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)已经在成千上万的 基因变异和成百上千的特征。然而，大多数协会的“功能效应”并非如此 还没有被阐明--也就是说，导致它们的因果变异和效应基因，以及组织和 它们的行动途径在很大程度上仍不为人所知。在过去的几年里，三类基因组 已经出现了用于推断GWAS关联的功能影响的数据：汇总关联统计 (SNPs和性状之间关联的效应大小和p值)，基因组注释(分析 调节活性和基因组功能元件)和生物信息学方法(通过计算预测 功能效果)。我们认为，目前汇总这些数据的资源存在两个缺口：第一，没有 当前资源的目标是全面管理和编目所有已知的数据或方法 可以帮助预测GWAS协会的功能影响；第二，现有资源是通过 (充其量)来自最初生成基因组数据和/或理解 如何更好地利用它们。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- 知识门户协会(A2FKP)-使用我们最初使用的通用软件平台 专为2型糖尿病开发。我们的方法利用一项关键创新来构建既具有 高质量和全面：我们与疾病专家社区合作，建立专门的知识 为他们提供门户，激励他们贡献他们的数据和专业知识，然后我们集成这些数据 与其他社区的资源一起使用，为用户提供全面的资源访问。 具体目标1解决在数据的全面性和质量方面存在的差距 关于全球气候变化联盟的功能影响的现有资源。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、汇总和整理11类疾病的数据。具体目标2解决当前的差距 用于预测功能影响的各种类型数据的模式和软件平台 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关联的功能影响的方法，将这些管道应用于聚集的数据 在目标1中，并将其输出转换为知识图中实体之间的关系。的目标是 具体目标3是为用户提供对汇总或计算的资源的直接和可视访问 目标1和目标2。它将开发REST API和门户网站，用于查询和可视化A2FKP内的数据。 意义：该项目将产生一个高质量和全面的基因组数据资源 以及用于预测GWAS协会的功能效应的方法。轻松访问这样的资源将 加快将全球气候变化协会的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.