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

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Haas, Mary E., Pirruccello, James P., Friedman, Samuel N., Wang, Minxian, Emdin, Connor A., Ajmera, Veeral H., Simon, Tracey G., Homburger, Julian R. +9 more·2021·Cell Genomics
DOI 10.1016/j.xgen.2021.100066PMC PMC8699145

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Abstract

Abstract Excess accumulation of liver fat – termed hepatic steatosis when fat accounts for > 5.5% of liver content – is a leading risk factor for end-stage liver disease and is strongly associated with important cardiometabolic disorders. Using a truth dataset of 4,511 UK Biobank participants with liver fat previously quantified via abdominal MRI imaging, we developed a machine learning algorithm to quantify liver fat with correlation coefficients of 0.97 and 0.99 in hold-out testing datasets and applied this algorithm to raw imaging data from an additional 32,192 participants. Among all 36,703 individuals with abdominal MRI imaging, median liver fat was 2.2%, with 6,250 (17%) meeting criteria for hepatic steatosis. Although individuals afflicted with hepatic steatosis were more likely to have been diagnosed with conditions such as obesity or diabetes, a prediction model based on clinical data alone without imaging could not reliably estimate liver fat content. To identify genetic drivers of variation in liver fat, we first conducted a common variant association study of 9.8 million variants, confirming three known associations for variants in the TM6SF2, APOE , and PNPLA3 genes and identifying five new variants associated with increased hepatic fat in or near the MARC1, ADH1B, TRIB1, GPAM and MAST3 genes. A polygenic score that integrated information from each of these eight variants was strongly associated with future clinical diagnosis of liver diseases. Next, we performed a rare variant association study in a subset of 11,021 participants with gene sequencing data available, identifying an association between inactivating variants in the APOB gene and substantially lower LDL cholesterol, but more than 10-fold increased risk of steatosis. Taken together, these results provide proof of principle for the use of machine learning algorithms on raw imaging data to enable epidemiologic studies and genetic discovery.

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  • Besides PNPLA3 p.I148M and TM6SF2 p.E167K, additional variants including common risk variants in MBOAT7, GCKR, GPAM, protective variants in HSD17B13, APOE and MTARC1, and rare variants in APOB, MTTP, CIDEB and ATG7 have been robustly associated with the risk of progressive MASLD.

    “Besides PNPLA3 p.I148M and TM6SF2 p.E167K, additional variants, including common risk variants in MBOAT7 , GCKR , GPAM , protective variants in HSD17B13 , APOE and MTARC1, and rare variants ( e.g. in APOB , MTTP, CIDEB and ATG7 ), have been robustly associated with the risk of progressive MASLD. 199 -205”

    From EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). (2024) · cites this paper as [205]

    The Results (GWAS identifies 5 newly associated loci; page not provided) associate PNPLA3, TM6SF2, GPAM, APOE and MTARC1 variants with increased liver fat, while describing GCKR and MBOAT7 associations as suggestive. The Results (Rare inactivating variants in APOB and MTTP; page not provided) associate those rare variants with steatosis, and the paper notes prior evidence linking HSD17B13 to advanced liver disease. This does not establish that every listed variant is robustly associated with progressive MASLD: the APOE and MTARC1 variants discussed increase rather than protect against liver fat, and searches for CIDEB and ATG7 found no relevant findings. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Variants in TM6SF2 as well as microsomal triglyceride transfer protein (MTTP) and APOB have already been identified as MASLD risk factors having direct influence over VLDL secretion.

    “Indeed, variants in TM6SF2 as well as microsomal triglyceride transfer protein (MTTP) and APOB have already been identified as MASLD risk factors having direct influence over VLDL secretion (7–10), and the current experiments add Hdlbp to this list.”

    From Loss of function of T-box 3 in the liver protects against MASLD (2025) · cites this paper as [8]

    The Results sections report associations of TM6SF2, APOB, and MTTP variants with increased liver fat. The APOB/MTTP section also describes their roles in exporting lipoproteins from the liver, but our copy does not establish that TM6SF2 variants directly affect VLDL secretion or measure a direct effect of these variants on VLDL secretion. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • A single nucleotide polymorphism in transmembrane-6 superfamily member 2 (TM6SF2) is strongly associated with MASLD risk.

    “Noteworthy among these is a single nucleotide polymorphism in transmembrane-6 superfamily member 2 (TM6SF2), which is strongly associated with MASLD risk (7–10).”

    From Loss of function of T-box 3 in the liver protects against MASLD (2025) · cites this paper as [8]

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Cited by

Sorted by citations

Authors
Haas, M. E., Pirruccello, J. P., Friedman, S. N., Wang, M., Emdin, C. A., Ajmera, V. H., Simon, T. G., Homburger, J. R., Guo, X., Budoff, M., Corey, K. E., Zhou, A. Y., Philippakis, A., Ellinor, P. T., Loomba, R., Batra, P., & Khera, A. V.
Affiliations
  • Broad Institute (US)
  • University of California San Diego (US)
  • Harvard University (US)
  • Color (United States) (US)
  • The Lundquist Institute (US)
Corresponding author
Khera, Amit V.
ORCID
Venue
Cell Genomics
Publisher
Elsevier BV
Published
2021-12
Volume
1
Issue
3
Pages
100066
ISSN
2666-979X (ISSN-L)
DOI
10.1016/j.xgen.2021.100066
PMC
PMC8699145
OpenAlex
W4200310144
Cited by
118
References
12
Citations by year
2026: 17 · 2025: 22 · 2024: 28 · 2023: 26 · 2022: 17 · 2021: 6 · 2020: 2
FWCI
13.87
Type
journal article
Language
en
Open access
Licence
Locations
  • Cell Genomics (publishedVersion, cc-by-nc-nd)
  • medRxiv (acceptedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
Funders
  • U.S. Department of Defense (5UL1TR001442, UL1TR001881, CA170674P2)
  • Massachusetts General Hospital
  • American Association for the Study of Liver Diseases
  • Broad Institute
  • National Institutes of Health (n01hc95164-17-0-1, 75N92020D00005, 75N92020D00003, UL1TR001420, n01hc95165-34-0-1, n01hc95161-24-0-1, n01hc095169-003, N01-HC- 95166, 75N92020D00006, n01hc95166-21-0-1, N01-HC9-5168, 75N92020D00001 ,, n01hc95162-31-0-1, n01hc95168-27-0-1, n01hc95160-18-0-1, n01hc095167-000, N01-HC- 95159, UL1TR001079, n01hc95163-22-0-1, n02hl64278-6-0-0, HHSN268201500003I, 75N92020D00004, n01hc95159-40-0-1, 75N92020D00007, UL1TR000040, 75N92020D00002, UL1TR001442, UL1-TR-, N01‐HC‐95161, N01- HC-95163, N01- HC-95165, N01‐HC‐95160, N01 HC95164, N01-HC-95167, N02-HL-64278, N01-HC95162, N01‐HC‐95169, DK-063491, UL1TR001881)
  • Medical Research Council (MC_PC_17228)
  • National Heart, Lung, and Blood Institute (DK-063491, N01 HC-95159, N01-HC-95160, N01-HC095169, R01HL071739, 75N92020D00001, N01 HC-95168, N01-HC-095165, 75N92020D00006, N01 HC95167, (N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-, N01 HC095163, 75N92020D00007, N01 HC-95161, N01 HC95165, N01HC95164, N01-HC-95167, N01-HC-95168, N01-HC-95169, N02 HL064278, N01-HC-95167, N01-HC-95168, 75N92020D00002, R01HL128914, UL1-TR-, N01-HC-095159, N01-HC, N01 HC095168, N02- HL-64278, N01-HC-095162, HC-95168, N01-HC-095161, 75N92020D00005, 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, UL1-TR-001420, HHSN268201500003C, HHSN26820150 0003I, 75N92020D00003, N01 HC095167, N01-HC-095160, N01 HC095166, N01 HC095164, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, N01‐HC‐95166, N01 HC-95162, HC95159, K24HL105780, 75N92020D00004, R01HL092577, HL64278, N01 HC95169)
  • National Human Genome Research Institute (K08HG010155, U01HG011719)
  • National Institute of Diabetes and Digestive and Kidney Diseases (UL1-TR001079, 75N92020D00005, DK063491, N01-HC-95163, N01-HC-95167, N01-HC-95166, N01-HC-95160, N01-HC-95169, N01-HC-95165, N02-HL-64278, K23DK119460, Diabetes Research Center (DRC) grant DK063491, R01DK106419, N01-HC-95168, N01-HC-95161, K23DK122104, N01-HC-95159, N01-HC-95162, N01-HC-95164, P30DK120515, P30DK063491)
  • National Institute of Environmental Health Sciences (P42ES010337)
  • National Center for Advancing Translational Sciences (N01-HC-95161, 75N92020D00006, N01-HC-95168, UL1-TR-000040, UL1-TR-001079, UL1-TR, N01-HC-95162, 75N92020D00005, N01-HC-95159, DK063491, N01-HC-95160, 75N92020D00002, HHSN268201500003I, N01-HC-95166, N01-HC-95169, N01-HC-95163, 75N92020D00003, N01-HC-95167, N01-HC-95165, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, N01-HC-95164, 75N92020D00004)
  • DOD Peer Reviewed Cancer Research Program (CA170674P2)
Topics
  • Health Sciences › Medicine › Epidemiology › Liver Disease Diagnosis and Treatment
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
Keywords
hepatic steatosis, liver fat quantification, abdominal MRI, machine learning, genetic variants, genome-wide association study, polygenic risk score, apolipoprotein B, microsomal triglyceride transfer protein, ADH1B, TRIB1, GPAM, genetic risk factors

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