scholarly journals Mendelian randomization analysis identified genes pleiotropically associated with the risk and prognosis of COVID-19

2020 ◽  
Author(s):  
Di Liu ◽  
Jingyun Yang ◽  
Bowen Feng ◽  
Wenjin Lu ◽  
Chuntao Zhao ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Global Pandemic ◽  
Genome Wide ◽  
Biological Interpretation ◽  
Disease Behavior ◽  
Randomization Analysis ◽  
Summary Data

Objectives: COVID-19 has caused a large global pandemic. Patients with COVID-19 exhibited considerable variation in disease behavior. Pervious genome-wide association studies have identified potential genetic variants involved in the risk and prognosis of COVID-19, but the underlying biological interpretation remains largely unclear. Methods: We applied the summary data-based Mendelian randomization (SMR) method to identify genes that were pleiotropically associated with the risk and various outcomes of COVID-19, including severe respiratory confirmed COVID-19 and hospitalized COVID-19. Results: In blood, we identified 2 probes, ILMN_1765146 and ILMN_1791057 tagging IFNAR2, that showed pleiotropic association with hospitalized COVID-19 (Beta; [SE]=0.42 [0.09], P=4.75E-06 and Beta; [SE]=-0.48 [0.11], P=6.76E-06, respectively). Although no other probes were significant after correction for multiple testing in both blood and lung, multiple genes as tagged by the top 5 probes were involved in inflammation or antiviral immunity, and several other tagged genes, such as PON2 and HPS5, were involved in blood coagulation. Conclusions: We identified IFNAR2 and other potential genes that could be involved in the susceptibility or prognosis of COVID-19. These findings provide important leads to a better understanding of the mechanisms of cytokine storm and venous thromboembolism in COVID-19 and potential therapeutic targets for the effective treatment of COVID-19.

scholarly journals Software application profile: mrrobust—a tool for performing two-sample summary Mendelian randomization analyses

2018 ◽  
Vol 48 (3) ◽  
pp. 684-690 ◽  
Author(s):  
Wes Spiller ◽  
Neil M Davies ◽  
Tom M Palmer
Keyword(s):  
Mendelian Randomization ◽  
Association Studies ◽  
Estimation Methods ◽  
Genome Wide Association Studies ◽  
Software Application ◽  
Genome Wide ◽  
Inverse Variance ◽  
Randomization Analysis ◽  
Summary Data ◽  
Weighted Estimation

Abstract Motivation In recent years, Mendelian randomization analysis using summary data from genome-wide association studies has become a popular approach for investigating causal relationships in epidemiology. The mrrobust Stata package implements several of the recently developed methods. Implementation mrrobust is freely available as a Stata package. General features The package includes inverse variance weighted estimation, as well as a range of median, modal and MR-Egger estimation methods. Using mrrobust, plots can be constructed visualizing each estimate either individually or simultaneously. The package also provides statistics such as IGX2, which are useful in assessing attenuation bias in causal estimates. Availability The software is freely available from GitHub [https://raw.github.com/remlapmot/mrrobust/master/].

scholarly journals Software Application Profile: mrrobust - a tool for performing two-sample summary Mendelian randomization analyses

2017 ◽  
Author(s):  
Wesley Spiller ◽  
Neil M. Davies ◽  
Tom M. Palmer
Keyword(s):  
Mendelian Randomization ◽  
Association Studies ◽  
Estimation Methods ◽  
Genome Wide Association Studies ◽  
Software Application ◽  
Genome Wide ◽  
Inverse Variance ◽  
Randomization Analysis ◽  
Summary Data ◽  
Weighted Estimation

AbstractMotivationIn recent years Mendelian randomization analysis using summary data from genome-wide association studies has become a popular approach for investigating causal relationships in epidemiology. The mrrobust Stata package implements several of the recently developed methods.Implementationmrrobust is freely available as a Stata package.General FeaturesThe package includes inverse variance weighted estimation, as well as a range of median and MR-Egger estimation methods. Using mrrobust, plots can be constructed visualising each estimate either individually or simultaneously. The package also provides statistics such as which are useful in assessing attenuation bias in causal estimates.AvailabilityThe software is freely available from GitHub [https://raw.github.com/remlapmot/mrrobust/master/].

scholarly journals Investigating genetically mimicked effects of statins via HMGCR inhibition on immune-related diseases in men and women using Mendelian randomization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Guoyi Yang ◽  
C. Mary Schooling
Keyword(s):  
Lupus Erythematosus ◽  
Multiple Testing ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Allergic Diseases ◽  
Genome Wide Association Studies ◽  
East Asians ◽  
Genome Wide

AbstractStatins have been suggested as a potential treatment for immune-related diseases. Conversely, statins might trigger auto-immune conditions. To clarify the role of statins in allergic diseases and auto-immune diseases, we conducted a Mendelian randomization (MR) study. Using established genetic instruments to mimic statins via 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) inhibition, we assessed the effects of statins on asthma, eczema, allergic rhinitis, rheumatoid arthritis (RA), psoriasis, type 1 diabetes, systemic lupus erythematosus (SLE), multiple sclerosis (MS), Crohn’s disease and ulcerative colitis in the largest available genome wide association studies (GWAS). Genetically mimicked effects of statins via HMGCR inhibition were not associated with any immune-related diseases in either study after correcting for multiple testing; however, they were positively associated with the risk of asthma in East Asians (odds ratio (OR) 2.05 per standard deviation (SD) decrease in low-density lipoprotein cholesterol (LDL-C), 95% confidence interval (CI) 1.20 to 3.52, p value 0.009). These associations did not differ by sex and were robust to sensitivity analysis. These findings suggested that genetically mimicked effects of statins via HMGCR inhibition have little effect on allergic diseases or auto-immune diseases. However, we cannot exclude the possibility that genetically mimicked effects of statins via HMGCR inhibition might increase the risk of asthma in East Asians.

scholarly journals Mendelian Randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

2018 ◽  
Author(s):  
Eleonora Porcu ◽  
Sina Rüeger ◽  
Kaido Lepik ◽  
Federico A. Santoni ◽  
Alexandre Reymond ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Regulation Of Gene Expression ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Biological Interpretation ◽  
Independent Association

AbstractGenome-wide association studies (GWAS) identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs (eQTLs), indicating their potential involvement in the regulation of gene expression.Here, we propose an advanced transcriptome-wide summary statistics-based Mendelian Randomization approach (called TWMR) that uses multiple SNPs jointly as instruments and multiple gene expression traits as exposures, simultaneously.When applied to 43 human phenotypes it uncovered 2,369 genes whose blood expression is putatively associated with at least one phenotype resulting in 3,913 gene-trait associations; of note, 36% of them had no genome-wide significant SNP nearby in previous GWAS analysis. Using independent association summary statistics (UKBiobank), we confirmed that the majority of these loci were missed by conventional GWAS due to power issues. Noteworthy among these novel links is educational attainment-associated BSCL2, known to carry mutations leading to a mendelian form of encephalopathy. We similarly unraveled novel pleiotropic causal effects suggestive of mechanistic connections, e.g. the shared genetic effects of GSDMB in rheumatoid arthritis, ulcerative colitis and Crohn’s disease.Our advanced Mendelian Randomization unlocks hidden value from published GWAS through higher power in detecting associations. It better accounts for pleiotropy and unravels new biological mechanisms underlying complex and clinical traits.

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

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.

scholarly journals Genomics-driven drug discovery based on disease-susceptibility genes

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.

Stroke Genetics: Turning Discoveries into Clinical Applications

Stroke ◽  
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.

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

2017 ◽  
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.

scholarly journals Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

2018 ◽  
Author(s):  
David M. Howard ◽  
Mark J. Adams ◽  
Toni-Kim Clarke ◽  
Jonathan D. Hafferty ◽  
Jude Gibson ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Drug Repositioning ◽  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Synaptic Structure ◽  
Genome Wide

AbstractMajor depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

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

2019 ◽  
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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