A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry.
A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry.
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DOI：
10.1176/appi.ajp.2011.11020191
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发表时间：
2011-10
期刊：
影响因子：
--
通讯作者：
Keller MC
中科院分区：
文献类型：
--
作者：
Duncan LE;Keller MC
精神病学中的基因与环境相互作用 (G×E) 研究通常使用候选 G×E (cG×E) 方法进行，类似于用于测试遗传主效应的候选基因关联方法。此类cG×E研究受到了广泛关注和好评，但cG×E研究结果仍存在争议。作者检查了精神病学文献中报告的许多阳性 cG×E 研究结果是否稳健，或者总的来说，cG×E 研究结果是否与发表偏倚、低统计功效和高错误发现率的存在相一致。作者对精神病学 cG×E 研究第一个十年（2000-2009 年）所有已发表研究（103 项研究）中提取的数据进行了分析。 96% 的新型 cG×E 研究具有显着性，而复制尝试中只有 27% 具有显着性。这些发现与新颖的 cG×E 研究中存在的发表偏倚一致，使得 cG×E 假设看起来比实际情况更加稳健。复制尝试之间似乎也存在发表偏差，因为正复制尝试的平均样本量小于负复制尝试。使用观察到的样本量进行功效计算表明，cG×E 研究的功效不足。低功效以及给定 cG×E 假设成立的可能较低的先验概率表明，大多数甚至所有积极的 cG×E 结果都代表了 I 类错误。在这个大数据和小影响的新时代，有必要重新调整对“突破性”发现的看法。与新颖的 cG×E 发现和间接复制相比，功能良好的直接复制值得更多关注。
Gene-by-environment interaction (G×E) studies in psychiatry have typically been conducted using a candidate G×E (cG×E) approach, analogous to the candidate gene association approach used to test genetic main effects. Such cG×E research has received widespread attention and acclaim, yet cG×E findings remain controversial. The authors examined whether the many positive cG×E findings reported in the psychiatric literature were robust or if, in aggregate, cG×E findings were consistent with the existence of publication bias, low statistical power, and a high false discovery rate. The authors conducted analyses on data extracted from all published studies (103 studies) from the first decade (2000–2009) of cG×E research in psychiatry. Ninety-six percent of novel cG×E studies were significant compared with 27% of replication attempts. These findings are consistent with the existence of publication bias among novel cG×E studies, making cG×E hypotheses appear more robust than they actually are. There also appears to be publication bias among replication attempts because positive replication attempts had smaller average sample sizes than negative ones. Power calculations using observed sample sizes suggest that cG×E studies are underpowered. Low power along with the likely low prior probability of a given cG×E hypothesis being true suggests that most or even all positive cG×E findings represent type I errors. In this new era of big data and small effects, a recalibration of views about “groundbreaking” findings is necessary. Well-powered direct replications deserve more attention than novel cG×E findings and indirect replications.
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