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Introduction Genetic associations for variants identified through genome-wide association studies (GWASs) tend to be overestimated in the original discovery data set as, if the association was underestimated, the variant may not have been detected. This bias, known as winner's curse, can affect Mendelian randomization estimates, but its severity and potential impact are unclear. Methods We performed an empirical investigation to assess the potential bias from winner's curse in practice. We considered Mendelian randomization estimates for the effect of body mass index (BMI) on coronary artery disease risk. We randomly divided a UK Biobank data set 100 times into three equally sized subsets. The first subset was treated as the 'discovery GWAS'. We compared genetic associations estimated in the discovery GWAS to those estimated in the other subsets for each of the 100 iterations. Results For variants associated with BMI at P  <   5 × 10–8 in at least one iteration, genetic associations with BMI were up to 5-fold greater in iterations in which the variant was associated with BMI at P  <   5 × 10–8 compared with its mean association across all iterations. If the minimum P -value for association with BMI was P  =   10–13 or lower, then this inflation was <25%. Mendelian randomization estimates were affected by winner's curse bias. However, bias did not materially affect results; all analyses indicated a deleterious effect of BMI on coronary artery disease risk. Conclusions Winner's curse can bias Mendelian randomization estimates, although its practical impact may not be substantial. If avoiding sample overlap is infeasible, analysts should consider performing a sensitivity analysis based on variants strongly associated with the exposure. [ABSTRACT FROM AUTHOR]

Background An increasing proportion of people have a body mass index (BMI) classified as overweight or obese and published studies disagree whether this will be beneficial or detrimental to health. We applied and evaluated two intergenerational instrumental variable methods to estimate the average causal effect of BMI on mortality in a cohort with many deaths: the parents of UK Biobank participants. Methods In Cox regression models, parental BMI was instrumented by offspring BMI using an 'offspring as instrument' (OAI) estimation and by offspring BMI-related genetic variants in a 'proxy-genotype Mendelian randomization' (PGMR) estimation. Results Complete-case analyses were performed in parents of 233 361 UK Biobank participants with full phenotypic, genotypic and covariate data. The PGMR method suggested that higher BMI increased mortality with hazard ratios per kg/m2 of 1.02 (95% CI: 1.01, 1.04) for mothers and 1.04 (95% CI: 1.02, 1.05) for fathers. The OAI method gave considerably higher estimates, which varied according to the parent–offspring pairing between 1.08 (95% CI: 1.06, 1.10; mother–son) and 1.23 (95% CI: 1.16, 1.29; father–daughter). Conclusion Both methods supported a causal role of higher BMI increasing mortality, although caution is required regarding the immediate causal interpretation of these exact values. Evidence of instrument invalidity from measured covariates was limited for the OAI method and minimal for the PGMR method. The methods are complementary for interrogating the average putative causal effects because the biases are expected to differ between them. [ABSTRACT FROM AUTHOR]

Background: Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables.Methods: We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR.Results: In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index.Conclusions: Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution. [ABSTRACT FROM AUTHOR]

Background To improve melanoma early detection, tools to predict personal risk based on genetic information (polygenic risk scores, PRS) have been developed, but require external validation. Methods We analysed invasive melanoma incidence in UK Biobank (UKB; n = 395,647; 1,651 cases) and the Melbourne Collaborative Cohort Study (MCCS, Australia; n = 4,765; 303 cases). Three PRS were evaluated: 68 genetic variants (SNPs) at 54 loci from a 2020 meta-analysis (PRS68); 50 SNPs significant in the 2020 meta-analysis excluding UKB (PRS50); 45 SNPs at 21 loci known pre-2020 (PRS45). 10-year melanoma risks were calculated from population-level cancer registry data by age group and sex, with and without PRS adjustment. Results All PRS were strongly associated with melanoma incidence, including after adjustment for age, sex, ethnicity, and ease of tanning. Predicted absolute melanoma risks based on age and sex alone underestimated melanoma incidence in UKB (ratio expected/observed cases E/O=0.65, 95% confidence interval 0.62-0.68) and MCCS (E/O=0.65, 0.57-0.73). For UKB, this was reduced by PRS-adjustment, e.g. PRS50-adjusted risks E/O=0.91 (0.87-0.95). Discriminative ability for PRS68- and PRS50-adjusted absolute risks was higher than for risks based on age and sex alone (deltaAUC 0.07-0.1, p < 0.0001), and higher than for PRS45-adjusted risks (deltaAUC 0.02-0.04, p < 0.001). Conclusions A PRS derived from a larger, more diverse meta-analysis improves melanoma risk prediction compared to an earlier PRS. Re-calibration of absolute risks may be necessary for application to specific populations. Key messages A genetic score can improve prediction of melanoma risk and might help tailor melanoma prevention and early detection strategies to different risk levels. [ABSTRACT FROM AUTHOR]

Background A personal history of keratinocyte carcinoma (KC) has been reported as a risk factor for developing subsequent primary cutaneous and non-cutaneous malignancies. However most evidence to date stems from observational studies which are prone to bias, confounding and reverse causation. Our aim was to examine this association using different Mendelian randomization (MR) approaches. Methods We performed a one-sample MR analysis using individual-level data from the UK Biobank (n = 394,306). This analysis was then validated in an independent dataset in the QSkin cohort (n = 16,896). Using 64 independent genetic variants known to be associated with KC, we generated a polygenic risk score (PRS) for each participant in the UK Biobank and the QSkin cohort. We then performed two-sample MR analyses using genome-wide association study (GWAS) summary statistics. We tested the association between genetically predicted KC and risk of subsequent cancer using logistic regression. Results Results from one-sample MR analyses in the UK Biobank indicated that a personal history of KC was significantly associated with cancer overall (excluding melanoma) (OR: 1.15, 95% CI: 1.10-1.20, per doubling the prevalence of KC). The results from the two-sample MR corroborate the findings from the one-sample MR, although the risk estimate was lower (OR: 1.05, 95% CI: 1.03-1.07). Conclusions Our MR analyses suggest that genetically predicted KC is a risk factor for developing subsequent primary malignancies. Key messages A personal history of KC may serve as a proxy marker of inherited cancer risk. [ABSTRACT FROM AUTHOR]

Background Mendelian randomisation uses genetic variants randomly allocated at conception as instrumental variables for an exposure. Methodological advances allow for mediation analysis to be carried out using Mendelian randomisation using either multivariable Mendelian randomisation or two-step Mendelian randomisation. Methods We use simulations and an applied example to demonstrate when multivariable Mendelian randomisation and two-step Mendelian randomisation methods are valid and how they relate to traditional phenotypic regression-based approaches to mediation. We demonstrate how Mendelian randomisation methods can relax assumptions required for causal inference in phenotypic mediation, as well as which Mendelian randomisation specific assumptions are required. We illustrate our methods in data from UK Biobank, estimating the role of body mass index mediating the association between education and cardiovascular outcomes. Results Both multivariable Mendelian randomization and two-step Mendelian randomization are unbiased when estimating the total effect, direct effect, indirect effect and proportion mediated when both confounding, and measurement error are present. Multivariable Mendelian Randomization can be used when multiple mediators are to be investigated in a single model. Conclusions Mendelian randomisation provides an opportunity to improve causal inference in mediation analysis. Although Mendelian randomisation specific assumptions apply, such as no weak instrument bias and no pleiotropic pathways, strong phenotypic assumptions of no confounding and no measurement error can be relaxed. Key messages Mendelian randomisation offers an opportunity to address bias by unmeasured confounding, measurement error and reverse causality in mediation analysis. [ABSTRACT FROM AUTHOR]