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首页> 外文期刊>Behavior Genetics: An International Journal Devoted to Research in the Inheritance of Behavior in Animals and Man >Applying Novel Methods for Assessing Individual- and Neighborhood-Level Social and Psychosocial Environment Interactions with Genetic Factors in the Prediction of Depressive Symptoms in the Multi-Ethnic Study of Atherosclerosis
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Applying Novel Methods for Assessing Individual- and Neighborhood-Level Social and Psychosocial Environment Interactions with Genetic Factors in the Prediction of Depressive Symptoms in the Multi-Ethnic Study of Atherosclerosis

机译：应用新方法评估个体和邻里水平的社会和心理环境与遗传因素的相互作用，在多族裔动脉粥样硬化研究中预测抑郁症状

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摘要

Complex illnesses, like depression, are thought to arise from the interplay between psychosocial stressors and genetic predispositions. Approaches that take into account both personal and neighborhood factors and that consider gene regions as well as individual SNPs may be necessary to capture these interactions across race and ethnic groups. We used novel gene-region based analysis methods [Sequence Kernel Association Test (SKAT) and meta-analysis (MetaSKAT), gene-environment set association test (GESAT)], as well as traditional linear models to identify gene region and SNP x psychosocial factor interactions at the individual- and neighborhood-level, across multiple race/ethnicities. Multiple regions identified in SKAT analyses showed evidence of a significant gene-region association with averaged depressive symptom scores across race/ethnicity (MetaSKAT p values < 0.001). One region x neighborhood-environment interaction was significantly associated with averaged depressive symptom score across race/ethnicity after multiple testing correction (chr 18:21454070-21494070, Fisher's combined p value = 0.001). The examination of gene regions jointly with environmental factors measured at multiple levels (individuals and their contexts) may shed light on the etiology of depressive illness across race/ethnicities.
机译：人们认为，诸如抑郁症之类的复杂疾病是由心理社会压力源与遗传易感性之间的相互作用引起的。可能需要考虑个人和邻域因素，并考虑基因区域以及单个SNP的方法，以捕获跨种族和族裔群体的这些相互作用。我们使用了新颖的基于基因区域的分析方法[序列核关联测试（SKAT）和荟萃分析（MetaSKAT），基因-环境集关联测试（GESAT）]，以及传统的线性模型来识别基因区域和SNP x心理社会跨多个种族/族裔在个人和邻里级进行因素交互。在SKAT分析中确定的多个区域显示出明显的基因区域关联以及种族/族裔之间平均抑郁症状评分的关联（MetaSKAT p值<0.001）。在多次测试校正后，跨种族/种族的一个地区x邻里与环境的相互作用与平均抑郁症状评分显着相关（chr 18：21454070-21494070，Fisher的综合p值= 0.001）。将基因区域与在多个水平（个体及其背景）下测得的环境因素一起检查，可能会揭示出种族/族裔抑郁症的病因。

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