英国牛津大学Gil McVean研究小组利用研发的新方法实现了在英国生物银行中利用不同医院数据鉴定不同疾病的遗传风险。这一研究成果2019年12月23日在线发表在国际学术期刊《自然—遗传学》上。

研究人员开发了一种疾病检测方法，将来自英国生物库中320644名参与者的19155种疾病分类代码的3025个全基因组独立基因座的遗传风险谱聚类，这代表了庞大而异类的群体。研究人员确定了339种不同的疾病关联概况，并使用多种方法将群集链接到潜在的生物学途径。研究人员展示了聚类如何分解疾病风险的方差和协方差，从而确定了潜在的生物学过程及其影响。研究人员验证了在确定疾病关系及其在影响治疗策略中的应用。

据了解，遗传风险因素经常影响多种人类常见疾病，从而提供对共同病理生理途径的了解以及治疗方法发展的机会。但是，疾病风险遗传图谱的系统鉴定受到多种限制，包括人口规模队列综合临床数据的可用性以及能够处理多表型数据固有规模和差异功效的合适统计方法的缺乏。

附：英文原文

Title: Identifying cross-disease components of genetic risk across hospital data in the UK Biobank

Author: Adrian Cortes, Patrick K. Albers, Calliope A. Dendrou, Lars Fugger, Gil McVean

Issue&Volume: 2019-12-23

Abstract: Genetic risk factors frequently affect multiple common human diseases, providing insight into shared pathophysiological pathways and opportunities for therapeutic development. However, systematic identification of genetic profiles of disease risk is limited by the availability of both comprehensive clinical data on population-scale cohorts and the lack of suitable statistical methodology that can handle the scale of and differential power inherent in multi-phenotype data. Here, we develop a disease-agnostic approach to cluster the genetic risk profiles for 3,025 genome-wide independent loci across 19,155 disease classification codes from 320,644 participants in the UK Biobank, representing a large and heterogeneous population. We identify 339 distinct disease association profiles and use multiple approaches to link clusters to the underlying biological pathways. We show how clusters can decompose the variance and covariance in risk for disease, thereby identifying underlying biological processes and their impact. We demonstrate the use of clusters in defining disease relationships and their potential in informing therapeutic strategies.

DOI: 10.1038/s41588-019-0550-4

Source: https://www.nature.com/articles/s41588-019-0550-4