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

该项目将评估转录补偿方法的性能，以检测基因表达与复杂人类特征的关联。全基因组关联研究已经成功地识别了与复杂人类特征相关的基因座。然而，支撑这些关联的生物学机制仍然知之甚少，而且在承诺将全球气候变化研究结果转化为改善健康结果方面进展甚微。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数据与相关组织的转录组学相结合，利用基因表达作为DNA变异和表型之间的中间特征，来推断潜在的因果生物学机制。这些技术使用遗传变异来研究在存在潜在的混杂因素(如生活方式/环境)的情况下，一些可改变的“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该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在相同的个体中需要GWAS和转录数据，这在大的队列中可能在财务上是不可行的。这一挑战的一个潜在解决方案是使用“双样本”技术：(I)利用外部数据资源(如基因类型-组织表达“GTEx”项目)来确定预测组织特异性基因表达的遗传变体的子集；(Ii)使用已确定的多变量预测因子将这些转录图谱输入现有的GWAs；以及(Iii)测试推测的转录图谱与表型之间的关联。这种方法的主要优点是，转录图谱可以免费地归因于大的、广泛的表型GWAs(除了计算)，并且由于表型状态或处理不会改变生殖系的遗传变异，“反向因果关系”不是一个主要的问题。在这个项目中，我们将评估转录拼接方法的性能，以检测基因表达与复杂的人类特征的关联。我们将使用计算机模拟，在一系列混淆非遗传因素的模型下，比较推测的转录和直接测量的基因表达的能力。我们将利用GTEx的可用资源开发多种组织中基因表达的多变量预测因子，并使用这些因子将转录图谱输入到监督团队可用的大规模、深表型GWA中，包括英国生物库(500,000个个体)、爱沙尼亚生物库(50,000个个体)和老龄队列遗传流行病学研究资源(100,000个个体)。除了包含关于DNA变异的信息(以GWAS数据和全基因组序列的形式)，这些数据集还包含各种表型测量，包括生物样本的数据(测量)、在评估访问中未收集的额外暴露数据(例如，来自基于网络的饮食问卷的数据)、以及与健康相关的结果的数据(通过与一系列与健康相关的记录的链接)。来自学生的输出将为未来GWAS的设计/分析提供信息，并将检测复杂人类特征的原因基因，从而提供对调节机制的洞察，以及对于与疾病相关的结果，为新的治疗干预确定潜在的药物靶点。该项目映射到BBSRC的战略研究重点“生物科学促进健康”和“世界级支撑生物科学”，旨在阐明支撑复杂表型结果的生物学机制，包括与健康相关的长期结果和与正常生理过程有关的结果。

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.