candidate genes for milk protein and fatty acid composition in Montbéliarde, Normande, and Holstein cows. We designed these variants on the custom part of the EuroG10K BeadChip (Illumina Inc., San Diego, CA), which is routinely used for genomic selection analyses in French dairy cattle. To validate the effects of these candidate variants on milk composition and to estimate their effects on cheesemaking properties, a genome-wide association study was performed on milk protein, fatty acid and mineral composition, as well as on 9 cheesemaking traits (3 laboratory cheese yields, 5 coagulation traits, and milk pH). All the traits were predicted from midinfrared spectra in the Montbéliarde cow population of the Franche-Comté region. A total of 194 candidate variants located in 24 genes and 17 genomic regions were imputed on 19,862 cows with phenotypes and genotyped with either the BovineSNP50 (Illumina Inc.) or the EuroG10K BeadChip. We then tested the effect of each SNP in a mixed linear model including random polygenic effects estimated with a genomic relationship matrix. We confirm here the effects of candidate causative variants located in 17 functional candidate genes on both cheesemaking properties and milk composition traits. In each candidate gene, we identified the most plausible causative variant: 4 are missense in the ALPL, SLC26A4, CSN3, and SCD genes, 7 are located in 5'UTR (AGPAT6), 3' untranslated region (GPT), or upstream (CSN1S1, CSN1S2, PAEP, DGAT1, and PICALM) regions, and 6 are located in introns of the SLC37A1, MGST1, CSN2, BRI3BP, FASN, and ANKH genes.

Control measures to prevent the increase of paratuberculosis prevalence in dairy cattle herds: an individual-based modelling approach

Paratuberculosis, a gastrointestinal disease caused by Mycobacterium avium subsp. paratuberculosis (Map), can lead to severe economic losses in dairy cattle farms. Current measures are aimed at controlling prevalence in infected herds, but are not fully effective. Our objective was to determine the most effective control measures to prevent an increase in adult prevalence in infected herds. We developed a new individual-based model coupling population and infection dynamics. Animals are characterized by their age (6 groups) and health state (6 states). The model accounted for all transmission routes and two control measures used in the field, namely reduced calf exposure to adult faeces and test-and-cull. We defined three herd statuses (low, moderate, and high) based on realistic prevalence ranges observed in French dairy cattle herds. We showed that the most relevant control measures depend on prevalence. Calf management and test-and-cull both were required to maximize the probability of stabilizing herd status. A reduced calf exposure was confirmed to be the most influential measure, followed by test frequency and the proportion of infected animals that were detected and culled. Culling of detected high shedders could be delayed for up to 3 months without impacting prevalence. Management of low prevalence herds is a priority since the probability of status stabilization is high after implementing prioritized measures. On the contrary, an increase in prevalence was particularly difficult to prevent in moderate prevalence herds, and was only feasible in high prevalence herds if the level of control was high.

A system-based analysis of the genetic determinism of udder conformation and health phenotypes across three French dairy cattle breeds.

Using GWAS to identify candidate genes associated with cattle morphology traits at a functional level is challenging. The main difficulty of identifying candidate genes and gene interactions associated with such complex traits is the long-range linkage disequilibrium (LD) phenomenon reported widely in dairy cattle. Systems biology approaches, such as combining the Association Weight Matrix (AWM) with a Partial Correlation in an Information Theory (PCIT) algorithm, can assist in overcoming this LD. Used in a multi-breed and multi-phenotype context, the AWM-PCIT could aid in identifying udder traits candidate genes and gene networks with regulatory and functional significance. This study aims to use the AWM-PCIT algorithm as a post-GWAS analysis tool with the goal of identifying candidate genes underlying udder morphology. We used data from 78,440 dairy cows from three breeds and with own phenotypes for five udder morphology traits, five production traits, somatic cell score and clinical mastitis. Cows were genotyped with medium (50k) or low-density (7 to 10k) chips and imputed to 50k. We performed a within breed and trait GWAS. The GWAS showed 9,830 significant SNP across the genome (p < 0.05). Five thousand and ten SNP did not map a gene, and 4,820 SNP were within 10-kb of a gene. After accounting for 1SNP:1gene, 3,651 SNP were within 10-kb of a gene (set1), and 2,673 significant SNP were further than 10-kb of a gene (set2). The two SNP sets formed 6,324 SNP matrix, which was fitted in an AWM-PCIT considering udder depth/ development as the key trait resulting in 1,013 genes associated with udder morphology, mastitis and production phenotypes. The AWM-PCIT detected ten potential candidate genes for udder related traits: ESR1, FGF2, FGFR2, GLI2, IQGAP3, PGR, PRLR, RREB1, BTRC, and TGFBR2.

Use of meta-analyses and joint analyses to select variants in whole genome sequences for genomic evaluation: An application in milk production of French dairy cattle breeds

As a result of the 1000 Bull Genome Project, it has become possible to impute millions of variants, with many of these potentially causative for traits of interest, for thousands of animals that have been genotyped with medium-density chips. This enormous source of data opens up very interesting possibilities for the inclusion of these variants in genomic evaluations. However, for computational reasons, it is not possible to include all variants in genomic evaluation procedures. One potential approach could be to select the most relevant variants based on the results of genome-wide association studies (GWAS); however, the identification of causative mutations is still difficult with this method, partly because of weak imputation accuracy for rare variants. To address this problem, this study assesses the ability of different approaches based on multi-breed GWAS (joint and meta-analyses) to identify single-nucleotide polymorphisms (SNP) for use in genomic evaluation in the 3 main French dairy cattle breeds. A total of 6,262 Holstein bulls, 2,434 Montbéliarde bulls, and 2,175 Normande bulls with daughter yield deviations for 5 milk production traits were imputed for 27 million variants. Within-breed and joint (including all 3 breeds) GWAS were performed and 3 models of meta-analysis were tested: fixed effect, random effect, and Z-score. Comparison of the results of within- and multi-breed GWAS showed that most of the quantitative trait loci identified using within-breed approaches were also found with multi-breed methods. However, the most significant variants identified in each region differed depending on the method used. To determine which approach highlighted the most predictive SNP for each trait, we used a marker-assisted best unbiased linear prediction model to evaluate lists of SNP generated by the different GWAS methods; each list contained between 25 and 2,000 candidate variants per trait, which were identified using a single within- or multi-breed GWAS approach. Among all the multi-breed methods tested in this study, variant selection based on meta-analysis (fixed effect) resulted in the most-accurate genomic evaluation (+1 to +3 points compared with other multi-breed approaches). However, the accuracies of genomic evaluation were always better when variants were selected using the results of within-breed GWAS. As has generally been found in studies of quantitative trait loci, these results suggest that part of the genetic variance of milk production traits is breed specific in Holstein, Montbéliarde, and Normande cattle.

Short communication: Genetic parameters for milk protein composition predicted using mid-infrared spectroscopy in the French Montbéliarde, Normande, and Holstein dairy cattle breeds.

Genetic parameters for the major milk proteins were estimated in the 3 main French dairy cattle breeds (i.e. Montbéliarde, Normande, and Holstein) as part of the PhénoFinlait program. The 6 major milk protein contents as well as the total protein content (PC) were estimated from mid-infrared spectrometry on 133,592 test-day milk samples from 20,434 cows in first lactation. Lactation means, expressed as a percentage of milk (protein contents) or of protein (protein fractions), were analyzed with an animal mixed model including fixed environmental effects (herd, year × month of calving, and spectrometer) and a random genetic effect. Genetic parameter estimates were very consistent across breeds. Heritability estimates (h2) were generally higher for protein fractions than for protein contents. They were moderate to high for αS1-casein, αS2-casein, β-casein, κ-casein, and α-lactalbumin (0.25 < h2 < 0.72). In each breed, β-lactoglobulin was the most heritable trait (0.61 < h2 < 0.86). Genetic correlations (rg) varied depending on how the percentage was expressed. The PC was strongly positively correlated with protein contents but almost genetically independent from protein fractions. Protein fractions were generally in opposition, except between κ-casein and α-lactalbumin (0.39 < rg < 0.46) and κ-casein and αS2-casein (0.36 < rg < 0.49). Between protein contents, rg estimates were positive, with highest values found between caseins (0.83 < rg < 0.98). In the 3 breeds, β-lactoglobulin was negatively correlated with caseins (-0.75 < rg < -0.08), in particular with κ-casein (-0.75 < rg < -0.55). These results, obtained from a large panel of cows of the 3 main French dairy cattle breeds, show that routinely collected mid-infrared spectra could be used to modify milk protein composition by selection.

Construction of a large collection of small genome variations in French dairy and beef breeds using whole-genome sequences.

BackgroundIn recent years, several bovine genome sequencing projects were carried out with the aim of developing genomic tools to improve dairy and beef production efficiency and sustainability.ResultsIn this study, we describe the first French cattle genome variation dataset obtained by sequencing 274 whole genomes representing several major dairy and beef breeds. This dataset contains over 28 million single nucleotide polymorphisms (SNPs) and small insertions and deletions. Comparisons between sequencing results and SNP array genotypes revealed a very high genotype concordance rate, which indicates the good quality of our data.ConclusionsTo our knowledge, this is the first large-scale catalog of small genomic variations in French dairy and beef cattle. This resource will contribute to the study of gene functions and population structure and also help to improve traits through genotype-guided selection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0268-z) contains supplementary material, which is available to authorized users.

P8006 Identification and characterization of copy number variations in cattle

Copy number variations (CNVs) are an important source of genetic changes. They are defined as a gain or loss of genomic region ranging from 50 bp to several megabases. CNVs have been shown to be associated with many diseases and some phenotypic traits in several species, including cattle. We used Pindel, Delly, BreakDancer, and CNVnator to identify CNVs using whole-genome sequencing data of 200 animals from eight French dairy and beef cattle breeds. We selected only deletions and duplications predicted by at least two tools and present in at least two animals. We identified a total of 29,132 autosomal deletions and duplications which cover between 31 to 34% (784 to 865 Mb) of the autosomal genome, with an average of 6,000 events per animal. Among these deletions and duplications, 27,690 were present in at least two animals. Out of theses, 26,417 events were deletions, 674 were duplications and 599 regions were both (deletion and duplication within the same region). We defined a CNV as deletion and duplication in the same region, and we termed this region as CNV-Region (CNVR). The size of CNVRs ranged from 100 bp to 9.3 Mb with a median of 1.3 kb and a mean of 45 kb. From the identified deletions and duplications, 8,283 overlapped with 9,733 annotated genes including 290 CNVRs overlapping with 974 annotated genes, including some genes known to be implicated in some traits of economic importance. Our study provides an extensive view of the CNVRs in French dairy and beef breeds. CNVRs with an effect on some commercially interesting phenotypes could be used to improve genetic selection of these eight French breeds.

Comparing power and precision of within-breed and multibreed genome-wide association studies of production traits using whole-genome sequence data for 5 French and Danish dairy cattle breeds.

The objective of this study was to compare mapping precision and power of within-breed and multibreed genome-wide association studies (GWAS) and to compare the results obtained by the multibreed GWAS with 3 meta-analysis methods. The multibreed GWAS was expected to improve mapping precision compared with a within-breed GWAS because linkage disequilibrium is conserved over shorter distances across breeds than within breeds. The multibreed GWAS was also expected to increase detection power for quantitative trait loci (QTL) segregating across breeds. GWAS were performed for production traits in dairy cattle, using imputed full genome sequences of 16,031 bulls, originating from 6 French and Danish dairy cattle populations. Our results show that a multibreed GWAS can be a valuable tool for the detection and fine mapping of quantitative trait loci. The number of QTL detected with the multibreed GWAS was larger than the number detected by the within-breed GWAS, indicating an increase in power, especially when the 2 Holstein populations were combined. The largest number of QTL was detected when all populations were combined. The analysis combining all breeds was, however, dominated by Holstein, and QTL segregating in other breeds but not in Holstein were sometimes overshadowed by larger QTL segregating in Holstein. Therefore, the GWAS combining all breeds except Holstein was useful to detect such peaks. Combining all breeds except Holstein resulted in smaller QTL intervals on average, but this outcome was not the case when the Holstein populations were included in the analysis. Although no decrease in the average QTL size was observed, mapping precision did improve for several QTL. Out of 3 different multibreed meta-analysis methods, the weighted z-scores model resulted in the most similar results to the full multibreed GWAS and can be useful as an alternative to a full multibreed GWAS. Differences between the multibreed GWAS and the meta-analyses were larger when different breeds were combined than when the 2 Holstein populations were combined.

Whole-genome scan to detect quantitative trait loci associated with milk protein composition in 3 French dairy cattle breeds.

In the context of the PhénoFinLait project, a genome-wide analysis was performed to detect quantitative trait loci (QTL) that affect milk protein composition estimated using mid-infrared spectrometry in the Montbéliarde (MO), Normande (NO), and Holstein (HO) French dairy cattle breeds. The 6 main milk proteins (α-lactalbumin, β-lactoglobulin, and αS1-, αS2-, β-, and κ-caseins) expressed as grams per 100g of milk (% of milk) or as grams per 100g of protein (% of protein) were estimated in 848,068 test-day milk samples from 156,660 cows. Genotyping was performed for 2,773 MO, 2,673 NO, and 2,208 HO cows using the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). Individual test-day records were adjusted for environmental effects and then averaged per cow to define the phenotypes analyzed. Quantitative trait loci detection was performed within each breed using a linkage disequilibrium and linkage analysis approach. A total of 39 genomic regions distributed on 20 of the 29 Bos taurus autosomes (BTA) were significantly associated with milk protein composition at a genome-wide level of significance in at least 1 of the 3 breeds. The 9 most significant QTL were located on BTA2 (133 Mbp), BTA6 (38, 47, and 87 Mbp), BTA11 (103 Mbp), BTA14 (1.8 Mbp), BTA20 (32 and 58 Mbp), and BTA29 (8 Mbp). The BTA6 (87 Mbp), BTA11, and BTA20 (58 Mbp) QTL were found in all 3 breeds, and they had highly significant effects on κ-casein, β-lactoglobulin, and α-lactalbumin, expressed as a percentage of protein, respectively. Each of these QTL explained between 13% (BTA14) and 51% (BTA11) of the genetic variance of the trait. Many other QTL regions were also identified in at least one breed. They were located on 14 additional chromosomes (1, 3, 4, 5, 7, 15, 17, 19, 21, 22, 24, 25, 26, and 27), and they explained 2 to 8% of the genetic variance of 1 or more protein composition traits. Concordance analyses, performed between QTL status and sequence-derived polymorphisms from 13 bulls, revealed previously known causal polymorphisms in LGB (BTA11) and GHR (BTA20 at 32 Mbp) and excluded some other previously described mutations. These results constitute a first step in identifying causal mutations and using routinely collected mid-infrared predictions in future genomic selection programs to improve bovine milk protein composition.

Description and factors of variation of the overall health score in French dairy cattle herds using the Welfare Quality® assessment protocol

Extensive information is available in the literature on the specific risk factors of the main health disorders afflicting dairy cattle herds. However, it remains difficult to manage a herd's overall health because measures to control one risk factor can exacerbate the risk of another disease. To achieve and maintain good overall herd health, livestock systems and management practices need to simultaneously take into account all of the main health disorders. We aimed to identify the characteristics of systems and practices conducive to good herd health using the Welfare Quality® assessment protocol for cattle. This protocol allows an assessment of the level of health and welfare at the herd level according to the opinion of a selected group of 13 experts from animal sciences. Our objectives were to (i) describe the distribution of dairy herds’ health scores in a representative sample of French dairy cattle herds, and (ii) to investigate systems (housing system, milking system, herd size, breed, farm location) and management practices associated with variations of the overall health score of dairy herds. This protocol was carried out on 130 farms between December 2010 and March 2011. A multivariable analysis of variance (ANOVA) was performed to investigate the factors of variation of the overall health score at the herd level.The overall health scores of the farms in the sample were classified as moderate for the vast majority of farms (95.4%) (mainly due to subclinical mastitis, dystocia and pain induced by disbudding/dehorning) and varied little between farms. Some livestock systems were associated with a higher overall health score: straw yards and milking parlors (P<0.0001), highland vs. lowland locations (P=0.013), Montbeliarde rather than Holstein breeds (P=0.006). Some management practices also were associated with a higher level of health: medium herd average parity (P=0.03), low proportion of dirty cows (P=0.002) and low proportion of cows with abnormal body condition (P=0.04). These results suggest that some systems contribute to better health and that improvement of health can be obtained in the short term by the modification of routine management practices.

Dairy goat demography and Q fever infection dynamics

Between 2007 and 2009, the largest human Q fever epidemic ever described occurred in the Netherlands. The source was traced back to dairy goat farms, where abortion storms had been observed since 2005. Since one putative cause of these abortion storms is the intensive husbandry systems in which the goats are kept, the objective of this study was to assess whether these could be explained by herd size, reproductive pattern and other demographic aspects of Dutch dairy goat herds alone. We adapted an existing, fully parameterized simulation model for Q fever transmission in French dairy cattle herds to represent the demographics typical for Dutch dairy goat herds. The original model represents the infection dynamics in a herd of 50 dairy cows after introduction of a single infected animal; the adapted model has 770 dairy goats. For a full comparison, herds of 770 cows and 50 goats were also modeled. The effects of herd size and goat versus cattle demographics on the probability of and time to extinction of the infection, environmental bacterial load and abortion rate were studied by simulation. The abortion storms could not be fully explained by demographics alone. Adequate data were lacking at the moment to attribute the difference to characteristics of the pathogen, host, within-herd environment, or a combination thereof. The probability of extinction was higher in goat herds than in cattle herds of the same size. The environmental contamination was highest within cattle herds, which may be taken into account when enlarging cattle farming systems.

Joint genomic evaluation of French dairy cattle breeds using multiple-trait models

BackgroundUsing a multi-breed reference population might be a way of increasing the accuracy of genomic breeding values in small breeds. Models involving mixed-breed data do not take into account the fact that marker effects may differ among breeds. This study was aimed at investigating the impact on accuracy of increasing the number of genotyped candidates in the training set by using a multi-breed reference population, in contrast to single-breed genomic evaluations.MethodsThree traits (milk production, fat content and female fertility) were analyzed by genomic mixed linear models and Bayesian methodology. Three breeds of French dairy cattle were used: Holstein, Montbéliarde and Normande with 2976, 950 and 970 bulls in the training population, respectively and 964, 222 and 248 bulls in the validation population, respectively. All animals were genotyped with the Illumina Bovine SNP50 array. Accuracy of genomic breeding values was evaluated under three scenarios for the correlation of genomic breeding values between breeds (rg): uncorrelated (1), rg = 0; estimated rg (2); high, rg = 0.95 (3). Accuracy and bias of predictions obtained in the validation population with the multi-breed training set were assessed by the coefficient of determination (R2) and by the regression coefficient of daughter yield deviations of validation bulls on their predicted genomic breeding values, respectively.ResultsThe genetic variation captured by the markers for each trait was similar to that estimated for routine pedigree-based genetic evaluation. Posterior means for rg ranged from −0.01 for fertility between Montbéliarde and Normande to 0.79 for milk yield between Montbéliarde and Holstein. Differences in R2 between the three scenarios were notable only for fat content in the Montbéliarde breed: from 0.27 in scenario (1) to 0.33 in scenarios (2) and (3). Accuracies for fertility were lower than for other traits.ConclusionsUsing a multi-breed reference population resulted in small or no increases in accuracy. Only the breed with a small data set and large genetic correlation with the breed with a large data set showed increased accuracy for the traits with moderate (milk) to high (fat content) heritability. No benefit was observed for fertility, a lowly heritable trait.