Thousands of years of natural and artificial selection have resulted in indigenous cattle breeds that are well-adapted to the environmental challenges of their local habitat and thereby are considered as valuable genetic resources. Understanding the genetic background of such adaptation processes can help us design effective breeding objectives to preserve local breeds and improve commercial cattle. To identify regions under putative selection, GGP HD 150 K single nucleotide polymorphism (SNP) arrays were used to genotype 106 individuals representing five Swedish breeds i.e. native to different regions and covering areas with a subarctic cold climate in the north and mountainous west, to those with a continental climate in the more densely populated south regions. Five statistics were incorporated within a framework, known as de-correlated composite of multiple signals (DCMS) to detect signatures of selection. The obtained p-values were adjusted for multiple testing (FDR < 5%), and significant genomic regions were identified. Annotation of genes in these regions revealed various verified and novel candidate genes that are associated with a diverse range of traits, including e.g. high altitude adaptation and response to hypoxia (DCAF8, PPP1R12A, SLC16A3, UCP2, UCP3, TIGAR), cold acclimation (AQP3, AQP7, HSPB8), body size and stature (PLAG1, KCNA6, NDUFA9, AKAP3, C5H12orf4, RAD51AP1, FGF6, TIGAR, CCND2, CSMD3), resistance to disease and bacterial infection (CHI3L2, GBP6, PPFIBP1, REP15, CYP4F2, TIGD2, PYURF, SLC10A2, FCHSD2, ARHGEF17, RELT, PRDM2, KDM5B), reproduction (PPP1R12A, ZFP36L2, CSPP1), milk yield and components (NPC1L1, NUDCD3, ACSS1, FCHSD2), growth and feed efficiency (TMEM68, TGS1, LYN, XKR4, FOXA2, GBP2, GBP5, FGD6), and polled phenotype (URB1, EVA1C). We identified genomic regions that may provide background knowledge to understand the mechanisms that are involved in economic traits and adaptation to cold climate in cattle. Incorporating p-values of different statistics in a single DCMS framework may help select and prioritize candidate genes for further analyses.

中文翻译：

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选择的签名揭示了五个瑞典牛品种中涉及经济性状和冷驯化的候选基因。

几千年的自然和人工选择已导致土著牛品种非常适应当地栖息地的环境挑战，因此被视为宝贵的遗传资源。了解此类适应过程的遗传背景可以帮助我们设计有效的育种目标，以保护当地品种并改良商品牛。为了确定推定选择的区域，使用GGP HD 150 K单核苷酸多态性（SNP）阵列对代表五个瑞典品种的106个个体进行基因分型，这些个体属于不同地区，并覆盖北部和西部山区的北极亚寒气候地区。在人口更稠密的南部地区具有大陆性气候。在一个框架中纳入了五项统计数据，被称为多信号解相关复合（DCMS），以检测选择的特征。调整获得的p值进行多次测试（FDR <5％），并鉴定出重要的基因组区域。这些区域中的基因注释揭示了各种经过验证的新颖候选基因，这些基因与多种特征相关，包括例如高海拔适应性和对缺氧的反应（DCAF8，PPP1R12A，SLC16A3，UCP2，UCP3，TIGAR），冷驯化（AQP3 ，AQP7，HSPB8），体型和身高（PLAG1，KCNA6，NDUFA9，AKAP3，C5H12orf4，RAD51AP1，FGF6，TIGAR，CCND2，CSMD3），对疾病和细菌感染的抵抗力（CHI3L2，GBP6，PPFIBP1，REP15，CYP4F2 ，PYURF，SLC10A2，FCHSD2，ARHGEF17，RELT，PRDM2，KDM5B），繁殖（PPP1R12A，ZFP36L2，CSPP1），牛奶产量和成分（NPC1L1，NUDCD3，ACSS1，FCHSD2），生长和饲料效率（TMEM68，TGS1，LYN，XKR4，FOXA2，GBP2，GBP5，FGD6）和轮询表型（URB1，EVA1C）。我们确定了可能提供背景知识的基因组区域，以了解与牛的经济性状和适应寒冷气候有关的机制。将不同统计量的p值合并到单个DCMS框架中可能有助于选择候选者基因并确定其优先级，以进行进一步分析。