Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Genome-wide association meta-analysis identifies 17 loci associated with nonalcoholic fatty liver disease

Nature Genetics volume 55, pages 1640–1650 (2023)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Characteristics of a subset of GOLDPlus genome-wide significant variants in GOLD ancestry-based cohorts.
Fig. 2: Effects of NAFLD-associated variants on other human diseases and traits using PheWAS clustering to identify distinct biological subgroupings.
Fig. 3: Effect of PheWAS subgroupings on human diseases and traits.
Fig. 4: Schematic providing biological context for PheWAS subgroupings.
Fig. 5: PheWAS Manhattan plot of NAFLD polygenic risk score.
Fig. 6: Associations between NAFLD polygenic risk score with NAFLD, cirrhosis and HCC in an independent cohort.

Similar content being viewed by others

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.

References

  1. Lazo, M. et al. Prevalence of nonalcoholic fatty liver disease in the United States: the third National Health and Nutrition Examination Survey, 1988–1994. Am. J. Epidemiol. 178, 38–45 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Portillo Sanchez, P. et al. High prevalence of nonalcoholic fatty liver disease in patients with type 2 diabetes mellitus and normal plasma aminotransferase levels. J. Clin. Endocrinol. Metab. 100, 2231–2238 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Dongiovanni, P. et al. Causal relationship of hepatic fat with liver damage and insulin resistance in nonalcoholic fatty liver. J. Intern. Med. 283, 356–370 (2018).

    Article  CAS  PubMed  Google Scholar 

  4. Lauridsen, B. K. et al. Liver fat content, non-alcoholic fatty liver disease, and ischaemic heart disease: Mendelian randomization and meta-analysis of 279 013 individuals. Eur. Heart J. 39, 385–393 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Stender, S. et al. Adiposity amplifies the genetic risk of fatty liver disease conferred by multiple loci. Nat. Genet. 49, 842–847 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Liu, Z. et al. Causal relationships between NAFLD, T2D and obesity have implications for disease subphenotyping. J. Hepatol. 73, 263–276 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Bianco, C. et al. Non-invasive stratification of hepatocellular carcinoma risk in non-alcoholic fatty liver using polygenic risk scores. J. Hepatol. 74, 775–782 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Parisinos, C. A. et al. Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis. J. Hepatol. 73, 241–251 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Crespo, J. et al. Are there predictive factors of severe liver fibrosis in morbidly obese patients with non-alcoholic steatohepatitis? Obes. Surg. 11, 254–257 (2001).

    Article  CAS  PubMed  Google Scholar 

  10. Younossi, Z. M. et al. The economic and clinical burden of nonalcoholic fatty liver disease in the United States and Europe. Hepatology 64, 1577–1586 (2016).

    Article  PubMed  Google Scholar 

  11. Romeo, S. et al. Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease. Nat. Genet. 40, 1461–1465 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Speliotes, E. K. et al. Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits. PLoS Genet. 7, e1001324 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Luukkonen, P. K. et al. MARC1 variant rs2642438 increases hepatic phosphatidylcholines and decreases severity of non-alcoholic fatty liver disease in humans. J. Hepatol. 73, 725–726 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Jamialahmadi, O. et al. Exome-wide association study on alanine aminotransferase identifies sequence variants in the GPAM and APOE associated with fatty liver disease. Gastroenterology 160, 1634–1646 (2021).

    Article  CAS  PubMed  Google Scholar 

  15. Kitamoto, A. et al. Association of polymorphisms in GCKR and TRIB1 with nonalcoholic fatty liver disease and metabolic syndrome traits. Endocr. J. 61, 683–689 (2014).

    Article  CAS  PubMed  Google Scholar 

  16. Mancina, R. M. et al. The MBOAT7-TMC4 variant rs641738 increases risk of nonalcoholic fatty liver disease in individuals of European descent. Gastroenterology 150, 1219–1230 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Nakajima, S. et al. Polymorphism of receptor-type tyrosine-protein phosphatase δ gene in the development of non-alcoholic fatty liver disease. J. Gastroenterol. Hepatol. 33, 283–290 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Palmer, N. D. et al. Allele-specific variation at APOE increases nonalcoholic fatty liver disease and obesity but decreases risk of Alzheimer’s disease and myocardial infarction. Hum. Mol. Genet. 30, 1443–1456 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Vilar-Gomez, E. et al. ADH1B*2 is associated with reduced severity of nonalcoholic fatty liver disease in adults, independent of alcohol consumption. Gastroenterology 159, 929–943 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Zheng, W. et al. MTP -493G>T polymorphism and susceptibility to nonalcoholic fatty liver disease: a meta-analysis. DNA Cell Biol. 33, 361–369 (2014).

    Article  CAS  PubMed  Google Scholar 

  21. Middleton, M. S. et al. Agreement between magnetic resonance imaging proton density fat fraction measurements and pathologist-assigned steatosis grades of liver biopsies from adults with nonalcoholic steatohepatitis. Gastroenterology 153, 753–761 (2017).

    Article  PubMed  Google Scholar 

  22. Saadeh, S. et al. The utility of radiological imaging in nonalcoholic fatty liver disease. Gastroenterology 123, 745–750 (2002).

    Article  PubMed  Google Scholar 

  23. Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    Article  CAS  PubMed  Google Scholar 

  24. Kahali, B. et al. A noncoding variant near PPP1R3B promotes liver glycogen storage and MetS, but protects against myocardial infarction. J. Clin. Endocrinol. Metab. 106, 372–387 (2021).

    Article  PubMed  Google Scholar 

  25. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lawlor, D. A. et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27, 1133–1163 (2008).

    Article  PubMed  Google Scholar 

  27. Chen, V. L. et al. Genome-wide association study of serum liver enzymes implicates diverse metabolic and liver pathology. Nat. Commun. 12, 816 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Emdin, C. A. et al. Association of genetic variation with cirrhosis: a multi-trait genome-wide association and gene-environment interaction study. Gastroenterology 160, 1620–1633 (2021).

    Article  CAS  PubMed  Google Scholar 

  29. Sveinbjornsson, G. et al. Multiomics study of nonalcoholic fatty liver disease. Nat. Genet. 54, 1652–1663 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Vujkovic, M. et al. A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation. Nat. Genet. 54, 761–771 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chambers, J. C. et al. Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nat. Genet. 43, 1131–1138 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kathiresan, S. et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat. Genet. 40, 189–197 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Beer, N. L. et al. The P446L variant in GCKR associated with fasting plasma glucose and triglyceride levels exerts its effect through increased glucokinase activity in liver. Hum. Mol. Genet. 18, 4081–4088 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ishizuka, Y. et al. TRIB1 downregulates hepatic lipogenesis and glycogenesis via multiple molecular interactions. J. Mol. Endocrinol. 52, 145–158 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. Bauer, R. C. et al. Tribbles-1 regulates hepatic lipogenesis through posttranscriptional regulation of C/EBPalpha. J. Clin. Invest. 125, 3809–3818 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Agius, L. Hormonal and metabolite regulation of hepatic glucokinase. Annu. Rev. Nutr. 36, 389–415 (2016).

    Article  CAS  PubMed  Google Scholar 

  37. Janssen, M. C. et al. Symptomatic lipid storage in carriers for the PNPLA2 gene. Eur. J. Hum. Genet. 21, 807–815 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Steneberg, P. et al. Hyperinsulinemia enhances hepatic expression of the fatty acid transporter Cd36 and provokes hepatosteatosis and hepatic insulin resistance. J. Biol. Chem. 290, 19034–19043 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Ipsen, D. H., Lykkesfeldt, J. & Tveden-Nyborg, P. Molecular mechanisms of hepatic lipid accumulation in non-alcoholic fatty liver disease. Cell. Mol. Life Sci. 75, 3313–3327 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Popineau, L. et al. Novel Grb14-mediated cross talk between insulin and p62/Nrf2 pathways regulates liver lipogenesis and selective insulin resistance. Mol. Cell. Biol. 36, 2168–2181 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Cooney, G. J. et al. Improved glucose homeostasis and enhanced insulin signalling in Grb14-deficient mice. EMBO J. 23, 582–593 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Michael, M. D. et al. Loss of insulin signaling in hepatocytes leads to severe insulin resistance and progressive hepatic dysfunction. Mol. Cell 6, 87–97 (2000).

    Article  CAS  PubMed  Google Scholar 

  43. Sirwi, A. & Hussain, M. M. Lipid transfer proteins in the assembly of apoB-containing lipoproteins. J. Lipid Res. 59, 1094–1102 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Adam, M. P. et al. (eds.). GeneReviews((R)) (University of Washington, 1993).

  45. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  Google Scholar 

  46. Polimanti, R. & Gelernter, J. ADH1B: from alcoholism, natural selection, and cancer to the human phenome. Am. J. Med. Genet. B Neuropsychiatr. Genet. 177, 113–125 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Gu, S. et al. Recent selection on a class I ADH locus distinguishes Southwest Asian populations including Ashkenazi Jews. Genes (Basel) 9, 452 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Macgregor, S. et al. Associations of ADH and ALDH2 gene variation with self report alcohol reactions, consumption and dependence: an integrated analysis. Hum. Mol. Genet. 18, 580–593 (2009).

    Article  CAS  PubMed  Google Scholar 

  49. Muenter, M. D., Perry, H. O. & Ludwig, J. Chronic vitamin A intoxication in adults. Hepatic, neurologic and dermatologic complications. Am. J. Med. 50, 129–136 (1971).

    Article  CAS  PubMed  Google Scholar 

  50. Shin, J. Y. et al. Nuclear envelope-localized torsinA-LAP1 complex regulates hepatic VLDL secretion and steatosis. J. Clin. Invest. 129, 4885–4900 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Innes, H. et al. Genome-wide association study for alcohol-related cirrhosis identifies risk loci in MARC1 and HNRNPUL1. Gastroenterology 159, 1276–1289.e7 (2020).

    Article  CAS  PubMed  Google Scholar 

  52. Xia, M. et al. Hepatic deletion of Mboat7 (Lpiat1) causes activation of SREBP-1c and fatty liver. J. Lipid Res. 62, 100031 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Landgraf, K. et al. FTO obesity risk variants are linked to adipocyte IRX3 expression and BMI of children—relevance of FTO variants to defend body weight in lean children? PLoS ONE 11, e0161739 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Kozlitina, J. et al. Exome-wide association study identifies a TM6SF2 variant that confers susceptibility to nonalcoholic fatty liver disease. Nat. Genet. 46, 352–356 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wang, Y. et al. PNPLA3, CGI-58, and inhibition of hepatic triglyceride hydrolysis in mice. Hepatology 69, 2427–2441 (2019).

    Article  CAS  PubMed  Google Scholar 

  56. Morton, A. M. et al. Apolipoproteins E and CIII interact to regulate HDL metabolism and coronary heart disease risk. JCI Insight 3, e98045 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Abul-Husn, N. S. et al. A protein-truncating HSD17B13 variant and protection from chronic liver disease. N. Engl. J. Med. 378, 1096–1106 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Fox, C. S. et al. Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women. PLoS Genet. 8, e1002695 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Hatters, D. M., Peters-Libeu, C. A. & Weisgraber, K. H. Apolipoprotein E structure: insights into function. Trends Biochem. Sci. 31, 445–454 (2006).

    Article  CAS  PubMed  Google Scholar 

  60. Mahdessian, H. et al. TM6SF2 is a regulator of liver fat metabolism influencing triglyceride secretion and hepatic lipid droplet content. Proc. Natl Acad. Sci. USA 111, 8913–8918 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. BasuRay, S. et al. Accumulation of PNPLA3 on lipid droplets is the basis of associated hepatic steatosis. Proc. Natl Acad. Sci. USA 116, 9521–9526 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Rondinone, C. M. et al. Protein tyrosine phosphatase 1B reduction regulates adiposity and expression of genes involved in lipogenesis. Diabetes 51, 2405–2411 (2002).

    Article  CAS  PubMed  Google Scholar 

  63. Zou, Y. et al. IRX3 promotes the browning of white adipocytes and its rare variants are associated with human obesity risk. EBioMedicine 24, 64–75 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Zeng, H. et al. CD36 promotes de novo lipogenesis in hepatocytes through INSIG2-dependent SREBP1 processing. Mol. Metab. 57, 101428 (2022).

    Article  CAS  PubMed  Google Scholar 

  65. Cignarelli, A. et al. Insulin and insulin receptors in adipose tissue development. Int. J. Mol. Sci. 20, 759 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Ong, K. T. et al. Adipose triglyceride lipase is a major hepatic lipase that regulates triacylglycerol turnover and fatty acid signaling and partitioning. Hepatology 53, 116–126 (2011).

    Article  CAS  PubMed  Google Scholar 

  67. Morales, L. D. et al. Further evidence supporting a potential role for ADH1B in obesity. Sci. Rep. 11, 1932 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Tanaka, Y. et al. LPIAT1/MBOAT7 depletion increases triglyceride synthesis fueled by high phosphatidylinositol turnover. Gut 70, 180–193 (2021).

    Article  CAS  PubMed  Google Scholar 

  69. Neschen, S. et al. Prevention of hepatic steatosis and hepatic insulin resistance in mitochondrial acyl-CoA:glycerol-sn-3-phosphate acyltransferase 1 knockout mice. Cell Metab. 2, 55–65 (2005).

    Article  CAS  PubMed  Google Scholar 

  70. Linden, D. et al. Liver-directed overexpression of mitochondrial glycerol-3-phosphate acyltransferase results in hepatic steatosis, increased triacylglycerol secretion and reduced fatty acid oxidation. FASEB J. 20, 434–443 (2006).

    Article  CAS  PubMed  Google Scholar 

  71. Klein, J. M. et al. The mitochondrial amidoxime-reducing component (mARC1) is a novel signal-anchored protein of the outer mitochondrial membrane. J. Biol. Chem. 287, 42795–42803 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Hussain, M. M. et al. Multiple functions of microsomal triglyceride transfer protein. Nutr. Metab. (Lond.) 9, 14 (2012).

    Article  CAS  PubMed  Google Scholar 

  73. Fernandes Silva, L. et al. An intronic variant in the GCKR gene is associated with multiple lipids. Sci. Rep. 9, 10240 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Douvris, A. et al. Functional analysis of the TRIB1 associated locus linked to plasma triglycerides and coronary artery disease. J. Am. Heart Assoc. 3, e000884 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Harris, T. B. et al. Age, Gene/Environment Susceptibility-Reykjavik Study: multidisciplinary applied phenomics. Am. J. Epidemiol. 165, 1076–1087 (2007).

    Article  PubMed  Google Scholar 

  76. Regan, E. A. et al. Genetic epidemiology of COPD (COPDGene) study design. COPD 7, 32–43 (2010).

    Article  PubMed  Google Scholar 

  77. Carr, J. J. et al. Calcified coronary artery plaque measurement with cardiac CT in population-based studies: standardized protocol of Multi-Ethnic Study of Atherosclerosis (MESA) and Coronary Artery Risk Development in Young Adults (CARDIA) study. Radiology 234, 35–43 (2005).

    Article  PubMed  Google Scholar 

  78. Speliotes, E. K. et al. Liver fat is reproducibly measured using computed tomography in the Framingham Heart Study. J. Gastroenterol. Hepatol. 23, 894–899 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Daniels, P. R. et al. Familial aggregation of hypertension treatment and control in the Genetic Epidemiology Network of Arteriopathy (GENOA) study. Am. J. Med. 116, 676–681 (2004).

    Article  PubMed  Google Scholar 

  80. Palmer, N. D. et al. Genetic variants associated with quantitative glucose homeostasis traits translate to type 2 diabetes in Mexican Americans: the GUARDIAN (Genetics Underlying Diabetes in Hispanics) Consortium. Diabetes 64, 1853–1866 (2015).

    Article  CAS  PubMed  Google Scholar 

  81. Liu, J. et al. Fatty liver, abdominal adipose tissue and atherosclerotic calcification in African Americans: the Jackson Heart Study. Atherosclerosis 224, 521–525 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Kramer, H. et al. Racial/ethnic differences in hypertension and hypertension treatment and control in the multi-ethnic study of atherosclerosis (MESA). Am. J. Hypertens. 17, 963–970 (2004).

    Article  PubMed  Google Scholar 

  83. Rampersaud, E. et al. The association of coronary artery calcification and carotid artery intima-media thickness with distinct, traditional coronary artery disease risk factors in asymptomatic adults. Am. J. Epidemiol. 168, 1016–1023 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Nat. Genet. 50, 1593–1599 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation (Springer International Publishing, 2015).

  86. Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31, 1116–1128 (2006).

    Article  PubMed  Google Scholar 

  87. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778 (IEEE, 2016).

  88. Namjou, B. et al. GWAS and enrichment analyses of non-alcoholic fatty liver disease identify new trait-associated genes and pathways across eMERGE Network. BMC Med. 17, 135 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Chen, V. L. et al. Genetic variants that associate with cirrhosis have pleiotropic effects on human traits. Liver Int. 40, 405–415 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Zhou, W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Dongiovanni, P. et al. Genetic variants regulating insulin receptor signalling are associated with the severity of liver damage in patients with non-alcoholic fatty liver disease. Gut 59, 267–273 (2010).

    Article  CAS  PubMed  Google Scholar 

  93. Feitosa, M. F. et al. The ERLIN1-CHUK-CWF19L1 gene cluster influences liver fat deposition and hepatic inflammation in the NHLBI Family Heart Study. Atherosclerosis 228, 175–180 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Chalasani, N. et al. Genome-wide association study identifies variants associated with histologic features of nonalcoholic fatty liver disease. Gastroenterology 139, 1567–1576 (2010).

    Article  PubMed  Google Scholar 

  95. Eslam, M. et al. Interferon-λ rs12979860 genotype and liver fibrosis in viral and non-viral chronic liver disease. Nat. Commun. 6, 6422 (2015).

    Article  CAS  PubMed  Google Scholar 

  96. Wiedmann, S. et al. Genetic variants within the LPIN1 gene, encoding lipin, are influencing phenotypes of the metabolic syndrome in humans. Diabetes 57, 209–217 (2008).

    Article  CAS  PubMed  Google Scholar 

  97. Shang, X. R. et al. GWAS-identified common variants with nonalcoholic fatty liver disease in Chinese children. J. Pediatr. Gastroenterol. Nutr. 60, 669–674 (2015).

    Article  CAS  PubMed  Google Scholar 

  98. Petta, S. et al. IL28B and PNPLA3 polymorphisms affect histological liver damage in patients with non-alcoholic fatty liver disease. J. Hepatol. 56, 1356–1362 (2012).

    Article  CAS  PubMed  Google Scholar 

  99. Kitamoto, T. et al. Genome-wide scan revealed that polymorphisms in the PNPLA3, SAMM50, and PARVB genes are associated with development and progression of nonalcoholic fatty liver disease in Japan. Hum. Genet. 132, 783–792 (2013).

    Article  CAS  PubMed  Google Scholar 

  100. Anstee, Q. M. et al. Genome-wide association study of non-alcoholic fatty liver and steatohepatitis in a histologically characterised cohort. J. Hepatol. 73, 505–515 (2020).

    Article  CAS  PubMed  Google Scholar 

  101. Ma, Y. et al. 17-β hydroxysteroid dehydrogenase 13 is a hepatic retinol dehydrogenase associated with histological features of nonalcoholic fatty liver disease. Hepatology 69, 1504–1519 (2019).

    Article  CAS  PubMed  Google Scholar 

  102. Park, S. L. et al. Genome-wide association study of liver fat: the Multiethnic Cohort Adiposity Phenotype Study. Hepatol. Commun. 4, 1112–1123 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Hemani, G. et al. The MR-base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Bowden, J. et al. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int. J. Epidemiol. 45, 1961–1974 (2016).

    PubMed  PubMed Central  Google Scholar 

Download references

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.

Author information

Author notes

  1. These authors contributed equally: Yanhua Chen, Xiaomeng Du.

  2. These authors jointly supervised this work: Patricia A. Peyser, Nicholette D. Palmer, Elizabeth K. Speliotes.

Authors and Affiliations

  1. Department of Internal Medicine, Division of Gastroenterology and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Yanhua Chen, Xiaomeng Du, Annapurna Kuppa, Bratati Kahali, Vincent L. Chen, Antonino Oliveri, Brian D. Halligan & Elizabeth K. Speliotes

  2. Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

    Mary F. Feitosa & Michael A. Province

  3. Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA

    Lawrence F. Bielak, Sharon L. R. Kardia & Patricia A. Peyser

  4. Department of Endocrinology, Diabetes and Nutrition, University of Maryland – Baltimore, Baltimore, MD, USA

    Jeffrey R. O’Connell & Kathleen A. Ryan

  5. Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA

    Solomon K. Musani, Adolfo Correa, Breland F. Crudup & Thomas H. Mosley Jr

  6. The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor–UCLA Medical Center, Torrance, CA, USA

    Xiuqing Guo, Yii-Der I. Chen, Kent D. Taylor & Jerome I. Rotter

  7. Centre for Brain Research, Indian Institute of Science, Bangalore, India

    Bratati Kahali

  8. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

    Albert V. Smith

  9. Icelandic Heart Association, Kopavogur, Iceland

    Gudny Eirksdottir & Vilmundur Gudnason

  10. Department of Family Medicine, University of California San Diego, San Diego, CA, USA

    Matthew A. Allison

  11. Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA

    Donald W. Bowden & Nicholette D. Palmer

  12. Department of Internal Medicine, Lundquist Institute at Harbor–UCLA, Torrance, CA, USA

    Matthew J. Budoff

  13. Department of Radiology, Vanderbilt University School of Medicine, Nashville, TN, USA

    John Jeffrey Carr & James G. Terry

  14. Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA

    Jill M. Norris, Kendra A. Young & John E. Hokanson

  15. Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA

    Lynne E. Wagenknecht

  16. Department of Medicine, Division of Pulmonary and Critical Care, Brigham and Women’s Hospital, Boston, MA, USA

    George R. Washko

  17. Department of Medicine, University of Iceland, Reykjavik, Iceland

    Vilmundur Gudnason

Authors
  1. Yanhua Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaomeng Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Annapurna Kuppa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mary F. Feitosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lawrence F. Bielak
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jeffrey R. O’Connell
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Solomon K. Musani
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiuqing Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bratati Kahali
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Vincent L. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Albert V. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Kathleen A. Ryan
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Gudny Eirksdottir
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Matthew A. Allison
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Donald W. Bowden
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Matthew J. Budoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. John Jeffrey Carr
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Yii-Der I. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Kent D. Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Antonino Oliveri
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Adolfo Correa
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Breland F. Crudup
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Sharon L. R. Kardia
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Thomas H. Mosley Jr
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Jill M. Norris
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. James G. Terry
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Jerome I. Rotter
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Lynne E. Wagenknecht
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Brian D. Halligan
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Kendra A. Young
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. John E. Hokanson
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. George R. Washko
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Vilmundur Gudnason
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Michael A. Province
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Patricia A. Peyser
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Nicholette D. Palmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Elizabeth K. Speliotes
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Corresponding author

Correspondence to Elizabeth K. Speliotes.

Ethics declarations

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.

Peer review

Peer review information

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

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.

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.

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.

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.

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.

Source data

Supplementary information

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.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-023-01497-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing