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Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbeliarde cows.

ABSTRACT: 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.

SUBMITTER: Sanchez MP

PROVIDER: S-EPMC6604208 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows.

Sanchez Marie-Pierre MP Ramayo-Caldas Yuliaxis Y Wolf Valérie V Laithier Cécile C El Jabri Mohammed M Michenet Alexis A Boussaha Mekki M Taussat Sébastien S Fritz Sébastien S Delacroix-Buchet Agnès A Brochard Mickaël M Boichard Didier D

Genetics, selection, evolution : GSE 20190701 1

<h4>Background</h4>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 ide ...[more]

PMID: 31262251

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Project description:The composition of raw milk is of major importance for dairy products, especially fat, protein, and casein (CN) contents, which are used worldwide in breeding programs for dairy species because of their role in human nutrition and in determining cheese yield (%CY). The aim of the study was to develop formulas based on detailed milk composition to disentangle the role of each milk component on %CY traits. To this end, 1,271 individual milk samples (1.5 L/cow) from Brown Swiss cows were processed according to a laboratory model cheese-making procedure. Fresh %CY (%CYCURD), total solids and water retained in the fresh cheese (%CYSOLIDS and %CYWATER), and 60-days ripened cheese (%CYRIPENED) were the reference traits and were used as response variables. Training-testing linear regression modeling was performed: 80% of observations were randomly assigned to the training set, 20% to the validation set, and the procedure was repeated 10 times. Four groups of predictive equations were identified, in which different combinations of predictors were tested separately to predict %CY traits: (i) basic composition, i.e., fat, protein, and CN, tested individually and in combination; (ii) udder health indicators (UHI), i.e., fat + protein or CN + lactose and/or somatic cell score (SCS); (iii) detailed protein profile, i.e., fat + protein fractions [CN fractions, whey proteins, and nonprotein nitrogen (NPN) compounds]; (iv) detailed protein profile + UHI, i.e., fat + protein fractions + NPN compounds and/or UHI. Aside from the positive effect of fat, protein, and total casein on %CY, our results allowed us to disentangle the role of each casein fraction and whey protein, confirming the central role of β-CN and κ-CN, but also showing α-lactalbumin (α-LA) to have a favorable effect, and β-lactoglobulin (β-LG) a negative effect. Replacing protein or casein with individual milk protein and NPN fractions in the statistical models appreciably increased the validation accuracy of the equations. The cheese industry would benefit from an improvement, through genetic selection, of traits related to cheese yield and this study offers new insights into the quantification of the influence of milk components in composite selection indices with the aim of directly enhancing cheese production.

Project description:BackgroundYak cows produce higher quality milk with higher concentrations of milk fat than dairy cows. Recently, studies have found the yak milk yield and milk fat percentage have decreased significantly over the past decade, highlighting the urgency for yak milk improvement. Therefore, we aimed to analyze how the gut microbiome impacts milk fat synthesis in Zhongdian yak cows.MethodsWe collected milk samples from Zhongdian yak cows and analyzed the milk fat percentage, selecting five Zhongdian yak cows with a very high milk fat percentage (>7%, 8.70 ± 1.89%, H group) and five Zhongdian yak cows with a very low milk fat percentage (<5%, 4.12 ± 0.43%, L group), and then obtained gut samples of these ten Zhongdian yak cows through rectal palpation. Gut metagenomics, metabolomics, and conjoint metagenomics and metabolomics analyses were performed on these samples, identifying taxonomic changes, functional changes, and changes in gut microbes-metabolite interactions within the milk fat synthesis-associated Zhongdian yak cows gut microbiome, to identify potential regulatory mechanisms of milk fat at the gut microbiome level in Zhongdian yak cows.ResultsThe metagenomics analysis revealed Firmicutes and Proteobacteria were significantly more abundant in the gut of the high-milk fat Zhongdian yak cows. These bacteria are involved in the biosynthesis of unsaturated fatty acids and amino acids, leading to greater efficiency in converting energy to milk fat. The metabolomics analysis showed that the elevated gut metabolites in high milk fat percentage Zhongdian yak cows were mainly enriched in lipid and amino acid metabolism. Using a combined metagenomic and metabolomics analysis, positive correlations between Firmicutes (Desulfocucumis, Anaerotignum, Dolosiccus) and myristic acid, and Proteobacteria (Catenovulum, Comamonas, Rubrivivax, Marivita, Succinimouas) and choline were found in the gut of Zhongdian yak cows. These interactions may be the main contributors to methanogen inhibition, producing less methane leading to higher-efficient milk fat production.ConclusionsA study of the gut microbe, gut metabolites, and milk fat percentage of Zhongdian yak cows revealed that the variations in milk fat percentage between yak cows may be caused by the gut microbes and their metabolites, especially Firmicutes-myristic acid and Proteobacteria-choline interactions, which are important to milk fat synthesis. Our study provides new insights into the functional roles of the gut microbiome in producing small molecule metabolites and contributing to milk performance traits in yak cows.

Dataset Information

Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbeliarde cows.

Publications

Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows.

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