{"title":"PsyRiskMR: a comprehensive resource for identifying psychiatric disorders risk factors through Mendelian randomization.","authors":"Xiaoyan Li, Aotian Shen, Lingli Fan, Yiran Zhao, Junfeng Xia","doi":"10.1016/j.biopsych.2024.11.018","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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 the modifiable risk factors becomes crucial for the diagnosis and management of psychiatric disorders. However, inferring the causal risk factors in these disorders from disparate data sources is challenging due to constraints in data collection and analytical capabilities.</p><p><strong>Methods: </strong>By leveraging the largest available genome-wide association studies (GWAS) summary statistics for ten psychiatric disorders and compiling an extensive set of risk factor datasets, including 71 psychiatric disorders-specific phenotypes, 3,935 brain imaging traits, and over 30 brain tissue/cell-specific xQTL datasets (covering 6 types of QTLs), we performed comprehensive Mendelian randomization (MR) analyses to explore the potential causal links between various exposures and psychiatric outcomes using genetic variants as instrumental variables.</p><p><strong>Results: </strong>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 MR evidence supporting 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.</p><p><strong>Conclusions: </strong>Our study offered a user-friendly PsyRiskMR database for the research community to browse, search, and download all MR results, potentially revealing new insights into the biological etiology of psychiatric disorders.</p>","PeriodicalId":8918,"journal":{"name":"Biological Psychiatry","volume":" ","pages":""},"PeriodicalIF":9.6000,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biological Psychiatry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.biopsych.2024.11.018","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Background: 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 the modifiable risk factors becomes crucial for the diagnosis and management of psychiatric disorders. However, inferring the causal risk factors in these disorders from disparate data sources is challenging due to constraints in data collection and analytical capabilities.
Methods: By leveraging the largest available genome-wide association studies (GWAS) summary statistics for ten psychiatric disorders and compiling an extensive set of risk factor datasets, including 71 psychiatric disorders-specific phenotypes, 3,935 brain imaging traits, and over 30 brain tissue/cell-specific xQTL datasets (covering 6 types of QTLs), we performed comprehensive Mendelian randomization (MR) analyses to explore the potential causal links between various exposures and psychiatric outcomes using genetic variants as instrumental variables.
Results: 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 MR evidence supporting 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.
Conclusions: Our study offered a user-friendly PsyRiskMR database for the research community to browse, search, and download all MR results, potentially revealing new insights into the biological etiology of psychiatric disorders.
期刊介绍：
Biological Psychiatry is an official journal of the Society of Biological Psychiatry and was established in 1969. It is the first journal in the Biological Psychiatry family, which also includes Biological Psychiatry: Cognitive Neuroscience and Neuroimaging and Biological Psychiatry: Global Open Science. The Society's main goal is to promote excellence in scientific research and education in the fields related to the nature, causes, mechanisms, and treatments of disorders pertaining to thought, emotion, and behavior. To fulfill this mission, Biological Psychiatry publishes peer-reviewed, rapid-publication articles that present new findings from original basic, translational, and clinical mechanistic research, ultimately advancing our understanding of psychiatric disorders and their treatment. The journal also encourages the submission of reviews and commentaries on current research and topics of interest.
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