Dataset Information

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

ABSTRACT:

Background & aims

MRI-based corrected T1 (cT1) is a non-invasive method to grade the severity of steatohepatitis and liver fibrosis. We aimed to identify genetic variants influencing liver cT1 and use genetics to understand mechanisms underlying liver fibroinflammatory disease and its link with other metabolic traits and diseases.

Methods

First, we performed a genome-wide association study (GWAS) in 14,440 Europeans, with liver cT1 measures, from the UK Biobank. Second, we explored the effects of the cT1 variants on liver blood tests, and a range of metabolic traits and diseases. Third, we used Mendelian randomisation to test the causal effects of 24 predominantly metabolic traits on liver cT1 measures.

Results

We identified 6 independent genetic variants associated with liver cT1 that reached the GWAS significance threshold (p <5×10^-8). Four of the variants (rs759359281 in SLC30A10, rs13107325 in SLC39A8, rs58542926 in TM6SF2, rs738409 in PNPLA3) were also associated with elevated aminotransferases and had variable effects on liver fat and other metabolic traits. Insulin resistance, type 2 diabetes, non-alcoholic fatty liver and body mass index were causally associated with elevated cT1, whilst favourable adiposity (instrumented by variants associated with higher adiposity but lower risk of cardiometabolic disease and lower liver fat) was found to be protective.

Conclusion

The association between 2 metal ion transporters and cT1 indicates an important new mechanism in steatohepatitis. Future studies are needed to determine whether interventions targeting the identified transporters might prevent liver disease in at-risk individuals.

Lay summary

We estimated levels of liver inflammation and scarring based on magnetic resonance imaging of 14,440 UK Biobank participants. We performed a genetic study and identified variations in 6 genes associated with levels of liver inflammation and scarring. Participants with variations in 4 of these genes also had higher levels of markers of liver cell injury in blood samples, further validating their role in liver health. Two identified genes are involved in the transport of metal ions in our body. Further investigation of these variations may lead to better detection, assessment, and/or treatment of liver inflammation and scarring.

SUBMITTER: Parisinos CA

PROVIDER: S-EPMC7372222 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundObservational studies have identified various associations between neuroimaging alterations and neuropsychiatric disorders. However, whether such associations could truly reflect causal relations remains still unknown.ResultsHere, we leveraged genome-wide association studies (GWAS) summary statistics for (1) 11 psychiatric disorders (sample sizes varied from n = 9,725 to 1,331,010); (2) 110 diffusion tensor imaging (DTI) measurement (sample size n = 17,706); (3) 101 region-of-interest (ROI) volumes, and investigate the causal relationship between brain structures and neuropsychiatric disorders by two-sample Mendelian randomization. Among all DTI-Disorder combinations, we observed a significant causal association between the superior longitudinal fasciculus (SLF) and the risk of Anorexia nervosa (AN) (Odds Ratio [OR] = 0.62, 95 % confidence interval: 0.50 ~ 0.76, P = 6.4 × 10- 6). Similar significant associations were also observed between the body of the corpus callosum (fractional anisotropy) and Alzheimer's disease (OR = 1.07, 95 % CI: 1.03 ~ 1.11, P = 4.1 × 10- 5). By combining all observations, we found that the overall p-value for DTI - Disorder associations was significantly elevated compared to the null distribution (Kolmogorov-Smirnov P = 0.009, inflation factor λ = 1.37), especially for DTI - Bipolar disorder (BP) (λ = 2.64) and DTI - AN (λ = 1.82). In contrast, for ROI-Disorder combinations, we only found a significant association between the brain region of pars triangularis and Schizophrenia (OR = 0.48, 95 % CI: 0.34 ~ 0.69, P = 5.9 × 10- 5) and no overall p-value elevation for ROI-Disorder analysis compared to the null expectation.ConclusionsAs a whole, we show that SLF degeneration may be a risk factor for AN, while DTI variations could be causally related to some neuropsychiatric disorders, such as BP and AN. Also, the white matter structure might have a larger impact on neuropsychiatric disorders than subregion volumes.

Project description:Rice yield per plant has a complex genetic architecture, which is mainly determined by its three component traits: the number of grains per panicle (GPP), kilo-grain weight (KGW), and tillers per plant (TP). Exploring ideotype breeding based on selection for genetically less complex component traits is an alternative route for further improving rice production. To understand the genetic basis of the relationship between rice yield and component traits, we investigated the four traits of two rice hybrid populations (575 + 1495 F1) in different environments and conducted meta-analyses of genome-wide association study (meta-GWAS). In total, 3589 significant loci for three components traits were detected, while only 3 loci for yield were detected. It indicated that rice yield is mainly controlled by minor-effect loci and hardly to be identified. Selecting quantitative trait locus/gene affected component traits to further enhance yield is recommended. Mendelian randomization design is adopted to investigate the genetic effects of loci on yield through component traits and estimate the genetic relationship between rice yield and its component traits by these loci. The loci for GPP or TP mainly had a positive genetic effect on yield, but the loci for KGW with different direction effects (positive effect or negative effect). Additionally, TP (Beta = 1.865) has a greater effect on yield than KGW (Beta = 1.016) and GPP (Beta = 0.086). Five significant loci for component traits that had an indirect effect on yield were identified. Pyramiding superior alleles of the five loci revealed improved yield. A combination of direct and indirect effects may better contribute to the yield potential of rice. Our findings provided a rationale for using component traits as indirect indices to enhanced rice yield, which will be helpful for further understanding the genetic basis of yield and provide valuable information for improving rice yield potential.

Project description:Aims/hypothesisFragility fractures may be a complication of diabetes, partly caused by chronic hyperglycaemia. We hypothesised that: (1) individuals with hyperglycaemia and diabetes have increased risk of fragility fracture; (2) hyperglycaemia is causally associated with increased risk of fragility fracture; and (3) diabetes and fragility fracture jointly associate with the highest risk of all-cause mortality.MethodsIn total, 117,054 individuals from the Copenhagen City Heart Study and the Copenhagen General Population Study (the Copenhagen studies) and 390,374 individuals from UK Biobank were included for observational and one-sample Mendelian randomisation (MR) analyses. Fragility fractures were defined as fractures at the hip, spine and arm (humerus/wrist), collected from national health registries. Summary data for fasting glucose and HbA1c concentrations from 196,743 individuals in the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) were combined with data on fragility fractures from the Copenhagen studies in two-sample MR analyses.ResultsHigher fasting and non-fasting glucose and HbA1c concentrations were associated with higher risk of any fragility fracture (p<0.001). Individuals with vs without diabetes had HRs for fragility fracture of 1.50 (95% CI 1.19, 1.88) in type 1 diabetes and 1.22 (1.13, 1.32) in type 2 diabetes. One-sample MR supported a causal association between high non-fasting glucose concentrations and increased risk of arm fracture in the Copenhagen studies and UK Biobank combined (RR 1.41 [1.11, 1.79], p=0.004), with similar results for fasting glucose and HbA1c in two-sample MR analyses (ORs 1.50 [1.03, 2.18], p=0.03; and 2.79 [1.12, 6.93], p=0.03, respectively). The corresponding MR estimates for any fragility fracture were 1.18 (1.00, 1.41), p=0.06; 1.36 (0.89, 2.09), p=0.15; and 2.47 (0.95, 6.43), p=0.06, respectively. At age 80 years, cumulative death was 27% in individuals with fragility fracture only, 54% in those with diabetes only, 67% in individuals with both conditions and 17% in those with neither.Conclusions/interpretationHyperglycaemia and diabetes are risk factors for fragility fracture and one- and two-sample MR analyses supported a causal effect of hyperglycaemia on arm fractures. Diabetes and previous fragility fracture jointly conferred the highest risk of death in the general population.