MRC Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, CB2 0SR, UK.
Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK.
Nat Commun. 2021 Feb 3;12(1):764. doi: 10.1038/s41467-020-20885-8.
Genome-wide association studies (GWAS) have identified thousands of genomic regions affecting complex diseases. The next challenge is to elucidate the causal genes and mechanisms involved. One approach is to use statistical colocalization to assess shared genetic aetiology across multiple related traits (e.g. molecular traits, metabolic pathways and complex diseases) to identify causal pathways, prioritize causal variants and evaluate pleiotropy. We propose HyPrColoc (Hypothesis Prioritisation for multi-trait Colocalization), an efficient deterministic Bayesian algorithm using GWAS summary statistics that can detect colocalization across vast numbers of traits simultaneously (e.g. 100 traits can be jointly analysed in around 1 s). We perform a genome-wide multi-trait colocalization analysis of coronary heart disease (CHD) and fourteen related traits, identifying 43 regions in which CHD colocalized with ≥1 trait, including 5 previously unknown CHD loci. Across the 43 loci, we further integrate gene and protein expression quantitative trait loci to identify candidate causal genes.
全基因组关联研究（GWAS）已经确定了数千个影响复杂疾病的基因组区域。下一步的挑战是阐明涉及的因果基因和机制。一种方法是使用统计共定位来评估多个相关特征（例如分子特征、代谢途径和复杂疾病）之间的共享遗传病因，以确定因果途径、优先考虑因果变体和评估多效性。我们提出了 HyPrColoc（多特征共定位的假设优先级），这是一种使用 GWAS 汇总统计数据的高效确定性贝叶斯算法，可以同时检测大量特征之间的共定位（例如，大约 1 秒内可以联合分析 100 个特征）。我们对冠心病（CHD）和 14 种相关特征进行了全基因组多特征共定位分析，确定了 43 个与 CHD 共定位的区域≥1 个特征，包括 5 个先前未知的 CHD 基因座。在这 43 个基因座中，我们进一步整合基因和蛋白质表达数量性状基因座，以确定候选因果基因。
