Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning to Advance Human Genome‐Wide Association Studies

Rafaella Sigala
,
Vasiliki Lagou
,
Aleksey Shmeliov
,
Sara Atito
ORCID logo ,
Samaneh Kouchaki
ORCID logo ,
Muhammad Awais
,
Inga Prokopenko
ORCID logo ,
Adam Mahdi
,
Ayse Demirkan
* ORCID logo
Version 1 : Received: 28 November 2023 / Approved: 28 November 2023 / Online: 28 November 2023 (15:41:32 CET)

A peer-reviewed article of this Preprint also exists.

Sigala, R.E.; Lagou, V.; Shmeliov, A.; Atito, S.; Kouchaki, S.; Awais, M.; Prokopenko, I.; Mahdi, A.; Demirkan, A. Machine Learning to Advance Human Genome-Wide Association Studies. Genes 2024, 15, 34. Sigala, R.E.; Lagou, V.; Shmeliov, A.; Atito, S.; Kouchaki, S.; Awais, M.; Prokopenko, I.; Mahdi, A.; Demirkan, A. Machine Learning to Advance Human Genome-Wide Association Studies. Genes 2024, 15, 34.

Abstract

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data is made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist’s perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.

Keywords

genome‐wide association; human genetics; machine learning

Subject

Biology and Life Sciences, Other

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download PDF

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0
Get PDF Cite Share 0
Bookmark BibSonomy Mendeley Reddit Delicious
×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.