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Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology.

Alipanahi, B; Hormozdiari, F; Behsaz, B; Cosentino, J; McCaw, ZR; Schorsch, E; Sculley, D; ... McLean, CY; + view all (2021) Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology. The American Journal of Human Genetics (AJHG) 10.1016/j.ajhg.2021.05.004. (In press). Green open access

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Abstract

Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; p ≤ 5 × 10-8) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 93 novel loci, significantly expanding our understanding of the genetic etiologies of glaucoma and VCDR. Pathway analyses support the biological significance of the novel hits to VCDR: select loci near genes involved in neuronal and synaptic biology or harboring variants are known to cause severe Mendelian ophthalmic disease. Finally, the ML-based GWAS results significantly improve polygenic prediction of VCDR and primary open-angle glaucoma in the independent EPIC-Norfolk cohort.

Type: Article
Title: Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology.
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.ajhg.2021.05.004
Publisher version: https://doi.org/10.1016/j.ajhg.2021.05.004
Language: English
Additional information: © 2021 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: GWAS, phenotyping, machine learning, glaucoma
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
URI: https://discovery.ucl.ac.uk/id/eprint/10129434
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