Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Cell. 2021 Apr 15;184(8):2068-2083.e11. doi: 10.1016/j.cell.2021.03.034.
Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.
了解人口健康差异是公平精准健康努力的重要组成部分。流行病学研究通常依赖于种族和民族的定义，但这些人口标签可能无法充分捕捉影响特定亚人群的疾病负担和环境因素。在这里，我们提出了一个框架，用于重新利用电子健康记录 (EHR) 中的数据，并结合基因组数据来探索可能影响疾病负担的人口联系。我们使用来自纽约市一个多元化生物库的数据，确定了 17 个具有最近遗传亲缘关系的社区。我们观察到 1177 个与特定群体相关的具有统计学意义的健康结果，并证明了导致孟德尔疾病的遗传变异的分离在统计学上存在显著差异。我们还表明，精细的人口结构可以影响群体内复杂疾病风险的预测。这项工作加强了将基因组数据与 EHR 相关联的实用性，并提供了一个框架，用于精细监测人口健康。
