基因组数据分析正日益成为畜牧业的一部分。因此,基因组信息的常规收集将是有效管理小型育种计划的宝贵资源,濒危人口。本文的目的是证明如何使用基因组数据来分析(1)连锁不平衡(LD),LD衰变和有效种群大小(NeLD);(2)基于纯合性(ROH)的近交水平和有效种群大小(NeROH);(3)使用小的品种内和其他品种的基因组信息预测基因组育种值(GEBV)。以蒂罗尔·格雷种群为例,目的是突出小品种基因组分析的潜力。除了我们自己的结果之外,我们还讨论了基因组学的额外用途来评估相关性,外加剂比例,和有害变体的继承。示例数据集包括218个蒂罗尔·格雷公牛基因型,这些都是人口中可用的人工智能公牛。经过标准质量控制限制后,仍有34,581个SNP用于分析。根据IlluminaGenCall和IlluminaGenTrain评分,采用单独的质量控制来确定ROH水平。导致211头公牛和33,604个SNP。LD被计算为10兆碱基对(Mb)区域内的SNP之间的平方相关系数。ROHs是基于覆盖至少4、8和16Mb的区域得出的,这表明动物在大约12、6和3代前有共同的祖先,分别。4Mb的相应平均近交系数(FROH)为4.0%,8Mb为2.9%,16Mb为1.6%。平均发电间隔为5.66年,估计NeROH为125(NeROH>16Mb),186(NeROH>8Mb)和370(NeROH>4Mb)表明严格避免群体中的近亲繁殖。LD被用作推断人口历史和Ne的替代方法。结果表明,NeLD持续下降,到100、10和5代以前的780、120和80,分别。基因组选择是为大型品种开发的,并且在大型品种中运行良好。同样的方法也适用于蒂罗尔灰牛,使用不同的参考人群。与预期相反,在品种参考种群内具有非常小的GEBV的准确性非常高,在0.13-0.91和0.12-0.63之间,当估计的育种值和退化的育种值被用作假表型时,分别。随后的分析证实了高准确性是验证集中伪表型的低可靠性的结果,因此受到父母平均水平的严重影响。多品种和跨品种参考集的准确性不一致且较低。基因组信息可能在小型品种的管理中起着至关重要的作用,即使它的主要用途不同于大型品种。它允许评估个体之间的相关性,近亲繁殖的趋势,并做出相应的决定。这些决定将基于真实的基因组结构,而不是传统的谱系信息,它可能丢失或不完整。我们强烈建议对属于小型品种的所有个体进行常规基因分型,以促进对濒危牲畜种群的有效管理。
Analysis of genomic data is increasingly becoming part of the livestock industry. Therefore, the routine collection of genomic information would be an invaluable resource for effective management of breeding programs in small, endangered populations. The objective of the paper was to demonstrate how genomic data could be used to analyse (1) linkage disequlibrium (LD), LD decay and the effective population size (NeLD); (2) Inbreeding level and effective population size (NeROH) based on runs of homozygosity (ROH); (3) Prediction of genomic breeding values (GEBV) using small within-breed and genomic information from other breeds. The Tyrol Grey population was used as an example, with the goal to highlight the potential of genomic analyses for small breeds. In addition to our own results we discuss additional use of genomics to assess relatedness, admixture proportions, and inheritance of harmful variants. The example data set consisted of 218 Tyrol Grey bull genotypes, which were all available AI bulls in the population. After standard quality control restrictions 34,581 SNPs remained for the analysis. A separate quality control was applied to determine ROH levels based on Illumina GenCall and Illumina GenTrain scores, resulting into 211 bulls and 33,604 SNPs. LD was computed as the squared correlation coefficient between SNPs within a 10 mega base pair (Mb) region. ROHs were derived based on regions covering at least 4, 8, and 16 Mb, suggesting that animals had common ancestors approximately 12, 6, and 3 generations ago, respectively. The corresponding mean inbreeding coefficients (F ROH) were 4.0% for 4 Mb, 2.9% for 8 Mb and 1.6% for 16 Mb runs. With an average generation interval of 5.66 years, estimated NeROH was 125 (NeROH>16 Mb), 186 (NeROH>8 Mb) and 370 (NeROH>4 Mb) indicating strict avoidance of close inbreeding in the population. The LD was used as an alternative method to infer the population history and the Ne. The results show a continuous decrease in NeLD, to 780, 120, and 80 for 100, 10, and 5 generations ago, respectively. Genomic selection was developed for and is working well in large breeds. The same methodology was applied in Tyrol Grey cattle, using different reference populations. Contrary to the expectations, the accuracy of GEBVs with very small within breed reference populations were very high, between 0.13-0.91 and 0.12-0.63, when estimated breeding values and deregressed breeding values were used as pseudo-phenotypes, respectively. Subsequent analyses confirmed the high accuracies being a consequence of low reliabilities of pseudo-phenotypes in the validation set, thus being heavily influenced by parent averages. Multi-breed and across breed reference sets gave inconsistent and lower accuracies. Genomic information may have a crucial role in management of small breeds, even if its primary usage differs from that of large breeds. It allows to assess relatedness between individuals, trends in inbreeding and to take decisions accordingly. These decisions would be based on the real genome architecture, rather than conventional pedigree information, which can be missing or incomplete. We strongly suggest the routine genotyping of all individuals that belong to a small breed in order to facilitate the effective management of endangered livestock populations.