Vast-scale linear mixed modelling genetic discovery approaches for genome- and exome-wide association analyses to enable therapeutic target validation

用于全基因组和外显子组关联分析的大规模线性混合建模遗传发现方法，以实现治疗靶点验证

• 批准号: MR/R025851/1
• 负责人: Oriol Canela-Xandri
• 金额: $ 38.18万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FR025851%2F1
• 关键词: Vast; scale; linear; mixed; modelling

Large-scale publicly available datasets, such as the UK Biobank (n=500,000 participants), which combine genome-wide genotyping and exome sequencing data with linkage to detailed phenotype measurement and electronic healthcare records have the opportunity to transform human genetic discovery analyses. Such datasets are transformative both in their scale and in the depth and diversity of quantitative and disease phenotypes available, and raised a strong interest both in the academia and the industry. In this regard, we have identified partners in Target Sciences (TSci) at GlaxoSmithKline (GSK), a leading team in the application of genetics in drug target discovery and validation. They have previously shown that drugs developed against targets with genetic support for the proposed disease are more likely to reach approval (PMID: 26121088), have used existing GWAS results to search for drug repurposing opportunities (PMID: 22491277) and to develop databases of gene-disease pairs to inform target discovery and validation decisions (PMID: 27899665, 28472345), and have used other biobank samples to influence selection of cardiovascular endpoints (PMID: 26791069) and search for drug repurposing opportunities (PMID: 27301456). GSK have previously performed large-scale targeted sequencing studies (PMID: 22604722) and recently funded exome sequencing of 50,000 participants in UK Biobank, with the aim of further supporting drug target discovery and validation. A major aim at GSK is to use UK Biobank data to conduct phenome-wide association studies (PheWAS), for variants known or predicted to affect gene function for drug targets of interest. The approach currently used is to test each single variant against thousands of disease traits, in the subset of unrelated individuals. However, this approach needs to be improved to distinguish between associations where the drug target variants are likely causal, from associations where the drug target variants are merely correlated (in linkage disequilibrium).Testing all variants (potentially thousands) in order to fine map in the genomic context of each association of interest is inefficient. A preferable approach is to conduct PheWAS and fine mapping in genomic context, by querying a database of genome-wide association results for all diseases and phenotypes of interest. To maximize discovery power and fine mapping resolution, it is preferable to populate this database with results calculated using in the largest possible sample size. However, an almost inevitable consequence of increasing sample sizes from human populations, is that a larger fraction of participants are related to other participants in the sample. Traditional approaches, such as removing one participant from each related pair, may lead to the removal of a significant proportion of participants from the analysis with consequent loss of statistical power. An alternative approach is using mixed linear model approaches to correct for population structure. However, these approaches require the development of new software tools to deal with large sample sizes, variants and numbers of phenotypes. However, GSK TSci scientists lack the technical expertise required to implement efficient mixed model association testing at the scale required, so this joint project is aimed to collaborate with them to develop the required methods to populate the database. Our work has the opportunity to be impactful on drug discovery and development.

大规模公开可用的数据集，如英国生物银行（n=500,000参与者），将全基因组基因分型和外显子组测序数据与详细的表型测量和电子医疗记录相结合，有机会改变人类基因发现分析。这些数据集在规模、数量和疾病表型的深度和多样性方面都具有变革性，引起了学术界和工业界的强烈兴趣。在这方面，我们在葛兰素史克公司（GSK）的目标科学公司（TSci）找到了合作伙伴，葛兰素史克公司是遗传学应用于药物靶点发现和验证的领先团队。他们之前的研究表明，针对有基因支持的靶点开发的药物更有可能获得批准（PMID: 26121088），利用现有的GWAS结果寻找药物再利用机会（PMID: 22491277），开发基因-疾病对数据库，为靶点发现和验证决策提供信息（PMID: 27899665, 28472345），并使用其他生物库样本影响心血管终点的选择（PMID: 26121088）。26791069)，寻找药物再利用的机会（PMID: 27301456）。GSK此前进行了大规模靶向测序研究（PMID: 22604722），最近资助了英国生物银行5万名参与者的外显子组测序，目的是进一步支持药物靶点的发现和验证。GSK的一个主要目标是利用UK Biobank的数据进行全表型关联研究（PheWAS），研究已知或预测会影响感兴趣药物靶点基因功能的变异。目前使用的方法是在不相关个体的子集中测试每个单一变体对数千种疾病特征的影响。然而，这种方法需要改进，以区分药物靶点变异可能是因果关系的关联，以及药物靶点变异仅仅是相关的关联（连锁不平衡）。测试所有的变异（可能是数千个），以便在每个感兴趣的关联的基因组环境中绘制精细的图谱是低效的。一种较好的方法是通过查询所有感兴趣的疾病和表型的全基因组关联结果数据库，在基因组背景下进行PheWAS和精细定位。为了最大限度地提高发现能力和精细的映射分辨率，最好使用使用尽可能大的样本量计算的结果填充该数据库。然而，随着人口样本数量的增加，一个几乎不可避免的结果是，样本中更大比例的参与者与其他参与者有关系。传统的方法，例如从每个相关对中删除一个参与者，可能会导致从分析中删除相当大比例的参与者，从而导致统计能力的丧失。另一种方法是使用混合线性模型方法来校正人口结构。然而，这些方法需要开发新的软件工具来处理大样本量、变异和表型数量。然而，GSK TSci科学家缺乏所需的技术专长，无法在所需的规模上实施有效的混合模型关联测试，因此该联合项目旨在与他们合作，开发所需的方法来填充数据库。我们的工作有机会对药物发现和开发产生影响。

项目成果

An atlas of genetic associations in UK Biobank.

• DOI: 10.1038/s41588-018-0248-z
• 发表时间: 2018-11
• 期刊: Nature genetics
• 影响因子: 30.8
• 作者: Canela-Xandri O;Rawlik K;Tenesa A
• 通讯作者: Tenesa A

A multi-tissue atlas of regulatory variants in cattle.

• DOI: 10.1038/s41588-022-01153-5
• 发表时间: 2022-09
• 期刊: Nature genetics
• 影响因子: 30.8
• 作者: Liu S;Gao Y;Canela-Xandri O;Wang S;Yu Y;Cai W;Li B;Xiang R;Chamberlain AJ;Pairo-Castineira E;D'Mellow K;Rawlik K;Xia C;Yao Y;Navarro P;Rocha D;Li X;Yan Z;Li C;Rosen BD;Van Tassell CP;Vanraden PM;Zhang S;Ma L;Cole JB;Liu GE;Tenesa A;Fang L
• 通讯作者: Fang L