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dc.contributor.authorYaghootkar, Hanieh
dc.contributor.authorPastel, E.
dc.contributor.authorKos, K.
dc.contributor.authorPitt, Andrew
dc.contributor.authorHudson, Michelle
dc.contributor.authorFrayling, Timothy M.
dc.date.accessioned2021-02-09T12:32:10Z
dc.date.available2021-02-09T12:32:10Z
dc.date.issued2020-08-14
dc.identifier.citationGlastonbury CA et al. Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits. PLoS Comput Biol. 2020 Aug 14;16(8):e1008044.en_US
dc.identifier.pmid32797044
dc.identifier.doi10.1371/journal.pcbi.1008044
dc.identifier.urihttps://rde.dspace-express.com/handle/11287/621608
dc.description.abstractGenetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between mean adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive mean adipocyte area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler: R2visceral = 0.94, P < 2.2 × 10-16, R2subcutaneous = 0.91, P < 2.2 × 10-16), and faster run times (10,000 images: 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that mean adipocyte area positively correlated with body mass index (BMI) (Psubq = 8.13 × 10-69, βsubq = 0.45; Pvisc = 2.5 × 10-55, βvisc = 0.49; average R2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts (Pmeta = 9.8 × 10-7). Lastly, we performed the largest GWAS and subsequent meta-analysis of mean adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations.en_US
dc.language.isoenen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.urlhttps://dx.plos.org/10.1371/journal.pcbi.1008044en_US
dc.rights: © 2020 Glastonbury et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectAdipocytesen_US
dc.subjectAdipose Tissueen_US
dc.subjectBody Mass Indexen_US
dc.subjectCell Sizeen_US
dc.subjectMachine Learningen_US
dc.subjectObesityen_US
dc.subjectPhenotypeen_US
dc.subjectPolymorphismen_US
dc.titleMachine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traitsen_US
dc.typeMeta-Analysisen_US
dc.identifier.journalPLoS Computational Biologyen_US
dc.identifier.pmcidPMC7449405
dc.description.noteThis article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.en_US
dc.description.fundingC.A.G received a pump priming grant from Novo Nordisk to carry out this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en_US
dc.type.versionPublisheden_US
dc.description.admin-notepublished version, accepted versionen_US


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: © 2020 Glastonbury et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as : © 2020 Glastonbury et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.