Genomic Selection for Bovine Tuberculosis Resistance in Dairy Cows

这种细菌，牛分枝杆菌，对世界范围内的养牛业产生了重大的经济，贸易，健康和福利影响，并对人类，其他驯养，野生和野生动物种群构成风险。这种病原体导致慢性呼吸道疾病，牛结核病（bTB），尽管60多年来进行了昂贵的根除计划，但bTB仍然是英国和爱尔兰共和国牛群中日益严重的问题。这些方案包括屠宰皮试呈阳性的动物，这表明动物已被感染。迫切需要替代控制策略。以前的研究表明，牛在bTB风险方面存在遗传差异，这为降低bTB风险提供了遗传选择的可能性。通过基因组资源和信息的进步，包括新的基因分型工具，如高密度单核苷酸多态性（SNP）芯片，为更有利的性状育种牲畜变得更快，更准确。这些“芯片”由数千个SNP标记组成，这些标记可以将基因组中的变异与性状中的变异联系起来。在我们之前的BBSRC CEDFAS研究中，使用了来自北方爱尔兰的bTB病例（定义为皮肤试验和病变阳性）和与之匹配的黑白花奶牛对照，我们可以解释ca。使用包含超过700，000个SNP的芯片，观察到的bTB状态变化的25%。我们现在建议在此信息的基础上，将其与来自英国和爱尔兰共和国其他牛种群的数据集（我们原则上已获得互惠许可）结合联合收割机，以开发bTB风险的强大基因组预测因子。然后，养牛业可以直接应用这些方法来选择bTB抗性。首先，我们将通过对另一组皮肤试验阳性但病变阴性的牛进行基因分型来增强我们最初研究的效力。这也将使我们能够澄清这种表型与我们对bTB病例的更严格定义之间的遗传关系。然后，我们将进行一项荟萃分析，将这个丰富的数据集与其他来源的数据集结合起来，并使用这个大型数据集来开发bTB风险的基因组预测因子。这些结果将为乳制品行业提供一个直接的工具，使其能够选择增加的bTB抗性，而不需要继续从目前感染的牛群中收集bTB表型。此外，我们将通过确定bTB抗性与产奶量和英国育种目标中其他经济上重要的性状的遗传关系，确保bTB抗性的选择不会对生产性状造成损害。我们还希望找到导致我们在bTB抗性中检测到的遗传差异的实际SNP。为了做到这一点，我们建议利用这样一个事实，即现在已经可以对单个动物的整个基因组进行测序;这通常会揭示许多新的SNP。此外，由于DNA序列中的插入和缺失（插入缺失和拷贝数变异）引起的遗传变化也越来越多地与性状变异相关，并且也可能支持抗病性。为了研究这种类型的变异，并确定更接近实际致病SNP的SNP，我们建议对具有最极端bTB风险的动物进行重新测序，这是由本基金早期开发的基因组预测因子确定的。这些信息，即与抗性增加相关的实际DNA变化的鉴定，将提高跨代基因组预测因子的准确性，并可能在其他品种中具有实用性。这些结果也将使我们能够探索抗M的潜在基础。牛感染，这可能会提高我们的能力，设计进一步的控制策略，这一棘手的疾病。

The bacterium, Mycobacterium bovis, has a major economic, trade, health and welfare impact on the cattle industry worldwide as well as posing a risk to humans, other domesticated, feral and wild animal populations. This pathogen causes the chronic respiratory disease, bovine tuberculosis (bTB), which remains an increasing problem in cattle herds in the UK and Republic of Ireland despite over sixty years of costly eradication programmes. These programmes included the slaughter of animals which are positive for a skin test which indicates that the animal has become infected. Alternative control strategies are urgently needed. Previous studies have suggested that cattle differ genetically in their risk of bTB, opening up the possibility of genetic selection for decreased risk of bTB. Breeding livestock for more favourable traits is becoming faster and more accurate through advances in genomic resources and information, including new genotyping tools such as high density single nucleotide polymorphism (SNP) chips. These 'chips' consist of thousands of SNP markers which can relate variation across the genome to variation seen in traits. In our previous BBSRC CEDFAS grant using bTB cases (defined as skin test and lesion positive) and herd-matched Holstein-Friesian controls from Northern Ireland, we could account for ca. 25% of the observed variation in bTB status using chips comprising over 700,000 SNPs. We now propose to build on this information and combine it with datasets derived from other cattle populations in the UK and Republic of Ireland (for which we have obtained reciprocal permission in principle) to develop robust genomic predictors of bTB risk. These could then be directly applied by the cattle industry to select for bTB resistance. First we will enhance the power of our original study by genotyping a further set of cattle which are skin test positive but lesion negative. This will also enable us to clarify the genetic relationship between this phenotype and our more strict definition of a bTB case. We will then conduct a meta-analysis combining this enriched dataset with that from other sources, and use this large dataset to develop genomic predictors of bTB risk. The results will provide a direct tool to the dairy industry, enabling it to select for increased bTB resistance without a continuing requirement to collect bTB phenotypes from cattle in currently infected herds. In addition, we will ensure that selection for bTB resistance is not detrimental to production traits by determining the genetic relationship of bTB resistance with milk production and other economically important traits in the UK breeding goal. We also want to find the actual SNPs that lead to the genetic differences we detect in bTB resistance. To do this, we propose to take advantage of the fact that it has now become feasible to sequence whole genomes of individual animals; this usually reveals many novel SNPs. Furthermore, genetic changes due to insertions and deletions (indels and copy number variants) in the DNA sequence are also increasingly associated with variation in traits and may underpin disease resistance as well. In order to investigate this type of variation and also identify SNPs closer to the actual causative SNPs, we propose to resequence animals with the most extreme bTB risk, as determined by the genomic predictors developed in the earlier part of the grant. This information, i.e. identification of the actual DNA changes associated with increased resistance, would improve the accuracy of the genomic predictors across generations and potentially have utility in other breeds. These results will also enable us to explore the underlying basis for resistance to M. bovis infection, which could advance our ability to design further control strategies for this intractable disease.