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 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/.

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

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.

scholarly journals Educational attainment impacts drinking behaviors and risk for alcohol dependence: results from a two-sample Mendelian randomization study with ~780,000 participants

2019 ◽  
Author(s):  
Daniel B. Rosoff ◽  
Toni-Kim Clarke ◽  
Mark J. Adams ◽  
Andrew M. McIntosh ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Educational Attainment ◽  
Alcohol Dependence ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Significant Snps ◽  
Genome Wide Association Studies ◽  
Reverse Causality ◽  
Genome Wide ◽  
Genetic Instruments

Abstract Observational studies suggest that lower educational attainment (EA) may be associated with risky alcohol use behaviors; however, these findings may be biased by confounding and reverse causality. We performed two-sample Mendelian randomization (MR) using summary statistics from recent genome-wide association studies (GWAS) with >780,000 participants to assess the causal effects of EA on alcohol use behaviors and alcohol dependence (AD). Fifty-three independent genome-wide significant SNPs previously associated with EA were tested for association with alcohol use behaviors. We show that while genetic instruments associated with increased EA are not associated with total amount of weekly drinks, they are associated with reduced frequency of binge drinking ≥6 drinks (ßIVW = −0.198, 95% CI, −0.297 to –0.099, PIVW = 9.14 × 10−5), reduced total drinks consumed per drinking day (ßIVW = −0.207, 95% CI, −0.293 to –0.120, PIVW = 2.87 × 10−6), as well as lower weekly distilled spirits intake (ßIVW = −0.148, 95% CI, −0.188 to –0.107, PIVW = 6.24 × 10−13). Conversely, genetic instruments for increased EA were associated with increased alcohol intake frequency (ßIVW = 0.331, 95% CI, 0.267–0.396, PIVW = 4.62 × 10−24), and increased weekly white wine (ßIVW = 0.199, 95% CI, 0.159–0.238, PIVW = 7.96 × 10−23) and red wine intake (ßIVW = 0.204, 95% CI, 0.161–0.248, PIVW = 6.67 × 10−20). Genetic instruments associated with increased EA reduced AD risk: an additional 3.61 years schooling reduced the risk by ~50% (ORIVW = 0.508, 95% CI, 0.315–0.819, PIVW = 5.52 × 10−3). Consistency of results across complementary MR methods accommodating different assumptions about genetic pleiotropy strengthened causal inference. Our findings suggest EA may have important effects on alcohol consumption patterns and may provide potential mechanisms explaining reported associations between EA and adverse health outcomes.

scholarly journals Selecting instruments for Mendelian randomization in the wake of genome-wide association studies

2016 ◽  
Vol 45 (5) ◽  
pp. 1600-1616 ◽  
Author(s):  
Daniel I Swerdlow ◽  
Karoline B Kuchenbaecker ◽  
Sonia Shah ◽  
Reecha Sofat ◽  
Michael V Holmes ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

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 Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments

2020 ◽  
Author(s):  
Jingshu Wang ◽  
Qingyuan Zhao ◽  
Jack Bowden ◽  
Gilbran Hemani ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Risk Factors ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Genetic Instruments

Over a decade of genome-wide association studies have led to the finding that significant genetic associations tend to spread across the genome for complex traits. The extreme polygenicity where "all genes affect every complex trait" complicates Mendelian Randomization studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing Mendelian Randomization methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE (Genome-wide mR Analysis under Pervasive PLEiotropy) to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using summary statistics from genome-wide association studies, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, adjust for confounding risk factors, and determine the causal direction. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and the potential pleiotropic pathways.

scholarly journals Causal Association between Serum Thyroid-Stimulating Hormone and Obesity: A Bidirectional Mendelian Randomization Study

2021 ◽  
Author(s):  
Xichang Wang ◽  
Xiaotong Gao ◽  
Yutong Han ◽  
Fan Zhang ◽  
Zheyu Lin ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Association Studies ◽  
Epidemiological Studies ◽  
Thyroid Stimulating Hormone ◽  
Genome Wide Association Studies ◽  
Causal Association ◽  
Free Triiodothyronine ◽  
The Impact ◽  
Serum Tsh ◽  
Stimulating Hormone

Abstract Context The association between serum thyroid-stimulating hormone (TSH) and obesity traits has been investigated previously in several epidemiological studies. However, the underlying causal association has not been established. Objective To determine and analyze the causal association between serum TSH level and obesity-related traits (BMI and obesity). Design, Setting, Participants The latest genome-wide association studies (GWASs) on TSH, BMI and obesity were searched to obtain full statistics. Bidirectional two-sample Mendelian randomization (MR) was performed to explore the causal relationship between serum TSH and BMI and obesity. The inverse variance-weighted (IVW) and MR-Egger methods were used to combine the estimation for each SNP. Based on the preliminary MR results, free thyroxine (fT4) and free triiodothyronine (fT3) levels were also set as outcomes to further analyze the impact of BMI on them. Main Outcome Measures BMI and obesity were treated as the outcomes to evaluate the effect of serum TSH on them, and TSH was set as the outcome to estimate the effect of BMI and obesity on it. Results Both IVW and MR-Egger results indicated that genetically driven serum TSH did not causally lead to changes in BMI or obesity. Moreover, the IVW method showed that the TSH level could be significantly elevated by genetically predicted high BMI (β=0.038, se=0.013, p=0.004). In further MR analysis, the IVW method indicated that BMI could causally increase the fT3 (β=10.123, se=2.523, p&lt;0.001) while not significantly affecting the fT4 level. Conclusion Together with fT3, TSH can be significantly elevated by an increase in genetically driven BMI.

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