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.

Intramuscular fat content is generally associated with improved sensory quality and better acceptability of fresh pork. However, conclusive evidence is still lacking for the biological mechanisms underlying i.m. fat content variability in pigs. The current study aimed to determine whether variations in i.m. fat content of longissimus muscle are related to i.m. adipocyte cellularity, lipid metabolism, or contractile properties of the whole muscle. To this end, crossbred (Large White x Duroc) pigs exhibiting either a high (2.82 +/- 0.38%, HF) or a low (1.15 +/- 0.14%, LF) lipid content in LM biopsies at 70 kg of BW were further studied at 107 +/- 7 kg of BW. Animals grew at the same rate, but HF pigs at slaughter presented fatter carcasses than LF pigs (P = 0.04). The differences in i.m. fat content between the 2 groups were mostly explained by variation in i.m. adipocyte number (+127% in HF compared with LF groups, P = 0.005). Less difference (+13% in HF compared with LF groups, P = 0.057) was noted in adipocyte diameter, and no significant variation was detected in whole-muscle lipogenic enzyme activities (acetyl-CoA carboxylase, P = 0.9; malic enzyme, P = 0.35; glucose-6-phosphate dehydrogenase, P = 0.75), mRNA levels of sterol-regulatory element binding protein-1 (P = 0.6), or diacylglycerol acyltransferase 1 (P = 0.6). Adipocyte fatty acid binding protein (FABP)-4 protein content in whole LM was 2-fold greater in HF pigs than in LF pigs (P = 0.05), and positive correlation coefficients were found between the FABP-4 protein level and adipocyte number (R2 = 0.47, P = 0.02) and lipid content (R2 = 0.58, P = 0.004). Conversely, there was no difference between groups relative to FABP-3 mRNA (P = 0.46) or protein (P = 0.56) levels, oxidative enzymatic activities (citrate synthase, P = 0.9; beta-hydroxyacyl-CoA dehydrogenase, P = 0.7), mitochondrial (P = 0.5) and peroxisomal (P = 0.12) oxidation rates of oleate, mRNA levels of genes involved in fatty acid oxidation (carnitine-palmitoyl-transferase 1, P = 0.98; peroxisome proliferator-activated receptor delta, P = 0.73) or energy expenditure (uncoupling protein 2, P = 0.92; uncoupling protein 3, P = 0.84), or myosin heavy-chain mRNA proportions (P > 0.49). The current study suggests that FABP-4 protein content may be a valuable marker of lipid accretion in LM and that i.m. fat content and myofiber type composition can be manipulated independently.

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.

BackgroundNumerous quantitative trait loci (QTL) have been detected in pigs over the past 20 years using microsatellite markers. However, due to the low density of these markers, the accuracy of QTL location has generally been poor. Since 2009, the dense genome coverage provided by the Illumina PorcineSNP60 BeadChip has made it possible to more accurately map QTL using genome-wide association studies (GWAS). Our objective was to perform high-density GWAS in order to identify genomic regions and corresponding haplotypes associated with production traits in a French Large White population of pigs.MethodsAnimals (385 Large White pigs from 106 sires) were genotyped using the PorcineSNP60 BeadChip and evaluated for 19 traits related to feed intake, growth, carcass composition and meat quality. Of the 64 432 SNPs on the chip, 44 412 were used for GWAS with an animal mixed model that included a regression coefficient for the tested SNPs and a genomic kinship matrix. SNP haplotype effects in QTL regions were then tested for association with phenotypes following phase reconstruction based on the Sscrofa10.2 pig genome assembly.ResultsTwenty-three QTL regions were identified on autosomes and their effects ranged from 0.25 to 0.75 phenotypic standard deviation units for feed intake and feed efficiency (four QTL), carcass (12 QTL) and meat quality traits (seven QTL). The 10 most significant QTL regions had effects on carcass (chromosomes 7, 10, 16, 17 and 18) and meat quality traits (two regions on chromosome 1 and one region on chromosomes 8, 9 and 13). Thirteen of the 23 QTL regions had not been previously described. A haplotype block of 183 kb on chromosome 1 (six SNPs) was identified and displayed three distinct haplotypes with significant (0.0001 < P < 0.03) associations with all evaluated meat quality traits.ConclusionsGWAS analyses with the PorcineSNP60 BeadChip enabled the detection of 23 QTL regions that affect feed consumption, carcass and meat quality traits in a LW population, of which 13 were novel QTL. The proportionally larger number of QTL found for meat quality traits suggests a specific opportunity for improving these traits in the pig by genomic selection.

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Email: Marie-Pierre Sanchez -marie-pierre.sanchez@jouy.inra.fr; Nathalie Iannuccelli -nathalie.iannuccelli@toulouse.inra.fr; Benjamin Basso -benjamin.basso@toulouse.inra.fr; Jean-Pierre Bidanel -jean-pierre.bidanel@jouy.inra.fr; Yvon Billon -yvon.billon@magneraud.inra.fr; Gilles Gandemer -gilles.gandemer@lusignan.inra.fr; Hélène Gilbert -helene.gilbert@jouy.inra.fr; Catherine Larzul* -catherine.larzul@jouy.inra.fr; Christian Legault -catherine.larzul@jouy.inra.fr; Juliette Riquet -juliette.riquet@toulouse.inra.fr; Denis Milan -denis.milan@toulouse.inra.fr; Pascale Le Roy -pascale.leroy@rennes.inra.fr * Corresponding author Abstract Background: Improving pork quality can be done by increasing intramuscular fat (IMF) content. This trait is influenced by quantitative trait loci (QTL) sought out in different pig populations. Considering the high IMF content observed in the Duroc pig, it was appealing to determine whether favourable alleles at a major gene or QTL could be found. The detection was performed in an experimental F2 Duroc × Large White population first by segregation analysis, then by QTL mapping using additional molecular information.

The aim of this work was to estimate whether genetic dissection of QTL on chromosomes 1, 2, 4, and 7, detected in an F2 Meishan x Large White population, can be achieved with a recombinant back-cross progeny test approach. For this purpose, a first generation of backcross (BC1) was produced by using frozen semen of F1 Large White x Meishan boars with Large White females. Four BC1 boars were selected because of their heterozygosity for at least 1 of the 4 regions. The BC1 boars were crossed with Large White sows, and the resulting BC2 offspring were measured for several growth and body composition traits. Contrary to the F2 animals, BC2 animals were also measured for meat quality traits in adductor, gluteus superficialis (GS), longissimus dorsi, and biceps femoris (BF) muscles. Each BC1 boar was tested for a total of 39 traits and for the 4 regions with statistical interval mapping analyses. The QTL effects obtained in BC1 families showed some differences compared with those described in F1 families. However, we confirmed QTL effects for growth in the SW1301-SW2512 markers interval on chromosome 1 and also for body composition in the SW1828-SW2512 markers interval on chromosome 1, in the SW2443-SWR783 markers interval on chromosome 2, and in the SW1369-SW632 markers interval on chromosome 7. In addition, we detected new QTL for growth traits on chromosome 2 and for meat quality traits on chromosomes 1 and 2. Growth of animals from weaning to the end of the test was influenced by the IGF2 gene region on chromosome 2. Concerning meat quality, ultimate pH of adductor, longissimus dorsi, and BF were affected by the interval delimited by UMNP3000 and SW2512 markers on chromosome 1, and a* of GS, L* of BF, and water-holding capacity of GS were affected by QTL located between marker loci SW2443 and SWR783 on chromosome 2. Recombinant progeny testing appeared to be a suitable strategy for the genetic dissection of the QTL investigated.

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.