重新审视全基因组关联研究：基因筛选和人工智能的经验教训如何揭示生物学机制。

Rethinking GWAS: how lessons from genetic screens and artificial intelligence could reveal biological mechanisms.

作者信息

Hazelett Dennis J

机构信息

Department of Computational Biomedicine at Cedars-Sinai Medical Center, West Hollywood, CA 90069, United States.

Cancer Prevention and Control-Samuel Oschin Cancer Center, Los Angeles, CA 90048, United States.

出版信息

Bioinformatics. 2025 Mar 29;41(4). doi: 10.1093/bioinformatics/btaf153.

DOI:10.1093/bioinformatics/btaf153

PMID:40198231

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12014097/

Abstract

MOTIVATION

Modern single-cell omics data are key to unraveling the complex mechanisms underlying risk for complex diseases revealed by genome-wide association studies (GWAS). Phenotypic screens in model organisms have several important parallels to GWAS which the author explores in this essay.

RESULTS

The author provides the historical context of such screens, comparing and contrasting similarities to association studies, and how these screens in model organisms can teach us what to look for. Then the author considers how the results of GWAS might be exhaustively interrogated to interpret the biological mechanisms underpinning disease processes. Finally, the author proposes a general framework for tackling this problem computationally, and explore the data, mechanisms, and technology (both existing and yet to be invented) that are necessary to complete the task.

AVAILABILITY AND IMPLEMENTATION

There are no data or code associated with this article.

摘要

动机

现代单细胞组学数据是揭示全基因组关联研究（GWAS）所揭示的复杂疾病风险背后复杂机制的关键。模式生物中的表型筛选与GWAS有几个重要的相似之处，作者在本文中对此进行了探讨。

结果

作者提供了此类筛选的历史背景，比较和对比了与关联研究的相似之处，以及模式生物中的这些筛选如何能教会我们寻找什么。然后，作者考虑了如何详尽地探究GWAS的结果，以解释支撑疾病过程的生物学机制。最后，作者提出了一个用于通过计算解决此问题的通用框架，并探索完成该任务所需的数据、机制和技术（包括现有和尚未发明的）。