Evaluation of methodology for transcriptomic imputation into genome-wide association studies of complex human traits to infer causal mechanisms

该项目将评估转录组学插补方法的性能，用于检测基因表达与复杂人类特征的关联。全基因组关联研究（GWAS）已经成功地鉴定了与复杂人类性状相关的基因座。然而，支撑这些关联的生物学机制仍然知之甚少，并且在将GWAS研究结果转化为改善健康结果的承诺方面进展甚微。解决这一挑战的一种方法是应用因果建模技术，如“孟德尔随机化”，通过将GWAS数据与相关组织中的转录组学相结合，使用基因表达作为DNA变异和表型之间的中间性状，来推断潜在的因果生物学机制。这些技术使用遗传变异来检查在存在潜在混杂因素（如生活方式/环境）的情况下某些可改变的“暴露”（如基因表达）对疾病的因果影响。与健康相关结果相关的转录本可能通过下调基因为新的干预措施提供药物靶点。孟德尔随机化的一个缺点是需要相同个体的GWAS和转录组学数据，这在大型队列中可能在经济上不可行。应对这一挑战的一个潜在解决方案是使用“双样本”技术，即：（如基因型-组织表达“GTEx”项目）以鉴定预测组织特异性基因表达的遗传变体的子集;（ii）使用鉴定的多变体预测因子将这些转录组谱插补到现有的GWAS中;和（iii）检验插补的转录组谱和表型之间的关联。这种方法的主要优点是，转录组学的档案可以插补到大，广泛的表型GWAS在没有成本（除了计算），和“反向因果关系”不是一个主要的问题，因为表型状态或治疗不改变种系遗传variation.In这个项目中，我们将评估的性能的转录组学插补方法检测基因表达与复杂的人类性状的关联。我们将使用计算机模拟来比较在一系列与非遗传因素混淆的模型下，插补转录组学与直接测量的基因表达的功效。我们将使用GTEx的可用资源开发多组织中基因表达的多变异预测因子，并使用这些预测因子将转录组学特征输入到包括英国生物样本库在内的监督团队可用的大规模、深度表型化GWAS中（500 000人），爱沙尼亚生物库（50 000人）和老龄化群体遗传流行病学研究资源（100 000人）。除了包含DNA变异信息（以GWAS数据和全基因组序列的形式）外，这些数据集还包含各种表型测量，包括生物样本数据（进行的测量）、评估访视时未收集的其他暴露数据（例如，来自基于网络的饮食问卷调查的数据），以及通过链接到一系列健康相关记录的健康相关结果的数据）。学生的输出将为GWAS的未来设计/分析提供信息，并将检测复杂人类特征的因果基因，从而提供对调控机制的洞察，并为疾病相关结果确定新的治疗干预的潜在药物靶点。该项目映射到BBSRC战略研究重点“健康生物科学”和“世界级基础生物科学”，旨在阐明复杂表型结果的生物学机制，包括长期健康相关结果和与正常生理过程相关的结果。

This project will evaluate the performance of transcriptomic imputation methods for detecting association of gene expression with complex human traits. Genome-wide association studies (GWAS) have been successful at identifying loci associated with complex human traits. However, the biological mechanisms underpinning these associations remain poorly understood, and there has been little progress in the promised translation of GWAS findings into improved health outcomes. One approach to address this challenge is to apply causal modelling techniques, such as "Mendelian randomisation", to infer potential causal biological mechanisms through integrating GWAS data with transcriptomics in relevant tissues, using gene expression as an intermediate trait between DNA variation and phenotype. These techniques use genetic variation to examine the causal effect on disease of some modifiable "exposure" (such as gene expression) in the presence of potential confounding factors (such as lifestyle/environment). Transcripts associated with health-related outcomes may provide drug targets for novel interventions through down-regulation of the gene. One disadvantage of Mendelian randomisation is the requirement for GWAS and transcriptomic data in the same individuals, which may be financially infeasible in large cohorts. A potential solution to this challenge is to use "two-sample" techniques that: (i) utilise external data resources (such as the Genotype-Tissue Expression "GTEx" Project) to identify subsets of genetic variants that predict tissue-specific gene expression; (ii) impute these transcriptomic profiles into existing GWAS using the identified multi-variant predictors; and (iii) test for association between imputed transcriptomic profiles and phenotype. The key advantages of this approach are that transcriptomic profiles can be imputed into large, extensively phenotyped GWAS at no cost (except computation), and "reverse causality" is not a major concern because phenotypic status or treatment does not alter germline genetic variation.In this project, we will evaluate the performance of transcriptomic imputation methods for detecting association of gene expression with complex human traits. We will use computer simulations to compare the power of imputed transcriptomics with directly measured gene expression under a range of models of confounding with non-genetic factors. We will develop multi-variant predictors of gene expression in multiple tissues using available resources from GTEx, and use these to impute transcriptomic profiles into large-scale, deeply phenotyped GWAS available to the supervisory team, including UK Biobank (500,000 individuals), Estonian Biobank (50,000 individuals), and the Resource for Genetic Epidemiology Research on Ageing Cohort (100,000 individuals). As well as containing information on DNA variation (in the form of GWAS data and whole-genome sequence), these data sets also contain a variety of phenotypic measurements including data (measurements taken) on biological samples, additional exposure data not collected at the assessment visit (e.g., data from web-based dietary questionnaires), and data on health-related outcomes via linkage to a range of health-related records).The output from the studentship will inform future design/analysis of GWAS, and will detect causal genes for complex human traits, thereby providing insight into regulatory mechanisms, and, for disease-related outcomes, identifying potential drug targets for novel therapeutic intervention. The project maps to the BBSRC strategic research priorities "Bioscience for Health" and "World Class Underpinning Bioscience" by aiming to elucidate biological mechanisms underpinning complex phenotypic outcomes, including both long-term health-related outcomes and those related to normal physiological processes.