Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Abstract

Background & aims: Excess liver iron content is common and is linked to the risk of hepatic and extrahepatic diseases. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases.

Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals from UK Biobank, whose liver iron level had been quantified by magnetic resonance imaging, before validating our findings in an independent cohort (n = 1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 25 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 traits and disease outcomes.

Results: We identified 3 independent genetic variants (rs1800562 [C282Y] and rs1799945 [H63D] in HFE and rs855791 [V736A] in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p <5 × 10^-8). The 2 HFE variants account for ∼85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease.

Conclusion: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases.

Lay summary: Excess liver iron content is common and is associated with liver diseases and metabolic diseases including diabetes, high blood pressure, and heart disease. We identified 3 genetic variants that are linked to an increased risk of developing higher liver iron content. We show that the same genetic variants are linked to higher risk of many diseases, but they may also be associated with some health advantages. Finally, we use genetic variants associated with waist-to-hip ratio as a tool to show that central obesity is causally associated with increased liver iron content.

Keywords: Genetics; Genome-wide association study; Iron; Magnetic resonance imaging; Metabolic syndrome; Metabolism.

Graphical abstract

Fig. 1

Study design. GWAS on liver iron content was performed in UK Biobank (N = 8,289) and replicated in IMI DIRECT (N = 1,115). GWAS, genome-wide association study; SNPs, single-nucleotide polymorphisms.

Fig. 2

Manhattan plot illustrating genetic variants associated with liver iron in UK Biobank. The x-axis is the chromosomal position and y axis is -log(P) for the association with each variant. The black line indicates genome-wide significance level (5 × 10⁻⁸). SNPs, single-nucleotide polymorphisms.

Fig. 3

Liver iron content per genotype group. The x-axis represents the 6 genotype groups based on the number of C282Y and H63D they carry. The y-axis represents the mean of liver iron (mg/g) per category. Error bars indicate 95% CIs. Numbers in brackets are the number of individuals per genotype category. WT, wild-type.

Fig. 4

The effect of 25 predominantly metabolic traits and diseases on liver iron content. The plot illustrates per allele effect of genetic variants associated with different metabolic traits. We have used alleles associated with higher adiposity, higher metabolic disease risk, higher glycaemic traits, adverse lipid levels, higher liver enzymes and adverse metabolic biomarkers profile. For comparison, the plot illustrates the effect of HFE C282Y, HFE H63D, TMPRSS6 V736A on liver iron content. Please refer to Table S10 for the results of 2-sample Mendelian randomisation analysis including 4 circulating iron biomarkers. The error bars indicate 95% CIs. ALP, alkaline phosphatase; ALT, alanine aminotransferase; BMI, body mass index; CRP, C-reactive protein; GGT, gamma-glutamyltransferase; HDL, high-density lipoprotein; LDL, low-density lipoprotein; NAFLD, non-alcoholic fatty liver disease; SHBG, sex hormone binding globulin. The statistical test illustrated on x-vector is from the Inverse Variance Weighted (IVW) method.

Fig. 5

Illustration of prioritised associations following phenome-wide association studies of rs1800562, rs1799945 and rs855791 and significant traits. Data from the UK Biobank and publicly available summary statistics. Blue indicates a positive association and red an inverse association, following correction for multiple testing (false discovery rate <5%). Continuous traits betas were scaled to per SD where appropriate for better visualisation. Effect on disease risk is given in log(odds ratio). BMI, body mass index; LDL, low-density lipoprotein.