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Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci

Nicholls, HL; John, CR; Watson, DS; Munroe, PB; Barnes, MR; Cabrera, CP; (2020) Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci. Frontiers in Genetics , 11 , Article 350. 10.3389/fgene.2020.00350. Green open access

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Abstract

Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS – the ability to detect genetic association by linkage disequilibrium (LD) – is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact.

Type: Article
Title: Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fgene.2020.00350
Publisher version: https://doi.org/10.3389/fgene.2020.00350
Language: English
Additional information: Copyright © 2020 Nicholls, John, Watson, Munroe, Barnes and Cabrera. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: machine learning, artificial intelligence, genome-wide association study, genomics, candidate gene, clinical translation, deep learning, data science
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10118992
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