Trans-ancestral rare variant association study with machine learning-based phenotyping for metabolic dysfunction-associated steatotic liver disease

• Published in: Genome Biology Vol. 26; no. 1; p. 50
• Main Authors: Chen, Robert, Petrazzini, Ben Omega, Duffy, Áine, Rocheleau, Ghislain, Jordan, Daniel, Bansal, Meena, Do, Ron
• Format: Journal Article
• Language: English
• Published: London BioMed Central 10.03.2025; Springer Nature B.V; BMC
• Subjects: ancestry; Animal Genetics and Genomics; Biobanks; Bioinformatics; Biomedical and Life Sciences; Cardiovascular disease; Case-Control Studies; Ecosystems; Enzymes; Evolutionary Biology; Fatty liver; Fatty Liver - genetics; Female; Genes; Genetic association studies; Genetic diversity; Genetic Predisposition to Disease; genome; Genome-wide association studies; Genome-Wide Association Study; Genomes; Human Genetics; Humans; Insights from trans-ancestral association study; Learning algorithms; Life Sciences; Liver cirrhosis; Liver diseases; Machine Learning; Male; Meta-analysis; Metabolic dysfunction-associated steatotic liver disease; Metabolism; Microbial Genetics and Genomics; Middle Aged; Pathogenesis; peroxisome proliferator-activated receptor gamma; Peroxisome proliferator-activated receptors; Phenotype; Phenotypes; Phenotyping; Plant Genetics and Genomics; Polymorphism, Single Nucleotide; Proteins; Steatosis; United Kingdom > UK; Metabolic dysfunction-associated steatotic liver disease; Genetic association studies; Machine learning
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-025-03518-5

Background Genome-wide association studies (GWAS) have identified common variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD). However, rare coding variant studies have been limited by phenotyping challenges and small sample sizes. We test associations of rare and ultra-rare coding variants with proton density fat fraction (PDFF) and MASLD case–control status in 736,010 participants of diverse ancestries from the UK Biobank, All of Us, and BioMe and performed a trans-ancestral meta-analysis. We then developed models to accurately predict PDFF and MASLD status in the UK Biobank and tested associations with these predicted phenotypes to increase statistical power. Results The trans-ancestral meta-analysis with PDFF and MASLD case–control status identifies two single variants and two gene-level associations in APOB , CDH5 , MYCBP2 , and XAB2 . Association testing with predicted phenotypes, which replicates more known genetic variants from GWAS than true phenotypes, identifies 16 single variants and 11 gene-level associations implicating 23 additional genes. Two variants were polymorphic only among African ancestry participants and several associations showed significant heterogeneity in ancestry and sex-stratified analyses. In total, we identified 27 genes, of which 3 are monogenic causes of steatosis ( APOB , G6PC1 , PPARG ), 4 were previously associated with MASLD ( APOB , APOC3 , INSR , PPARG ), and 23 had supporting clinical, experimental, and/or genetic evidence. Conclusions Our results suggest that trans-ancestral association analyses can identify ancestry-specific rare and ultra-rare coding variants in MASLD pathogenesis. Furthermore, we demonstrate the utility of machine learning in genetic investigations of difficult-to-phenotype diseases in trans-ancestral biobanks.