Infrastructure and Core Activities of the Social Science Genetic Association Consortium

社会科学遗传协会联合会(SSGAC)是一个研究网络，为 行为表型全基因组关联研究的大规模跨学科合作。 因为可信的结果通常需要大量的发现样本，SSGAC的主要职能是进行 在不同的数据集进行全基因组关联研究，然后严格合并结果。对这件事 最后，SSGAC还促进收集跨数据集的协调和测量良好的表型 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，提供给研究界。 这项建议的首要目标是维护和发展SSGAC的核心基础设施。这个 SSGAC产生的资源将继续为一般的行为基因组学领域服务， 特别是衰老的行为基因组学。具体目标是： ·在更大的范围内对健康和衰老相关的行为表型进行全基因组关联研究 现在已经可以获得的样品。目前计划的研究包括对教育的分析 成就、总体风险承受能力和生活满意度，都是在史无前例的大样本中进行的。此外，我们 随着数据的获得，将对更多的表型进行大规模研究。 ·促进研究人员使用SSGAC的成果，方法是：(A)在SSGAC的网站上维护已公布的成果； 以及(B)根据SSGAC的规定，向参与数据集提供某些未公布的结果 数据可用性政策。 ·建立“多基因分数资料库”，这些分数是根据有用的遗传数据构建的变量 献给研究人员。我们将建立资料库，为参与的数据集提供多基因得分。 ·当新的遗传关联结果可用时，更新和扩展资料库。我们也将欢迎 任何希望参与的新数据集。每一次发布都将伴随着明确的文档 描述使用的方法和基础数据。 ·创建和分发软件，允许数据集使用相同的 协调方法学作为储存库，但不需要与SSGAC共享其基因数据。 该软件将在GitHub存储库公开提供，该存储库有一个问答论坛，潜在用户可以在该论坛上 可以发问。我们还将编写界面友好的软件手册。

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by (a) maintaining published results on the SSGAC's website, and (b) providing participating datasets with certain unpublished results, in accordance with the SSGAC's Data Availability Policy.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.