Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits

Craig A Glastonbury, Sara L Pulit, Julius Honecker, Jenny C Censin, Samantha Laber, Hanieh Yaghootkar, Nilufer Rahmioglu, Emilie Pastel, Katerina Kos, Andrew Pitt, Michelle Hudson, Christoffer Nellåker, Nicola L Beer, Hans Hauner, Christian M Becker, Krina T Zondervan, Timothy M Frayling, Melina Claussnitzer and Cecilia M Lindgren

PLOS Computational Biology, 2020, vol. 16, issue 8, 1-21

Abstract: Genetic 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

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1008044

DOI: 10.1371/journal.pcbi.1008044

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