超越统计显著性的视角:发现有意义的效应

H. Edenberg
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引用次数: 4

摘要

精神病学的全基因组关联研究(GWASs)最近发现了许多影响多种疾病和相关特征的基因座,尽管相关基因座的数量在精神病学和药物使用特征之间差异很大。GWAS的成功引发了人们的思考,即如何超越对基因座(包含大量变异)的识别,识别它们内部实际上导致性状风险的功能变异,并从那里识别相关的基因、途径和机制。希望对关键基因和途径的了解将有助于更好地诊断、治疗和预防这些复杂的疾病。2020年9月2日,美国国立卫生研究院(NIH)的三个研究所主办了一场名为“超越统计显著性:寻找有意义的影响”的虚拟会议,致力于探讨这些问题。会议的结构——首先是演讲,然后是三组主题的讨论部分,所有愿意参加的人都通过讨论,最后是一般性讨论作为总结——允许活跃的互动和许多想法的展示。伊丽莎白·霍夫曼博士在会议开幕式上说,会议的目标是“通过让来自不同学科的研究人员参与讨论超越统计意义的有意义的科学,为识别、分析和解释有意义的影响制定最佳实践建议。”有一个结论是,这些问题很难,没有放之四海而皆准的答案,这应该不会让任何人感到惊讶。本次会议的总结已在https://apps1.seiservices.com/meaningfuleffects/上发布,其中包括演讲和讨论的细节。其中一些问题已经在最近发布到bioRxiv[2]的预印本中与NIH青少年大脑认知发展研究[1]有关。我不想在此重述会议摘要,只想对讨论的关键问题和未来可能的方向提出一个看法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Perspective on Beyond Statistical Significance: Finding Meaningful Effects
Genome-wide association studies (GWASs) in psychiatry have recently identified many loci that affect a wide variety of disorders and related traits, although the number of associated loci varies widely among psychiatric and substance-use traits. The successes of GWAS have triggered thoughts about how to move beyond identification of loci (which contain a very large number of variants), to identification of the functional variants within them that actually contribute to the risk for the traits, and from there to identifying the genes, pathways, and mechanisms involved. The hope is that knowledge of the key genes and pathways will lead to better diagnosis, treatment, and prevention of these complex disorders. On September 2, 2020, three institutes within the US National Institutes of Health (NIH) sponsored a virtual meeting entitled “Beyond Statistical Significance: Finding Meaningful Effects” devoted to exploring these issues. The structure of the meeting – initial presentations followed by discussion sections on 3 sets of topics through which all willing participants cycled and a general discussion as a wrap-up – allowed for lively interaction and the presentation of many ideas. Dr. Elizabeth Hoffman opened the meeting by stating its objective was “To develop best practice recommendations for identifying, analyzing, and interpreting meaningful effects by engaging researchers from a range of disciplines in discussions of meaningful science that go beyond statistical significance.” It should not surprise anyone that one conclusion was that these questions are difficult and there are no one-size-fits-all answers. A good summary of the meeting has been posted at https://apps1.seiservices.com/meaningfuleffects/, with details of the presentations and the discussions. Some of these issues have been discussed in relation to the NIH Adolescent Brain Cognitive Development study [1] in a preprint recently posted to bioRxiv [2]. I will not recapitulate the meeting summary here, but rather provide a perspective on the key questions discussed, and potential future directions.
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