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

大规模公开可用的数据集，如英国生物库(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。这样的数据集在其规模以及可用数量和疾病表型的深度和多样性方面都具有变革性，并引起了学术界和工业界的强烈兴趣。在这方面，我们已经在葛兰素史克(GSK)的靶标科学(TSCI)领域找到了合作伙伴，GSK是在药物靶标发现和验证中应用遗传学的领先团队。他们之前已经表明，针对针对拟议疾病的基因支持的目标开发的药物更有可能获得批准(PMID：26121088)，已经使用现有的GWAS结果来搜索药物再利用机会(PMID：22491277)，并开发基因-疾病对数据库以提供靶标发现和验证决定(PMID：27899665,28472345)，并使用其他生物库样本来影响心血管终点的选择(PMID：26791069)和搜索药物再用途机会(PMID：27301456)。葛兰素史克此前曾对英国生物库的50,000名参与者进行了大规模的靶向测序研究(PMID：22604722)，最近还资助了50,000名参与者的外显子组测序，目的是进一步支持药物靶标的发现和验证。葛兰素史克的一个主要目标是使用英国生物库数据进行表型全组关联研究(Phewas)，寻找已知或预测会影响感兴趣药物靶标的基因功能的变异。目前使用的方法是在无关个体的子集中测试每个单一变异与数千种疾病特征的对比。然而，这种方法需要改进，以区分药物靶标变异可能是因果关系的关联，以及药物靶标变异只是相关的关联(在连锁不平衡情况下)。为了在每个感兴趣的关联的基因组环境中精细定位，测试所有变异(可能是数千个)是低效的。一种更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的数据库，在基因组环境中进行Phewas和精细作图。为了最大限度地提高发现能力和精细映射分辨率，最好在此数据库中填充使用尽可能大的样本大小计算的结果。然而，从人类群体中增加样本量的一个几乎不可避免的后果是，更大比例的参与者与样本中的其他参与者相关。传统方法，例如从每对相关的参与者中删除一名参与者，可能会导致将相当大比例的参与者从分析中删除，从而丧失统计能力。另一种方法是使用混合线性模型方法来修正种群结构。然而，这些方法需要开发新的软件工具来处理大样本量、变种和数量的表型。然而，葛兰素史克TSCI的科学家缺乏在所需规模上实施高效混合模型关联测试所需的技术专业知识，因此该联合项目旨在与他们合作开发填充数据库所需的方法。我们的工作有机会对药物发现和开发产生影响。

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.