Abstract
Nonalcoholic fatty liver disease (NAFLD) is common and partially heritable and has no effective treatments. We carried out a genome-wide association study (GWAS) meta-analysis of imaging (n = 66,814) and diagnostic code (3,584 cases versus 621,081 controls) measured NAFLD across diverse ancestries. We identified NAFLD-associated variants at torsin family 1 member B (TOR1B), fat mass and obesity associated (FTO), cordon-bleu WH2 repeat protein like 1 (COBLL1)/growth factor receptor-bound protein 14 (GRB14), insulin receptor (INSR), sterol regulatory element-binding transcription factor 1 (SREBF1) and patatin-like phospholipase domain-containing protein 2 (PNPLA2), as well as validated NAFLD-associated variants at patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily 2 (TM6SF2), apolipoprotein E (APOE), glucokinase regulator (GCKR), tribbles homolog 1 (TRIB1), glycerol-3-phosphate acyltransferase (GPAM), mitochondrial amidoxime-reducing component 1 (MARC1), microsomal triglyceride transfer protein large subunit (MTTP), alcohol dehydrogenase 1B (ADH1B), transmembrane channel like 4 (TMC4)/membrane-bound O-acyltransferase domain containing 7 (MBOAT7) and receptor-type tyrosine-protein phosphatase δ (PTPRD). Implicated genes highlight mitochondrial, cholesterol and de novo lipogenesis as causally contributing to NAFLD predisposition. Phenome-wide association study (PheWAS) analyses suggest at least seven subtypes of NAFLD. Individuals in the top 10% and 1% of genetic risk have a 2.5-fold to 6-fold increased risk of NAFLD, cirrhosis and hepatocellular carcinoma. These genetic variants identify subtypes of NAFLD, improve estimates of disease risk and can guide the development of targeted therapeutics.
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Data availability
Meta-analysis results from this study are available at http://www.med.umich.edu/spelioteslab/ and at GWAS Catalog (GCP ID: GCP000662). GOLD Consortium and MGI individual-level data are governed by patient privacy requirements and available to those having the mandatory IRB approvals. The eMERGE NAFLD cohort was previously described, and summary statistics are publicly available (https://www.ebi.ac.uk/gwas/studies/GCST008468). FinnGen data freeze 4 summary statistics are publicly available (https://www.finngen.fi/fi). UKBB genomic and phenotypic data supporting this publication are available upon application (https://ukbiobank.ac.uk). Otherwise, all data used to generate figures can be found in supplementary tables or in the above publicly available datasets. Source data are provided with this paper.
Code availability
Data analyses were performed using publicly available codes or software.
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Acknowledgements
AGES was funded by the National Institutes of Health (NIH; contracts N01-AG-1-2100 and HHSN271201200022C), the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association) and the Althingi (the Icelandic Parliament). Support for FamHS was provided by the National Heart, Lung and Blood Institute (NHLBI; grants R01 HL087700 and R01 HL117078) and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; grant R01 DK089256 to M.A.P.). FHS is conducted and supported by the NHLBI in collaboration with Boston University (contracts N01-HC-25195, HHSN268201500001I and 75N92019D00031). Funding for SHARe Affymetrix genotyping was provided by NHLBI (contract N02-HL64278). SHARe Illumina genotyping was provided under an agreement between Illumina and Boston University. The Old Order Amish liver phenotyping is supported by NIH grants and contracts (U01 HL072515 and P30 DK72488) and analysis methods by U01 HL137181 (to J.R.O.). Support for the GENOA study was provided by the NIH (grants HL085571 to P.A.P. and HL087660 to S.L.R.K.) and NHLBI (HL100245). Support for the IRASFS was provided by the NHLBI (grants R01 HL060944, R01 HL061019, R01 HL060919, R01 HL060894 and R01 HL061210 to X.G., D.W.B., J.M.N., J.I.R., L.E.W. and N.D.P.). Genotyping and analysis were supported by NIDDK (grants DK085175 and R01 DK118062). JHS is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the NHLBI and the National Institute on Minority Health and Health Disparities (NIMHD). We also thank the staff and participants of the JHS. MESA and the MESA SHARe projects are conducted and supported by the NHLBI in collaboration with MESA investigators. Support for MESA is provided by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079 and UL1-TR-001420, UL1TR001881, DK063491 and R01HL105756. Funding for SHARe genotyping was provided by NHLBI (contract N02-HL-64278). Genotyping was performed at Affymetrix and the Broad Institute of Harvard and MIT (Boston, MA) using the Affymetrix Genome-Wide Human SNP Array 6.0. We thank the other investigators, the staff and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutes can be found at http://www.mesa-nhlbi.org. L.F.B. was supported by R01 HL071739 for all measures of NAFLD in MESA. OOA studies are supported by grants and contracts from NIH, including U01 HL072515, U01 HL84756, U01 HL137181 and P30 DK72488. We acknowledge the MGI participants, Precision Health at the University of Michigan, the University of Michigan Medical School Central Biorepository and the University of Michigan Advanced Genomics Core for providing data and specimen storage, management, processing and distribution services. We also acknowledge the Center for Statistical Genetics in the Department of Biostatistics at the School of Public Health for genotype data curation, imputation and management in support of the research reported in this publication. COPDGene is supported by NHLBI (U01 HL089897 and U01 HL089856) as well as through contributions made to an industry advisory board comprised of AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Novartis, Pfizer, Siemens and Sunovion. Liver fat measures in COPDGene were gathered under HL122464. Analyses in the UKBB were done under approved project 18120 (to E.K.S.). E.K.S., Y.C., A.K., X.D., A.O. and B.D.H. are supported by NIH (grants R01 DK106621 and R01 DK107904 to E.K.S.) and The University of Michigan Department of Internal Medicine. N.D.P. and E.K.S. are supported by NIH (grants R01 DK128871 to N.D.P. and E.K.S.; R01DK131787 to E.K.S.). V.L.C. was supported in part by an American Association for the Study of Liver Disease Clinical, Translational and Outcomes Research Award. We acknowledge the participants and investigators of the FinnGen study. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the NHLBI; the National Institutes of Health; the US Department of Health and Human Services; Framingham Heart Study or Boston University.
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E.K.S. led the conceptualization, methodology development and funding of the project. P.A.P., N.D.P. and E.K.S provided supervision of the project. E.K.S., A.K. and N.D.P. led project management. Analysis were conducted by Y.C. (lead), X.D. (lead), B.K., M.F.F., L.F.B., K.A.R., S.K.M., K.A.Y., X.G., A.V.S., A.K., A.O., N.D.P. and B.F.C. Study resources were provided by N.D.P., D.W.B., L.E.W., J.R.O., S.K.M., K.D.T., S.L.R.K., T.H.M., A.C., J.I.R., V.G., J.M.N., M.A.P., P.A.P., J.E.H., G.R.W. and E.K.S. Data curation was performed by M.A.A., M.J.B., J.J.C., J.G.T., Y.-D.I.C., G.E., B.D.H. and E.K.S; Y.C., X.D., N.D.P., P.A.P. and E.K.S participated in central results interpretation. Paper draft preparation and editing was performed by E.K.S. (lead), Y.C., A.K., V.L.C., X.D., A.O. and N.D.P. Final review: all authors. All authors had access to the study data and reviewed and approved the final manuscript.
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Competing interests
The Regents of the University of Michigan and E.K.S. have a pending patent on the use of systems and methods for analysis of samples associated with NAFLD and related conditions. V.L.C. received grant funding from KOWA and AstraZeneca. J.J.C. and Vanderbilt University Medical Center receive research funding from NIH, IBM Watson Health, GE Healthcare and Theratechnologies. G.R.W. is a cofounder and equity shareholder in Quantitative Imaging Solutions, a company that provides consulting services for image and data analytics. G.R.W.’s spouse works for Biogen. Grants or contracts from NIH, Department of Defense (DoD) and Boehringer Ingelheim made payments to G.R.W.’s institution. G.R.W. received consulting fees from Pulmonx, Vertex, Janssen Pharmaceuticals, Pieris Therapeutics and Intellia Therapeutics. G.R.W. also received payments from Pulmonx for participation on a Data Safety Monitoring Board or Advisory Board. The remaining authors declare no competing interests.
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Nature Genetics thanks Stefano Romeo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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Extended data
Extended Data Fig. 1
GOLDPlus NAFLD measures meta-analysis study design.
Extended Data Fig. 2
European GOLDPlus NAFLD measures meta-analysis schematic.
Extended Data Fig. 3 Characteristics of GOLDPlus genome-wide significant variants in GOLD ancestry-based cohorts.
For each variant, the characteristics are shown for the GOLD ancestry-based analysis including: associated gene, NAFLD increasing effect allele (EA), effect allele frequency (EAF), effect/beta and 95% confidence interval (CI), Cochran’s Q heterogeneity I2 metric (HetSq) and heterogeneity P-value (HetPVal), EA P-value (P), and sample size (N). Results are for meta-analysis of GOLD European ancestry (red), African ancestry (blue), Hispanic ancestry (green), Chinese ancestry (purple), and all ancestries pooled (black). The estimates of the effect sizes (Beta) and 95% confidence interval in bidirectional testing within each ancestry and for all the ancestries combined were shown in the forest plots. The data underlying these plots are provided as Source Data.
Extended Data Fig. 4 Characteristics of GOLDPlus genome-wide significant variants in GOLD sex-specific cohorts.
For each variant, the characteristics are shown for the GOLD sex-specific analysis including: associated gene, NAFLD increasing effect allele (EA), effect allele frequency (EAF), effect/beta and 95% confidence interval (CI), Cochran’s Q heterogeneity I2 metric (HetSq) and heterogeneity P-value (HetPVal), EA P-value (P), and sample size (N). Results are for meta-analysis of GOLD cohort males (blue), females (red), and pooled sexes (black). The estimates of the effect sizes (Beta) and 95% confidence interval in bidirectional testing within each ancestry and for all the ancestries combined were shown in the forest plots. The data underlying these plots are provided as Source Data.
Extended Data Fig. 5 DEPICT analysis of biological enrichment of NAFLD associated variants.
Height of the bar represents the nominal −log10P-value of enrichment of GWAS associated genes with physiological systems, cells, and tissues. Dark orange shading represents statistical significance at false discovery rate (FDR) < 0.05. The data underlying these plots are provided as Source Data.
Extended Data Fig. 6 Two-sample Mendelian randomization analysis for casual associations between NAFLD associated variants and fibrosis/cirrhosis and esophageal varices.
a,b, Data represent the effect/beta and 95% confidence intervals for the inverse variance weighted (IVW) and MR-Egger analyses for (a) NAFLD exposure (GOLD cohort, n = 11 instruments) and K74:fibrosis/cirrhosis outcome (UKBB) (MR-Egger P-value = 1.88 × 10-3, IVW p-value = 8.65 × 10-5) and (b) NAFLD exposure (GOLD cohort, n = 11 instruments) and I85:esophageal varices outcome (UKBB) (MR-Egger P-value = 9.36 × 10-4, IVW P-value = 3.51 × 10-4). c,d, The crosshairs on the plots represent the effect and 95% confidence intervals for each SNP-NAFLD or SNP-outcome association for (c) NAFLD exposure (GOLD cohort, n = 10 instruments) and K74:fibrosis/cirrhosis outcome (UKBB) and (d) NAFLD exposure (GOLD cohort, n = 10 instruments) and I85:esophageal varices outcome (UKBB). The data underlying these plots are provided as Source Data.
Extended Data Fig. 7 Two-sample Mendelian randomization analysis for casual associations between BMI, waist circumference associated variants and NAFLD.
a,b, Data are presented as effect/beta and 95% confidence intervals for MR-Egger and inverse variance weighted (IVW) methods for (a) waist circumference GWAS (UKBB, n = 217 instruments) and GOLD cohort outcome (MR-Egger P-value = 3.6 × 10-2, IVW P-value = 3.71 × 10-4) and (b) BMI GWAS (UKBB, n = 293 instruments) and GOLD cohort outcome (MR-Egger P-value = 0.02, IVW P-value = 1.02 × 10-7). c,d, The crosshairs on the plots represent the effect/beta and 95% confidence intervals for each SNP-NAFLD or SNP-outcome association for (c) waist circumference GWAS (UKBB, n = 211 instruments) and GOLD cohort outcome and (d) BMI GWAS (UKBB, n = 283 instruments) and GOLD cohort outcome. The data underlying these plots are provided as Source Data.
Extended Data Fig. 8 Convolutional neural network schematic for UKBB MRI liver imaging (PCC values).
Scatter plot of predicted UKBB MRI-PDFF values versus ‘true’ UKBB MRI-PDFF values (as determined by Perspectum Diagnostics). a,b, Pearson correlation coefficients (PCC) are shown for (a) gradient echo image protocol and (b) IDEAL image protocol. The data underlying these plots are provided as Source Data.
Supplementary information
Supplementary Information
Supplementary Fig. 1.
Supplementary Tables
Supplementary Tables 1–19.
Source data
Source Data Fig. 1
Source data to produce forest plots.
Source Data Fig. 2
Source data containing effect sizes for NAFLD-associated variants on human diseases and traits.
Source Data Fig. 3
Source data to produce forest plots.
Source Data Fig. 5
Source data to produce Manhattan plot.
Source Data Fig. 6
Source data to plot associations.
Source Data Extended Data Fig. 3
Source data to produce forest plots.
Source Data Extended Data Fig. 4
Source data to produce forest plots.
Source Data Extended Data Fig. 5
Source data that includes data from DEPICT analysis.
Source Data Extended Data Fig. 6
Source data that includes data from MR analysis.
Source Data Extended Data Fig. 7
Source data that includes data from MR analysis.
Source Data Extended Data Fig. 8
Source data that includes true and predicted PDFF.
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Chen, Y., Du, X., Kuppa, A. et al. Genome-wide association meta-analysis identifies 17 loci associated with nonalcoholic fatty liver disease. Nat Genet 55, 1640–1650 (2023). https://doi.org/10.1038/s41588-023-01497-6
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DOI: https://doi.org/10.1038/s41588-023-01497-6
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