Genetic discovery from the multitude of phenotypes extractable from routine healthcare data can transform understanding of the human phenome and accelerate progress toward precision medicine. However, a critical question when analyzing high-dimensional and heterogeneous data is how best to interrogate increasingly specific subphenotypes while retaining statistical power to detect genetic associations. Here we develop and employ a new Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to analyze genetic variants against UK Biobank disease phenotypes derived from self-reporting and hospital episode statistics. Our method displays a more than 20% increase in power to detect genetic effects over other approaches and identifies new associations between classical human leukocyte antigen (HLA) alleles and common immune-mediated diseases (IMDs). By applying the approach to genetic risk scores (GRSs), we show the extent of genetic sharing among IMDs and expose differences in disease perception or diagnosis with potential clinical implications.

中文翻译：

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英国生物库中树状结构常规医疗保健数据的遗传关联贝叶斯分析。

从可从常规医疗保健数据中提取的众多表型中进行基因发现可以改变对人类表型的理解，并加速向精准医学的发展。然而，在分析高维和异质数据时，一个关键问题是如何最好地询问越来越具体的亚表型，同时保留检测遗传关联的统计能力。在这里，我们开发并采用了一个新的贝叶斯分析框架，该框架利用诊断分类的层次结构来分析来自自我报告和医院事件统计数据的 UK Biobank 疾病表型的遗传变异。与其他方法相比，我们的方法检测遗传效应的能力提高了 20% 以上，并确定了经典人类白细胞抗原 (HLA) 等位基因与常见免疫介导疾病 (IMD) 之间的新关联。通过将该方法应用于遗传风险评分 (GRS)，我们展示了 IMD 之间遗传共享的程度，并揭示了具有潜在临床意义的疾病感知或诊断差异。