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

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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Deep resequencing identifies candidate functional genes in leprosy GWAS loci

PLoS Neglected Tropical Diseases ◽

10.1371/journal.pntd.0010029 ◽

2021 ◽

Vol 15 (12) ◽

pp. e0010029

Author(s):

Vinicius M. Fava ◽

Monica Dallmann-Sauer ◽

Marianna Orlova ◽

Wilian Correa-Macedo ◽

Nguyen Van Thuc ◽

...

Keyword(s):

Genetic Epidemiology ◽

Genetic Factors ◽

Association Studies ◽

Genome Wide Association Studies ◽

Mycobacterial Disease ◽

Protein Coding ◽

The Past ◽

Genome Wide ◽

Host Genetic ◽

Coding Variants

Leprosy is the second most prevalent mycobacterial disease globally. Despite the existence of an effective therapy, leprosy incidence has consistently remained above 200,000 cases per year since 2010. Numerous host genetic factors have been identified for leprosy that contribute to the persistently high case numbers. In the past decade, genetic epidemiology approaches, including genome-wide association studies (GWAS), identified more than 30 loci contributing to leprosy susceptibility. However, GWAS loci commonly encompass multiple genes, which poses a challenge to define causal candidates for each locus. To address this problem, we hypothesized that genes contributing to leprosy susceptibility differ in their frequencies of rare protein-altering variants between cases and controls. Using deep resequencing we assessed protein-coding variants for 34 genes located in GWAS or linkage loci in 555 Vietnamese leprosy cases and 500 healthy controls. We observed 234 nonsynonymous mutations in the targeted genes. A significant depletion of protein-altering variants was detected for the IL18R1 and BCL10 genes in leprosy cases. The IL18R1 gene is clustered with IL18RAP and IL1RL1 in the leprosy GWAS locus on chromosome 2q12.1. Moreover, in a recent GWAS we identified an HLA-independent signal of association with leprosy on chromosome 6p21. Here, we report amino acid changes in the CDSN and PSORS1C2 genes depleted in leprosy cases, indicating them as candidate genes in the chromosome 6p21 locus. Our results show that deep resequencing can identify leprosy candidate susceptibility genes that had been missed by classic linkage and association approaches.

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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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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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Genetic Methodologies and Applications

10.1093/med/9780190681425.003.0001 ◽

2017 ◽

Author(s):

Shaun M. Purcell

Keyword(s):

Mental Illness ◽

Genetic Risk ◽

Genetic Basis ◽

Common Mental Disorders ◽

Association Studies ◽

Genome Wide Association Studies ◽

The Past ◽

Genome Wide ◽

Experimental Approaches ◽

Large Families

Mental illness is highly heritable, yet it has been difficult historically to identify the specific genes that comprise that risk. This difficulty resides in the fact that the genetic risk for all common mental disorders is polygenic, with perhaps hundreds of genetic variations, each of small effect, contributing to the overall risk. Despite these challenges, the field has made dramatic advances over the past decade in beginning to understand the genetic basis of mental illness. This chapter provides an overview of the experimental approaches used, beginning with epidemiology and population genetics to define the heritability of an illness, to classic studies of large families and linkage disequilibrium analysis, to genome-wide investigations including genome-wide association studies (GWAS), exome sequencing, and whole genome sequencing. Increasingly, these genetic advances are being understood within the biological context of disease pathophysiology.

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

Molecular Psychiatry ◽

10.1038/s41380-019-0535-9 ◽

2019 ◽

Cited By ~ 4

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.

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

International Journal of Epidemiology ◽

10.1093/ije/dyw088 ◽

2016 ◽

Vol 45 (5) ◽

pp. 1600-1616 ◽

Cited By ~ 96

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

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

International Journal of Epidemiology ◽

10.1093/ije/dyy195 ◽

2018 ◽

Vol 48 (3) ◽

pp. 684-690 ◽

Cited By ~ 34

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/].

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