Rigoletto

De cada obra s'ha digitalitzat un programa sencer. De la resta s'han digitalitzat les parts que són diferents.Director: José Sabate

Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals

peer-reviewedH.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

La sélection génétique des races bovines allaitantes en France : Un dispositif et des outils innovants au service des filières viande

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

Genetic parameters for milk calcium content predicted by MIR spectroscopy in three French breeds

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 MIR and 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 genomic sequences to identify causal mutations for Ca

ldentification de mutations candidates affectant la composition du lait en acides gras et protéines par analyse d’association sur la séquence du génome dans les races Holstein, Montbéliarde et Normande

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.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

Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals

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