Li Xiaoyan, Shen Aotian, Fan Lingli, Zhao Yiran, Xia Junfeng
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China.
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China.
Biol Psychiatry. 2025 Jul 15;98(2):126-134. doi: 10.1016/j.biopsych.2024.11.018. Epub 2024 Dec 4.
Psychiatric disorders pose an enormous economic and emotional burden on individuals, their families, and society. Given that the current analysis of the pathogenesis of psychiatric disorders remains challenging and time consuming, elucidating modifiable risk factors is crucial for the diagnosis and management of psychiatric disorders. However, inferring causal risk factors for these disorders from disparate data sources is challenging due to constraints in data collection and analytical capabilities.
By leveraging the largest available genome-wide association study summary statistics for 10 psychiatric disorders and compiling an extensive set of risk factor datasets, including 71 psychiatric disorder-specific phenotypes, 3935 brain imaging traits, and over 30 brain tissue/cell-specific quantitative trait loci datasets (covering 6 types of quantitative trait loci), we performed comprehensive Mendelian randomization analyses to explore the potential causal links between various exposures and psychiatric outcomes using genetic variants as instrumental variables.
After Bonferroni correction for multiple testing, we identified multiple potential risk factors for psychiatric disorders (including phenotypic-level and molecular-level traits) and provided robust Mendelian randomization evidence that supports these associations utilizing rigorous sensitivity analyses and colocalization analyses. Furthermore, we have established the PsyRiskMR database (http://bioinfo.ahu.edu.cn/PsyRiskMR/), which serves as an interactive platform for showcasing and querying risk factors for psychiatric disorders.
Our study offers a user-friendly PsyRiskMR database for the research community to browse, search, and download all Mendelian randomization results, potentially revealing new insights into the biological etiology of psychiatric disorders.
精神疾病给个人、其家庭和社会带来了巨大的经济和情感负担。鉴于目前对精神疾病发病机制的分析仍然具有挑战性且耗时，阐明可改变的风险因素对于精神疾病的诊断和管理至关重要。然而，由于数据收集和分析能力的限制，从不同数据源推断这些疾病的因果风险因素具有挑战性。
通过利用现有的针对10种精神疾病的最大规模全基因组关联研究汇总统计数据，并汇编一组广泛的风险因素数据集，包括71种精神疾病特异性表型、3935种脑成像特征以及超过30个脑组织/细胞特异性数量性状位点数据集（涵盖6种数量性状位点类型），我们进行了全面的孟德尔随机化分析，以使用基因变异作为工具变量来探索各种暴露与精神疾病结局之间的潜在因果联系。
在对多重检验进行Bonferroni校正后，我们确定了精神疾病的多个潜在风险因素（包括表型水平和分子水平特征），并通过严格的敏感性分析和共定位分析提供了有力的孟德尔随机化证据来支持这些关联。此外，我们建立了PsyRiskMR数据库（http://bioinfo.ahu.edu.cn/PsyRiskMR/），该数据库作为一个交互式平台，用于展示和查询精神疾病的风险因素。
我们的研究为研究界提供了一个用户友好的PsyRiskMR数据库，用于浏览、搜索和下载所有孟德尔随机化结果，可能揭示精神疾病生物学病因的新见解。
