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. 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. 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.

中文翻译：

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基于序列的GWAS，网络和途径分析揭示了与Montbéliarde奶牛的奶酪制造特性和牛奶成分相关的基因

通常通过分析中红外光谱来评估奶牛的牛奶质量。这种方法还可用于预测牛奶的奶酪制作性能（CMP）和成分。当这种高通量表型分析方法与来自奶牛基因型分析数据的全基因组序列数据的有效估算相结合时，它将为进行基因组分析提供独特而强大的框架。这项研究的目的是使用这种方法来鉴定与Montbéliarde品种的牛奶CMP及其组成相关的基因和基因网络。牛奶奶酪的产量，凝结特性，牛奶pH值以及蛋白质，脂肪酸，矿物质，柠檬酸盐和乳糖的含量可通过MIR光谱进行预测。初产Montbéliarde奶牛的36种表型（来自189个测试日的1,442,371个测试日记录，调整了817头母牛的非遗传影响，并平均每头母牛。使用1000 Bull Genomes Project（包含2333只动物）的Run6，在序列水平上估算了可用于19,586头母牛的50 K基因型。在全基因组关联研究（GWAS）中评估了850万个变体的个体作用，该研究导致检测到59个QTL区，其中大多数对CMP和牛奶成分具有非常显着的影响。GWAS的结果进一步经历了关联权重矩阵和偏相关和信息论方法，我们确定了一组736个关联基因。其中，著名的酪蛋白PAEP和DGAT1，以及数十种其他基因，例如SLC37A1，ALPL，MGST1，SEL1L3，GPT，BRI3BP，SCD，GPAT4，FASN和ANKH，解释了CMP性状表型变异的12％到30％。我们还能够确定代谢途径（例如磷酸和磷脂代谢以及无机阴离子转运）和关键调控基因，例如与牛奶成分功能相关的PPARA，ASXL3和bta-mir-200c。通过使用在全基因组序列水平上将GWAS与网络和途径分析相集成的方法，我们提出了候选变体，这些变体解释了CMP性状的表型变异的很大一部分，因此可以被包括在基因组评估模型中，以改善牛奶的CMP。 Montbéliarde奶牛。和bta-mir-200c在功能上与牛奶成分相关。通过使用在全基因组序列水平上将GWAS与网络和途径分析相集成的方法，我们提出了候选变体，这些变体解释了CMP性状的表型变异的很大一部分，因此可以被包括在基因组评估模型中，以改善牛奶的CMP。 Montbéliarde奶牛。和bta-mir-200c在功能上与牛奶成分相关。通过使用在全基因组序列水平上将GWAS与网络和途径分析相集成的方法，我们提出了候选变体，这些变体解释了CMP性状的表型变异的很大一部分，因此可以被包括在基因组评估模型中，以改善牛奶的CMP。 Montbéliarde奶牛。