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. Genes2024, 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.
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. Genes2024, 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.