Liver Fat
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Entries
Haas ME (ML Liver Fat GWAS)
PUBMED_LINK
FULL NAME
Machine Learning Enables New Insights into Genetic Contributions to Liver Fat Accumulation
DESCRIPTION
Developed an abdominal MRI-based machine-learning regression model (gradient-boosted regression on raw MRI signal intensities) to accurately estimate liver fat from UK Biobank abdominal MRI scans (correlation 0.97-0.99 with ground truth). Trained on 4,511 participants with gold-standard MRI biomarker measurements and applied to 32,192 additional individuals. GWAS identified 8 associated variants (5 novel: MTARC1, ADH1B, TRIB1, GPAM, MAST3) and a polygenic score strongly associated with future chronic liver disease risk (HR>1.32 per SD, p<9e-17).
KEYWORDS
MRI signal regression, liver fat quantification, abdominal MRI, hepatic steatosis, gradient boosting, UK Biobank
TITLE
Machine learning enables new insights into genetic contributions to liver fat accumulation.
Main citation
Haas ME, Pirruccello JP, Friedman SN, Wang M, ...&, Khera AV. (2021) Machine learning enables new insights into genetic contributions to liver fat accumulation. Cell Genom, 1 (3). doi:10.1016/j.xgen.2021.100066. PMID 34957434
ABSTRACT
Excess liver fat, called hepatic steatosis, is a leading risk factor for end-stage liver disease and cardiometabolic diseases but often remains undiagnosed in clinical practice because of the need for direct imaging assessments. We developed an abdominal MRI-based machine-learning algorithm to accurately estimate liver fat from a truth dataset of 4,511 middle-aged UK Biobank participants, enabling quantification in 32,192 additional individuals. A genome-wide association study of common genetic variants and liver fat replicated three known associations and identified five newly associated variants.
DOI
10.1016/j.xgen.2021.100066