Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Mental Illness Research Education and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania.
Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Biol Psychiatry. 2021 Feb 1;89(3):236-245. doi: 10.1016/j.biopsych.2020.06.026. Epub 2020 Jul 6.
Prediction of disease risk is a key component of precision medicine. Common traits such as psychiatric disorders have a complex polygenic architecture, making the identification of a single risk predictor difficult. Polygenic risk scores (PRSs) denoting the sum of an individual's genetic liability for a disorder are a promising biomarker for psychiatric disorders, but they require evaluation in a clinical setting.
We developed PRSs for 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, cross disorder, attention-deficit/hyperactivity disorder, and anorexia nervosa) and 17 nonpsychiatric traits in more than 10,000 individuals from the Penn Medicine Biobank with accompanying electronic health records. We performed phenome-wide association analyses to test their association across disease categories.
Four of the 6 psychiatric PRSs were associated with their primary phenotypes (odds ratios from 1.2 to 1.6). Cross-trait associations were identified both within the psychiatric domain and across trait domains. PRSs for coronary artery disease and years of education were significantly associated with psychiatric disorders, largely driven by an association with tobacco use disorder.
We demonstrated that the genetic architecture of electronic health record-derived psychiatric diagnoses is similar to ascertained research cohorts from large consortia. Psychiatric PRSs are moderately associated with psychiatric diagnoses but are not yet clinically predictive in naïve patients. Cross-trait associations for these PRSs suggest a broader effect of genetic liability beyond traditional diagnostic boundaries. As identification of genetic markers increases, including PRSs alongside other clinical risk factors may enhance prediction of psychiatric disorders and associated conditions in clinical registries.
疾病风险预测是精准医学的关键组成部分。常见的特征，如精神障碍，具有复杂的多基因结构，使得识别单个风险预测因子变得困难。多基因风险评分（PRS）表示个体患某种疾病的遗传易感性总和，是精神障碍的一种很有前途的生物标志物，但需要在临床环境中进行评估。
我们在宾夕法尼亚大学医学生物库中对 10000 多名伴有电子健康记录的个体进行了 6 种精神障碍（精神分裂症、双相情感障碍、重度抑郁症、跨障碍、注意缺陷/多动障碍和神经性厌食症）和 17 种非精神疾病特征的 PRS 开发，并进行了表型全基因组关联分析，以检验它们在疾病类别中的相关性。
6 种精神 PRS 中的 4 种与主要表型相关（比值比为 1.2 至 1.6）。在精神领域和特征领域内都发现了跨特征的关联。冠心病和受教育年限的 PRS 与精神障碍显著相关，主要是与烟草使用障碍相关。
我们证明了电子健康记录衍生的精神诊断的遗传结构与大型联盟的确定研究队列相似。精神 PRS 与精神诊断中度相关，但在未经治疗的患者中尚未具有临床预测性。这些 PRS 的跨特征关联表明，遗传易感性的影响超出了传统诊断边界，更为广泛。随着遗传标记的识别增加，包括 PRS 在内的其他临床危险因素可能会提高临床登记处对精神障碍和相关疾病的预测能力。
