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Power GM, Sanderson E, Pagoni P, Fraser A, Morris T, Prince C, Frayling TM, Heron J, Richardson TG, Richmond R, Tyrrell J, Warrington N, Davey Smith G, Howe LD, Tilling KM. Methodological approaches, challenges, and opportunities in the application of Mendelian randomisation to lifecourse epidemiology: A systematic literature review. Eur J Epidemiol 2023:10.1007/s10654-023-01032-1. [PMID: 37938447 DOI: 10.1007/s10654-023-01032-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/21/2023] [Indexed: 11/09/2023]
Abstract
Diseases diagnosed in adulthood may have antecedents throughout (including prenatal) life. Gaining a better understanding of how exposures at different stages in the lifecourse influence health outcomes is key to elucidating the potential benefits of disease prevention strategies. Mendelian randomisation (MR) is increasingly used to estimate causal effects of exposures across the lifecourse on later life outcomes. This systematic literature review explores MR methods used to perform lifecourse investigations and reviews previous work that has utilised MR to elucidate the effects of factors acting at different stages of the lifecourse. We conducted searches in PubMed, Embase, Medline and MedRXiv databases. Thirteen methodological studies were identified. Four studies focused on the impact of time-varying exposures in the interpretation of "standard" MR techniques, five presented methods for repeat measures of the same exposure, and four described methodological approaches to handling multigenerational exposures. A further 127 studies presented the results of an applied research question. Over half of these estimated effects in a single generation and were largely confined to the exploration of questions regarding body composition. The remaining mostly estimated maternal effects. There is a growing body of research focused on the development and application of MR methods to address lifecourse research questions. The underlying assumptions require careful consideration and the interpretation of results rely on select conditions. Whilst we do not advocate for a particular strategy, we encourage practitioners to make informed decisions on how to approach a research question in this field with a solid understanding of the limitations present and how these may be affected by the research question, modelling approach, instrument selection, and data availability.
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Affiliation(s)
- Grace M Power
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Panagiota Pagoni
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Abigail Fraser
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tim Morris
- Centre for Longitudinal Studies, Social Research Institute, University College London, London, UK
| | - Claire Prince
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Timothy M Frayling
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Jon Heron
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Rebecca Richmond
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Jessica Tyrrell
- Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Nicole Warrington
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Frazer Institute, University of Queensland, Woolloongabba, Queensland, Australia
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- NIHR Bristol Biomedical Research Centre Bristol, University Hospitals Bristol and Weston NHS Foundation Trust, University of Bristol, Bristol, UK
| | - Laura D Howe
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Kate M Tilling
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
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Tylee DS, Lee YK, Wendt FR, Pathak GA, Levey DF, De Angelis F, Gelernter J, Polimanti R. An Atlas of Genetic Correlations and Genetically Informed Associations Linking Psychiatric and Immune-Related Phenotypes. JAMA Psychiatry 2022; 79:667-676. [PMID: 35507366 PMCID: PMC9069342 DOI: 10.1001/jamapsychiatry.2022.0914] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Importance Certain psychiatric and immune-related disorders are reciprocal risk factors. However, the nature of these associations is unclear. Objective To characterize the pleiotropy between psychiatric and immune-related traits, as well as risk factors of hypothesized relevance. Design, Setting, and Participants This genetic association study was conducted from July 10, 2020, to January 15, 2022. Analyses used genome-wide association (GWA) statistics related to 14 psychiatric traits; 13 immune-related phenotypes, ie, allergic, autoimmune, and inflammatory disorders; and 15 risk factors related to health-related behaviors, social determinants of health, and stress response. Genetically correlated psychiatric-immune pairs were assessed using 2-sample mendelian randomization (MR) with sensitivity analyses and multivariable adjustment for genetic associations of third variables. False discovery rate correction (Q value < .05) was applied for each analysis. Exposures Genetic associations. Main Outcomes and Measures Genetic correlations and MR association estimates with SEs and P values. A data-driven approach was used that did not test a priori planned hypotheses. Results A total of 44 genetically correlated psychiatric-immune pairs were identified, including 31 positive correlations (most consistently involving asthma, Crohn disease, hypothyroidism, and ulcerative colitis) and 13 negative correlations (most consistently involving allergic rhinitis and type 1 diabetes). Correlations with third variables were especially strong for psychiatric phenotypes. MR identified 7 associations of psychiatric phenotypes on immune-related phenotypes that were robust to multivariable adjustment, including the positive association of (1) the psychiatric cross-disorder phenotype with asthma (odds ratio [OR], 1.04; 95% CI, 1.02-1.06), Crohn disease (OR, 1.09; 95% CI, 1.05-1.14), and ulcerative colitis (OR, 1.09; 95% CI, 1.05-1.14); (2) major depression with asthma (OR, 1.25; 95% CI, 1.13-1.37); (3) schizophrenia with Crohn disease (OR, 1.12; 95% CI, 1.05-1.18) and ulcerative colitis (OR, 1.14; 95% CI, 1.07-1.21); and a negative association of risk tolerance with allergic rhinitis (OR, 0.77; 95% CI, 0.67-0.92). Conclusions and Relevance Results of this genetic association study suggest that genetic liability for psychiatric disorders was associated with liability for several immune disorders, suggesting that vertical pleiotropy related to behavioral traits (or correlated third variables) contributes to clinical associations observed in population-scale data.
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Affiliation(s)
- Daniel S. Tylee
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut
| | - Yu Kyung Lee
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Frank R. Wendt
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut
| | - Gita A. Pathak
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut
| | - Daniel F. Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut
| | - Flavio De Angelis
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut
- Departments of Genetics and of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
- Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut
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