Imperial College London
Alternate

ProfessorJimmyBell

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Visiting Professor
 
 
 
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Contact

 

+44 (0)20 3506 4608jimmy.bell Website

 
 
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Location

 

Hammersmith HospitalHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Liu:2020:10.1101/2020.07.14.187070,
author = {Liu, Y and Basty, N and Whitcher, B and Bell, JD and Sorokin, E and van, Bruggen N and Thomas, L and Cule, M},
doi = {10.1101/2020.07.14.187070},
publisher = {eLife Sciences Publications Ltd},
title = {Genetic architecture of 11 abdominal organ traits derived from abdominal MRI using deep learning},
url = {http://dx.doi.org/10.1101/2020.07.14.187070},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - Cardiometabolic diseases are an increasing global health burden. While well established socioeconomic, environmental, behavioural, and genetic risk factors have been identified, our understanding of the drivers and mechanisms underlying these complex diseases remains incomplete. A better understanding is required to develop more effective therapeutic interventions. Magnetic resonance imaging (MRI) has been used to assess organ health in a number of studies, but large-scale population-based studies are still in their infancy. Using 38,683 abdominal MRI scans in the UK Biobank, we used deep learning to systematically quantify parameters from individual organs (liver, pancreas, spleen, kidneys, lungs and adipose depots), and demonstrate that image derived phenotypes (volume, fat and iron content) reflect organ health and disease. We show that these traits have a substantial heritable component (8%-44%), and identify 93 independent genome-wide significant associations, including 3 associations with liver fat and one with liver iron that have not previously been reported, and 73 in traits that have not previously been studied. Overall our work demonstrates the utility of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues of the abdomen, and to generate new insights into the genetic architecture of complex traits.
AU  - Liu,Y
AU  - Basty,N
AU  - Whitcher,B
AU  - Bell,JD
AU  - Sorokin,E
AU  - van,Bruggen N
AU  - Thomas,L
AU  - Cule,M
DO  - 10.1101/2020.07.14.187070
PB  - eLife Sciences Publications Ltd
PY  - 2020///
TI  - Genetic architecture of 11 abdominal organ traits derived from abdominal MRI using deep learning
UR  - http://dx.doi.org/10.1101/2020.07.14.187070
ER  -
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