Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis

Parisinos, Constantinos; Wilman, H.; Thomas, E. Louise; Kelly, Matt; Nicholls, Rowan C.; McGonigle, John; Neubauer, Stefan; Hingorani, Aroon D.; Patel, Riyaz; Hemingway, Harry; Bell, Jimmy D.; Banerjee, Rajarshi; Yaghootkar, Hanieh

doi:10.1016/j.jhep.2020.03.032

Cited by 91 publications

(110 citation statements)

References 69 publications

Supporting

Mentioning

100

Contrasting

Unclassified

Order By: Relevance

“…Looking for the subset of these phenotypes available in our scan of Thr95Ile, we do find a nominally significant increase in BMD and a suggestive increase in MCH but no detectable increase in erythrocyte distribution width. By contrast, we find that the a common intronic SNP in SLC30A10 recently reported to associate with liver MRI cT1, a sign of steatohepatitis and fibrosis 36 , is in complete linkage with the major allele of Thr95Ile, suggesting an independent genetic mechanism but also providing independent evidence of the role of SLC30A10 SNPs in liver health.…”

Section: Comparison Of Thr95ile Phenotypes To Slc30a10 Common Variantcontrasting

confidence: 78%

“…A recent study not yet in the GWAS catalog reported an association between another common intronic SNP in SLC30A10 (rs759359281; MAF in White British, 5.6%) and liver MRI-derived cT1 measures, a proxy for liver fibrosis and steatohepatitis 36 . However, the reported cT1increasing allele (liver disease risk allele) of rs759359281, which is the minor allele, is in complete linkage (D' = 1) with the major allele of Thr95Ile (rs188273166); in other words, the cT1increasing allele and Thr95Ile liver disease risk allele occur on different haplotypes, suggesting that the mechanism of this reported cT1 association is independent of Thr95Ile.…”

Section: Linkage Of Thr95ile To Gwas Snps At Slc30a10mentioning

confidence: 99%

See 1 more Smart Citation

GWAS of serum ALT and AST reveals an association ofSLC30A10Thr95Ile with hypermanganesemia symptoms

Ward

Quenneville

et al. 2020

Preprint

View full text Add to dashboard Cite

22To better understand molecular pathways underlying liver health and disease, we performed 23 genome-wide association studies (GWAS) on circulating levels of alanine aminotransferase (ALT) 24 and aspartate aminotransferase (AST) across 408,300 subjects from four ethnic groups in the UK 25 Biobank, focusing on variants associating with both enzymes. Of these variants, the strongest 26 effect is a rare (MAF in White British = 0.12%) missense variant in the gene encoding manganese 27 efflux transporter SLC30A10, Thr95Ile (rs188273166), associating with a 5.9% increase in ALT 28 and a 4.2% increase in AST. Carriers have higher prevalence of all-cause liver disease (OR = 1.70; 29 95% CI = 1.24 to 2.34) and higher prevalence of extrahepatic bile duct cancer (OR = 23.8; 95% 30 CI = 9.1 to 62.1) compared to non-carriers. Over 4% of the cases of extrahepatic 31 cholangiocarcinoma in the UK Biobank carry SLC30A10 Thr95Ile. Unlike variants in SLC30A10 32 known to cause the recessive syndrome hypermanganesemia with dystonia-1 (HMNDYT1), the 33 Thr95Ile variant has a detectable effect even in the heterozygous state. Also unlike HMNDYT1-34 causing variants, Thr95Ile results in a protein that is properly trafficked to the plasma membrane 35 when expressed in HeLa cells. These results suggest that coding variation in SLC30A10 impacts 36 liver health in more individuals than the small population of HMNDYT1 patients. 37 are sensitive biomarkers of liver injury; in particular, alanine aminotransferase (ALT) and aspartate 44 aminotransferase (AST) are released into the circulation during damage to hepatocyte 45 membranes 1,2 . One powerful approach for understanding the molecular basis of liver disease has 46 been to perform genome-wide association studies (GWAS) of levels of circulating liver enzymes 47 across large population samples 1,3-13 . Combined GWAS of ALT and AST have previously revealed 48 genetic associations providing potential therapeutic targets for liver disease such as PNPLA3 14 and 49HSD17B13 15 . To further study the genetics of hepatocellular damage, we performed GWAS on 50 circulating levels of ALT and AST in 408,300 subjects, meta-analyzed across four ethnic groups 51 in the UK Biobank. 52 Results 53 GWAS summary 54We performed a GWAS of ALT and AST in four sub-populations in the UK Biobank (Asian or 55 Asian British, Black or Black British, Chinese, and Black or Black British; sample sizes, number 56 of variants tested, and lGC values, Supplementary Table 1; genome-wide significant associations, 57 Supplementary Table 2; Manhattan and QQ plots for each enzyme and sub-population, 58 Supplementary Figures 1 and 2). After meta-analyzing across sub-populations to obtain a single 59 set of genome-wide p-values for each enzyme (Manhattan plots, Figure 1), we found 244 and 277 60 independent loci associating at p < 5 x 10 -8 with ALT and AST, respectively, defined by lead SNPs 61 separated by at least 500 kilobases and pairwise linkage disequilibrium (LD) r 2 less than 0.2. 62Enzyme levels were strongly associated w...

show abstract

Section: Comparison Of Thr95ile Phenotypes To Slc30a10 Common Variantcontrasting

confidence: 78%

Section: Linkage Of Thr95ile To Gwas Snps At Slc30a10mentioning

confidence: 99%

GWAS of serum ALT and AST reveals an association ofSLC30A10Thr95Ile with hypermanganesemia symptoms

Ward

Quenneville

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Summary statistics for the liver fat CVAS, as well as the machine learning model architectures and learned weights will be made available at the Cardiovascular Disease Knowledge Portal (http://broadcvdi.org/home/portalHome) and the ML4CVD modeling framework will be made available via GitHub repository at time of publication. (Abul-Husn et al, 2018;Buch et al, 2015;Feitosa et al, 2013;Gellert-Kristensen et al, 2020;Ma et al, 2019;Mancina et al, 2016;Palmer et al, 2013;Parisinos et al, 2020;Speliotes et al, 2011)…”

Section: Data Availabilitymentioning

confidence: 99%

Machine learning enables new insights into clinical significance of and genetic contributions to liver fat accumulation

Haas

Friedman

Emdin

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…While genetic variants at most of these loci such as MTARC1, GCKR, SUGP1 (where the lead variant was in linkage disequilibrium with a variant at TM6SF2), and PNPLA3 have been associated with some form of liver diseases. [33][34][35][36] TRIB1, encoding tribbles pseudokinase 1 and LMO3, encoding LIM domain only 3 may be new NAFLD loci. However, additional validation and ne-mapping studies will be required, especially for the genetic signal at LMO3, which encodes an oncogene that, to our knowledge, has not been previously associated with disease or metabolic traits.…”

Section: Phenotypic Consequences Of Non-alcoholic Fatty Liver Diseasementioning

confidence: 99%

“…Genetic variation at APOE has been linked with NAFLD in another study. 36 Although the biological relevance of variation at the FTO locus is still a matter of debate, FTO is a well-characterized genetic locus for obesity. 40 Lipoprotein lipase (LPL) on the other hand is a key enzyme that regulates the catabolism of triglycerides-rich lipoproteins in adipose tissue, skeletal muscle and heart.…”

Section: Phenotypic Consequences Of Non-alcoholic Fatty Liver Diseasementioning

confidence: 99%

Electronic Health Record-Based Genome-Wide Meta-Analysis and Mendelian Randomization Identify Metabolic and Phenotypic Consequences of Non-Alcoholic Fatty Liver Disease

Ghodsian

Abner

Gobeil

et al. 2020

Preprint

View full text Add to dashboard Cite

Non-alcoholic fatty liver disease (NAFLD) has been associated with several blood biomarkers and chronic diseases. Whether these associations underlie causal effects remains to be determined. We aimed at identifying blood metabolites, blood proteins and human diseases that are causally impacted by the presence of NAFLD using Mendelian randomization. We created a NAFLD genetic instrument from NAFLD loci (MTARC1, GCKR, LPL, TRIB1, LMO3, FTO, TM6SF2, APOE and PNPLA3) identified in a new electronic health record based-GWAS meta-analysis (6715 cases and 682,748 controls). We found a potentially causal effect of NAFLD on tyrosine metabolism as well as on blood levels of eight proteins that could potentially represent new early biomarkers of NAFLD. Using results from the UK Biobank, FinnGen and the COVID-19 Host Genetics Initiative, we found that NAFLD was not causally associated with diseases outside the spectrum of liver diseases, suggesting that the resolution of NAFLD might not prevent other diseases.

show abstract

Genome-wide and Mendelian randomisation studies of liver MRI yield insights into the pathogenesis of steatohepatitis

Cited by 91 publications

References 69 publications

GWAS of serum ALT and AST reveals an association ofSLC30A10Thr95Ile with hypermanganesemia symptoms

GWAS of serum ALT and AST reveals an association ofSLC30A10Thr95Ile with hypermanganesemia symptoms

Machine learning enables new insights into clinical significance of and genetic contributions to liver fat accumulation

Electronic Health Record-Based Genome-Wide Meta-Analysis and Mendelian Randomization Identify Metabolic and Phenotypic Consequences of Non-Alcoholic Fatty Liver Disease

Contact Info

Product

Resources

About