Lu Tianyuan, Silveira Patrícia Pelufo, Greenwood Celia M T
Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.
Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, QC, Canada.
Front Neurosci. 2023 Jul 18;17:1143496. doi: 10.3389/fnins.2023.1143496. eCollection 2023.
Both genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated.
Leveraging a meta-analysis of major depressive disorder genome-wide association studies ( = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank ( = 130,092 European ancestry individuals). In a UK Biobank test dataset ( = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression.
In the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14-1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05-1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively.
We have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression.
遗传和早期生活风险因素在成人抑郁症的发病机制和进展中均起重要作用。然而，这些风险因素之间的相互作用及其对风险预测模型的附加价值尚未完全阐明。
利用对重度抑郁症全基因组关联研究的荟萃分析（45591例病例和97674例对照），我们在英国生物银行的一个模型选择数据集中（130092名欧洲血统个体）使用LDpred开发并优化了抑郁症的多基因风险评分。在一个英国生物银行测试数据集（278730名欧洲血统个体）中，我们测试了多基因风险评分与早期生活风险因素是否相互关联，并比较了它们与抑郁症表型的关联。最后，我们通过逐步回归进行联合预测建模，将该多基因风险评分与早期生活风险因素相结合，并评估该模型在识别抑郁症高危个体方面的性能。
在英国生物银行测试数据集中，抑郁症的多基因风险评分与多种早期生活风险因素中度相关。例如，多基因风险评分增加一个标准差与遭受频繁家庭暴力的几率增加1.16倍相关（95%置信区间：1.14 - 1.19），以及儿童时期无法获得医疗服务的几率增加1.09倍相关（95%置信区间：1.05 - 1.14）。然而，多基因风险评分与抑郁症表型的关联比大多数早期生活风险因素更强。一个整合了多基因风险评分、早期生活风险因素、年龄和性别的联合预测模型在预测严格定义的重度抑郁症时的曲线下面积（AUROC）为0.6766，而一个没有多基因风险评分的模型和一个没有任何早期生活风险因素的模型的AUROC分别为0.6593和0.6318。
我们开发了一个多基因风险评分以部分捕捉抑郁症的遗传易感性。尽管遗传和早期生活风险因素可能相关，但联合预测模型尽管在幅度上改善有限，但仍改善了风险分层，并且可作为更好地识别抑郁症高危个体的工具进行探索。
