Multidimensional plasma lipid composition and its causal association with type 2 diabetes mellitus: A Mendelian randomization study

BACKGROUND AND AIM

Recent research extends our knowledge of plasma lipid species, building on established links between serum lipid levels and Type 2 Diabetes Mellitus (T2DM) risk. Identifying the causal roles of these lipid species is key to improving T2DM risk assessment.

METHODS AND RESULTS

This study employs Mendelian randomization (MR) to investigate the causal relationship between 179 lipid species across 13 lipid categories and T2DM. Summary-level data were sourced from genome-wide association studies. The primary analytical methods included the inverse variance weighted (IVW) approach and the Wald ratio, complemented by a series of sensitivity analyses to ensure the robustness of results. The IVW analysis reveals a significant causal association between elevated levels of ceramide (d40:2) (OR = 1.071, 95% CI 1.034-1.109, P = 1.36 × 10-4), sphingomyelin (d38:1) (OR = 1.052, 95% CI 1.028-1.077, P = 1.80 × 10-5), and triacylglycerol (56:8) (OR = 1.174, 95% CI 1.108-1.243, P = 4.65 × 10-8), and an increased risk of T2DM. Conversely, Wald ratio analysis indicates that higher levels of phosphatidylcholine (O-16:1_16:0) (OR = 0.928, 95% CI 0.892-0.966, P = 2.37 × 10-4), phosphatidylcholine (O-16:1_20:4) (OR = 0.932, 95% CI 0.897-0.967, P = 2.37 × 10-4), and phosphatidylcholine (O-18:2_20:4) (OR = 0.872, 95% CI 0.812-0.935, P = 1.24 × 10-4) are significantly associated with a reduced risk of T2DM. Furthermore, suggestive causal evidence for 22 additional lipid species was identified.

CONCLUSIONS

This MR study establishes a causal relationship between specific lipid classes in modulating the risk of T2DM. It offers new insights for risk assessment and potential therapeutic targets in T2DM.

The causal relationship between major depression disorder and thyroid diseases: A Mendelian randomization study and mediation analysis

BACKGROUND

Studies have been conducted on the relationship between depression and thyroid diseases and function, its causal relationship remains unclear.

METHODS

Using summary statistics of genome-wide association studies of European and East Asian ancestry, we conducted 2-sample bidirectional Mendelian randomization to estimate the association between MDD and thyroid function (European: normal range TSH, T4, T3, fT4, TPOAb levels and TPOAb-positives; East Asian: T4) and thyroid diseases (hypothyroidism, hyperthyroidism, and Hashimoto's thyroiditis), and used Mediation analysis to evaluate potential mediators (alcohol intake, antidepressant) of the association and calculate the mediated proportions.

RESULTS

It was observed a significant causal association between MDD on hypothyroidism (P = 8.94 × 10-5), hyperthyroidism (P = 8.68 × 10-3), and hashimoto's thyroiditis (P = 3.97 × 10-5) among European ancestry, which was mediated by Alcohol intake (alcohol intake versus 10 years previously for hypothyroidism (P = 0.026), hashimoto's thyroiditis (P = 0.042), and alcohol intake frequency for hypothyroidism (P = 0.015)) and antidepressant (for hypothyroidism (P = 0.008), hashimoto's thyroiditis (P = 0.010)), but not among East Asian ancestry (PMDD-hypothyroidism = 0.016, but β direction was different; PMDD-hyperthyroidism = 0.438; PMDD-hashimoto's thyroiditis = 0.496). There was no evidence for bidirectional causal association between thyroid function mentioned above and MDD among both ancestry (all P > 0.05).

CONCLUSION

We importantly observed a significant causal association between MDD on risk of hypothyroidism, hyperthyroidism, and hashimoto's thyroiditis among European ancestry, and Alcohol intake and antidepressant as mediators for prevention of hypothyroidism, hashimoto's thyroiditis attributable to MDD.

A metabolome-wide Mendelian randomization study prioritizes causal circulating metabolites for reproductive disorders including primary ovarian insufficiency, polycystic ovary syndrome, and abnormal spermatozoa

BACKGROUND

Accumulating studies have highlighted the significant role of circulating metabolomics in the etiology of reproductive system disorders. However, the causal effects between genetically determined metabolites (GDMs) and reproductive diseases, including primary ovarian insufficiency (POI), polycystic ovary syndrome (PCOS), and abnormal spermatozoa (AS), still await thorough clarification.

METHODS

With the currently most comprehensive genome-wide association studies (GWAS) data of metabolomics, systematic two-sample Mendelian randomization (MR) analyses were conducted to disclose causal associations between 1,091 blood metabolites and 309 metabolite ratios with reproductive disorders. The inverse-variance weighted (IVW) method served as the primary analysis approach, and multiple effective MR methods were employed as complementary analyses including MR-Egger, weighted median, constrained maximum likelihood (cML-MA), contamination mixture method, robust adjusted profile score (MR-RAPS), and debiased inverse-variance weighted method. Heterogeneity and pleiotropy were assessed via MR-Egger intercept and Cochran's Q statistical analysis. Outliers were detected by Radial MR and MR-PRESSO methods. External replication and metabolic pathway analysis were also conducted.

RESULTS

Potential causal associations of 63 GDMs with POI were unearthed, and five metabolites with strong causal links to POI were emphasized. Two metabolic pathways related to the pathogenesis of POI were pinpointed. Suggestive causal effects of 70 GDMs on PCOS were detected, among which 7 metabolites stood out for strong causality with elevated PCOS risk. Four metabolic pathways associated with PCOS mechanisms were recognized. For AS, 64 GDMs as potential predictive biomarkers were identified, particularly highlighting two metabolites for their strong causal connections with AS. Three pathways underneath the AS mechanism were identified. Multiple assessments were conducted to further confirm the reliability and robustness of our causal inferences.

CONCLUSION

By extensively assessing the causal implications of circulating GDMs on reproductive system disorders, our study underscores the intricate and pivotal role of metabolomics in reproductive ill-health, laying a theoretical foundation for clinical strategies from metabolic insights.

Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics

Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices. Our study focused on the following crucial aspects: type I error control in the presence of various confounding scenarios (e.g., population stratification, pleiotropy, and family-level confounders like assortative mating), the accuracy of causal effect estimates, replicability, and power. By comprehensively evaluating the performance of compared methods over one thousand exposure-outcome trait pairs, our study not only provides valuable insights into the performance and limitations of the compared methods but also offers practical guidance for researchers to choose appropriate MR methods for causal inference.

Eye-brain connections revealed by multimodal retinal and brain imaging genetics

The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.

MRBEE: A bias-corrected multivariable Mendelian randomization method

Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes, which is becoming increasingly popular because of its ability to handle summary statistics from genome-wide association studies. However, existing MR approaches often suffer the bias from weak instrumental variables, horizontal pleiotropy and sample overlap. We introduce MRBEE (MR using bias-corrected estimating equation), a multivariable MR method capable of simultaneously removing weak instrument and sample overlap bias and identifying horizontal pleiotropy. Our extensive simulations and real data analyses reveal that MRBEE provides nearly unbiased estimates of causal effects, well-controlled type I error rates and higher power than comparably robust methods and is computationally efficient. Our real data analyses result in consistent causal effect estimates and offer valuable guidance for conducting multivariable MR studies, elucidating the roles of pleiotropy, and identifying total 42 horizontal pleiotropic loci missed previously that are associated with myopia, schizophrenia, and coronary artery disease.

The impact of cannabis use on erectile dysfunction and sex hormones: a Mendelian randomization analysis

Previous study has highlighted an association between cannabis use (CU) and an increased risk of erectile dysfunction (ED), potentially due to indirect effects on sex hormonal balance. However, the evidence remains controversial, and the causal relationship is unclear. This study utilized genome-wide association study (GWAS) data to investigate the causal relationships between cannabis use disorder (CUD), lifetime cannabis use (LCU), and ED, as well as levels of sex hormones including estradiol (E2), bioavailable testosterone (BT), follicle-stimulating hormone (FSH), and luteinizing hormone (LH) through Mendelian randomization (MR) analysis. The primary method of analysis was the inverse variance weighted (IVW) method. Data from the FinnGen and UK Biobank were used for replication and meta-analysis. The results indicated no causal relationship between genetically predicted CUD (OR = 0.97, 95% CI 0.87-1.10, P = 0.66) and LCU (OR = 1.13, 95% CI 0.84-1.50, P = 0.42) with the risk of ED. The meta-analysis provided consistent evidence (P > 0.05). No causal relationships were found between CUD and LCU with E2(CUD: β = 0.00, 95% CI 0.00-0.01, P = 0.37; LCU: β = 0.00, 95% CI -0.02-0.01, P = 0.62), BT (CUD: β = 0.00, 95% CI -0.03-0.02, P = 0.90; LCU: β = 0.02, 95% CI -0.04-0.09, P = 0.46), FSH (CUD: β = 0.01, 95% CI -0.18-0.20, P = 0.92; LCU: β = 0.01, 95% CI -0.44-0.47, P = 0.95), and LH (CUD: β = 0.01, 95% CI -0.18-0.21, P = 0.90; LCU: β = 0.13, 95% CI -0.22-0.49, P = 0.46). Sensitivity analyses detected no evidence of horizontal pleiotropy or heterogeneity, ensuring the robustness of the results. In conclusion, this MR analysis did not provide evidence supporting a causal relationship between CU and ED or sex hormone levels.

Selecting Invalid Instruments to Improve Mendelian Randomization with Two-Sample Summary Data

Mendelian randomization (MR) is a widely-used method to estimate the causal relationship between a risk factor and disease. A fundamental part of any MR analysis is to choose appropriate genetic variants as instrumental variables. Genome-wide association studies often reveal that hundreds of genetic variants may be robustly associated with a risk factor, but in some situations investigators may have greater confidence in the instrument validity of only a smaller subset of variants. Nevertheless, the use of additional instruments may be optimal from the perspective of mean squared error even if they are slightly invalid; a small bias in estimation may be a price worth paying for a larger reduction in variance. For this purpose, we consider a method for "focused" instrument selection whereby genetic variants are selected to minimise the estimated asymptotic mean squared error of causal effect estimates. In a setting of many weak and locally invalid instruments, we propose a novel strategy to construct confidence intervals for post-selection focused estimators that guards against the worst case loss in asymptotic coverage. In empirical applications to: (i) validate lipid drug targets; and (ii) investigate vitamin D effects on a wide range of outcomes, our findings suggest that the optimal selection of instruments does not involve only a small number of biologically-justified instruments, but also many potentially invalid instruments.

Deciphering the brain-gut axis: elucidating the link between cerebral cortex structures and functional gastrointestinal disorders via integrated Mendelian randomization

Background

Observational studies have suggested associations between functional gastrointestinal disorders (FGIDs) and variations in the cerebral cortex. However, the causality of these relationships remains unclear, confounded by anxiety and depression. To clarify these causal relationships and explore the mediating roles of anxiety and depression, we applied univariate, multivariable, and mediation Mendelian randomization (MR) analyses.

Method

We utilized genome-wide association study (GWAS) summary data from the FinnGen database and the ENIGMA consortium, identifying genetic variants associated with irritable bowel syndrome (IBS), functional dyspepsia (FD), and cerebral cortex structures. Data on anxiety and depression came from FinnGen and a large meta-analysis. Utilizing a bidirectional univariate MR approach, we explored correlations between FD, IBS, and cortex variations. Then, independent effects were assessed through multivariable MR. A meta-analysis of these results, incorporating data from two cohorts, aimed to increase precision. We also explored the potential mediating roles of anxiety and depression.

Results

Our findings indicate a negative causal correlation between FD and the thickness of the rostral anterior cingulate cortex (rACC) across both global and regional adjustments (β = -0.142, 95% confidence interval (CI): -0.209 to-0.074, P.FDR = 0.004; β = -0.112, 95%CI: -0.163 to-0.006, P.FDR = 0.003) and a positive causal correlation with the globally adjusted thickness of the superior frontal gyrus (SFG) (β = 0.107, 95%CI: 0.062 to 0.153, P.FDR = 0.001). The causal correlation with the rACC persisted after multiple variable adjustments (β = -0.137, 95% CI: -0.187 to-0.087, P.FDR = 1.81 × 10-5; β = -0.109, 95%CI: -0.158 to-0.06, P.FDR = 0.002). A significant causal association was found between globally adjusted surface area of the caudal anterior cingulate cortex (cACC) and IBS (odds ratio = 1.267, 95%CI: 1.128 to 1.424, P.FDR = 0.02). The analysis showed that neither anxiety nor depression mediated the relationship between FGIDs and cerebral cortex structures.

Conclusion

Our research provides significant MR evidence of a bidirectional causal relationship between FGIDs and the cerebral cortex structures. This evidence not only confirms the two-way communication along the brain-gut axis but also illuminates the underlying pathophysiology, paving the way for identifying potential therapeutic approaches.

Body mass index, smoking behavior, and depression mediated the effects of schizophrenia on chronic obstructive pulmonary disease: trans-ethnic Mendelian-randomization analysis

Background

Previous studies have highlighted the association between schizophrenia (SCZ) and chronic obstructive pulmonary disease (COPD), yet the causal relationship remains unestablished.

Methods

Under the genome-wide significance threshold (P<5×10-8), data from individuals of European (EUR) and East Asian (EAS) ancestries with SCZ were selected for analysis. Univariable Mendelian randomization (MR) explored the causal relationship between SCZ and COPD. Linkage disequilibrium score (LDSC) regression was used to calculate genetic correlation, while multivariable and mediation MR further investigated the roles of six confounding factors and their mediating effects. The primary method utilized was inverse-variance weighted (IVW), complemented by a series of sensitivity analyses and false discovery rate (FDR) correction.

Results

LDSC analysis revealed a significant genetic correlation between SCZ and COPD within EUR ancestry (rg = 0.141, P = 6.16×10-7), with no such correlation found in EAS ancestry. IVW indicated a significant causal relationship between SCZ and COPD in EUR ancestry (OR = 1.042, 95% CI 1.013-1.071, P = 0.003, PFDR = 0.015). Additionally, replication datasets provide evidence of consistent causal associations(P < 0.05 & PFDR < 0.05). Multivariable and mediation MR analyses identified body mass index (BMI)(Mediation effect: 50.57%, P = 0.02), age of smoking initiation (Mediation effect: 27.42%, P = 0.02), and major depressive disorder (MDD) (Mediation effect: 60.45%, P = 6.98×10-5) as partial mediators of this causal relationship. No causal associations were observed in EAS (OR = 0.971, 95% CI 0.875-1.073, P = 0.571, PFDR = 0.761) ancestry. No causal associations were found in the reverse analysis across the four ancestries (P > 0.05 & PFDR > 0.05).

Conclusions

This study confirmed a causal relationship between SCZ and the risk of COPD in EUR ancestry, with BMI, smoking, and MDD serving as key mediators. Future research on a larger scale is necessary to validate the generalizability of these findings across other ancestries.

Shared genetic architecture between autoimmune disorders and B-cell acute lymphoblastic leukemia: insights from large-scale genome-wide cross-trait analysis

BACKGROUND

To study the shared genetic structure between autoimmune diseases and B-cell acute lymphoblastic leukemia (B-ALL) and identify the shared risk loci and genes and genetic mechanisms involved.

METHODS

Based on large-scale genome-wide association study (GWAS) summary-level data sets, we observed genetic overlaps between autoimmune diseases and B-ALL, and cross-trait pleiotropic analysis was performed to detect shared pleiotropic loci and genes. A series of functional annotation and tissue-specific analysis were performed to determine the influence of pleiotropic genes. The heritability enrichment analysis was used to detect crucial immune cells and tissues. Finally, bidirectional Mendelian randomization (MR) methods were utilized to investigate the casual associations.

RESULTS

Our research highlighted shared genetic mechanisms between seven autoimmune disorders and B-ALL. A total of 73 pleiotropic loci were identified at the genome-wide significance level (P < 5 × 10-8), 16 of which had strong evidence of colocalization. We demonstrated that several loci have been previously reported (e.g., 17q21) and discovered some novel loci (e.g., 10p12, 5p13). Further gene-level identified 194 unique pleiotropic genes, for example IKZF1, GATA3, IKZF3, GSDMB, and ORMDL3. Pathway analysis determined the key role of cellular response to cytokine stimulus, B cell activation, and JAK-STAT signaling pathways. SNP-level and gene-level tissue enrichment suggested that crucial role pleiotropic mechanisms involved in the spleen, whole blood, and EBV-transformed lymphocytes. Also, hyprcoloc and stratified LD score regression analyses revealed that B cells at different developmental stages may be involved in mechanisms shared between two different diseases. Finally, two-sample MR analysis determined causal effects of asthma and rheumatoid arthritis on B-ALL.

CONCLUSIONS

Our research proved shared genetic architecture between autoimmune disorders and B-ALL and shed light on the potential mechanism that might involve in.

Genetically Predicted Apolipoprotein E Levels with the Risk of Panvascular Diseases: A Mendelian Randomization Study

The aim of this study was to comprehensively assess the causal relationship between the overall genetic effect of circulating ApoE levels and panvascular lesions using newer genome-wide association data and two-sample bidirectional Mendelian randomization (MR) analysis. Two-way MR using single-nucleotide polymorphisms of circulating ApoE as instrumental variables was performed using the highest-priority Genome-wide association study (GWAS) data, with factor-adjusted and data-corrected statistics, to estimate causal associations between circulating ApoE levels and 10 pan-vascular diseases in > 500,000 UK Biobank participants, > 400,000 participants of Finnish ancestry, and numerous participants in a consortium of predominantly European ancestry. Meta-analysis was conducted to assess positive results. After correcting for statistical results, elevated circulating ApoE levels were shown to have a significant protective effect against Cerebral ischemia (CI) [IVW odds ratio (OR) 0.888, 95% Confidence Interval (CI): 0.823-0.958, p = 2.3 × 10-3], Coronary heart disease [IVW OR 0.950,95% CI: 0.924-0.976, p = 2.0 × 10-4] had a significant protective effect and potentially suggestive protective causality against Angina pectoris [IVW odds ratio (OR) 0.961, 95%CI: 0.931-0.991, p = 1.1 × 10-2]. There was a potential causal effect for increased risk of Heart failure (HF) [IVW ratio (OR) 1.040, 95%CI: 1.006-1.060, p = 1.8 × 10-2]. (Bonferroni threshold p < 0.0026, PFDR < 0.05) Reverse MR analysis did not reveal significant evidence of a causal effect of PVD on changes in circulating ApoE levels. Meta-analysis increases reliability of results. Elevated circulating ApoE levels were particularly associated with an increased risk of heart failure. Elevated ApoE levels reduce the risk of cerebral ischemia, coronary heart disease, and angina pectoris, reflecting a protective effect. The possible pathophysiological role of circulating ApoE levels in the development of panvascular disease is emphasized.

Causal relationship between PCOS and related sex hormones with oral inflammatory diseases: a bidirectional Mendelian randomization study

Background

Observational studies have identified a strong association between polycystic ovary syndrome (PCOS) and hormone levels related to oral inflammatory diseases. To better understand the relationship between them, we conducted an analysis using a two-sample Mendelian randomization (MR) approach.

Methods

We gathered summary statistical data from previously published genome-wide association studies (GWAS) on PCOS and three sex hormones (AMH, Estradiol, LH) along with four oral inflammatory diseases (painful gums, loose teeth, mouth ulcers, and toothache). We selected single nucleotide polymorphisms (SNPs) as instrumental variables and employed four types of MR analysis methods to evaluate causal relationships between exposure and outcome. Finally, the robustness of our results was further validated through sensitivity tests and reverse MR.

Results

We observed that PCOS could increase the risk of mouth ulcers (ORIVW= 1.0013, 95%CI: 1.0001-1.0025, PIVW = 0.0278), painful gums (ORIVW= 1.0015, 95%CI:1.0003-1.0027, PIVW = 0.0163), and loose teeth (ORIVW= 1.0014, 95%CI: 1.0001-1.0027, PIVW = 0.0328). Moreover, LH was also found to increase the risk of mouth ulcers (ORIVW= 1.0031, 95%CI: 0.0001-1.0062, PIVW = 0.0457). MR-Egger regression, weighted mode, and WE indicated similar results. Additionally, we discovered no causal link between PCOS and toothache (PIVW>0.05), LH and painful gums, loose teeth, or toothache (PIVW>0.05), or AMH and Estradiol level with any of the four oral diseases (PIVW>0.05).

Conclusion

Our research provides new insights and references for exploring the effects of PCOS and related hormones on oral inflammatory lesions. For patients with PCOS, especially those with elevated LH levels, early intervention measures should be taken to prevent the occurrence of oral inflammatory diseases.

Inverse association between type 2 diabetes and hepatocellular carcinoma in East Asian populations

Aims

To investigate the potential association between type 2 diabetes (T2D) and hepatocellular carcinoma (HCC) in East Asian populations using Mendelian randomization (MR) analyses.

Methods

Bidirectional Mendelian randomization (MR) studies were conducted using summary statistics from genome-wide association studies (GWAS) related to T2D and HCC. The potential effects of confounders such as chronic hepatitis B, chronic hepatitis C, body mass index, and alcohol intake frequency were corrected using a multivariate MR study. Various MR methods, including the inverse variance weighted (IVW) approach, were used to estimate the associations between T2D and HCC. Sensitivity analysis and assessment of heterogeneity were performed to ensure the robustness of the results.

Results

In the forward MR study, the IVW approach of MR analysis suggested an inverse association between T2D and HCC, with a risk odds ratio of 0.8628 (95% confidence interval [CI], 0.7888-0.9438). Furthermore, even after adjusting for BMI, chronic hepatitis B, and alcohol intake frequency, this study still supports the inverse association between T2D and HCC. Additional MR methods provided further support for this relationship. Sensitivity analysis and assessment of heterogeneity confirmed the robustness of the results. The reverse MR analysis did not show a clear impact of genetic liability to HCC on reduced risk of T2D(OR=0.9788; 95% CI, 0.9061-1.0574).

Conclusion

This study provides evidence of an inverse association between T2D and HCC in East Asian populations using MR analysis. Further studies are warranted to validate these findings.

Conditional inference in cis-Mendelian randomization using weak genetic factors

Mendelian randomization (MR) is a widely used method to estimate the causal effect of an exposure on an outcome by using genetic variants as instrumental variables. MR analyses that use variants from only a single genetic region (cis-MR) encoding the protein target of a drug are able to provide supporting evidence for drug target validation. This paper proposes methods for cis-MR inference that use many correlated variants to make robust inferences even in situations, where those variants have only weak effects on the exposure. In particular, we exploit the highly structured nature of genetic correlations in single gene regions to reduce the dimension of genetic variants using factor analysis. These genetic factors are then used as instrumental variables to construct tests for the causal effect of interest. Since these factors may often be weakly associated with the exposure, size distortions of standard t-tests can be severe. Therefore, we consider two approaches based on conditional testing. First, we extend results of commonly-used identification-robust tests for the setting where estimated factors are used as instruments. Second, we propose a test which appropriately adjusts for first-stage screening of genetic factors based on their relevance. Our empirical results provide genetic evidence to validate cholesterol-lowering drug targets aimed at preventing coronary heart disease.

Genetic risk converges on regulatory networks mediating early type 2 diabetes

Type 2 diabetes mellitus (T2D), a major cause of worldwide morbidity and mortality, is characterized by dysfunction of insulin-producing pancreatic islet β cells1,2. T2D genome-wide association studies (GWAS) have identified hundreds of signals in non-coding and β cell regulatory genomic regions, but deciphering their biological mechanisms remains challenging3-5. Here, to identify early disease-driving events, we performed traditional and multiplexed pancreatic tissue imaging, sorted-islet cell transcriptomics and islet functional analysis of early-stage T2D and control donors. By integrating diverse modalities, we show that early-stage T2D is characterized by β cell-intrinsic defects that can be proportioned into gene regulatory modules with enrichment in signals of genetic risk. After identifying the β cell hub gene and transcription factor RFX6 within one such module, we demonstrated multiple layers of genetic risk that converge on an RFX6-mediated network to reduce insulin secretion by β cells. RFX6 perturbation in primary human islet cells alters β cell chromatin architecture at regions enriched for T2D GWAS signals, and population-scale genetic analyses causally link genetically predicted reduced RFX6 expression with increased T2D risk. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs and individuals, and thus we anticipate that this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits using GWAS data.

MendelianRandomization v0.9.0: updates to an R package for performing Mendelian randomization analyses using summarized data

The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added or edited in the package since version 0.5.0, when we last described the package and its contents. The main additions to the package since that time are: 1) new robust methods for performing Mendelian randomization, particularly in the cases of bias from weak instruments and/or winner's curse, and pleiotropic variants, 2) methods for performing Mendelian randomization with correlated variants using dimension reduction to summarize large numbers of highly correlated variants into a limited set of principal components, 3) functions for calculating first-stage F statistics, representing instrument strength, in both univariable and multivariable contexts, and with uncorrelated and correlated genetic variants. We also discuss some pragmatic issues relating to the use of correlated variants in Mendelian randomization.

MendelianRandomization v0.9.0: updates to an R package for performing Mendelian randomization analyses using summarized data

The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added or edited in the package since version 0.5.0, when we last described the package and its contents. The main additions to the package since that time are: 1) new robust methods for performing Mendelian randomization, particularly in the cases of bias from weak instruments and/or winner's curse, and pleiotropic variants, 2) methods for performing Mendelian randomization with correlated variants using dimension reduction to summarize large numbers of highly correlated variants into a limited set of principal components, 3) functions for calculating first-stage F statistics, representing instrument strength, in both univariable and multivariable contexts, and with uncorrelated and correlated genetic variants. We also discuss some pragmatic issues relating to the use of correlated variants in Mendelian randomization.

Abdominal obesity mediates the causal relationship between depression and the risk of gallstone disease: retrospective cohort study and Mendelian randomization analyses

OBJECTIVE

Our study aimed to explore the causal effect of depression on the risk of gallstone disease, and the mediation effects of metabolic traits.

METHODS

A retrospective cohort study on Chinese elderly from the Dongfeng-Tongji cohort (including 18,141 individuals) was conducted to estimate the adverse effect of probable depression on the risk of gallstone disease. Two-sample Mendelian randomization was performed in European and East-Asian ancestries, to verify the causal relationship between major depression and gallstone disease. We further applied two-step Mendelian randomization to explore the mediation effects of metabolic traits.

RESULTS

In the cohort study, probable depression was associated with an increased risk of gallstone disease within 5 years, with RR (95% CI) of 1.33 (1.12, 1.58) in multivariable regression, and 1.34 (1.11, 1.61) following propensity score weighting. Bidirectional Mendelian randomization in European ancestry revealed a positive causal effect (OR: 1.21; 95% CI: 1.07 to 1.37) of genetically predicted major depression liability on gallstone disease, based on the inverse variance weighted method. Little evidence was presented from other complementary approaches, and the analysis in East-Asian ancestry (IVW estimated OR: 1.03; 95% CI: 0.92 to 1.15). The indirect effect via waist circumference and HDL-C were 1.06 (95% CI: 1.02 to 1.10) and 1.01 (95% CI: 1.00 to 1.01) respectively, which mediated 25.8% and 3.78% of the causal relationship.

CONCLUSIONS

Our study suggested a higher risk of gallstone disease in the population with probable depression, while the two-sample Mendelian randomization provided weak evidence for the causal relationship, which was moderately mediated by abdominal obesity.

Assessing the causal relationship between immune traits and systemic lupus erythematosus by bi-directional Mendelian randomization analysis

Previous studies have observed relationships between immune cells and systemic lupus erythematosus (SLE), but their causal links remain undetermined. Based on the public available genome-wide association studies (GWAS) summary statistics, we conducted two-sample Mendelian randomization (MR) to evaluate the associations between 731 immune phenotypes and SLE pairs. Pairwise pleiotropy analysis was performed to identify pleiotropic genes for significant immunophenotype-SLE pairs. A comprehensive gene function analysis was undertaken to explore the mechanisms of immune cells in SLE. By using the instrumental variables extracted from GWAS data, we observed that increased levels of five immune phenotypes were causally associated with SLE risk (FDR < 0.05), that were CD20 on IgD+ CD38- naïve, BAFF-R on IgD+ CD38dim, CD39+ secreting Treg AC, CD14- CD16+ monocyte AC, and HLA DR on CD14+ monocyte. Pairwise gene-based analyses identified a total of 38 pleiotropic genes for 5 significant pairs identified and gene set enrichment analysis revealed the involvement of the identified pleiotropic genes in complex pathways (i.e., systemic lupus erythematosus, an integral component of luminal side of endoplasmic reticulum membrane, C-type lectin receptor signaling pathway and regulation of hormone secretion). This study demonstrates that the immune response influences the progression of SLE in a complex pattern. These findings greatly improve our understanding of the interaction between immune response and SLE risk and also aid in the design of therapeutic strategies from an immunological perspective.

Mendelian randomization mixed-scale treatment effect robust identification and estimation for causal inference

Standard Mendelian randomization (MR) analysis can produce biased results if the genetic variant defining an instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment variable. We provide novel identification conditions for the causal effect of a treatment in the presence of unmeasured confounding by leveraging a possibly invalid IV for which both the IV independence and exclusion restriction assumptions may be violated. The proposed Mendelian randomization mixed-scale treatment effect robust identification (MR MiSTERI) approach relies on (i) an assumption that the treatment effect does not vary with the possibly invalid IV on the additive scale; (ii) that the confounding bias does not vary with the possibly invalid IV on the odds ratio scale; and (iii) that the residual variance for the outcome is heteroskedastic with respect to the possibly invalid IV. Although assumptions (i) and (ii) have, respectively, appeared in the IV literature, assumption (iii) has not; we formally establish that their conjunction can identify a causal effect even with an invalid IV. MR MiSTERI is shown to be particularly advantageous in the presence of pervasive heterogeneity of pleiotropic effects on the additive scale. We propose a simple and consistent three-stage estimator that can be used as a preliminary estimator to a carefully constructed efficient one-step-update estimator. In order to incorporate multiple, possibly correlated, and weak invalid IVs, a common challenge in MR studies, we develop a MAny Weak Invalid Instruments (MR MaWII MiSTERI) approach for strengthened identification and improved estimation accuracy. Both simulation studies and UK Biobank data analysis results demonstrate the robustness of the proposed methods.

Instrumental variable model average with applications in Mendelian randomization

Mendelian randomization is a technique used to examine the causal effect of a modifiable exposure on a trait using an observational study by utilizing genetic variants. The use of many instruments can help to improve the estimation precision but may suffer bias when the instruments are weakly associated with the exposure. To overcome the difficulty of high-dimensionality, we propose a model average estimator which involves using different subsets of instruments (single nucleotide polymorphisms, SNPs) to predict the exposure in the first stage, followed by weighting the submodels' predictions using penalization by common penalty functions such as least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). The model averaged predictions are then used as a genetically predicted exposure to obtain the estimation of the causal effect on the response in the second stage. The novelty of our model average estimator also lies in that it allows the number of submodels and the submodels' sizes to grow with the sample size. The practical performance of the estimator is examined in a series of numerical studies. We apply the proposed method on a real genetic dataset investigating the relationship between stature and blood pressure.

Guidelines for performing Mendelian randomization investigations: update for summer 2023

This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.

An empirical investigation into the impact of winner's curse on estimates from Mendelian randomization

INTRODUCTION

Genetic associations for variants identified through genome-wide association studies (GWASs) tend to be overestimated in the original discovery data set as, if the association was underestimated, the variant may not have been detected. This bias, known as winner's curse, can affect Mendelian randomization estimates, but its severity and potential impact are unclear.

METHODS

We performed an empirical investigation to assess the potential bias from winner's curse in practice. We considered Mendelian randomization estimates for the effect of body mass index (BMI) on coronary artery disease risk. We randomly divided a UK Biobank data set 100 times into three equally sized subsets. The first subset was treated as the 'discovery GWAS'. We compared genetic associations estimated in the discovery GWAS to those estimated in the other subsets for each of the 100 iterations.

RESULTS

For variants associated with BMI at P < 5 × 10-8 in at least one iteration, genetic associations with BMI were up to 5-fold greater in iterations in which the variant was associated with BMI at P < 5 × 10-8 compared with its mean association across all iterations. If the minimum P-value for association with BMI was P = 10-13 or lower, then this inflation was <25%. Mendelian randomization estimates were affected by winner's curse bias. However, bias did not materially affect results; all analyses indicated a deleterious effect of BMI on coronary artery disease risk.

CONCLUSIONS

Winner's curse can bias Mendelian randomization estimates, although its practical impact may not be substantial. If avoiding sample overlap is infeasible, analysts should consider performing a sensitivity analysis based on variants strongly associated with the exposure.

Tobacco and alcohol consumption and the risk of frailty and falling: a Mendelian randomisation study

Background

Epidemiological data have suggested that tobacco and alcohol consumption were associated with the risk of frailty and falling, but it is yet unclear whether these associations are of a causal nature. Thus, we conducted two-sample Mendelian randomisation analysis using genetic instruments to determine the causal associations of tobacco and alcohol consumption on frailty and falls.

Methods

Independent instrumental variables strongly (p<5E–09) associated with tobacco and alcohol consumption were obtained from the genome-wide association study (GWAS) and Sequencing Consortium of Alcohol and Nicotine use (up to 2 669 029 participants). Summary statistics of the frailty index (FI, N=175 226) and falling risk (N=451 179) were from the two latest published GWAS datasets on FI and falling risk.

Results

Using the inverse-variance weighted method, our results showed that genetically determined initiation of smoking was significantly associated with an increased FI (β=0.34, 95% CI=0.29 to 0.40, p=5.48E–33) and risk of falling (OR=1.39, 95% CI=1.30 to 1.50, p=1.01E–20). In addition, the age of initiation of smoking and cigarettes consumption per day was negatively and positively associated with both FI and falls, respectively. Current smokers were prone to having a higher FI and falling risk than individuals who quit smoking. There was no significant causal association between alcohol use and the risk of frailty and falling. Similar results were obtained using other statistical approaches with good stability.

Conclusions

Our findings demonstrate that tobacco use, but not alcohol drinking, significantly increases the risk of frailty and falling. Future studies are warranted to clarify the underlying physiopathological mechanisms.

Support Interval for Two-Sample Summary Data-Based Mendelian Randomization

The summary-data-based Mendelian randomization (SMR) method is gaining popularity in estimating the causal effect of an exposure on an outcome. In practice, the instrument SNP is often selected from the genome-wide association study (GWAS) on the exposure but no correction is made for such selection in downstream analysis, leading to a biased estimate of the effect size and invalid inference. We address this issue by using the likelihood derived from the sampling distribution of the estimated SNP effects in the exposure GWAS and the outcome GWAS. This likelihood takes into account how the instrument SNPs are selected. Since the effective sample size is 1, the asymptotic theory does not apply. We use a support for a profile likelihood as an interval estimate of the causal effect. Simulation studies indicate that this support has robust coverage while the confidence interval implied by the SMR method has lower-than-nominal coverage. Furthermore, the variance of the two-stage least squares estimate of the causal effect is shown to be the same as the variance used for SMR for one-sample data when there is no selection.

Associations between polyunsaturated fatty acid concentrations and Parkinson's disease: A two-sample Mendelian randomization study

Introduction

Observational studies demonstrated controversial effect of polyunsaturated fatty acids (PUFAs) on Parkinson's disease (PD) with limited causality evidence. Randomized control trials showed possible improvement in PD symptoms with PUFA supplement but had small study population and limited intervention time.

Methods

A two-sample Mendelian randomization was designed to evaluate the causal relevance between PUFAs and PD, using genetic variants of PUFAs as instrumental variables and PD data from the largest genome-wide association study as outcome. Inverse variance weighted (IVW) method was applied to obtain the primary outcome. Mendelian randomization Egger regression, weighted median and weighted mode methods were exploited to assist result analyses. Strict Mendelian randomization and multivariable Mendelian randomization (MVMR) were used to estimate direct effects of PUFAs on PD, eliminating pleiotropic effect. Debiased inverse variance weighted estimator was implemented when weak instrument bias was introduced into the analysis. A variety of sensitivity analyses were utilized to assess validity of the results.

Results

Our study included 33,674 PD cases and 449,056 controls. Higher plasma level of arachidonic acid (AA) was associated with a 3% increase of PD risk per 1-standard deviation (SD) increase of AA (IVW; Odds ratio (OR)=1.03 [95% confidence interval (CI) 1.01-1.04], P = 2.24E-04). After MVMR (IVW; OR=1.03 [95% CI 1.02-1.04], P =6.15E-08) and deletion of pleiotropic single-nucleotide polymorphisms overlapping with other lipids (IVW; OR=1.03 [95% CI 1.01-1.05], P =5.88E-04), result was still significant. Increased level of eicosapentaenoic acid (EPA) showed possible relevance with increased PD risk after adjustment of pleiotropy (MVMR; OR=1.05 [95% CI 1.01-1.08], P =5.40E-03). Linoleic acid (LA), docosahexaenoic acid (DHA), docosapentaenoic acid (DPA) and alpha-linolenic acid (ALA) were found not causally relevant to PD risk. Various sensitivity analyses verified the validity of our results. In conclusion, our findings from Mendelian randomization suggested that elevated levels of AA and possibly EPA might be linked to a higher risk of PD. No association between PD risk and LA, DHA, DPA, or ALA was found.

Discussion

The odds ratio for plasma AA and PD risk was weak. It is important to approach our results with caution in clinical practice and to conduct additional studies on the relationship between PUFAs and PD risk.

Considering strategies for SNP selection in genetic and polygenic risk scores

Genetic risk scores (GRS) and polygenic risk scores (PRS) are weighted sums of, respectively, several or many genetic variant indicator variables. Although they are being increasingly proposed for clinical use, the best ways to construct them are still actively debated. In this commentary, we present several case studies illustrating practical challenges associated with building or attempting to improve score performance when there is expected to be heterogeneity of disease risk between cohorts or between subgroups of individuals. Specifically, we contrast performance associated with several ways of selecting single nucleotide polymorphisms (SNPs) for inclusion in these scores. By considering GRS and PRS as predictors that are measured with error, insights into their strengths and weaknesses may be obtained, and SNP selection approaches play an important role in defining such errors.

Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics

Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false-positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here, we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which 1) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and 2) allows the inclusion of more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects.

Credible Mendelian Randomization Studies in the Presence of Selection Bias Using Control Exposures

Selection bias is increasingly acknowledged as a limitation of Mendelian randomization (MR). However, few methods exist to assess this issue. We focus on two plausible causal structures relevant to MR studies and illustrate the data-generating process underlying selection bias via simulation studies. We conceptualize the use of control exposures to validate MR estimates derived from selected samples by detecting potential selection bias and reproducing the exposure-outcome association of primary interest based on subject matter knowledge. We discuss the criteria for choosing the control exposures. We apply the proposal in an MR study investigating the potential effect of higher transferrin with stroke (including ischemic and cardioembolic stroke) using transferrin saturation and iron status as control exposures. Theoretically, selection bias affects associations of genetic instruments with the outcome in selected samples, violating the exclusion-restriction assumption and distorting MR estimates. Our applied example showing inconsistent effects of genetically predicted higher transferrin and higher transferrin saturation on stroke suggests the potential selection bias. Furthermore, the expected associations of genetically predicted higher iron status on stroke and longevity indicate no systematic selection bias. The routine use of control exposures in MR studies provides a valuable tool to validate estimated causal effects. Like the applied example, an antagonist, decoy, or exposure with similar biological activity as the exposure of primary interest, which has the same potential selection bias sources as the exposure-outcome association, is suggested as the control exposure. An additional or a validated control exposure with a well-established association with the outcome is also recommended to explore possible systematic selection bias.