Machine learning enables new insights into genetic contributions to liver fat accumulation

• Published in: Cell genomics Vol. 1; no. 3; p. 100066
• Main Authors: Haas, Mary E., Pirruccello, James P., Friedman, Samuel N., Wang, Minxian, Emdin, Connor A., Ajmera, Veeral H., Simon, Tracey G., Homburger, Julian R., Guo, Xiuqing, Budoff, Matthew, Corey, Kathleen E., Zhou, Alicia Y., Philippakis, Anthony, Ellinor, Patrick T., Loomba, Rohit, Batra, Puneet, Khera, Amit V.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 08-12-2021; Elsevier
• Subjects: GWAS; hepatic steatosis; liver fat; machine learning; MRI; NAFLD; nonalcoholic fatty liver disease; polygenic risk score; whole-exome sequencing; nonalcoholic fatty liver disease; NAFLD; GWAS; MRI; liver fat; machine learning; whole-exome sequencing; polygenic risk score; hepatic steatosis
• ISSN: 2666-979X; 2666-979X
• DOI: 10.1016/j.xgen.2021.100066

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 (correlation coefficients, 0.97–0.99) from a truth dataset of 4,511 middle-aged UK Biobank participants, enabling quantification in 32,192 additional individuals. 17% of participants had predicted liver fat levels indicative of steatosis, and liver fat could not have been reliably estimated based on clinical factors such as BMI. A genome-wide association study of common genetic variants and liver fat replicated three known associations and identified five newly associated variants in or near the MTARC1, ADH1B, TRIB1, GPAM, and MAST3 genes (p < 3 × 10−8). A polygenic score integrating these eight genetic variants was strongly associated with future risk of chronic liver disease (hazard ratio > 1.32 per SD score, p < 9 × 10−17). Rare inactivating variants in the APOB or MTTP genes were identified in 0.8% of individuals with steatosis and conferred more than 6-fold risk (p < 2 × 10−5), highlighting a molecular subtype of hepatic steatosis characterized by defective secretion of apolipoprotein B-containing lipoproteins. We demonstrate that our imaging-based machine-learning model accurately estimates liver fat and may be useful in epidemiological and genetic studies of hepatic steatosis.
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•A machine-learning algorithm precisely quantified liver fat in 36,703 individuals•17% of imaged participants had excess liver fat but were largely undiagnosed•8 common genetic variants and polygenic score associated with liver fat and liver disease•Rare variants in APOB or MTTP genes highlight a molecular subtype of steatosis
Haas et al. report a machine-learning algorithm used to precisely quantify liver fat, a leading driver of end-stage liver disease, from abdominal MRI imaging data of 36,703 UK Biobank participants. They identify common and rare genetic variants influencing liver fat and demonstrate utility for epidemiological studies.