BackgroundGenome-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.ResultsThirty-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).ConclusionsGWAS 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 materialThe online version of this article (doi:10.1186/s12711-017-0344-z) contains supplementary material, which is available to authorized users.

Cattle production faces new challenges regarding sustainability with its three pillars -economic, societal and environmental. The following three main factors will drive dairy cattle selection in the future: (1) During a long period, intensive selection for enhanced productivity has deteriorated most functional traits, some reaching a critical point and needing to be restored. This is especially the case for the Holstein breed and for female fertility, mastitis resistance, longevity and metabolic diseases.(2) Genomic selection offers two new opportunities: as the potential genetic gain can be almost doubled, more traits can be efficiently selected; phenotype recording can be decoupled from selection and limited to several thousand animals. (3) Additional information from other traits can be used, either from existing traditional recording systems at the farm level or from the recent and rapid development of new technologies and precision farming. Milk composition (i.e. mainly fatty acids) should be adapted to better meet human nutritional requirements. Fatty acids can be measured through a new interpretation of the usual medium infrared spectra. Milk composition can also provide additional information about reproduction and health. Modern milk recorders also provide new information, that is, on milking speed or on the shape of milking curves. Electronic devices measuring physiological or activity parameters can predict physiological status like estrus or diseases, and can record behavioral traits. Slaughterhouse data may permit effective selection on carcass traits. Efficient observatories should be set up for early detection of new emerging genetic defects. In the near future, social acceptance of cattle production could depend on its capacity to decrease its ecological footprint. The first solution consists in increasing survival and longevity to reduce replacement needs and the number of nonproductive animals. At the individual level, selection on rumen activity may lead to decreased methane production and concomitantly to improved feed efficiency. A major effort should be dedicated to this new field of research and particularly to rumen flora metagenomics. Low input in cattle production is very important and tomorrow's cow will need to adapt to a less intensive production environment, particularly lower feed quality and limited care. Finally, global climate change will increase pathogen pressure, thus more accurate predictors for disease resistance will be required.

Mid-infrared (MIR) spectrometry was used to estimate the fatty acid (FA) composition in cow, ewe, and goat milk. The objectives were to compare different statistical approaches with wavelength selection to predict the milk FA composition from MIR spectra, and to develop equations for FA in cow, goat, and ewe milk. In total, a set of 349 cow milk samples, 200 ewe milk samples, and 332 goat milk samples were both analyzed by MIR and by gas chromatography, the reference method. A broad FA variability was ensured by using milk from different breeds and feeding systems. The methods studied were partial least squares regression (PLS), first-derivative pretreatment + PLS, genetic algorithm + PLS, wavelets + PLS, least absolute shrinkage and selection operator method (LASSO), and elastic net. The best results were obtained with PLS, genetic algorithm + PLS and first derivative + PLS. The residual standard deviation and the coefficient of determination in external validation were used to characterize the equations and to retain the best for each FA in each species. In all cases, the predictions were of better quality for FA found at medium to high concentrations (i.e., for saturated FA and some monounsaturated FA with a coefficient of determination in external validation >0.90). The conversion of the FA expressed in grams per 100mL of milk to grams per 100g of FA was possible with a small loss of accuracy for some FA.

Background Milk quality in dairy cattle is routinely assessed via analysis of mid-infrared (MIR) spectra; this approach can also be used to predict the milk’s cheese-making properties (CMP) and composition. When this method of high-throughput phenotyping is combined with efficient imputations of whole-genome sequence data from cows’ genotyping data, it provides a unique and powerful framework with which to carry out genomic analyses. The goal of this study was to use this approach to identify genes and gene networks associated with milk CMP and composition in the Montbéliarde breed. Results Milk cheese yields, coagulation traits, milk pH and contents of proteins, fatty acids, minerals, citrate, and lactose were predicted from MIR spectra. Thirty-six phenotypes from primiparous Montbéliarde cows (1,442,371 test-day records from 189,817 cows) were adjusted for non-genetic effects and averaged per cow. 50 K genotypes, which were available for a subset of 19,586 cows, were imputed at the sequence level using Run6 of the 1000 Bull Genomes Project (comprising 2333 animals). The individual effects of 8.5 million variants were evaluated in a genome-wide association study (GWAS) which led to the detection of 59 QTL regions, most of which had highly significant effects on CMP and milk composition. The results of the GWAS were further subjected to an association weight matrix and the partial correlation and information theory approach and we identified a set of 736 co-associated genes. Among these, the well-known caseins, PAEP and DGAT1 , together with dozens of other genes such as SLC37A1 , ALPL , MGST1 , SEL1L3 , GPT , BRI3BP , SCD , GPAT4 , FASN , and ANKH , explained from 12 to 30% of the phenotypic variance of CMP traits. We were further able to identify metabolic pathways (e.g., phosphate and phospholipid metabolism and inorganic anion transport) and key regulator genes, such as PPARA , ASXL3, and bta - mir - 200c that are functionally linked to milk composition. Conclusions By using an approach that integrated GWAS with network and pathway analyses at the whole-genome sequence level, we propose candidate variants that explain a substantial proportion of the phenotypic variance of CMP traits and could thus be included in genomic evaluation models to improve milk CMP in Montbéliarde cows. Electronic supplementary material The online version of this article (10.1186/s12711-019-0473-7) contains supplementary material, which is available to authorized users.

Agroecology uses natural processes and local resources rather than chemical inputs to ensure production while limiting the environmental footprint of livestock and crop production systems. Selecting to achieve a maximization of target production criteria has long proved detrimental to fitness traits. However, since the 1990s, developments in animal breeding have also focussed on animal robustness by balancing production and functional traits within overall breeding goals. We discuss here how an agroecological perspective should further shift breeding goals towards functional traits rather than production traits. Breeding for robustness aims to promote individual adaptive capacities by considering diverse selection criteria which include reproduction, animal health and welfare, and adaptation to rough feed resources, a warm climate or fluctuating environmental conditions. It requires the consideration of genotype × environment interactions in the prediction of breeding values. Animal performance must be evaluated in low-input systems in order to select those animals that are adapted to limiting conditions, including feed and water availability, climate variations and diseases. Finally, we argue that there is no single agroecological animal type, but animals with a variety of profiles that can meet the expectations of agroecology. The standardization of both animals and breeding conditions indeed appears contradictory to the agroecological paradigm that calls for an adaptation of animals to local opportunities and constraints in weakly artificialized systems tied to their physical environment.

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Cheese-making properties of pressed cooked cheeses (PCC) and soft cheeses (SC) were predicted from mid-infrared (MIR) spectra. The traits that were best predicted by MIR spectra (as determined by comparison with reference measurements) were 3 measures of laboratory cheese yield, 5 coagulation traits, and 1 acidification trait for PCC (initial pH; pH). Coefficients of determination of these traits ranged between 0.54 and 0.89. These 9 traits as well as milk composition traits (fatty acid, protein, mineral, lactose, and citrate content) were then predicted from 1,100,238 MIR spectra from 126,873 primiparous Montbéliarde cows. Using this data set, we estimated the corresponding genetic parameters of these traits by REML procedures. A univariate or bivariate repeatability animal model was used that included the fixed effects of herd × test day × spectrometer, stage of lactation, and year × month of calving as well as the random additive genetic, permanent environmental, and residual effects. Heritability estimates varied between 0.37 and 0.48 for the 9 cheese-making property traits analyzed. Coagulation traits were the ones with the highest heritability (0.42 to 0.48), whereas cheese yields and pH had the lowest heritability (0.37 to 0.39). Strong favorable genetic correlations, with absolute values between 0.64 and 0.97, were found between different measures of cheese yield, between coagulation traits, between cheese yields and coagulation traits, and between coagulation traits measured for PCC and SC. In contrast, the genetic correlations between milk pH and CY or coagulation traits were weak (-0.08 to 0.09). The genetic relationships between cheese-making property traits and milk composition were moderate to high. In particular, high levels of proteins, fatty acids, Ca, P, and Mg in milk were associated with better cheese yields and improved coagulation. Proteins in milk were strongly genetically correlated with coagulation traits and, to a lesser extent, with cheese yields, whereas fatty acids in milk were more genetically correlated with cheese yields than with coagulation traits. This study, carried out on a large scale in Montbéliarde cows, shows that MIR predictions of cheese yields and milk coagulation properties are sufficiently accurate to be used for genetic analyses. Cheese-making traits, as predicted from MIR spectra, are moderately heritable and could be integrated into breeding objectives without additional phenotyping cost, thus creating an opportunity for efficient improvement via selection.

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 (h) were generally higher for protein fractions than for protein contents. They were moderate to high for α-casein, α-casein, β-casein, κ-casein, and α-lactalbumin (0.25 < h < 0.72). In each breed, β-lactoglobulin was the most heritable trait (0.61 < h < 0.86). Genetic correlations (r) 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 < r < 0.46) and κ-casein and α-casein (0.36 < r < 0.49). Between protein contents, r estimates were positive, with highest values found between caseins (0.83 < r < 0.98). In the 3 breeds, β-lactoglobulin was negatively correlated with caseins (-0.75 < r < -0.08), in particular with κ-casein (-0.75 < r < -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.