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peer-reviewed H.D.D., A.J.C., P.J.B. and B.J.H. would like to acknowledge the Dairy Futures Cooperative Research Centre for funding. H.P. and R.F. acknowledge funding from the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr ‘Synbreed—Synergistic Plant and Animal Breeding’ (grant 0315527B). H.P., R.F., R.E. and K.-U.G. acknowledge the Arbeitsgemeinschaft Süddeutscher Rinderzüchter, the Arbeitsgemeinschaft Österreichischer Fleckviehzüchter and ZuchtData EDV Dienstleistungen for providing genotype data. A. Bagnato acknowledges the European Union (EU) Collaborative Project LowInputBreeds (grant agreement 222623) for providing Brown Swiss genotypes. Braunvieh Schweiz is acknowledged for providing Brown Swiss phenotypes. H.P. and R.F. acknowledge the German Holstein Association (DHV) and the Confederación de Asociaciones de Frisona Española (CONCAFE) for sharing genotype data. H.P. was financially supported by a postdoctoral fellowship from the Deutsche Forschungsgemeinschaft (DFG) (grant PA 2789/1-1). D.B. and D.C.P. acknowledge funding from the Research Stimulus Fund (11/S/112) and Science Foundation Ireland (14/IA/2576). M.S. and F.S.S. acknowledge the Canadian Dairy Network (CDN) for providing the Holstein genotypes. P.S. acknowledges funding from the Genome Canada project entitled ‘Whole Genome Selection through Genome Wide Imputation in Beef Cattle’ and acknowledges WestGrid and Compute/Calcul Canada for providing computing resources. J.F.T. was supported by the National Institute of Food and Agriculture, US Department of Agriculture, under awards 2013-68004-20364 and 2015-67015-23183. A. Bagnato, F.P., M.D. and J.W. acknowledge EU Collaborative Project Quantomics (grant 516 agreement 222664) for providing Brown Swiss and Finnish Ayrshire sequences and genotypes. A.C.B. and R.F.V. acknowledge funding from the public–private partnership ‘Breed4Food’ (code BO-22.04-011- 001-ASG-LR) and EU FP7 IRSES SEQSEL (grant 317697). A.C.B. and R.F.V. acknowledge CRV (Arnhem, the Netherlands) for providing data on Dutch and New Zealand Holstein and Jersey bulls. Stature is affected by many polymorphisms of small effect in humans1. In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes2,3. Here we use data from cattle to compare the genetic architecture of stature to those in humans and dogs. We conducted a meta-analysis for stature using 58,265 cattle from 17 populations with 25.4 million imputed whole-genome sequence variants. Results showed that the genetic architecture of stature in cattle is similar to that in humans, as the lead variants in 163 significantly associated genomic regions (P < 5 × 10−8) explained at most 13.8% of the phenotypic variance. Most of these variants were noncoding, including variants that were also expression quantitative trait loci (eQTLs) and in ChIP–seq peaks. There was significant overlap in loci for stature with humans and dogs, suggesting that a set of common genes regulates body size in mammals.

Background Genome-wide association studies (GWAS) were performed at the sequence level to identify candidate mutations that affect the expression of six major milk proteins in Montbéliarde (MON), Normande (NOR), and Holstein (HOL) dairy cattle. Whey protein (α-lactalbumin and β-lactoglobulin) and casein (αs1, αs2, β, and κ) contents were estimated by mid-infrared (MIR) spectrometry, with medium to high accuracy (0.59 ≤ R2 ≤ 0.92), for 848,068 test-day milk samples from 156,660 cows in the first three lactations. Milk composition was evaluated as average test-day measurements adjusted for environmental effects. Next, we genotyped a subset of 8080 cows (2967 MON, 2737 NOR, and 2306 HOL) with the BovineSNP50 Beadchip. For each breed, genotypes were first imputed to high-density (HD) using HD single nucleotide polymorphisms (SNPs) genotypes of 522 MON, 546 NOR, and 776 HOL bulls. The resulting HD SNP genotypes were subsequently imputed to the sequence level using 27 million high-quality sequence variants selected from Run4 of the 1000 Bull Genomes consortium (1147 bulls). Within-breed, multi-breed, and conditional GWAS were performed. Results Thirty-four distinct genomic regions were identified. Three regions on chromosomes 6, 11, and 20 had very significant effects on milk composition and were shared across the three breeds. Other significant effects, which partially overlapped across breeds, were found on almost all the autosomes. Multi-breed analyses provided a larger number of significant genomic regions with smaller confidence intervals than within-breed analyses. Combinations of within-breed, multi-breed, and conditional analyses led to the identification of putative causative variants in several candidate genes that presented significant protein–protein interactions enrichment, including those with previously described effects on milk composition (SLC37A1, MGST1, ABCG2, CSN1S1, CSN2, CSN1S2, CSN3, PAEP, DGAT1, AGPAT6) and those with effects reported for the first time here (ALPL, ANKH, PICALM). Conclusions GWAS applied to fine-scale phenotypes, multiple breeds, and whole-genome sequences seems to be effective to identify candidate gene variants. However, although we identified functional links between some candidate genes and milk phenotypes, the causality between candidate variants and milk protein composition remains to be demonstrated. Nevertheless, the identification of potential causative mutations that underlie milk protein composition may have immediate applications for improvements in cheese-making. Electronic supplementary material The online version of this article (doi:10.1186/s12711-017-0344-z) contains supplementary material, which is available to authorized users.

Hérité de la loi sur l’Élevage de 1966, le dispositif génétique français a permis la mise en place d’un vaste recueil de phénotypes en ferme et en station. Toutes ces collectes ont pu être valorisées collectivement au travers de nombreuses évaluations génétiques, et notamment les évaluations nationales sur les données recueillies en ferme appelées « IBOVAL ». Ces évaluations ont évolué tant d'un point de vue méthodologique (évaluations polygéniques et maintenant génomiques) que sur l’éventail des caractères valorisés. La filière de production de viande bovine dispose aujourd’hui d’outils génétiques performants permettant d’évaluer les reproducteurs bovins allaitants, de les sélectionner sur leurs aptitudes bouchères et leur qualités maternelles en ferme et en station (contrôle individuel ou sur descendance). Le panel de caractères traités (naissance, sevrage, post-sevrage, reproduction, aptitudes bouchères) permet d’élaborer des objectifs de sélection adaptés aux orientations raciales, aux contraintes de la filière et de l’élevage. Les programmes de sélection utilisant ces outils génèrent un progrès génétique. Celui-ci est diffusé efficacement, même si la faible pénétration de l’insémination animale reste un facteur limitant. Enfin, l’arrivée de la génomique, les changements organisationnels induits par le nouveau règlement zootechnique européen et le contexte difficile de l’élevage vont entraîner des évolutions au niveau des outils et des objectifs de sélection., The French genetic improvement organisation of beef cattle, the legacy of the French Livestock act of 1966, produces a collection of a wide range of phenotypes obtained on farm and in testing stations. With these, numerous genetic evaluations were developed, in particular evaluations based on data collected on farms called “IBOVAL”. The methodology used (with recently the inclusion of genomic information) as well as the number of traits evaluated have evolved with time. Today, adequate genetic tools are available in France to predict genetic values of breeding animals, to select them on their fattening abilities or their maternal abilities. The range of traits (at birth, weaning, post-weaning as well as reproductive and fattening abilities) allows the development of breeding goals adapted to the farmers’ needs, the breed objectives and the context of the beef industry. The selection programs using these tools do generate genetic gains. The dissemination of these gains is efficient even though the beef cattle sector has a low rate of insemination. With the introduction of genomics and the organisational changes resulting from the new European regulation as well as the difficult situation of the beef industry, these genetic tools and breeding goals will continue to evolve.

International audience

International audience; In 2010, a routine genetic evaluation on occurrence of clinical mastitis in three main dairy cattle breeds-- Montbéliarde (MO), Normande (NO) and Holstein (HO)--was implemented in France. Records were clinical mastitis events reported by farmers to milk recording technicians and the analyzed trait was the binary variable describing the occurrence of a mastitis case within the first 150 days of the first three lactations. Genetic parameters of clinical mastitis were estimated for the three breeds. Low heritability estimates were found: between 2% and 4% depending on the breed. Despite its low heritability, the trait exhibits genetic variation so efficient genetic improvement is possible. Genetic correlations with other traits were estimated, showing large correlations (often>0.50, in absolute value) between clinical mastitis and somatic cell score (SCS), longevity and some udder traits. Correlation with milk yield was moderate and unfavorable (ρ=0.26 to 0.30). High milking speed was genetically associated with less mastitis in MO (ρ=-0.14) but with more mastitis in HO (ρ=0.18). A two-step approach was implemented for routine evaluation: first, a univariate evaluation based on a linear animal model with permanent environment effect led to pre-adjusted records (defined as records corrected for all non-genetic effects) and associated weights. These data were then combined with similar pre-adjusted records for others traits in a multiple trait BLUP animal model. The combined breeding values for clinical mastitis obtained are the official (published) ones. Mastitis estimated breeding values (EBV) were then combined with SCSs EBV into an udder health index, which receives a weight of 14.5% to 18.5% in the French total merit index (ISU) of the three breeds. Interbull genetic correlations for mastitis occurrence were very high (ρ=0.94) with Nordic countries, where much stricter recording systems exist reflecting a satisfactory quality of phenotypes as reported by the farmers. They were lower (around 0.80) with countries supplying SCS as a proxy for the international evaluation on clinical mastitis.

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.

Paramètres génétiques du taux de calcium, prédit à partir des spectres moyen infrarouge, dans le lait des 3 principales races bovines laitières françaises. 22. Rencontres autour des Recherches sur les Ruminants

National audience

International audience; The aims of this study were to develop an equation to estimate calcium content (Ca) in bovine milk, using mid-infrared (MIR) spectroscopy and to determine Ca genetic parameters. To develop the Ca equation, 300 milk samples were selected from PhénoFinlait milkbank to cover a large range of breeding practices (3 breeds, different areas, seasons, lactation numbers, diets, etc.). Those samples were both analyzed by MIRand by atomic absorption spectrometry which is the reference method for Ca measurement. 210 out of the 300 samples were used as calibration dataset and the remaining 90 were used as independent validation set. The determination coefficient of validation of the equation (Rv2) reached 0.79 and its residual standard deviation (sy,x) was 4%. Genetic parameters of Ca were estimated for the three French major dairy breeds (Prim’holstein (HOL), Montbéliarde (MON), Normande (NOR)). Ca equation was applied to 35,326 spectral records collected from 6,723 first lactation HOL cows, 28,508 spectral records collected from 5,590 first lactation NOR cows and 50,505 spectral records collected from 6,330 first lactation MON cows. Three different models were used to estimate genetic parameters (1) an individual test-day repeatability model,(2) a lactation model, where the trait is the average of test-day records and (3) a test-day random regression model. The heritabilities of Ca estimated with lactation model were 0.44 in HOL, 0.74 in NOR and 0.70 in MON. The coefficients of genetic variation were 3.6, 4.3 and 4.2 in HOL, NOR and MON respectively. And data from more than 8,000 cows in the 3 breeds will be used for the next step: analysis of genomicsequences to identify causal mutations for Ca.

International audience

International audience

Cette étude présente les principaux résultats d’estimation de paramètres génétiques et de détection de QTL obtenus dans le cadre du programme PhénoFinlait sur les caractères de composition en Acides Gras (AG) et protéines du lait dans trois races bovines (Holstein, Montbéliarde et Normande), deux races ovines (Lacaune et Manech Tête Rousse) et deux races caprines (Alpine et Saanen). La composition du lait est estimée à partir de la spectrométrie dans le moyen infrarouge. Les paramètres génétiques sont estimés à partir des données de 102 000 contrôles laitiers de 22 000 vaches en première lactation, 67 000 contrôles de 20 000 brebis, et 45 000 contrôles de 13 700 chèvres. Ils sont très homogènes entre espèces et entre races. En revanche, ils dépendent beaucoup du mode d’expression des caractères, exprimés en proportion du lait ou de la matière. Exprimés en teneur dans le lait, les AG saturés présentent une héritabilité plus élevée que les insaturés chez les bovins et les ovins, mais l’écart est plus faible quand ils sont exprimés en teneur dans le gras. Chez les caprins, les estimations d’héritabilité sont plus élevées pour les caractères exprimés en teneur dans la matière grasse. Les mesures d’AG sont fortement corrélées entre stades de lactation, à l’exception du premier mois qui apparaît comme un caractère assez différent. Les corrélations génétiques sont positives entre AG saturés et entre AG insaturés. Entre AG saturés et insaturés, les corrélations sont positives pour les AG exprimés en teneur dans le lait mais négatives quand les AG sont exprimés en pourcentage de la matière grasse. Les AG saturés sont très fortement corrélés au taux butyreux du lait. Concernant les protéines, les estimations d’héritabilité sont très élevées pour la bêta-lactoglobuline, assez élevées pour les caséines, plus modérées pour l’alpha-lactalbumine. Concernant les corrélations, il existe une forte analogie entre AG et protéines. Ainsi, les caséines sont fortement corrélées entre elles et fortement liées au taux protéique. Leur corrélation avec les protéines sériques est positive quand les protéines sont exprimées en teneur dans le lait, mais très négatives quand elles sont exprimées en teneur dans les protéines. [br/] Les analyses de détection de QTL reposent sur les données de 7 800 vaches, 1 800 brebis et 2 300 chèvres génotypées avec des puces SNP pangénomiques. En moyenne, 9 QTL d’AG ont été détectés par caractère et par race bovine. Les QTL les plus importants ont été trouvés sur les chromosomes 14 (gène DGAT1), 5, 19, 27, 17, 11 et 13. On observe une forte co-localisation de QTL entre AG du même type, reflétant leur origine métabolique commune. Une fraction notable de ces QTL semble partagée entre races. 22 à 29 QTL sont détectés en moyenne pour chaque taux de protéine. Les plus significatifs se situent sur les chromosomes 6 (2 régions QTL, régions des gènes ABCG2 et des caséines), 11 (gène de la bêta-lactoglobuline) et 20 (gène GHR vers 32 Mb, mais aussi vers 58Mb). Le gène DGAT1 affecte également de nombreuses protéines exprimées en teneur dans le lait. [br/] Ces résultats indiquent que la composition fine du lait pourrait être modifiée par sélection, même si les grands équilibres entre composants peuvent difficilement être bouleversés. Il est ainsi possible d’augmenter la fraction de caséines dans les protéines. Il est aussi possible d’augmenter la fraction d’AG insaturés dans le lait, mais sans doute au prix d’une diminution du taux butyreux., This study presents the main genetic results obtained from the PhénoFinlait project with regards to genetic parameters and QTL detection for milk composition in fatty acids (FA) and proteins in three dairy cattle breeds (Montbeliarde, Normande, and Holstein), two goat breeds (Alpine and Saanen) and two sheep breeds (Lacaune and Manech Tete Rousse). Milk composition was estimated from midinfra red spectrometry. Genetic parameters were estimated from about 102,000 test-day records from 22,000 cows in first lactation, 67,000 records from 20,000 ewes and 45,000 records from 13,700 goats. Genetic parameter results were very homogeneous across species and breeds. They were found to be sensitive to the mode of expression of the traits, in % of milk or in % of fat or protein. Expressed in % of milk, test-day saturated FA (SAT) had higher heritability estimates than unsaturated FA (UNSAT) but this difference was smaller when traits were in % of fat. In goats, the results were markedly different with higher heritability estimates found for traits expressed in % of fat. FA measurements were highly genetically correlated across different stages of lactation except in the beginning of the lactation. Genetic correlation estimates were found to be positive across saturated FA, and also across unsaturated FA. Between saturated and unsaturated FA, correlation estimates were positive when FA were expressed in % milk but negative when FA were expressed in % fat. Saturated FA were strongly correlated with fat content. With regards to proteins, heritability estimates were very high for beta-lactoglobulin, moderate to high for caseins, moderate for alpha-lactalbumin. Correlation pattern showed a strong similarity between FA and proteins. Indeed, caseins were strongly correlated with each other and with protein content. Their correlation with whey proteins was positive or very negative, whether proteins were expressed in % milk or in % proteins. [br/] QTL detection analyses were based on pangenomic genotyping data of 7800 cows, 1800 ewes, and 2300 goats. On average, 9 QTL were detected per FA trait and cattle breed. The most important QTL were found on chromosome 14 (DGAT1 gene), 5, 19, 27, 17, 11, and 13. A strong co-location of QTL was observed for FA sharing a common metabolic origin. A large proportion of the QTL seems to be shared across breeds. Twenty-two to 29 QTL were detected for each protein. The most significant QTL were found on chromosome 6 (2 regions close to ABCG2 gene and to casein cluster), 11 (beta-lactoglobulin gene), and 20 (2 regions, around GHR gene and around 58 Mb). The DGAT1 gene (BTA14, around 1.8 Mb) was also found to affect many proteins when expressed in % of milk. [br/] These results show that milk composition in FA or proteins can be significantly modified by selection, even if the major characteristics cannot be changed. For instance, it is possible to increase the casein percentage in total proteins. It is also possible to increase the unsaturated FA fraction in fat, but at the expense of a decrease in fat content in milk.

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International audience

International audience

International audience

International audience

National audience; This study, part of the PhénoFinlait project, was aimed at identifying the genes and mutations responsible for the genetic variability of milk fatty acids (FA) and proteins in Holstein, Montbéliarde and Normandy dairy cattle breeds. For 23 FA and 6 proteins, the analyzed phenotype was the mean of mid infra-red test-day measurements, adjusted for environmental effects. Whole genome sequences (WGS) of 8746 cows genotyped with the 50k Illumina chip were imputed with FImpute software, using 1147 sequenced bulls of the “1000 bull genomes” project as a reference. Over 28 million polymorphisms were predicted. GWAS analysis was carried out within breed with GCTA software and the model included a marker effect and a polygenic effect. Overall, 30 to 40 QTL regions were detected per trait over the genome. Many regions already detected with the 50k chip were confirmed but location confidence intervals were smaller. From this first analysis, several 500kb – 3Mb long regions were defined and analyzed by BayesC. This multi-marker method strongly reduces the effect of long distance linkage disequilibrium and pinpoints a limited number of candidate mutations. Breed comparison and functional annotation of variants also provide information. This work is illustrated by 10 examples on chromosomes 5, 14, 17, 19 and 27 for FA and 1, 2, 6, 11, and 20 for proteins. This study shows the value of WGS to identify candidate causal variants underlying the genetic variation of complex traits.; Cette étude réalisée dans le cadre du programme PhénoFinlait vise à identifier les gènes et les mutations responsables de la variabilité génétique de la composition du lait en acides gras (AG) et protéines dans les trois races bovines laitières Holstein, Montbéliarde et Normande. Pour 23 AG et 6 protéines, le phénotype analysé est la moyenne des mesures par moyen infra rouge à chaque contrôle, corrigées pour les effets de milieu. Les séquences du génome complet de 8746 vaches génotypées avec la puce Illumina 50k ont été reconstituées avec le logiciel FImpute, en utilisant comme référence la séquence de 1147 taureaux du projet « 1000 génomes bovins ». Au total, 28 millions de polymorphismes ont été prédits. L’analyse GWAS a été conduite intra-race avec le logiciel GCTA, le modèle incluant un effet marqueur et un effet polygénique. Cette approche a permis de caractériser 30 à 40 régions par caractère sur tout le génome. On retrouve la majorité des régions déjà détectées avec la puce 50k, mais les intervalles de localisation sont plus précis. Cette première analyse a permis de définir des régions de l’ordre de 500 kb à 3 Mb, analysées par BayesC. Cette méthode multi-marqueurs réduit considérablement l’effet du déséquilibre de liaison à grande distance et permet de proposer des mutations causales candidates. La comparaison entre races et l’annotation des variants aident au choix entre ces propositions. Ce travail est illustré avec 10 exemples sur les chromosomes 5, 14, 17, 19 et 27 pour les AG et 1, 2, 6, 11, et 20 pour les protéines. Cette étude montre l’intérêt du séquençage de génomes entiers pour identifier les mutations responsables du déterminisme génétique des caractères.