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

TL;DR

This essay explores how insights from genetic screens and AI can help uncover biological mechanisms behind complex diseases identified by GWAS.

Contribution

The paper proposes a new computational framework for interpreting GWAS results using lessons from genetic screens and AI.

Findings

Genetic screens in model organisms offer parallels to GWAS that can guide biological interpretation.

A computational framework is proposed to exhaustively interrogate GWAS results for biological mechanisms.

Existing and future data, mechanisms, and technologies are discussed for advancing GWAS interpretation.

Abstract

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.

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.

There are no data or code associated with this article.

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