Likelihood-based Mendelian randomization analysis with automated instrument selection and horizontal pleiotropic modeling

Causal Associations between Immune Cell Phenotypes and Varicose Veins: A Mendelian Randomization Analysis

BACKGROUND

Varicose veins (VVs) are a common chronic venous disorder with a complex pathophysiology involving immune dysregulation, inflammation, and genetic predisposition. This study aims to identify immune-related causal factors in the pathogenesis of VVs using Mendelian randomization (MR).

METHODS

A 2-sample MR analysis was conducted to assess the causal relationships between immune cell phenotypes and VVs. Genetic variants were used as instrumental variables, and data were derived from the Finland and genome-wide association study catalog. Inverse variance weighting (IVW) was used as the primary method, supported by sensitivity analyses such as MR-Egger, weighted median, and weighted mode.

RESULTS

A total of 79 immunophenotypes were identified as significantly associated with VVs, including 19 related to B-cells, 6 to conventional dendritic cells, 6 to T-cell maturation stages, 6 to monocytes, 12 to myeloid cells, 11 to T cells, B cells, and natural killer cells, and 17 to regulatory T-cells (Tregs). Of these, 8 immunophenotypes remained significant after multiple testing correction (false discovery rate <0.05). Specifically, high expression of cluster of differentiation 86 (CD86) on myeloid dendritic cells, CD33 expression on myeloid cells (e.g., basophils and human leukocyte antigen DR + subsets), increased side scatter area (SSC-A) on lymphocytes, and associations with CD39+ Tregs showed significant correlations with VVs.

CONCLUSION

This study highlights the involvement of immune cells in the pathogenesis of VVs. High expression of CD86 on myeloid DCs and SSC-A on lymphocytes may serve as potential markers for predicting VVs risk, while the protective role of CD39+ Tregs suggests a new direction for immune modulation therapy. Further research is needed to validate these findings across diverse populations and explore their clinical applications.

Genetic Evidence for GLP-1 and GIP Receptors as Targets for Treatment and Prevention of MASLD/MASH

BACKGROUND AND AIMS

Glucagon-like peptide-1 receptor (GLP1R) agonists and glucose-dependent insulinotropic polypeptide receptor (GIPR) agonists may help treat metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH). However, their definitive effects are still unclear. Our study aims to clarify this uncertainty.

METHODS

We utilised conventional Mendelian randomisation (MR) analysis to explore potential causal links between plasma GLP-1/GIP concentrations and MASLD and its related traits. Next, we conducted drug-target MR analysis using highly expressed tissue data to assess the effects of corresponding drug perturbation on these traits. Finally, mediation analysis was performed to ascertain whether the potential causal effect is direct or mediated by other MASLD-related traits.

RESULTS

Circulating 2-h GLP-1 and GIP concentrations measured during an oral glucose tolerance test showed hepatoprotective effects on MASLD risk (ORGLP-1 = 0.168 [95% CI 0.033-0.839], p = 0.030; ORGIP = 0.331 [95% CI 0.222-0.494], p = 6.31 × 10-8). GLP1R expression in the blood had a minimal causal effect on MASLD risk, whereas GIPR expression significantly affected MASLD risk (OR = 0.671 [95% CI 0.531-0.849], p = 9.07 × 10-4). Expression levels of GLP1R or GIPR in the blood significantly influenced MASLD-related clinical traits. Mediation analysis revealed that GIPR expression protected against MASLD, even after adjusting for type 2 diabetes or body mass index.

CONCLUSIONS

GLP-1/GIP receptor agonists offer promise in lowering MASLD/MASH risk. GIP receptor agonists can exert direct and indirect effects on MASLD mediated by weight reduction or glycemic control improvement.

Leveraging pleiotropic clustering to address high proportion correlated horizontal pleiotropy in Mendelian randomization studies

Mendelian randomization harnesses genetic variants as instrumental variables to infer causal relationships between exposures and outcomes. However, certain genetic variants can affect both the exposure and the outcome through a shared factor. This phenomenon, called correlated horizontal pleiotropy, may result in false-positive causal findings. Here, we propose a Pleiotropic Clustering framework for Mendelian randomization, PCMR. PCMR detects correlated horizontal pleiotropy and extends the zero modal pleiotropy assumption to enhance causal inference in trait pairs with correlated horizontal pleiotropic variants. Simulations show that PCMR can effectively detect correlated horizontal pleiotropy and avoid false positives in the presence of correlated horizontal pleiotropic variants, even when they constitute a high proportion of the variants connecting both traits (e.g., 30-40%). In datasets consisting of 48 exposure-common disease pairs, PCMR detects horizontal correlated pleiotropy in 7 out of the exposure-common disease pairs, and avoids detecting false positive causal links. Additionally, PCMR can facilitate the integration of biological information to exclude correlated horizontal pleiotropic variants, enhancing causal inference. We apply PCMR to study causal relationships between three common psychiatric disorders as examples.

Evaluation of the causal relationship between 28 circulating biomarkers and osteoarthritis : a bidirectional Mendelian randomization study

Aims

Circulating biochemistry markers are commonly used to monitor and detect disease-induced dysfunctions including osteoarthritis (OA). However, the causal nature of this relationship is nevertheless largely unknown, due to unmeasured confounding factors from observational studies. We aimed to reveal the causal relationship between 28 circulating biochemistry markers and OA pathogenesis.

Methods

We conducted a comprehensive bidirectional two-sample Mendelian randomization (MR) study between 28 circulating biomarkers and six OA types, using large-scale genome-wide association study (GWAS) summary statistics data from a UK Biobank cohort (n = 450,243) and the latest OA meta-analysis (n = 826,690). We replicated the significant results of low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC) in an independent large GWAS dataset obtained from the Global Lipids Genetics Consortium (GLGC) (n > 800,000).

Results

Using 73 to 792 instrumental variables for biomarkers, this large MR analysis identified 11 causal associations at the Bonferroni corrected significance level of 2.98 × 10-4, involving seven biomarkers and five OA types. LDL-C (odds ratio (OR) per SD increase 0.90, 95% CI 0.86 to 0.93), apolipoprotein B (OR 0.86, 95% CI 0.82 to 0.91), TC (OR 0.90, 95% CI 0.86 to 0.94), calcium (OR 0.82, 95% CI 0.75 to 0.90), and glucose (OR 0.81, 95% CI 0.73 to 0.89) are causally associated with a reduced risk of OA, while phosphate (OR 1.18, 95% CI 1.08 to 1.30) and aspartate aminotransferase (OR 1.15, 95% CI 1.07 to 1.24) are causally associated with an increased risk. Analysis of GLGC summary statistics successfully replicated LDL-C (OR 0.93, 95% CI 0.90 to 0.96) and TC (OR 0.92, 95% CI 0.89 to 0.95).

Conclusion

This comprehensive bidirectional MR analysis provides new insights into the prevention and treatment of OA, as well as understanding the biological mechanism underlying OA pathogenesis.

A robust and powerful GWAS method for family trios supporting within-family Mendelian randomization analysis

Effect size estimates in genome-wide association studies (GWAS) and Mendelian randomization (MR) studies for independent individuals may be biased due to dynastic effect (DE) and residual population stratification (RPS). Existing GWAS methods for family trios effectively controlled such biases, while only using parental and offspring's genotypes and offspring's phenotype, and not incorporating parental phenotypes, which causes loss in estimation accuracy and test power. Therefore, we proposed a novel GWAS method based on structural equation modelling for family trios, denoted by FT-SEM. FT-SEM simultaneously uses parental and offspring's genotypes and phenotypes. Simulation results demonstrate that FT-SEM substantially improves estimation accuracy and test power while controlling bias and type I error rate. Using family trios from Minnesota Center for Twin and Family Research (MCTFR), we found that DE and RPS greatly distort the results only based on independent individuals, and FT-SEM effectively corrects such biases. Combining the GWAS results from MCTFR with existing summary data, we performed several two-sample MR analyses. We observed that the effects of BMI on nicotine, alcohol consumption and behavior disorder were due to bias rather than causality. Our findings underscore the necessity of using families to validate the results of GWAS and MR, and highlight FT-SEM's advantages.

Investigating the causal effects of childhood and adulthood adiposity on later life mental health outcome: a Mendelian randomization study

BACKGROUND

Obesity particularly during childhood is considered a global public health crisis and has been linked with later life health consequences including mental health. However, there is lack of causal understanding if childhood body size has a direct effect on mental health or has an indirect effect after accounting for adulthood body size.

METHODS

Two-sample Mendelian randomization (MR) was performed to estimate the total effect and direct effect (accounting for adulthood body size) of childhood body size on anxiety and depression. We used summary statistics from a genome-wide association study (GWAS) of UK Biobank (n = 453,169) and large-scale consortia of anxiety (Million Veteran Program) and depression (Psychiatric Genomics Consortium) (n = 175,163 and n = 173,005, respectively).

RESULTS

Univariable MR did not indicate genetically predicted effects of childhood body size with later life anxiety (beta = - 0.05, 95% CI = - 0.13, 0.02) and depression (OR = 1.06, 95% CI = 0.94, 1.20). However, using multivariable MR, we observed that the higher body size in childhood reduced the risk of later life anxiety (beta = - 0.19, 95% CI = - 0.29, - 0.08) and depression (OR = 0.83, 95% CI = 0.71, 0.97) upon accounting for the effect of adulthood body size. Both univariable and multivariable MR indicated that higher body size in adulthood increased the risk of later life anxiety and depression.

CONCLUSIONS

Higher body size in adulthood may increase the risk of anxiety and depression, independent of childhood higher body size. In contrast, higher childhood body size does not appear to be a risk factor for later life anxiety and depression.

MR-SPLIT: A novel method to address selection and weak instrument bias in one-sample Mendelian randomization studies

Mendelian Randomization (MR) is a widely embraced approach to assess causality in epidemiological studies. Two-stage least squares (2SLS) method is a predominant technique in MR analysis. However, it can lead to biased estimates when instrumental variables (IVs) are weak. Moreover, the issue of the winner's curse could emerge when utilizing the same dataset for both IV selection and causal effect estimation, leading to biased estimates of causal effects and high false positives. Focusing on one-sample MR analysis, this paper introduces a novel method termed Mendelian Randomization with adaptive Sample-sPLitting with cross-fitting InstrumenTs (MR-SPLIT), designed to address bias issues due to IV selection and weak IVs, under the 2SLS IV regression framework. We show that the MR-SPLIT estimator is more efficient than its counterpart cross-fitting MR (CFMR) estimator. Additionally, we introduce a multiple sample-splitting technique to enhance the robustness of the method. We conduct extensive simulation studies to compare the performance of our method with its counterparts. The results underscored its superiority in bias reduction, effective type I error control, and increased power. We further demonstrate its utility through the application of a real-world dataset. Our study underscores the importance of addressing bias issues due to IV selection and weak IVs in one-sample MR analyses and provides a robust solution to the challenge.

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.

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.

A robust cis-Mendelian randomization method with application to drug target discovery

Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to investigate causal relationships between traits. Unlike conventional MR, cis-MR focuses on a single genomic region using only cis-SNPs. For example, using cis-pQTLs for a protein as exposure for a disease opens a cost-effective path for drug target discovery. However, few methods effectively handle pleiotropy and linkage disequilibrium (LD) of cis-SNPs. Here, we propose cisMR-cML, a method based on constrained maximum likelihood, robust to IV assumption violations with strong theoretical support. We further clarify the severe but largely neglected consequences of the current practice of modeling marginal, instead of conditional genetic effects, and only using exposure-associated SNPs in cis-MR analysis. Numerical studies demonstrated our method's superiority over other existing methods. In a drug-target analysis for coronary artery disease (CAD), including a proteome-wide application, we identified three potential drug targets, PCSK9, COLEC11 and FGFR1 for CAD.

Mendelian randomization: causal inference leveraging genetic data

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples - the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

simmrd: An open-source tool to perform simulations in Mendelian randomization

Mendelian randomization (MR) has become a popular tool for inferring causality of risk factors on disease. There are currently over 45 different methods available to perform MR, reflecting this extremely active research area. It would be desirable to have a standard simulation environment to objectively evaluate the existing and future methods. We present simmrd, an open-source software for performing simulations to evaluate the performance of MR methods in a range of scenarios encountered in practice. Researchers can directly modify the simmrd source code so that the research community may arrive at a widely accepted framework for researchers to evaluate the performance of different MR methods.

CYP2A6 Activity and Cigarette Consumption Interact in Smoking-Related Lung Cancer Susceptibility

Cigarette smoke, containing both nicotine and carcinogens, causes lung cancer. However, not all smokers develop lung cancer, highlighting the importance of the interaction between host susceptibility and environmental exposure in tumorigenesis. Here, we aimed to delineate the interaction between metabolizing ability of tobacco carcinogens and smoking intensity in mediating genetic susceptibility to smoking-related lung tumorigenesis. Single-variant and gene-based associations of 43 tobacco carcinogen-metabolizing genes with lung cancer were analyzed using summary statistics and individual-level genetic data, followed by causal inference of Mendelian randomization, mediation analysis, and structural equation modeling. Cigarette smoke-exposed cell models were used to detect gene expression patterns in relation to specific alleles. Data from the International Lung Cancer Consortium (29,266 cases and 56,450 controls) and UK Biobank (2,155 cases and 376,329 controls) indicated that the genetic variant rs56113850 C>T located in intron 4 of CYP2A6 was significantly associated with decreased lung cancer risk among smokers (OR = 0.88, 95% confidence interval = 0.85-0.91, P = 2.18 × 10-16), which might interact (Pinteraction = 0.028) with and partially be mediated (ORindirect = 0.987) by smoking status. Smoking intensity accounted for 82.3% of the effect of CYP2A6 activity on lung cancer risk but entirely mediated the genetic effect of rs56113850. Mechanistically, the rs56113850 T allele rescued the downregulation of CYP2A6 caused by cigarette smoke exposure, potentially through preferential recruitment of transcription factor helicase-like transcription factor. Together, this study provides additional insights into the interplay between host susceptibility and carcinogen exposure in smoking-related lung tumorigenesis.

SIGNIFICANCE

The causal pathway connecting CYP2A6 genetic variability and activity, cigarette consumption, and lung cancer susceptibility in smokers highlights the need for behavior modification interventions based on host susceptibility for cancer prevention.

Brain tissue- and cell type-specific eQTL Mendelian randomization reveals efficacy of FADS1 and FADS2 on cognitive function

Epidemiological studies suggested an association between omega-3 fatty acids and cognitive function. However, the causal role of the fatty acid desaturase (FADS) gene, which play a key role in regulating omega-3 fatty acids biosynthesis, on cognitive function is unclear. Hence, we used two-sample Mendelian randomization (MR) to estimate the gene-specific causal effect of omega-3 fatty acids (N = 114,999) on cognitive function (N = 300,486). Tissue- and cell type-specific effects of FADS1/FADS2 expression on cognitive function were estimated using brain tissue cis-expression quantitative trait loci (cis-eQTL) datasets (GTEx, N ≤ 209; MetaBrain, N ≤ 8,613) and single cell cis-eQTL data (N = 373), respectively. These causal effects were further evaluated in whole blood cis-eQTL data (N ≤ 31,684). A series of sensitivity analyses were conducted to validate MR assumptions. Leave-one-out MR showed a FADS gene-specific effect of omega-3 fatty acids on cognitive function [β = -1.3 × 10-2, 95% confidence interval (CI) (-2.2 × 10-2, -5 × 10-3), P = 2 × 10-3]. Tissue-specific MR showed an effect of increased FADS1 expression in cerebellar hemisphere and FADS2 expression in nucleus accumbens basal ganglia on maintaining cognitive function, while decreased FADS1 expression in nine brain tissues on maintaining cognitive function [colocalization probability (PP.H4) ranged from 71.7% to 100.0%]. Cell type-specific MR showed decreased FADS1/FADS2 expression in oligodendrocyte was associated with maintaining cognitive function (PP.H4 = 82.3%, respectively). Increased FADS1/FADS2 expression in whole blood showed an effect on cognitive function maintenance (PP.H4 = 86.6% and 88.4%, respectively). This study revealed putative causal effect of FADS1/FADS2 expression in brain tissues and blood on cognitive function. These findings provided evidence to prioritize FADS gene as potential target gene for maintenance of cognitive function.

An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset

Mendelian randomization (MR) is an effective approach for revealing causal risk factors that underpin complex traits and diseases. While MR has been more widely applied under two-sample settings, it is more promising to be used in one single large cohort given the rise of biobank-scale datasets that simultaneously contain genotype data, brain imaging data, and matched complex traits from the same individual. However, most existing multivariable MR methods have been developed for two-sample setting or a small number of exposures. In this study, we introduce a one-sample multivariable MR method based on partial least squares and Lasso regression (MR-PL). MR-PL is capable of considering the correlation among exposures (e.g., brain imaging features) when the number of exposures is extremely upscaled, while also correcting for winner's curse bias. We performed extensive and systematic simulations, and demonstrated the robustness and reliability of our method. Comprehensive simulations confirmed that MR-PL can generate more precise causal estimates with lower false positive rates than alternative approaches. Finally, we applied MR-PL to the datasets from UK Biobank to reveal the causal effects of 36 white matter tracts on 180 complex traits, and showed putative white matter tracts that are implicated in smoking, blood vascular function-related traits, and eating behaviors.

Modeling of horizontal pleiotropy identifies possible causal gene expression in systemic lupus erythematosus

Background

Systemic lupus erythematosus (SLE) is a chronic autoimmune condition with complex causes involving genetic and environmental factors. While genome-wide association studies (GWASs) have identified genetic loci associated with SLE, the functional genomic elements responsible for disease development remain largely unknown. Mendelian Randomization (MR) is an instrumental variable approach to causal inference based on data from observational studies, where genetic variants are employed as instrumental variables (IVs).

Methods

This study utilized a two-step strategy to identify causal genes for SLE. In the first step, the classical MR method was employed, assuming the absence of horizontal pleiotropy, to estimate the causal effect of gene expression on SLE. In the second step, advanced probabilistic MR methods (PMR-Egger, MRAID, and MR-MtRobin) were applied to the genes identified in the first step, considering horizontal pleiotropy, to filter out false positives. PMR-Egger and MRAID analyses utilized whole blood expression quantitative trait loci (eQTL) and SLE GWAS summary data, while MR-MtRobin analysis used an independent eQTL dataset from multiple immune cell types along with the same SLE GWAS data.

Results

The initial MR analysis identified 142 genes, including 43 outside of chromosome 6. Subsequently, applying the advanced MR methods reduced the number of genes with significant causal effects on SLE to 66. PMR-Egger, MRAID, and MR-MtRobin, respectively, identified 13, 7, and 16 non-chromosome 6 genes with significant causal effects. All methods identified expression of PHRF1 gene as causal for SLE. A comprehensive literature review was conducted to enhance understanding of the functional roles and mechanisms of the identified genes in SLE development.

Conclusions

The findings from the three MR methods exhibited overlapping genes with causal effects on SLE, demonstrating consistent results. However, each method also uncovered unique genes due to different modelling assumptions and technical factors, highlighting the complementary nature of the approaches. Importantly, MRAID demonstrated a reduced percentage of causal genes from the Major Histocompatibility complex (MHC) region on chromosome 6, indicating its potential in minimizing false positive findings. This study contributes to unraveling the mechanisms underlying SLE by employing advanced probabilistic MR methods to identify causal genes, thereby enhancing our understanding of SLE pathogenesis.

Causal relationship between dietary factors and breast cancer risk: A Mendelian randomization study

Background

Previous studies have discovered an association between dietary factors and breast cancer. However, few studies have used Mendelian randomization (MR) to assess the potential causal relationship between dietary factors and breast cancer.

Methods

The exposure datasets for fresh fruit intake, dried fruit intake, salad/raw vegetable intake, cooked vegetable intake, oily fish intake, non-oily fish intake, cheese intake, and bread intake were obtained from the UK Biobank. The outcome dataset was extracted from the Breast Cancer Association Consortium (BCAC). We used the inverse variance weighted (IVW) method as the primary approach for the two-sample MR analysis. To ensure the accuracy of the results, we conducted heterogeneity and horizontal pleiotropy analyses. Additionally, multivariable MR analysis was conducted to ensure the stability of the results.

Results

Dried fruit intake was found to be a protective factor for overall breast cancer (outliers excluded: OR: 0.549; 95 % CI: 0.429-0.702; p = 1.75 × 10-6). Subtype analyses showed that dried fruit intake was inversely associated with both estrogen receptor-positive (ER+) breast cancer (outliers excluded: OR: 0.669; 95 % CI: 0.512-0.875; p = 0.003) and ER-negative (ER-) breast cancer (OR: 0.559; 95 % CI: 0.379-0.827; p = 0.004), while fresh fruit intake was inversely associated with ER- breast cancer (excluded outliers: OR: 0.510; 95 % CI: 0.308-0.846; p = 0.009). No significant causal relationship was found between other dietary intakes and breast cancer. After adjusting for the effects of possible confounders, the causal relationships found by the two-sample MR analysis remained.

Conclusion

Our study provides evidence that dried fruit intake may reduce the risk of both ER+ and ER- breast cancer, and fresh fruit intake may reduce the risk of ER- breast cancer. Other factors included in this study were not linked to breast cancer.

Shared genetic architecture between attention-deficit/hyperactivity disorder and lifespan

There is evidence linking ADHD to a reduced life expectancy. The mortality rate in individuals with ADHD is twice that of the general population and it is associated with several factors, such as unhealthy lifestyle behaviors, social adversity, and mental health problems that may in turn increase mortality rates. Since ADHD and lifespan are heritable, we used data from genome-wide association studies (GWAS) of ADHD and parental lifespan, as proxy of individual lifespan, to estimate their genetic correlation, identify genetic loci jointly associated with both phenotypes and assess causality. We confirmed a negative genetic correlation between ADHD and parental lifespan (rg = -0.36, P = 1.41e-16). Nineteen independent loci were jointly associated with both ADHD and parental lifespan, with most of the alleles that increased the risk for ADHD being associated with shorter lifespan. Fifteen loci were novel for ADHD and two were already present in the original GWAS on parental lifespan. Mendelian randomization analyses pointed towards a negative causal effect of ADHD liability on lifespan (P = 1.54e-06; Beta = -0.07), although these results were not confirmed by all sensitivity analyses performed, and further evidence is required. The present study provides the first evidence of a common genetic background between ADHD and lifespan, which may play a role in the reported effect of ADHD on premature mortality risk. These results are consistent with previous epidemiological data describing reduced lifespan in mental disorders and support that ADHD is an important health condition that could negatively affect future life outcomes.

Impairment of kidney function and kidney cancer: A bidirectional Mendelian randomization study

BACKGROUND

Many observational epidemiology studies discovered that kidney cancer and impaired kidney function have a bidirectional relationship. However, it remains unclear whether these two kinds of traits are causally linked. In this study, we aimed to investigate the bidirectional causal relation between kidney cancer and kidney function biomarkers (creatinine-based estimated glomerular filtration rate (eGFRcrea), cystatin C-based estimated glomerular filtration rate (eGFRcys), blood urea nitrogen (BUN), serum urate, and urinary albumin-to-creatinine ratio (UACR)).

METHODS

For both directions, single-nucleotide polymorphisms (SNPs), as genetic instruments, for the five kidney function traits were selected from up to 1,004,040 individuals, and SNPs for kidney cancer were from 408,786 participants(1338 cases). In the main analysis, we applied two state-of-the-art MR methods, namely, contamination mixture and Robust Adjusted Profile Score to downweight the effect of weak instrument bias, pleiotropy, and extreme outliers. We additionally conducted traditional MR analyses as sensitivity analyses. Summary-level data of European ancestry were extracted from UK Biobank, Chronic Kidney Disease Genetics Consortium, and Kaiser Permanente.

RESULTS

Based on 99 SNPs, we found that the eGFRcrea had a significant negative causal effect on the risk of kidney cancer (OR = 0.007, 95% CI:2.6 × 10-4 -0.569, p = 0.041). After adjusting for body composition or diabetes, urate had a significant negative causal effect on kidney cancer (OR <1, p < 0.05). For UACR, it showed a strong causal effect on kidney cancer, after adjusting for body composition (OR = 14.503, 95% CI: 2.546-96.001, p = 0.032). Due to lacking significant signals and effect power for the reverse MR, further investigations are warranted.

CONCLUSIONS

Our study suggested a potential causal effect of damaged kidney function on kidney cancer. EGFRcrea and UACR might be causally associated with kidney cancer, especially when patients were comorbid with obesity or diabetes. We called for larger sample-size studies to further unravel the underlying causal relationship and the exact mechanism.

Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases

Proteome-wide Mendelian randomization (MR) shows value in prioritizing drug targets in Europeans but with limited evidence in other ancestries. Here, we present a multi-ancestry proteome-wide MR analysis based on cross-population data from the Global Biobank Meta-analysis Initiative (GBMI). We estimated the putative causal effects of 1,545 proteins on eight diseases in African (32,658) and European (1,219,993) ancestries and identified 45 and 7 protein-disease pairs with MR and genetic colocalization evidence in the two ancestries, respectively. A multi-ancestry MR comparison identified two protein-disease pairs with MR evidence in both ancestries and seven pairs with specific effects in the two ancestries separately. Integrating these MR signals with clinical trial evidence, we prioritized 16 pairs for investigation in future drug trials. Our results highlight the value of proteome-wide MR in informing the generalizability of drug targets for disease prevention across ancestries and illustrate the value of meta-analysis of biobanks in drug development.