US-UK BBSRC-NIFA Collab-Reassembly of cattle immune gene clusters for quantitative analysis

美英 BBSRC-NIFA 合作——牛免疫基因簇重组用于定量分析

• 批准号: BB/M027155/1
• 负责人: John Hammond
• 金额: $ 48.65万
• 依托单位: The Pirbright Institute
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM027155%2F1
• 关键词: US; UK; BBSRC; NIFA; Collab

Since livestock were first domesticated approximately 10,000 years ago they have been selectively bred for desirable traits. Traditional genetic improvement using measurable traits and animal pedigrees has been very successful, particularly to increase production in important livestock species. The result today is a plethora of different breeds that are particularly suited for different environments or types of production, e.g. dairy and beef cattle. However within most livestock populations there is considerable amount of variation that has never been exploited during selective breeding. As the global demand for food increases rapidly, the demand for livestock improvement is escalating. As a consequence of this demand and recent advances in technology, it is now possible to inform breeding strategies based on the animal's genome sequence. In cattle this has been made possible through the characterisation of nearly 800,000 single nucleotide polymorphisms (SNPs) identified in cattle genomes. Rather than having to sequence the whole genome of each animal, rapid identification of SNPs that are associated with parentage, productive traits or breed composition allows for breeding decisions to be made earlier in an animal's life. Unknown animals with no phenotypic data can then be assessed solely on their SNP genotype and their breeding values calculated. This method of genomic selection is now widely used by cattle breeding companies.However, as with any young technology, problems remain. If regions of the genome are very variable between individuals and/or very repetitive it is difficult to identify SNPs that can be screened by the genotyping technology. There are several highly variable and repetitive immune gene complexes in mammalian genomes which have a fundamental role in disease resistance and responses to vaccines. Moreover these regions have evolved this complexity, at least in part, to combat rapidly evolving pathogens. In cattle, we have identified that the current SNPs do not cover three large and vital immune gene complexes, and to a large extent these complexes have not been assembled in the current genome builds. The validation of SNPs for use in genotyping relies upon an accurate genome assembly over the region the SNP is located; therefore this further compounds the problem. Ultimately the current technology is not yet able to type for genetic markers associated with important immune genes that are likely to influence health and disease resistance traits.Cattle possess a pool of natural genetic diversity that has evolved to counter rapidly evolving pathogens that cannot yet be selected for using genomics. We propose to develop the tools to utilise this diversity to improve health and disease resistance traits in cattle. Building on our initial assemblies of these gene complexes, we will assemble these genomic regions in many individuals to characterise the extent a large structural variation. Existing short whole genome sequence reads from > 30 individuals will then be aligned to these larger regions, alongside other publically available sequence datasets. By targeting these regions, it will be possible to identify and validate suitable SNPs, even those at low frequency, which will then be incorporated into a genotyping platform. The utility of this tool will then be tested by genotyping a herd of cattle that display differential disease resistance to bovine tuberculosis, a complex disease that is known to involve a genetic component and is influenced by the gene complexes we are targeting in this study. Ultimately we envisage that these markers can then be incorporated into current and future genotyping technologies to improve disease resistance in cattle through selective breeding.

自从大约10，000年前家畜首次被驯化以来，它们一直被选择性地培育以获得理想的性状。利用可测量的性状和动物谱系的传统遗传改良非常成功，特别是在提高重要牲畜品种的产量方面。今天的结果是过多的不同品种，特别适合不同的环境或生产类型，例如奶牛和肉牛。然而，在大多数牲畜种群中，有相当数量的变异在选择性育种过程中从未被利用过。随着全球对粮食需求的迅速增长，对牲畜改良的需求也在不断升级。由于这种需求和最近的技术进步，现在可以根据动物的基因组序列来制定育种策略。在牛中，通过对牛基因组中鉴定的近80万个单核苷酸多态性（SNP）进行表征，这已经成为可能。快速鉴定与亲子关系、生产性状或品种组成相关的SNP，而不是必须对每只动物的全基因组进行测序，这允许在动物生命的早期做出育种决定。没有表型数据的未知动物可以仅根据其SNP基因型进行评估，并计算其育种值。这种基因组选择方法现已被养牛公司广泛使用。然而，与任何年轻技术一样，问题仍然存在。如果基因组的区域在个体之间非常可变和/或非常重复，则难以鉴定可通过基因分型技术筛选的SNP。在哺乳动物基因组中存在几种高度可变和重复的免疫基因复合物，它们在疾病抵抗和对疫苗的应答中具有基本作用。此外，这些区域已经进化出这种复杂性，至少部分是为了对抗快速进化的病原体。在牛中，我们已经确定，目前的SNP不包括三个大的和重要的免疫基因复合物，在很大程度上，这些复合物还没有组装在目前的基因组构建。用于基因分型的SNP的验证依赖于SNP所在区域上的准确基因组组装;因此这进一步使问题复杂化。最后，目前的技术还不能对与重要免疫基因相关的遗传标记进行分型，这些基因可能会影响健康和抗病特性。牛拥有一个天然的遗传多样性库，这些遗传多样性已经进化到可以对抗快速进化的病原体，这些病原体还不能被选择用于基因组学。我们建议开发工具，利用这种多样性来改善牛的健康和抗病性状。在我们最初组装这些基因复合体的基础上，我们将在许多个体中组装这些基因组区域，以避免大的结构变异。然后将来自> 30个个体的现有短全基因组序列读数与这些较大区域以及其他可获得的序列数据集进行比对。通过靶向这些区域，将有可能识别和验证合适的SNP，即使是低频率的SNP，然后将其纳入基因分型平台。该工具的实用性，然后将测试一群牛的基因分型，显示不同的疾病抵抗牛结核病，一种复杂的疾病，已知涉及的遗传成分，并受到我们在这项研究中的目标基因复合体的影响。最终，我们设想，这些标记可以被纳入目前和未来的基因分型技术，以提高抗病牛通过选择性育种。

项目成果

The antibody loci of the domestic goat (Capra hircus).

• DOI: 10.1007/s00251-017-1033-3
• 发表时间: 2018-05
• 期刊: Immunogenetics
• 影响因子: 3.2
• 作者: Schwartz JC;Philp RL;Bickhart DM;Smith TPL;Hammond JA
• 通讯作者: Hammond JA

The evolution of the natural killer complex; a comparison between mammals using new high-quality genome assemblies and targeted annotation.

• DOI: 10.1007/s00251-017-0973-y
• 发表时间: 2017-04
• 期刊: Immunogenetics
• 影响因子: 3.2
• 作者: Schwartz JC;Gibson MS;Heimeier D;Koren S;Phillippy AM;Bickhart DM;Smith TP;Medrano JF;Hammond JA
• 通讯作者: Hammond JA

Single-molecule sequencing and chromatin conformation capture enable de novo reference assembly of the domestic goat genome.

• DOI: 10.1038/ng.3802
• 发表时间: 2017-04
• 期刊: Nature genetics
• 影响因子: 30.8
• 作者: Bickhart DM;Rosen BD;Koren S;Sayre BL;Hastie AR;Chan S;Lee J;Lam ET;Liachko I;Sullivan ST;Burton JN;Huson HJ;Nystrom JC;Kelley CM;Hutchison JL;Zhou Y;Sun J;Crisà A;Ponce de León FA;Schwartz JC;Hammond JA;Waldbieser GC;Schroeder SG;Liu GE;Dunham MJ;Shendure J;Sonstegard TS;Phillippy AM;Van Tassell CP;Smith TP
• 通讯作者: Smith TP