This paper reports an exploratory study based on quantitative genomic analysis in dairy traits of American Alpine goats. The dairy traits are quality-determining components in goat milk, cheese, ice cream, etc. Alpine goat phenotypes for quality components have been routinely recorded for many years and deposited in the Council on Dairy Cattle Breeding (CDCB) repository. The data collected were used to conduct an exploratory genome-wide association study (GWAS) from 72 female Alpine goats originating from locations throughout the U.S. Genotypes were identified with the Illumina Goat 50K single-nucleotide polymorphisms (SNP) BeadChip. The analysis used a polygenic model where the dropping criterion was a . The initial dataset was composed of ~60,000 rows of SNPs and 21 columns of phenotypic traits and composed of 53,384 scaffolds containing other informative data points used for genomic predictive power. Phenotypic association with the 50K BeadChip revealed 26,074 reads of candidate genes. These candidate genes segregated as separate novel SNPs and were identified as statistically significant regions for genome and chromosome level trait associations. Candidate genes associated differently for each of the following phenotypic traits: test day milk yield (13,469 candidate genes), test day protein yield (25,690 candidate genes), test day fat yield (25,690 candidate genes), percentage protein (25,690 candidate genes), percentage fat (25,690 candidate genes), and percentage lactose content (25,690 candidate genes). The outcome of this study supports elucidation of novel genes that are important for livestock species in association to key phenotypic traits. Validation towards the development of marker-based selection that provides precision breeding methods will thereby increase the breeding value.

中文翻译：

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全基因组关联研究对美国高山山羊奶高产量和高质量的基因组预测能力的研究。

本文报告了一项基于定量基因组分析的美国高山山羊乳品性状的探索性研究。乳制品特性是山羊奶，奶酪，冰淇淋等中决定质量的成分。高质量成分的高山山羊表型已被常规记录多年，并保存在奶牛育种委员会（CDCB）资料库中。收集到的数据用于对来自全美各地的72只雌性高山山羊进行探索性全基因组关联研究（GWAS）。用Illumina Goat 50K单核苷酸多态性（SNP）BeadChip鉴定了基因型。分析使用多基因模型，其中丢弃标准为。初始数据集由约60,000行SNP和21列表型性状组成，由53,384个支架组成，这些支架包含用于基因组预测能力的其他信息性数据点。与50K BeadChip的表型关联揭示了26,074条候选基因的读数。这些候选基因被分离为单独的新SNP，并被确定为基因组和染色体水平性状关联的统计学显着区域。下列表型性状的相关候选基因不同：试验日产奶量（13,469个候选基因），试验日产蛋白量（25,690个候选基因），试验日产脂量（25,690个候选基因），蛋白质百分率（25,690个候选基因），脂肪百分比（25,690个候选基因）和乳糖含量百分比（25,690个候选基因）。这项研究的结果支持阐明对与关键表型性状有关的牲畜物种重要的新基因。