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Cited by 271Genetic Associations and EpidemiologyGenetics and Physical PerformanceGenetic and phenotypic traits in livestock

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

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Paternoster, Lavinia, Tilling, Kate, Davey Smith, George·2017·PLOS Genetics
DOI 10.1371/journal.pgen.1006944PMC PMC5628782

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Abstract

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, but this has yet to be realized 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.A valuable proposed outcome of the GWAS era is the identification of novel therapeutic targets [1-4].As of 3 April 2017, the GWAS Catalog contained 2,854 publications and 33,674 unique SNP-trait associations [5].The large majority of these studies investigate genetic variation related to the presence (or occurrence) of disease.Such variants, though they may be informative for the prevention of disease, have unclear utility in informing disease treatment.If variants implicate etiological mechanisms of import for disease onset but sometimes of little relevance to disease progression, then the use of case/control GWAS as evidence to inform disease treatment-related drug discovery could be misleading.As an obvious example, consider GWAS of lung cancer.The lead variants identified in such GWAS tag a locus related to the heaviness of cigarette smoking [6], supporting the overwhelming evidence that smoking causes lung cancer.However, the cessation of smoking is hardly an efficacious treatment strategy after the onset of disease, although not smoking is a highly effective means of very substantially reducing the risk of developing lung cancer in the first place.Examples of factors causing both disease incidence and disease progression exist-for example, low-density lipoprotein (LDL) cholesterol levels clearly influence the risk of initial coronary events, and lowering LDL cholesterol reduces the risk of subsequent events.However, it is not necessarily the case that risk factors will influence both disease onset and disease progression-for example, a recent GWAS of Crohn disease observed independent genetic variants for the risk of onset and progression [7] and reported a negative genetic correlation (estimated through linkage disequilibrium [LD]

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Crossref · 10.1371/journal.pgen.1006944

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Figures

Fig 1 DAG of Mendelian randomization method. Abbreviation: DAG, directed acyclic graph.
Fig 1 DAG of Mendelian randomization method. Abbreviation: DAG, directed acyclic graph.CC BY · PubMed Central
Fig 2 DAG demonstrating the issue of collider bias in studies with participants selected according to disease status. In this situation, collider bias can induce an association (dashed line) between any factors (A, C, and U) that affect disease incidence (or other study selection criteria). When 1 or more of these factors are also associated with disease progression (C, U), a path is opened up from A to disease progression through the induced association. If A is a genetic risk factor, it can appear that there is an association between genetic risk factor A and disease progression only because of the induced association with C or U. If C is measured and can be adjusted for, then the induced association is blocked, but unmeasured U cannot be adjusted for in the analysis. Only when the genetic risk factor for progression is not also a risk factor for incidence (i.e., B) will it not be affected by selection bias. The arrows in Figure 2 show causal paths between variables—e.g., that variable A causes disease incidence. A collider is a variable which has 2 paths entering it, e.g., disease incidence. A path is blocked by a collider—i.e., the path from A to disease progression is blocked by disease incidence. If a collider is conditioned on, then that path is unblocked—i.e., if disease incidence is conditioned upon, then the path from A to disease progression becomes unblocked (i.e., collider bias may occur). Abbreviation: DAG, directed acyclic graph.
Fig 2 DAG demonstrating the issue of collider bias in studies with participants selected according to disease status. In this situation, collider bias can induce an association (dashed line) between any factors (A, C, and U) that affect disease incidence (or other study selection criteria). When 1 or more of these factors are also associated with disease progression (C, U), a path is opened up from A to disease progression through the induced association. If A is a genetic risk factor, it can appear that there is an association between genetic risk factor A and disease progression only because of the induced association with C or U. If C is measured and can be adjusted for, then the induced association is blocked, but unmeasured U cannot be adjusted for in the analysis. Only when the genetic risk factor for progression is not also a risk factor for incidence (i.e., B) will it not be affected by selection bias. The arrows in Figure 2 show causal paths between variables—e.g., that variable A causes disease incidence. A collider is a variable which has 2 paths entering it, e.g., disease incidence. A path is blocked by a collider—i.e., the path from A to disease progression is blocked by disease incidence. If a collider is conditioned on, then that path is unblocked—i.e., if disease incidence is conditioned upon, then the path from A to disease progression becomes unblocked (i.e., collider bias may occur). Abbreviation: DAG, directed acyclic graph.CC BY · PubMed Central
Fig 3 DAG to demonstrate how the introduction of collider bias through the selection of cases (grey paths) can impact an MR analysis between an exposure and disease progression as an outcome. Associations are induced because SNP causes disease (via exposure), and thus conditioning on disease induces an association between all variables causing disease. In a model not adjusting for exposure (e.g., relating SNP to progression), there is an association between SNP and the confounders, which biases the SNP-progression association. Abbreviations: DAG, direct acyclic graph; MR, Mendelian randomization.
Fig 3 DAG to demonstrate how the introduction of collider bias through the selection of cases (grey paths) can impact an MR analysis between an exposure and disease progression as an outcome. Associations are induced because SNP causes disease (via exposure), and thus conditioning on disease induces an association between all variables causing disease. In a model not adjusting for exposure (e.g., relating SNP to progression), there is an association between SNP and the confounders, which biases the SNP-progression association. Abbreviations: DAG, direct acyclic graph; MR, Mendelian randomization.CC BY · PubMed Central

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Cites

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Research works printed but not linked: 2

  1. MR Munafo, Collider Scope: How selection bias can induce spurious associations, Int J Epidemiol
  2. F Balazard, 2017, Disease as collider: a new case-only method to discover environmental factors in complex diseases with genetic risk estimation, BioRxiv

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Metadata

Authors
Paternoster, L., Tilling, K., & Davey Smith, G.
Affiliations
University of Bristol (GB)
Corresponding author
  • Paternoster, Lavinia
  • Tilling, Kate
ORCID
Venue
PLOS Genetics
Publisher
Public Library of Science (PLoS)
Published
2017-10-05
Volume
13
Issue
10
Pages
e1006944
ISSN
  • 1553-7390 (ISSN-L)
  • 1553-7404
DOI
10.1371/journal.pgen.1006944
PMC
PMC5628782
OpenAlex
W2951286101
Cited by
271
References
38
Citations by year
2026: 10 · 2025: 16 · 2024: 37 · 2023: 37 · 2022: 38 · 2021: 47 · 2020: 36 · 2019: 23 · 2018: 21 · 2017: 6
FWCI
3.08
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Genetics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Explore Bristol Research (submittedVersion, cc-by)
Funders
Medical Research Council (MC_UU_12013, MCUU12013/1)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Physical Performance
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Keywords
drug target identification, disease progression, genetic epidemiology, Mendelian randomization, genome-wide association study, drug discovery, genetic correlation, Crohn's disease, LDL cholesterol, smoking and lung cancer
MeSH terms
Coronary Disease, Humans, Phenotype, Risk Factors, Disease Progression, Genetic Predisposition to Disease, Genetic Diseases, Inborn, Genome-Wide Association Study, Mendelian Randomization Analysis

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