Genomic analysis for managing small and endangered populations: a case study in Tyrol Grey cattle.
Genomic analysis for managing small and endangered populations: a case study in Tyrol Grey cattle.
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DOI：
10.3389/fgene.2015.00173
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发表时间：
2015
影响因子：
3.7
通讯作者：
Sölkner J
中科院分区：
文献类型：
--
作者：
Mészáros G;Boison SA;Pérez O'Brien AM;Ferenčaković M;Curik I;Da Silva MV;Utsunomiya YT;Garcia JF;Sölkner J
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濒危种群育种计划的宝贵资源。本文的目的是展示如何使用基因组数据来分析：(1)连锁不平衡（LD）、LD衰减和有效种群大小（NeLD）；(2)基于纯合子数（ROH）的近交水平和有效群体大小（NeROH）；(3)利用品种内和其他品种的基因组信息预测基因组育种值（GEBV）。以蒂罗尔灰犬为例，目的是强调对小型品种进行基因组分析的潜力。除了我们自己的结果之外，我们还讨论了基因组学的其他应用，以评估有害变异的亲缘关系、混合比例和遗传。示例数据集包括218个蒂罗尔灰牛基因型，这些基因型是种群中所有可用的AI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Illumina GenCall和Illumina GenTrain评分，采用单独的质量控制来确定ROH水平，结果得到211个公牛和33,604个snp。LD计算为10兆碱基对（Mb）区域内snp之间的相关系数的平方。ROHs是基于至少覆盖4、8和16 Mb的区域得出的，这表明动物分别在大约12、6和3代以前有共同的祖先。4 Mb、8 Mb和16 Mb组的平均近交系数（FROH）分别为4.0%、2.9%和1.6%。平均世代间隔为5.66年，估计NeROH为125 (NeROH>16 Mb), 186 （NeROH>8 Mb）和370 (NeROH>4 Mb)，表明该种群严格避免近近亲繁殖。将LD作为推断种群历史和Ne的另一种方法。结果显示，在100代、10代和5代之前，NeLD分别下降到780、120和80。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。同样的方法应用于蒂罗尔灰牛，使用不同的参考种群。与预期相反，当使用估计育种值和去压力育种值作为伪表型时，品种参考群体中非常小的gebv的准确性非常高，分别在0.13-0.91和0.12-0.63之间。随后的分析证实，高准确性是验证集中伪表型的低可靠性的结果，因此受到亲本平均值的严重影响。多品种和跨品种的参考集给出了不一致和较低的准确性。基因组信息可能在小型品种的管理中发挥关键作用，即使其主要用途与大型品种不同。它允许评估个体之间的亲缘关系，近交的趋势，并作出相应的决定。这些决定将基于真实的基因组结构，而不是传统的家谱信息，后者可能缺失或不完整。我们强烈建议对一个小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 (FROH) 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.
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影响因子：
2.6
作者：
Flury, C.;Tapio, M.;Rieder, S.
通讯作者：
Rieder, S.
影响因子：
2.4
作者：
Frkonja, A.;Gredler, B.;Soelkner, J.
通讯作者：
Soelkner, J.
影响因子：
4.5
作者：
Kadri NK;Sahana G;Charlier C;Iso-Touru T;Guldbrandtsen B;Karim L;Nielsen US;Panitz F;Aamand GP;Schulman N;Georges M;Vilkki J;Lund MS;Druet T
通讯作者：
Druet T
DOI：
10.1186/1297-9686-43-34
发表时间：
2011-10-11
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
Calus MP;Mulder HA;Bastiaansen JW
通讯作者：
Bastiaansen JW
DOI：
10.1186/1297-9686-41-55
发表时间：
2009-12-31
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
Garrick DJ;Taylor JF;Fernando RL
通讯作者：
Fernando RL