Genetic risk of fatty liver disease and mortality in the general population: A Mendelian randomization study

Background & Aims

Fatty liver disease has been associated with higher all-cause as well as liver-related, ischemic heart disease (IHD)-related and extrahepatic cancer-related mortality in observational epidemiological studies. We tested the hypothesis that fatty liver disease is a causal risk factor for higher mortality.

Methods

We genotyped seven genetic variants known to be associated with fatty liver disease (in PNPLA3, TM6SF2, HSD17B13, MTARC1, MBOAT7, GCKR, and GPAM) in 110 913 individuals from the Danish general population. Hepatic steatosis was measured by hepatic computed tomography in n = 6965. Using a Mendelian randomization framework, we tested whether genetically proxied hepatic steatosis and/or elevated plasma alanine transaminase (ALT) was associated with liver-related mortality.

Results

During a median follow-up of 9.5 years, 16 119 individuals died. In observational analyses, baseline elevated plasma ALT was associated with higher all-cause (1.26-fold), liver-related (9-fold), and extrahepatic cancer-related (1.25-fold) mortality. In genetic analyses, the risk alleles in PNPLA3, TM6SF2, and HSD17B13 were individually associated with higher liver-related mortality. The largest effects were seen for the PNPLA3 and TM6SF2 risk alleles, for which homozygous carriers had 3-fold and 6-fold, respectively, higher liver-related mortality than non-carriers. None of the risk alleles, individually or combined into risk scores, were robustly associated with all-cause, IHD-related, or extrahepatic cancer-related mortality. In instrumental variable analyses, genetically proxied hepatic steatosis and higher plasma ALT were associated with liver-related mortality.

Conclusions

Human genetic data support that fatty liver disease is a causal driver of liver-related mortality.     

Statistical methods for cis-Mendelian randomization with two-sample summary-level data

Mendelian randomization (MR) is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data MR analyses with many correlated variants from a single gene region, particularly on cis-MR studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis, and Bayesian variable selection. In a simulation study, we show that the various methods have comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of low-density lipoprotein-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions, respectively.     

Mendelian randomization study and meta-analysis exploring the causality of age at menarche and the risk of intracerebral hemorrhage and ischemic stroke

Background

The relationship between the age at menarche (AAM) and the risk of intracerebral hemorrhage (ICH) and ischemic stroke (IS) is still up for debate. The purpose of this study was to investigate potential causal connections between them.

Methods

Genome-wide association analysis (GWAS) of AAM conducted by the MRC-IEU consortium was utilized for association analyses of ICH and IS by two-sample Mendelian randomization (MR) study. AAM data of the within-family GWAS consortium were used as replication phase data to verify the causal relationship between each other. Inverse variance weighting (IVW) method was the primary method used in this MR study. For additional proof, the weighted median estimation, MR-Egger regression, MR-PRESSO test, and MR-Robust Adjusted Profile Score evaluation were performed. The Cochran's Q test and the MR-PRESSO global test were used, respectively, to examine the sensitivity and pleiotropy. Random effects meta-analysis was utilized to analyze the causal data from the two consortiums to further explore the causality between AAM and ICH, IS.

Results

We found that the AAM was causally linked with the risk of ICH (OR = 0.48, 95% CI: 0.28–0.80, p = 0.006). On the contrary, the causal effect from AAM to IS (OR = 0.98, 95% CI: 0.91–1.06, p = 0.64) has not been confirmed. For all subtypes of ICH, we found that nonlobar intracerebral hemorrhage (NLICH, OR = 0.41, 95% CI: 0.23–0.75, p = 0.004) but not lobar intracerebral hemorrhage (LICH, OR = 0.65, 95% CI: 0.34–1.24, p = 0.19) was associated with AAM without surprise. Similarly, we used the within-family GWAS consortium data to explore causality and found that AAM may reduce the risk of ICH (OR = 0.78, 95% CI: 0.72–0.86, p = 9.5 × 10⁻⁸) and NLICH (OR = 0.68, 95% CI: 0.61–0.75, p = 3.4 × 10⁻¹³) by IVW methods, but is not related to IS (OR = 0.97, 95% CI: 0.93–1.02, p = 0.26). These findings are further supported by the meta-analysis. Both Cochran's Q test and the MR-PRESSO global test failed to detect the presence of sensitivity.

Conclusion

AAM and ICH, particularly NLICH, are causally related, but not LICH, IS, or its subtypes in European population.     

Bidirectional causal associations between type 2 diabetes and COVID-19

Observational studies have reported high comorbidity between type 2 diabetes (T2D) and severe COVID-19. However, the causality between T2D and COVID-19 has yet to be validated. We performed genetic correlation and Mendelian randomization (MR) analyses to assess genetic relationships and potential causal associations between T2D and three COVID-19 outcomes (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infection, COVID-19 hospitalization, and critical COVID-19). Molecular pathways connecting SARS-CoV-2 and COVID-19 were reconstructed to extract insights into the potential mechanisms underlying the connection. We identified a high genetic overlap between T2D and each COVID-19 outcome (genetic correlations 0.21–0.28). The MR analyses indicated that genetic liability to T2D confers a causal effect on hospitalized COVID-19 (odds ratio 1.08, 95% confidence interval [CI] 1.04–1.12) and critical COVID-19 (1.09, 1.03–1.16), while genetic liability to SARS-CoV-2 infection exerts a causal effect on T2D (1.25, 1.00–1.56). There was suggestive evidence that T2D was associated with an increased risk for SARS-CoV-2 infection (1.02, 1.00–1.03), while critical COVID-19 (1.06, 1.00–1.13) and hospitalized COVID-19 (1.09, 0.99–1.19) were associated with an increased risk for T2D. Pathway analysis identified a panel of immunity-related genes that may mediate the links between T2D and COVID-19 at the molecular level. Our study provides robust support for the bidirectional causal associations between T2D and COVID-19. T2D may contribute to amplifying the severity of COVID-19, while the liability to COVID-19 may increase the risk for T2D.     

Clinical exome sequencing findings in 1589 patients

Clinical exome sequencing (CES) is important for the diagnosis of Mendelian diseases, which are clinically and etiologically heterogeneous. Sharing of large amounts of CES data associated with clinical findings will increase the accuracy of variant interpretation. We performed a retrospective study to state the diagnostic yield of CES in 1589 patients with a wide phenotypic spectrum. CES was performed using the Sophia Clinical Exome Sequencing Kit with 4493 genes, followed by sequencing on a NextSeq 500 system. The diagnosis rate was 36.8% when only pathogenic and likely pathogenic variants were included. Consanguineous unions and positive family history were associated with a high diagnostic yield. The neurological disease group had the highest number of patients. The groups with high diagnosis rates were ear, eye, and muscle disease groups. Seven candidate genes (EFHC2, HSPB3, FAAH2, ITGB1, GYG2, CD177, and CSTF2T) that are not yet associated with human diseases were identified. Owing to the high diagnostic yield of CES compared with that of other genetic tests, it can be used as a standard diagnostic test in patients with rare genetic disorders that require a wide differential diagnosis, especially in laboratories with limited resources.