Using Mendelian randomisation to assess causality in observational studies

被引:90
|
作者
Pagoni, Panagiota [1 ,2 ]
Dimou, Niki L. [3 ]
Murphy, Neil [3 ]
Stergiakouli, Evie [1 ,2 ,4 ]
机构
[1] Univ Bristol, Integrat Epidemiol Unit, MRC, Bristol, Avon, England
[2] Univ Bristol, Populat Hlth Sci, Bristol, Avon, England
[3] Int Agcy Res Canc, F-69372 Lyon, France
[4] Univ Bristol, Sch Oral & Dent Sci, Bristol, Avon, England
关键词
OBESITY; INSTRUMENTS; DEPRESSION; INSIGHTS; BIAS;
D O I
10.1136/ebmental-2019-300085
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Objective Mendelian randomisation (MR) is a technique that aims to assess causal effects of exposures on disease outcomes. The paper aims to present the main assumptions that underlie MR, the statistical methods used to estimate causal effects and how to account for potential violations of the key assumptions. Methods We discuss the key assumptions that should be satisfied in an MR setting. We list the statistical methodologies used in two-sample MR when summary data are available to estimate causal effects (ie, Wald ratio estimator, inverse-variance weighted and maximum likelihood method) and identify/adjust for potential violations of MR assumptions (ie, MR-Egger regression and weighted Median approach). We also present statistical methods and graphical tools used to evaluate the presence of heterogeneity. Results We use as an illustrative example of a published two-sample MR study, investigating the causal association of body mass index with three psychiatric disorders (ie, bipolar disorder, schizophrenia and major depressive disorder). We highlight the importance of assessing the results of all available methods rather than each method alone. We also demonstrate the impact of heterogeneity in the estimation of the causal effects. Conclusions MR is a useful tool to assess causality of risk factors in medical research. Assessment of the key assumptions underlying MR is crucial for a valid interpretation of the results.
引用
收藏
页码:67 / 71
页数:5
相关论文
共 50 条
  • [1] Using molecular genetic information to infer causality in observational data: Mendelian randomisation
    Taylor, Amy E.
    Ware, Jennifer J.
    Gage, Suzanne H.
    Smith, George Davey
    Munafo, Marcus R.
    CURRENT OPINION IN BEHAVIORAL SCIENCES, 2015, 2 : 39 - 45
  • [2] Mendelian Randomization as an Approach to Assess Causality Using Observational Data
    Sekula, Peggy
    Del Greco, Fabiola M.
    Pattaro, Cristian
    Koettgen, Anna
    JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY, 2016, 27 (11): : 3253 - 3265
  • [3] USING MENDELIAN RANDOMISATION TO INFER CAUSALITY IN DEPRESSION AND ANXIETY RESEARCH
    Gage, Suzanne H.
    Smith, George Davey
    Zammit, Stanley
    Hickman, Matthew
    Munafo, Marcus R.
    DEPRESSION AND ANXIETY, 2013, 30 (12) : 1185 - 1193
  • [4] Cigarette smoking and personality: interrogating causality using Mendelian randomisation
    Sallis, Hannah M.
    Smitho, George Davey
    Munafo, Marcus R.
    PSYCHOLOGICAL MEDICINE, 2019, 49 (13) : 2197 - 2205
  • [5] How to interpret studies using Mendelian randomisation
    Kim, Min Seo
    Song, Minku
    Shin, Jae Il
    Won, Hong-Hee
    BMJ EVIDENCE-BASED MEDICINE, 2023, 28 (04) : 251 - 254
  • [6] Hyperglycaemia, diabetes and risk of fragility fractures: observational and Mendelian randomisation studies
    Emanuelsson, Frida
    Afzal, Shoaib
    Jorgensen, Niklas R.
    Nordestgaard, Borge G.
    Benn, Marianne
    DIABETOLOGIA, 2024, 67 (02) : 301 - 311
  • [7] Hyperglycaemia, diabetes and risk of fragility fractures: observational and Mendelian randomisation studies
    Frida Emanuelsson
    Shoaib Afzal
    Niklas R. Jørgensen
    Børge G. Nordestgaard
    Marianne Benn
    Diabetologia, 2024, 67 : 301 - 311
  • [8] Education and myopia: assessing the direction of causality by mendelian randomisation
    Mountjoy, Edward
    Davies, Neil M.
    Plotnikov, Denis
    Smith, George Davey
    Rodriguez, Santiago
    Williams, Cathy E.
    Guggenheim, Jeremy A.
    Atan, Denize
    BMJ-BRITISH MEDICAL JOURNAL, 2018, 361
  • [9] Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration
    Skrivankova, Veronika W.
    Richmond, Rebecca C.
    Woolf, Benjamin A. R.
    Davies, Neil M.
    Swanson, Sonja A.
    VanderWeele, Tyler J.
    Timpson, Nicholas J.
    Higgins, Julian P. T.
    Dimou, Niki
    Langenberg, Claudia
    Loder, Elizabeth W.
    Golub, Robert M.
    Egger, Matthias
    Smith, George Davey
    Richards, J. Brent
    BMJ-BRITISH MEDICAL JOURNAL, 2021, 375
  • [10] Mendelian randomisation and causal inference in observational epidemiology
    Sheehan, Nuala A.
    Didelez, Vanessa
    Burton, Paul R.
    Tobin, Martin D.
    PLOS MEDICINE, 2008, 5 (08) : 1205 - 1210