Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics

AbstractMendelian randomization (MR) is a valuable tool for detecting evidence of causal relationships using genetic variant associations. Opportunities to apply MR are growing rapidly with the number of genome-wide association studies (GWAS) with publicly available results. However, existing MR methods rely on strong assumptions that are often violated, leading to false positives. Many methods have been proposed loosening these assumptions. However, it has remained challenging to account for correlated pleiotropy, which arises when variants affect both traits through a heritable shared factor. We propose a new MR method, Causal Analysis Using Summary Effect Estimates (CAUSE), that accounts for correlated and uncorrelated horizontal pleiotropic effects. We demonstrate in simulations that CAUSE is more robust to correlated pleiotropy than other methods. Applied to traits studied in recent GWAS, we find that CAUSE detects causal relationships with strong literature support and avoids identifying most unlikely relationships. Our results suggest that many pairs of traits identified as causal using alternative methods may be false positives due to horizontal pleiotropy.

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MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqaa028 ◽

2020 ◽

Vol 2 (2) ◽

Cited By ~ 2

Author(s):

Qing Cheng ◽

Yi Yang ◽

Xingjie Shi ◽

Kar-Fu Yeung ◽

Can Yang ◽

...

Keyword(s):

Risk Factors ◽

Linkage Disequilibrium ◽

Genetic Variants ◽

Mendelian Randomization ◽

Association Studies ◽

Alternative Methods ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Causal Relationships ◽

Disease Outcomes

Abstract The proliferation of genome-wide association studies (GWAS) has prompted the use of two-sample Mendelian randomization (MR) with genetic variants as instrumental variables (IVs) for drawing reliable causal relationships between health risk factors and disease outcomes. However, the unique features of GWAS demand that MR methods account for both linkage disequilibrium (LD) and ubiquitously existing horizontal pleiotropy among complex traits, which is the phenomenon wherein a variant affects the outcome through mechanisms other than exclusively through the exposure. Therefore, statistical methods that fail to consider LD and horizontal pleiotropy can lead to biased estimates and false-positive causal relationships. To overcome these limitations, we proposed a probabilistic model for MR analysis in identifying the causal effects between risk factors and disease outcomes using GWAS summary statistics in the presence of LD and to properly account for horizontal pleiotropy among genetic variants (MR-LDP) and develop a computationally efficient algorithm to make the causal inference. We then conducted comprehensive simulation studies to demonstrate the advantages of MR-LDP over the existing methods. Moreover, we used two real exposure–outcome pairs to validate the results from MR-LDP compared with alternative methods, showing that our method is more efficient in using all-instrumental variants in LD. By further applying MR-LDP to lipid traits and body mass index (BMI) as risk factors for complex diseases, we identified multiple pairs of significant causal relationships, including a protective effect of high-density lipoprotein cholesterol on peripheral vascular disease and a positive causal effect of BMI on hemorrhoids.

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Investigating Casual Associations among Gut Microbiota, Metabolites and Neurodegenerative Diseases: A Mendelian Randomization Study

10.21203/rs.3.rs-885681/v1 ◽

2021 ◽

Author(s):

Jin-Tai Yu ◽

Jing Ning ◽

Shu-Yi Huang ◽

Shi-Dong Chen ◽

Yu-Xiang Yang ◽

...

Keyword(s):

Gut Microbiota ◽

Neurodegenerative Diseases ◽

Protective Factor ◽

Mendelian Randomization ◽

Association Studies ◽

Genome Wide Association Studies ◽

Causal Relationships ◽

Genome Wide ◽

Underlying Mechanisms ◽

Lateral Sclerosis

Abstract Background Recent studies had explored that the gut microbiota was associated with neurodegenerative diseases (including Alzheimer’s disease (AD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS)) through the gut-brain axis, among which metabolic pathways played an important role. However, the underlying causality remained unclear. Our study aimed to evaluate potential causal relationships between gut microbiota, metabolites and neurodegenerative diseases through Mendelian randomization (MR) approach. Methods We selected genetic variants associated with gut microbiota traits (N = 18340) and gut microbiota-derived metabolites (N = 7824) from genome-wide association studies (GWASs). Summary statistics of neurodegenerative diseases were obtained from IGAP (AD: 17008 cases; 37154 controls), IPDGC (PD: 37 688 cases; 141779 controls) and IALSC (ALS: 20806 cases; 59804 controls) respectively. Results A total of 19 gut microbiota traits were found to be causally associated with risk of neurodegenerative diseases, including 1 phylum, 2 classes, 2 orders, 2 families and 12 genera. We found genetically predicted greater abundance of Ruminococcus, at genus level (OR:1.245, 95%CI:1.103,1.405; P = 0.0004) was significantly related to higher risk of ALS. We also found suggestive association between 12 gut microbiome-dependent metabolites and neurodegenerative diseases. For serotonin pathway, our results revealed serotonin as protective factor of PD, and kynurenine as risk factor of ALS. Besides, reduction of glutamine was found causally associated with occurrence of AD. Conclusions Our study firstly applied a two-sample MR approach to detect causal relationships among gut microbiota, gut metabolites and the risk of AD, PD and ALS, and we revealed several causal relationships. These findings may provide new targets for treatment of these neurodegenerative diseases, and may offer valuable insights for further researches on the underlying mechanisms.

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Genetic Contribution of Endometriosis to the Risk of Developing Hormone-Related Cancers

International Journal of Molecular Sciences ◽

10.3390/ijms22116083 ◽

2021 ◽

Vol 22 (11) ◽

pp. 6083

Author(s):

Aintzane Rueda-Martínez ◽

Aiara Garitazelaia ◽

Ariadna Cilleros-Portet ◽

Sergi Marí ◽

Rebeca Arauzo ◽

...

Keyword(s):

Mendelian Randomization ◽

Clear Cell ◽

Association Studies ◽

Epidemiological Data ◽

Epidemiological Studies ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Genomic Analyses ◽

Gynecological Disorder ◽

Robust Evidence

Endometriosis is a common gynecological disorder that has been associated with endometrial, breast and epithelial ovarian cancers in epidemiological studies. Since complex diseases are a result of multiple environmental and genetic factors, we hypothesized that the biological mechanism underlying their comorbidity might be explained, at least in part, by shared genetics. To assess their potential genetic relationship, we performed a two-sample mendelian randomization (2SMR) analysis on results from public genome-wide association studies (GWAS). This analysis confirmed previously reported genetic pleiotropy between endometriosis and endometrial cancer. We present robust evidence supporting a causal genetic association between endometriosis and ovarian cancer, particularly with the clear cell and endometrioid subtypes. Our study also identified genetic variants that could explain those associations, opening the door to further functional experiments. Overall, this work demonstrates the value of genomic analyses to support epidemiological data, and to identify targets of relevance in multiple disorders.

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Genomics-driven drug discovery based on disease-susceptibility genes

Inflammation and Regeneration ◽

10.1186/s41232-021-00158-7 ◽

2021 ◽

Vol 41 (1) ◽

Author(s):

Kyuto Sonehara ◽

Yukinori Okada

Keyword(s):

Drug Discovery ◽

Drug Targets ◽

Disease Susceptibility ◽

Mendelian Randomization ◽

Association Studies ◽

Drug Repurposing ◽

Susceptibility Genes ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Disease Associations

AbstractGenome-wide association studies have identified numerous disease-susceptibility genes. As knowledge of gene–disease associations accumulates, it is becoming increasingly important to translate this knowledge into clinical practice. This challenge involves finding effective drug targets and estimating their potential side effects, which often results in failure of promising clinical trials. Here, we review recent advances and future perspectives in genetics-led drug discovery, with a focus on drug repurposing, Mendelian randomization, and the use of multifaceted omics data.

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Stroke Genetics: Turning Discoveries into Clinical Applications

Stroke ◽

10.1161/strokeaha.121.032616 ◽

2021 ◽

Author(s):

Martin Dichgans ◽

Nathalie Beaufort ◽

Stephanie Debette ◽

Christopher D. Anderson

Keyword(s):

Mendelian Randomization ◽

Association Studies ◽

Clinical Applications ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Neuroprotective Agents ◽

Experimental Systems ◽

Genome Wide ◽

Rapid Pace

The field of medical and population genetics in stroke is moving at a rapid pace and has led to unanticipated opportunities for discovery and clinical applications. Genome-wide association studies have highlighted the role of specific pathways relevant to etiologically defined subtypes of stroke and to stroke as a whole. They have further offered starting points for the exploration of novel pathways and pharmacological strategies in experimental systems. Mendelian randomization studies continue to provide insights in the causal relationships between exposures and outcomes and have become a useful tool for predicting the efficacy and side effects of drugs. Additional applications that have emerged from recent discoveries include risk prediction based on polygenic risk scores and pharmacogenomics. Among the topics currently moving into focus is the genetics of stroke outcome. While still at its infancy, this field is expected to boost the development of neuroprotective agents. We provide a brief overview on recent progress in these areas.

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Genetic epidemiology and Mendelian randomization for informing disease therapeutics: conceptual and methodological challenges

10.1101/126599 ◽

2017 ◽

Cited By ~ 4

Author(s):

Lavinia Paternoster ◽

Kate Tilling ◽

George Davey Smith

Keyword(s):

Genetic Epidemiology ◽

Mendelian Randomization ◽

Association Studies ◽

Genome Wide Association Studies ◽

Disease Treatment ◽

The Past ◽

Genome Wide ◽

Perceived Benefit ◽

Progression Of Disease ◽

High Yields

The past decade has been proclaimed as a hugely successful era of gene discovery through the high yields of many genome-wide association studies (GWAS). However, much of the perceived benefit of such discoveries lies in the promise that the identification of genes that influence disease would directly translate into the identification of potential therapeutic targets (1-4), but this has yet to be realised at a level reflecting expectation. One reason for this, we suggest, is that GWAS to date have generally not focused on phenotypes that directly relate to the progression of disease, and thus speak to disease treatment.

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Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis

10.1101/865956 ◽

2019 ◽

Cited By ~ 1

Author(s):

Simon Haworth ◽

Pik Fang Kho ◽

Pernilla Lif Holgerson ◽

Liang-Dar Hwang ◽

Nicholas J. Timpson ◽

...

Keyword(s):

Dental Caries ◽

Causal Inference ◽

Mendelian Randomization ◽

Association Studies ◽

List Type ◽

Genome Wide Association Studies ◽

Causal Relationships ◽

Link Type ◽

Alternative Method ◽

Causal Variable

AbstractBackgroundHypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments.MethodsWe developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed a causal architecture plot. We apply this process to body mass index and lipid traits as exemplars of traits where there is strong prior expectation for causal effects and dental caries and periodontitis as exemplars of traits where there is a need for causal inference.ResultsThe results for lipids and BMI suggest that these traits are best viewed as creating consequences on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health.ConclusionsThe automated process is available as part of the MASSIVE pipeline from the Complex-Traits Genetics Virtual Lab (https://vl.genoma.io) and results are available in (https://view.genoma.io). We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a way visualizing the overall causal map of the human phenome.Key messagesThe latent causal variable approach uses summary statistics from genome-wide association studies to estimate a parameter termed genetic causality proportion.Systematic estimation of genetic causality proportion for many pairs of traits provides an alternative method for phenome-wide causal inference with some theoretical and practical advantages compared to phenome-wide Mendelian randomization.Using this approach, we confirm that lipid traits are an upstream risk factor for other traits and diseases, and we identify that dental diseases are predominantly a downstream consequence of other traits rather than a cause of poor systemic health.The method assumes no bidirectional causality and no confounding by environmental correlates of genotypes, so care is needed when these assumptions are not met.We developed an automated and accessible pipeline for estimating phenome-wide causal relationships and generating interactive visual summaries.

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A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

10.1101/563379 ◽

2019 ◽

Cited By ~ 2

Author(s):

Tom G Richardson ◽

Gibran Hemani ◽

Tom R Gaunt ◽

Caroline L Relton ◽

George Davey Smith

Keyword(s):

Gene Expression ◽

Genetic Variants ◽

Complex Traits ◽

Mendelian Randomization ◽

Drug Repositioning ◽

Association Studies ◽

Thyroid Tissue ◽

Genome Wide Association Studies ◽

Tissue Specific ◽

Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

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bGWAS: an R package to perform Bayesian genome wide association studies

Bioinformatics ◽

10.1093/bioinformatics/btaa549 ◽

2020 ◽

Vol 36 (15) ◽

pp. 4374-4376

Author(s):

Ninon Mounier ◽

Zoltán Kutalik

Keyword(s):

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Biological Mechanisms ◽

Genome Wide ◽

Related Risk

Abstract Summary Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. Availability and implementation bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

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Inference of causal relationships between sleep-related traits and 1,527 phenotypes using genetic data

SLEEP ◽

10.1093/sleep/zsaa154 ◽

2020 ◽

Author(s):

Luis M García-Marín ◽

Adrián I Campos ◽

Nicholas G Martin ◽

Gabriel Cuéllar-Partida ◽

Miguel E Rentería

Keyword(s):

Sleep Duration ◽

Association Studies ◽

Genome Wide Association Studies ◽

Causal Relationships ◽

Physical And Mental Health ◽

Related Factors ◽

Study Objective ◽

Genome Wide ◽

Increased Risk ◽

Level Statistics

Abstract Study Objective Sleep is essential for both physical and mental health, and there is a growing interest in understanding how different factors shape individual variation in sleep duration, quality and patterns, or confer risk for sleep disorders. The present study aimed to identify novel inferred causal relationships between sleep-related traits and other phenotypes, using a genetics-driven hypothesis-free approach not requiring longitudinal data. Methods We used summary-level statistics from genome-wide association studies and the latent causal variable (LCV) method to screen the phenome and infer causal relationships between seven sleep-related traits (insomnia, daytime dozing, easiness of getting up in the morning, snoring, sleep duration, napping, and morningness) and 1,527 other phenotypes. Results We identify 84 inferred causal relationships. Among other findings, connective tissue disorders increase insomnia risk and reduce sleep duration; depression-related traits increase insomnia and daytime dozing; insomnia, napping and snoring are affected by obesity and cardiometabolic traits and diseases; and working with asbestos, thinner, or glues may increase insomnia risk, possibly through an increased risk of respiratory disease or socio-economic related factors. Conclusion Overall, our results indicate that changes in sleep variables are predominantly the consequence, rather than the cause, of other underlying phenotypes and diseases. These insights could inform the design of future epidemiological and interventional studies in sleep medicine and research.

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