小柯机器人

近日，英国牛津大学Gil McVean等研究人员合作发现，英国生物库中合并症的年龄依赖性主题建模确定具有不同遗传风险的疾病亚型。这一研究成果于2023年10月9日在线发表在国际学术期刊《自然—遗传学》上。

研究人员介绍了一种年龄依赖性主题建模（ATM）方法，该方法可为大型电子病历数据集中数百种不同疾病的纵向记录提供低等级表示。研究人员将ATM应用于282957份英国生物库样本，识别出52种具有异质性合并症特征的疾病；对211908份“我们所有人”样本的分析得出了一致的结果。

研究人员根据52种异质性疾病的并发症特征定义了这些疾病的亚型，并使用多基因风险评分（PRS）比较了不同疾病亚型的遗传风险，进而确定了18种疾病亚型的多基因风险评分与同一疾病的其他亚型存在显著差异。研究人员进一步确定了对疾病风险具有亚型依赖性影响的特定遗传变异。总之，ATM确定了具有不同全基因组和特定位点遗传风险特征的疾病亚型。

据了解，对电子健康记录（EHR）中的纵向数据进行分析有可能改善临床诊断并实现个性化医疗，这促使人们努力从患者的合并症信息中识别疾病亚型

附：英文原文

Title: Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk

Author: Jiang, Xilin, Zhang, Martin Jinye, Zhang, Yidong, Durvasula, Arun, Inouye, Michael, Holmes, Chris, Price, Alkes L., McVean, Gil

Issue&Volume: 2023-10-09

Abstract: The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. We applied ATM to 282,957 UK Biobank samples, identifying 52 diseases with heterogeneous comorbidity profiles; analyses of 211,908 All of Us samples produced concordant results. We defined subtypes of the 52 heterogeneous diseases based on their comorbidity profiles and compared genetic risk across disease subtypes using polygenic risk scores (PRSs), identifying 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease. We further identified specific genetic variants with subtype-dependent effects on disease risk. In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.

DOI: 10.1038/s41588-023-01522-8

Source: https://www.nature.com/articles/s41588-023-01522-8

Nature Genetics：《自然—遗传学》，创刊于1992年。隶属于施普林格·自然出版集团，最新IF：41.307
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