Li Yujue, Xue Fei, Li Bingxuan, Yang Yilin, Fan Zirui, Shu Juan, Yang Xiaochen, Wang Xiyao, Lin Jinjie, Copana Carlos, Zhao Bingxin
Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.
Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.
bioRxiv. 2024 Mar 16:2023.04.28.538585. doi: 10.1101/2023.04.28.538585.
As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summary statistics for thousands of phenotypes can be both logistically challenging and computationally demanding. In this paper, we introduce BIGA (https://bigagwas.org/), a website that aims to offer unified data analysis pipelines and processed data resources for cross-trait genetic architecture analyses using GWAS summary statistics. We have developed a framework to implement statistical genetics tools on a cloud computing platform, combined with extensive curated GWAS data resources. Through BIGA, users can upload data, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights.
随着大规模生物样本库提供越来越多的深度表型和基因组数据，全基因组关联研究（GWAS）正在迅速揭示各种复杂性状和疾病背后的遗传结构。GWAS出版物通常会公开其汇总级数据（GWAS汇总统计量），从而能够进一步探索从不同研究和队列中收集的表型之间的遗传重叠。然而，系统地分析数千种表型的高维GWAS汇总统计量在逻辑上具有挑战性，并且对计算要求很高。在本文中，我们介绍了BIGA（https://bigagwas.org/），这是一个旨在提供统一数据分析管道和经过处理的数据资源的网站，用于使用GWAS汇总统计量进行跨性状遗传结构分析。我们开发了一个框架，以便在云计算平台上实现统计遗传学工具，并结合大量经过整理的GWAS数据资源。通过BIGA，用户可以上传数据、提交任务并共享结果，为研究社区提供了一个整合GWAS数据并产生新见解的便捷工具。
