Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk. Separate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk. This study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.

中文翻译：

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基于序列的混种奶牛个体牛奶中红外波数的全基因组关联研究

傅里叶变换中红外 (FT-MIR) 光谱提供了一种高通量且廉价的方法来预测牛奶样品中的牛奶成分和其他新特性。虽然已经对 FT-MIR 预测性状进行了许多全基因组关联研究 (GWAS)，但很少有针对单个 FT-MIR 波数的 GWAS。我们使用 38,085 头新西兰混种奶牛的全基因组推算序列，对 895 个单独的 FT-MIR 波数表型进行了 GWAS，并评估了这些直接表型在识别候选因果基因和变异方面的价值，并提高了我们对牛奶的理化性质。对 895 个单独的 FT-MIR 波数表型中的每一个进行单独的 GWAS，确定了 450 个具有显着 FT-MIR 波数 QTL 的 1-Mbp 基因组区域，与 246 个具有 QTL 的 1-Mbp 基因组区域相比，FT-MIR 预测的牛奶成分特征。使用乳腺 RNA-seq 数据和基因注释信息确定了 38 个共定位和共分离表达 QTL (eQTL) 和 31 个 FT-MIR 波数表型的蛋白质序列突变，后者包括 ABO 基因中的无效突变在改变牛奶寡糖谱方面具有潜在作用。对于这些分析中涉及的候选致病基因，我们检查了相关位点与中红外光谱中每个波数之间的关联强度。这揭示了基因组远处基因座组的共享关联模式，突出了通过它们在泌乳中的生物学作用及其对牛奶化学成分的假定影响相关联的基因座簇。这项研究证明了 FT-MIR 波数表型在提高我们对牛奶成分的理解方面的效用，与从 FT-MIR 预测的成分特征中发现的相比，它提供了更多的 QTL 和假定的致病基因和变体。检查感兴趣位点的中红外光谱的显着性模式进一步突出了关联的共性，这可能反映了牛奶成分的物理化学特性。