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多基因分数和环境因素对个人抑郁程度差异的综合影响
Anastasiya Kazantseva1,2, Yuliya Davydova1,2, Renata Enikeeva1,2
1Institute of Biochemistry and Genetics-Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences, 450054 Ufa, Russia.
Genes
|July 29, 2023
概括
多基因分数 (PGS) 有助于评估抑郁风险,特别是与社会因素相结合时. 使用PGS和社会数据的模型改善了欧亚大陆的年轻人,特别是女性的抑郁症预测.
科学领域:
- 行为遗传学行为遗传学
- 精神病学是一个精神病学.
- 人口遗传学 人口遗传学
背景情况:
- 抑郁症是一种多因素的疾病,受遗传和环境的影响.
- 多基因分数 (PGS) 提供了一种方法来评估对复杂特征的遗传倾向.
- 将社会和生活方式因素与PGS整合起来,对于理解抑郁症中"遗传性缺失"至关重要.
研究的目的:
- 评估多基因分数 (PGS) 结合社会/生活方式因素在预测抑郁水平方面的有效性.
- 评估从西欧GWAS数据中获得的PGS的可转移性到东欧人口.
- 调查来自欧亚大陆的伏尔加-乌拉尔地区 (VUR) 的高等教育学生的抑郁风险.
主要方法:
- 开发了回归模型,其中纳入了PGS (基于27个SNP) 和社会/生活方式因素.
- 在1065名年轻成年人 (18-25岁) 中,使用贝克抑郁 inventory (BDI) 测量抑郁水平.
- 使用来自西欧GWAS的PGS进行了敏感性分析,用于抑郁症和神经病症.
主要成果:
- 权重的PGS模型解释了2.4%的抑郁差异 (p = 3.42 × 10−7).
- 添加社会参数显著改善了模型预测 (调整后r2=15%,p <2.2 × 10−16).
- 模型显示女性的预测能力更高,社会因素大大提高了预测能力 (调整后r2=17%,p <2.2 × 10−16).
- 来自西欧GWAS的临床抑郁症PGS没有有效地预测样本中的BDI得分.
- 神经病症的PGS显示出相关性,但影响主要是由社会/生活方式因素驱动的.
结论:
- 构建的PGS模型,特别是当纳入社会/生活方式因素时,有助于理解抑郁症的个体间变化.
- 这些模型的预测能力通过包括外部因素,如童年抚养的具体情况来增强.
- 在PGS建模中使用特定种族的影响估计对于准确的个人风险评估至关重要.
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