The Association to Function Knowledge Portal: a genomic data resource for translating GWAS associations to biological effects

摘要 全基因组关联研究（GWAS）已经在数千个基因组之间产生了关联。 遗传变异和数百种特征。然而，大多数协会的“功能效应”并没有 尚未阐明-也就是说，因果变异和效应基因负责他们，和组织， 它们的作用途径在很大程度上仍然未知。在过去的几年里，三类基因组 已经出现了用于推断GWAS关联的功能效应的数据：关联统计汇总 （SNP和性状之间关联的效应大小和p值），基因组注释（ 调节活性和基因组功能元件）和生物信息学方法（计算预测 功能效果）。我们认为，在目前的资源，汇总这些数据存在两个差距：第一，没有 目前的资源旨在全面地整理和编目所有已知的，以及所有数据或方法， 可以帮助预测，GWAS协会的功能效果;第二，现有资源的开发， (at最好）来自最初产生基因组数据和/或理解基因组数据的专家的有限参与。 如何最好地使用它们。我们建议通过建立一个新的基因组社区资源来解决这些差距- 功能关联知识门户（A2 FKP）-使用通用软件平台， 2型糖尿病的治疗方法我们的方法利用了一个关键的创新来构建一个既能 高质量和全面：我们与疾病专家社区合作，建立专门的知识 门户网站，激励他们贡献自己的数据和专业知识，然后我们整合这些数据 与其他社区的社区一起，为用户提供全面的资源。 具体目标1解决以下方面所汇总数据的全面性和质量方面的差距： 关于GWAS关联的功能影响的当前资源。它将建立和管理 与广泛的疾病，数据和方法专家合作，然后与这些社区合作 识别、汇总和管理11类疾病的数据。具体目标2解决目前 模式和软件平台的无数类型的数据用于预测功能的影响， GWAS协会。它将通过生物信息学建立处理遗传和基因组数据集的管道 预测GWAS关联的功能效应的方法，将这些管道应用于聚合的数据 在目标1中，并将其输出转换为知识图中实体之间的关系。的目标 具体目标3是向用户提供对在 目标1和2。它将开发REST API和门户网站，用于查询和可视化A2 FKP中的数据。 意义：该项目将产生高质量和全面的基因组数据资源 以及预测GWAS关联的功能效应的方法。容易获得这样的资源将 加快GWAS关联转化为对复杂疾病的洞察的步伐。

Abstract Genome wide association studies (GWAS) have produced associations between many thousands of genetic variants and many hundreds of traits. The “functional effects” of most associations, however, have not yet been elucidated – that is, the causal variants and effector genes responsible for them, and the tissues and pathways through which they act, remain largely unknown. Over the past few years, three classes of genomic data have arisen for inferring the functional effects of GWAS associations: summary association statistics (effect sizes and p-values for associations between SNPs and traits), genomic annotations (assays of regulatory activity and genomic functional elements), and bioinformatic methods (computationally predicted functional effects). We argue that two gaps exist in the current resources that aggregate these data: first, no current resource aims to comprehensively curate and catalog all that is known, and all data or methods that could help predict, the functional effects of GWAS associations; second, existing resources are developed with (at best) limited involvement from experts who either originally generated the genomic data and/or understand how to best use them. We propose to address these gaps by building a new genomic community resource – the Association to Function Knowledge Portal (A2FKP) – using a general software platform we initially developed for type 2 diabetes. Our approach makes use of a key innovation to build a resource that is both high quality and comprehensive: we collaborate with disease expert communities to build dedicated knowledge portals for them, motivating them to contribute their data and expertise, and we then integrate these data alongside those of other communities, providing users with access a comprehensive resource. Specific aim 1 addresses gaps in the comprehensiveness and quality of the data aggregated by current resources regarding the functional effects of GWAS associations. It will establish and manage collaborations with a wide range of disease, data, and method experts, and then work with these communities to identify, aggregate, and curate data for 11 classes of disease. Specific aim 2 addresses gaps in current schemas and software platforms for the myriad types of data used for predicting the functional effects of GWAS associations. It will build pipelines for processing genetic and genomic datasets through bioinformatic methods for predicting the functional effects of GWAS associations, apply these pipelines to data aggregated in Aim 1, and transform their outputs to relationships among entities in a knowledge graph. The goal of specific aim 3 is to provide users with direct and visual access to the resources aggregated or computed in Aims 1 and 2. It will develop REST APIs and web portals for querying and visualizing data within the A2FKP. Significance: The project would produce a high quality and comprehensive genomic resource of data and methods for predicting the functional effects of GWAS associations. Easy access to such a resource will accelerate the pace by which GWAS associations can be translated to insights into complex disease.