Infrastructure and Core Activities of the Social Science Genetic Association Consortium

社会科学遗传协会联盟（SSGAC）是一个研究网络，提供了一个平台， 行为表型的全基因组关联研究的大规模跨学科合作。 由于可信的结果通常需要大量的发现样本，因此SSGAC的主要功能是进行 在不同的数据集中进行全基因组关联研究，然后严格地将结果联合收割机结合起来。本 最后，SSGAC还促进跨数据集收集协调和良好测量的表型， 开发新的方法工具。SSGAC致力于传播其研究成果， 以及随附的软件和手册。 本提案的总体目标是维护和开发SSGAC的核心基础设施。的 SSGAC产生的资源将继续服务于行为基因组学领域， 尤其是衰老的行为基因组学。具体目标是： ·在更大范围内对健康和衰老相关行为表型进行全基因组关联研究 现在已经有了样品。目前计划的研究包括分析教育 成就，一般风险承受能力和生活满意度，所有这些都是前所未有的大样本。另外我们 将在获得数据后对其他表型进行大规模研究。 ·通过以下方式促进研究人员使用SSGAC结果：（a）在SSGAC网站上维护已发表的结果， 以及（B）根据SSGAC的 数据可用性政策。 ·开发一个“多基因评分库”，这是从有用的遗传数据构建的变量 研究人员。我们将建立存储库，为参与数据集提供多基因评分。 ·随着新的遗传关联结果的出现，更新和扩展知识库。我们也欢迎 任何希望参与的新数据集。每个版本都将附有文档， 描述了使用的方法和基础数据。 ·创建和传播软件，允许数据集使用相同的方法创建自己的多基因分数 与知识库统一的方法，但不需要与SSGAC共享其遗传数据。 该软件将在GitHub存储库上公开提供，其中包括一个Q&A论坛，潜在用户可以在该论坛上访问该软件。 可以问问题我们还将准备用户友好的软件手册。

The Social Science Genetic Association Consortium (SSGAC) is a research network that provides a platform for large-scale, interdisciplinary collaborations on genome-wide association studies of behavioral phenotypes. Because credible results often require large discovery samples, a primary function of the SSGAC is to conduct genome-wide association studies conducted in different datasets and then rigorously combine the results. To this end, the SSGAC also promotes the collection of harmonized and well-measured phenotypes across datasets and the development of new methodological tools. The SSGAC is committed to disseminating its research findings, as well as accompanying software and manuals, to the research community. The overarching goal of this proposal is to maintain and develop the core infrastructure of the SSGAC. The resources produced by the SSGAC will continue to serve the field of behavioral genomics in general, and the behavioral genomics of aging in particular. The Specific Aims are: • Conduct genome-wide association studies of health and aging-relevant behavioral phenotypes in much larger samples that have now become available. Currently planned studies include analyses of educational attainment, general risk tolerance, and life satisfaction, all in unprecedentedly large samples. In addition, we will undertake large-scale studies of additional phenotypes as data becomes available. • Facilitate researchers' use of SSGAC results by (a) maintaining published results on the SSGAC's website, and (b) providing participating datasets with certain unpublished results, in accordance with the SSGAC's Data Availability Policy. • Develop a “Repository of Polygenic Scores,” which are variables constructed from genetic data that are useful to researchers. We will set up the Repository to provide polygenic scores to participating datasets. • Update and expand the Repository as new genetic-association results become available. We will also welcome any new datasets who wish to participate. Each release will be accompanied by documentation that clearly describes methods used and the underlying data. • Create and disseminate software that will allow datasets to create their own polygenic scores, using the same harmonized methodology as the Repository but without needing to share their genetic data with the SSGAC. The software will be publicly available on a GitHub repository featuring a Q&A forum where potential users can ask questions. We will also prepare user-friendly software manual.