The MRC IEU OpenGWAS data infrastructure

Elsworth, Ben; Lyon, M.; Alexander, Tessa; Liu, Yi; Matthews, Peter; Hallett, Jon; Bates, Paul; Palmer, Tom; Haberland, Valeriia; Smith, George Davey; Zheng, Jie; Haycock, Philip; Gaunt, Tom R.; Hemani, Gibran

doi:10.1101/2020.08.10.244293

Cited by 673 publications

(637 citation statements)

References 39 publications

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“…We also conducted split-sample GWAS and Mendelian randomization analysis using UK Biobank data, in which we randomly split UK Biobank into halves, and for each half conducted a GWAS for each health condition and risk factor using the MRC IEU UK Biobank GWAS pipeline. 35 The results of the two GWASs were used to create PRSs for the other half of UK Biobank avoiding sample overlap, 36 and we repeated the Mendelian randomization analysis with the two PRSs separately, then combined the two results with fixed-effect meta-analysis to give a single estimate. The split-sample analysis: (i) allowed us to analyse lifetime smoking, as this has only been generated in UK Biobank, and thus no previous GWAS could have been used to inform the PRS; (ii) allowed us to potentially increase the size and power of the GWASs, possibly improving the predictive ability of the PRSs; and (iii) guaranteed homogeneity of the GWASs and analysis populations, which removes the potential bias from using data from an external GWAS to inform the creation of the PRSs, for example, through differences in populations giving different effects of SNPs.…”

Section: Methodsmentioning

confidence: 99%

The causal effects of health conditions and risk factors on social and socioeconomic outcomes: Mendelian randomization in UK Biobank

Harrison

Davies

Dickson

et al. 2020

International Journal of Epidemiology

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Background We aimed to estimate the causal effect of health conditions and risk factors on social and socioeconomic outcomes in UK Biobank. Evidence on socioeconomic impacts is important to understand because it can help governments, policy makers and decision makers allocate resources efficiently and effectively. Methods We used Mendelian randomization to estimate the causal effects of eight health conditions (asthma, breast cancer, coronary heart disease, depression, eczema, migraine, osteoarthritis, type 2 diabetes) and five health risk factors [alcohol intake, body mass index (BMI), cholesterol, systolic blood pressure, smoking] on 19 social and socioeconomic outcomes in 336 997 men and women of White British ancestry in UK Biobank, aged between 39 and 72 years. Outcomes included annual household income, employment, deprivation [measured by the Townsend deprivation index (TDI)], degree-level education, happiness, loneliness and 13 other social and socioeconomic outcomes. Results Results suggested that BMI, smoking and alcohol intake affect many socioeconomic outcomes. For example, smoking was estimated to reduce household income [mean difference = -£22 838, 95% confidence interval (CI): -£31 354 to -£14 321] and the chance of owning accommodation [absolute percentage change (APC) = -20.8%, 95% CI: -28.2% to -13.4%], of being satisfied with health (APC = -35.4%, 95% CI: -51.2% to -19.5%) and of obtaining a university degree (APC = -65.9%, 95% CI: -81.4% to -50.4%), while also increasing deprivation (mean difference in TDI = 1.73, 95% CI: 1.02 to 2.44, approximately 216% of a decile of TDI). There was evidence that asthma decreased household income, the chance of obtaining a university degree and the chance of cohabiting, and migraine reduced the chance of having a weekly leisure or social activity, especially in men. For other associations, estimates were null. Conclusions Higher BMI, alcohol intake and smoking were all estimated to adversely affect multiple social and socioeconomic outcomes. Effects were not detected between health conditions and socioeconomic outcomes using Mendelian randomization, with the exceptions of depression, asthma and migraines. This may reflect true null associations, selection bias given the relative health and age of participants in UK Biobank, and/or lack of power to detect effects.

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Section: Methodsmentioning

confidence: 99%

The causal effects of health conditions and risk factors on social and socioeconomic outcomes: Mendelian randomization in UK Biobank

Harrison

Davies

Dickson

et al. 2020

International Journal of Epidemiology

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“…Participants from the UK Biobank were randomly allocated to one of two split halves of the genetic data. We then generated lifetime smoking scores in sample one of these two samples, and ran a GWAS with the UK Biobank pipeline, 38 following the exact method as described elsewhere. 24…”

Section: Methodsmentioning

confidence: 99%

Examining the effect of smoking on suicidal ideation and attempts: triangulation of epidemiological approaches

et al. 2020

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BackgroundPrevious literature has demonstrated a strong association between cigarette smoking, suicidal ideation and suicide attempts. This association has not previously been examined in a causal inference framework and could have important implications for suicide prevention strategies. AimsWe aimed to examine the evidence for an association between smoking behaviours (initiation, smoking status, heaviness, lifetime smoking) and suicidal thoughts or attempts by triangulating across observational and Mendelian randomisation analyses. MethodFirst, in the UK Biobank, we calculated observed associations between smoking behaviours and suicidal thoughts or attempts. Second, we used Mendelian randomisation to explore the relationship between smoking and suicide attempts and ideation, using genetic variants as instruments to reduce bias from residual confounding and reverse causation. ResultsOur observational analysis showed a relationship between smoking behaviour, suicidal ideation and attempts, particularly between smoking initiation and suicide attempts (odds ratio, 2.07; 95% CI 1.91-2.26; P < 0.001). The Mendelian randomisation analysis and single-nucleotide polymorphism analysis, however, did not support this (odds ratio for lifetime smoking on suicidal ideation, 0.050; 95% CI −0.027 to 0.127; odds ratio on suicide attempts, 0.053; 95% CI, −0.003 to 0.110). Despite past literature showing a positive dose-response relationship, our results showed no clear evidence for a causal effect of smoking on suicidal ideation or attempts. ConclusionsThis was the first Mendelian randomisation study to explore the effect of smoking on suicidal ideation and attempts. Our results suggest that, despite observed associations, there is no clear evidence for a causal effect.

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“…[7] All GWASs were conducted using the MRC Integrative Epidemiology Unit Pipeline with a BOLT-LMM model to account for population stratification. [15] All six GWASs were adjusted for age, sex and 40 principal components.…”

Section: Methodsmentioning

confidence: 99%

The consequences of adjustment, correction and selection in genome-wide association studies used for two-sample Mendelian randomization

Walker

Harrison

Carter

et al. 2020

Preprint

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Introduction: Genome-wide association studies (GWASs) often adjust for covariates, correct for medication use, or select on medication users. If these summary statistics are used in two-sample Mendelian randomization analyses, estimates may be biased. We used simulations to investigate how GWAS adjustment, correction and selection affects these estimates and performed an analysis in UK Biobank to provide an empirical example. Methods: We simulated six GWASs: no adjustment for a covariate, correction for medication use, or selection on medication users; adjustment only; selection only; correction only; both adjustment and selection; and both adjustment and correction. We then ran two-sample Mendelian randomization analyses using these GWASs to evaluate bias. We also performed equivalent GWASs using empirical data from 318,147 participants in UK Biobank with systolic blood pressure as the exposure and body mass index as the covariate and ran two-sample Mendelian randomization with coronary heart disease as the outcome. Results: The simulation showed that estimates from GWASs with selection can produce biased two-sample Mendelian randomization estimates. Yet, we observed relatively little difference between empirical estimates of the effect of systolic blood pressure on coronary artery disease across the six scenarios. Conclusions: Given the potential for bias from using GWASs with selection on Mendelian randomization estimates demonstrated in our simulation, and the reduced sample size of these GWAS, this approach should be deprioritized. However, based on our empirical results, using adjusted, corrected or selected GWASs is unlikely to make a large difference to two-sample Mendelian randomization estimates in practice.

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The MRC IEU OpenGWAS data infrastructure

Cited by 673 publications

References 39 publications

The causal effects of health conditions and risk factors on social and socioeconomic outcomes: Mendelian randomization in UK Biobank

The causal effects of health conditions and risk factors on social and socioeconomic outcomes: Mendelian randomization in UK Biobank

Examining the effect of smoking on suicidal ideation and attempts: triangulation of epidemiological approaches

The consequences of adjustment, correction and selection in genome-wide association studies used for two-sample Mendelian randomization

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