MR-Corr2: a two-sample Mendelian randomization method that accounts for correlated horizontal pleiotropy using correlated instrumental variants

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

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.

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.

A Bayesian approach to Mendelian randomization using summary statistics in the univariable and multivariable settings with correlated pleiotropy

Mendelian randomization uses genetic variants as instrumental variables to make causal inferences on the effect of an exposure on an outcome. Due to the recent abundance of high-powered genome-wide association studies, many putative causal exposures of interest have large numbers of independent genetic variants with which they associate, each representing a potential instrument for use in a Mendelian randomization analysis. Such polygenic analyses increase the power of the study design to detect causal effects; however, they also increase the potential for bias due to instrument invalidity. Recent attention has been given to dealing with bias caused by correlated pleiotropy, which results from violation of the "instrument strength independent of direct effect" assumption. Although methods have been proposed that can account for this bias, a number of restrictive conditions remain in many commonly used techniques. In this paper, we propose a Bayesian framework for Mendelian randomization that provides valid causal inference under very general settings. We propose the methods MR-Horse and MVMR-Horse, which can be performed without access to individual-level data, using only summary statistics of the type commonly published by genome-wide association studies, and can account for both correlated and uncorrelated pleiotropy. In simulation studies, we show that the approach retains type I error rates below nominal levels even in high-pleiotropy scenarios. We demonstrate the proposed approaches in applied examples in both univariable and multivariable settings, some with very weak instruments.

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.

MRBEE: A novel bias-corrected multivariable Mendelian Randomization method

Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes and can apply to summary data from genome-wide association studies (GWAS). Since GWAS summary statistics are subject to estimation errors, most existing MR approaches suffer from measurement error bias, whose scale and direction are influenced by weak instrumental variables and GWAS sample overlap, respectively. We introduce MRBEE (MR using Bias-corrected Estimating Equation), a novel multivariable MR method capable of simultaneously removing measurement error bias and identifying horizontal pleiotropy. In simulations, we showed that MRBEE is capable of effectively removing measurement error bias in the presence of weak instrumental variables and sample overlap. In two independent real data analyses, we discovered that the causal effect of BMI on coronary artery disease risk is entirely mediated by blood pressure, and that existing MR methods may underestimate the causal effect of cannabis use disorder on schizophrenia risk compared to MRBEE. MRBEE possesses significant potential for advancing genetic research by providing a valuable tool to study causality between multiple risk factors and disease outcomes, particularly as a large number of GWAS summary statistics become publicly available.

Genetic liability to multi-site chronic pain increases the risk of cardiovascular disease

BACKGROUND

Observational studies have shown associations between multi-site chronic pain (MCP) and cardiovascular disease. However, it remains unclear whether these associations are causal. Therefore, this study aimed to assess the causal associations between MCP and cardiovascular disease and identify possible mediators between them.

METHODS

A two-sample Mendelian randomisation analysis was applied in this study. The summary data for MCP were obtained from a genome-wide association study that included 387 649 individuals from the UK Biobank, whereas summary-level data for cardiovascular disease and its subtypes were obtained from relevant genome-wide association studies. Finally, summary-level data for common cardiovascular risk factors and inflammatory biomarkers were leveraged to identify possible mediators.

RESULTS

Genetic liability to multi-site chronic pain is associated with higher risks for coronary artery disease (CAD), myocardial infarction (MI), heart failure (HF), and stroke, with a combined odds ratio (OR) of 1.537 (per site increment in MCP; 95% confidence interval [CI]: 1.271-1.858; P=0.0001) for CAD, 1.604 (95% CI: 1.277-2.014; P=0.0005) for MI, 1.722 (95% CI: 1.423-2.083; P<0.00001) for HF, and 1.332 (95% CI: 1.093-1.623; P=0.00001) for stroke. Genetic liability to MCP was found to be associated with mental disorders, smoking initiation, physical activity, BMI, and lipid metabolites. Multivariable Mendelian randomisation suggested a mediating role for mental disorders, smoking initiation, physical activity, and BMI in the relationship between multi-site chronic pain and cardiovascular disease.

CONCLUSIONS

Our findings provide new insights into the role of multi-site chronic pain in cardiovascular disease. Additionally, we identified several modifiable risk factors for reducing cardiovascular disease.

Mendelian randomization accounting for complex correlated horizontal pleiotropy while elucidating shared genetic etiology

Mendelian randomization (MR) harnesses genetic variants as instrumental variables (IVs) to study the causal effect of exposure on outcome using summary statistics from genome-wide association studies. Classic MR assumptions are violated when IVs are associated with unmeasured confounders, i.e., when correlated horizontal pleiotropy (CHP) arises. Such confounders could be a shared gene or inter-connected pathways underlying exposure and outcome. We propose MR-CUE (MR with Correlated horizontal pleiotropy Unraveling shared Etiology and confounding), for estimating causal effect while identifying IVs with CHP and accounting for estimation uncertainty. For those IVs, we map their cis-associated genes and enriched pathways to inform shared genetic etiology underlying exposure and outcome. We apply MR-CUE to study the effects of interleukin 6 on multiple traits/diseases and identify several S100 genes involved in shared genetic etiology. We assess the effects of multiple exposures on type 2 diabetes across European and East Asian populations.

Mendelian randomization analysis of 37 clinical factors and coronary artery disease in East Asian and European populations

BACKGROUND

Coronary artery disease (CAD) remains the leading cause of mortality worldwide despite enormous efforts devoted to its prevention and treatment. While many genetic loci have been identified to associate with CAD, the intermediate causal risk factors and etiology have not been fully understood. This study assesses the causal effects of 37 heritable clinical factors on CAD in East Asian and European populations.

METHODS

We collected genome-wide association summary statistics of 37 clinical factors from the Biobank Japan (42,793 to 191,764 participants) and the UK Biobank (314,658 to 442,817 participants), paired with summary statistics of CAD from East Asians (29,319 cases and 183,134 controls) and Europeans (91,753 cases and 311,344 controls). These clinical factors covered 12 cardiometabolic traits, 13 hematological indices, 7 hepatological and 3 renal function indices, and 2 serum electrolyte indices. We performed univariable and multivariable Mendelian randomization (MR) analyses in East Asians and Europeans separately, followed by meta-analysis.

RESULTS

Univariable MR analyses identified reliable causal evidence (P < 0.05/37) of 10 cardiometabolic traits (height, body mass index [BMI], blood pressure, glycemic and lipid traits) and 4 other clinical factors related to red blood cells (red blood cell count [RBC], hemoglobin, hematocrit) and uric acid (UA). Interestingly, while generally consistent, we identified population heterogeneity in the causal effects of BMI and UA, with higher effect sizes in East Asians than those in Europeans. After adjusting for cardiometabolic factors in multivariable MR analysis, red blood cell traits (RBC, meta-analysis odds ratio 1.07 per standard deviation increase, 95% confidence interval 1.02-1.13; hemoglobin, 1.10, 1.03-1.16; hematocrit, 1.10, 1.04-1.17) remained significant (P < 0.05), while UA showed an independent causal effect in East Asians only (1.12, 1.06-1.19, P = 3.26×10-5).

CONCLUSIONS

We confirmed the causal effects of 10 cardiometabolic traits on CAD and identified causal risk effects of RBC, hemoglobin, hematocrit, and UA independent of traditional cardiometabolic factors. We found no causal effects for 23 clinical factors, despite their reported epidemiological associations. Our findings suggest the physiology of red blood cells and the level of UA as potential intervention targets for the prevention of CAD.

Investigating Causal Relationships Between Psychiatric Traits and Intracranial Aneurysms: A Bi-directional Two-Sample Mendelian Randomization Study

Background Despite psychiatric traits were associated with intracranial aneurysms (IAs) in observational studies, their causal relationships remain largely undefined. We aimed to assess the causality between psychiatric traits and IAs.

Methods We firstly collected the genome-wide association statistics of IAs (sample size, n = 79,429) and ten psychiatric traits from Europeans, including insomnia (n = 1,331,010), mood instability (n = 363,705), anxiety disorder (n = 83,566), major depressive disorder (MDD) (n = 480,359), subjective wellbeing (n = 388,538), attention deficit/hyperactivity disorder (ADHD) (n = 53,293), autism spectrum disorder (ASD) (n = 46,350), bipolar disorder (BIP) (n = 51,710), schizophrenia (SCZ) (n = 105,318), and neuroticism (n = 168,105). We then conducted a series of bi-directional two-sample Mendelian randomization (MR) analyses, of which the Robust Adjusted Profile Score (RAPS) was the primary method to estimate the causal effects between these psychiatric traits and IAs.

Results We found that insomnia exhibited a significant risk effect on IAs with the odds ratio (OR) being 1.22 (95% CI: 1.11–1.34, p = 4.61 × 10^–5) from the RAPS method. There was suggestive evidence for risk effect of mood instability on IAs (RAPS, OR = 4.16, 95% CI: 1.02–17.00, p = 0.047). However, no clear evidence of causal effects on IAs for the rest eight psychiatric traits (anxiety disorder, MDD, subjective wellbeing, ADHD, ASD, BIP, SCZ, and neuroticism) was identified. In the reverse MR analyses, no causal effects of IAs on psychiatric traits were found.

Conclusions Our findings provide strong evidence for a causal risk effect of insomnia on IAs and suggestive evidence for mood instability as a causal risk effect on IAs. These results could inform the prevention and clinical intervention of IAs.