A large number of pig breeds are distributed around the world, their features and characteristics vary among breeds, and they are valuable resources. Understanding the underlying genetic mechanisms that explain across-breed variation can help breeders develop improved pig breeds. In this study, we performed GWAS using a standard mixed linear model with three types of genome variants (SNP, InDel, and CNV) that were identified from public, whole-genome, sequencing data sets. We used 469 pigs of 57 breeds, and we identified and analyzed approximately 19 million SNPs, 1.8 million InDels, and 18,016 CNVs. We defined six biological phenotypes by the characteristics of breed features to identify the associated genome variants and candidate genes, which included coat color, ear shape, gradient zone, body weight, body length, and body height. A total of 37 candidate genes was identified, which included 27 that were reported previously (e.g., PLAG1 for body weight), but the other 10 were newly detected candidate genes (e.g., ADAMTS9 for coat color). Our study indicated that using GWAS across a modest number of breeds with high density genome variants provided efficient mapping of complex traits.

中文翻译：

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涵盖57个猪品种的全基因组变异可全面鉴定构成品种特征的遗传特征

世界各地分布着大量的猪品种，它们的特征和特性因品种而异，它们是宝贵的资源。了解解释杂交变异的潜在遗传机制可以帮助育种者发展改良的猪种。在这项研究中，我们使用标准混合线性模型对GWAS进行了研究，该模型具有从公共全基因组测序数据集中确定的三种类型的基因组变异（SNP，InDel和CNV）。我们使用了57个品种的469头猪，我们鉴定并分析了大约1900万个SNP，180万个InDels和18,016个CNV。我们通过品种特征的特征定义了六种生物表型，以鉴定相关的基因组变体和候选基因，包括外壳颜色，耳朵形状，梯度带，体重，体长和体高。共鉴定出37个候选基因，其中包括27个先前报告的基因（例如，体重为PLAG1），而其他10个是新检测到的候选基因（例如，为毛色为ADAMTS9）。我们的研究表明，在适度数量的具有高密度基因组变异的品种中使用GWAS可以有效地绘制复杂性状。