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Mendelian randomization (MR) overcomes some of the limitations of causal interpretation in observational studies. Two Mendelian randomization studies have applied novel approaches to instrumental variable selection in methylation data, identifying bidirectional causal effects of CPT1A and triglycerides, as well as of RNMT and C6orf42, on high-density lipoprotein cholesterol response to fenofibrate. Peakman Harbord (2015). Winner's curse. A has received support from industry (Medtronic and Roche Diagnostics) in relation to her biomarker research. #if you repeat this you will typically get a non-significant result, #let's repeat this 1,000 times and see how often we get a significant results, #proportion of tests that are significant, #let's plot the difference in the means for this estimates, the "effect size", "Estimated effect sizes (1000 simulations) for sample of size n=", #this is the effect size for all our estimates, #add a line to indicate where the true population mean lies, #let's look only at the tests that are significant at an alpha=0.05, #this is the effect sizes for the significant results. Yeung, Shiu Lun [corrected to Au Yeung, Shiu Lun]. Davey Smith JF As this is the denominator of the MR ratio estimate, it means that the estimated effect of WHR adjusted for BMI for female cancers (breast and ovarian) may be exaggerated and those for prostate cancers underestimated. G The following (in alphabetical order of first name) kindly provided useful comments on an earlier draft of this commentary: George Davey Smith (University of Bristol), Gibran Hermani (University of Bristol), Maria-Carolina Borge (Federal University of Pelotas), Neil Pearce (London School of Hygiene & Tropical Medicine), Philip Haycock (University of Bristol), Rachel Freathy (University of Exeter) and Richard Martin (University of Bristol). Taylor 1 Furthermore, they note that their results are consistent with a recent one-sample MR study that found inverse associations of BMI with breast cancer in pre- and postmenopausal women, though at the time of writing this commentary that paper appears to be unpublished. However, we still lack such access. 1 Concepts of MR and Instrumental variable (IV) methods motivation, assumptions, inference goals, merits and limitations two-stage least squares (2SLS) method from econometrics literature et al.  found that testosterone increased the density of bone mineral and decreased body fat. DO In addition, it would have advantages from having individual participant data rather than summary data, though the very select nature of some large biobanks (the response rate for UK Biobank was less than 5%) might introduce additional biases. Results obtained using 4 MR methods (inverse-variance weighted [IVW], Mendelian randomization-Egger [MR-Egger], weighted median, and MR-PRESSO [Mendelian Randomization Pleiotropy Residual Sum and Outlier]) are presented as a heat map representing causal estimates (1 SD of BP index per 1 SD of cell count). Columns are ‘hgnc_symbol’: HUGO Gene Nomenclature … Now genetic epidemiologists have shown us how to provide complete open-access summary data, and it is likely that over the coming decade important and impactful use will be made of these data. . DA Mendelian randomization (MR) has been used to estimate the causal effect of body mass index (BMI) on particular traits thought to be affected by BMI. . 2,3,9 It is notable, for example, that Gao et al. As a result the one-sample MR effect estimate will be an underestimate of the true causal effect 10, • Using two non-overlapping samples avoids this. B This issue is not discussed by Gao et al. Weak instrument bias in one-sample MR results in bias towards the confounded multivariable regression result, but in two-sample MR the bias is towards the null ( Table 1 ). TW Hingorani SG PMID: 23500241 S NM D DC Locke • With summary data from large GWAS consortia, not clear how these methods could be applied currently. largely base their conclusions on findings with a P -value equivalent to < 0.05 after multiple testing. It also has additional strengths and limitations in comparison with one-sample MR, which are summarized in Table 1 . This may be due to the winner’s curse because in the stage 1 we rank all 172 estimates of the allele score on the outcomes, such that the highest ranked are more likely to be higher than their respective true values because of the random variation of these sample estimates about their true values. United Nations' Sustainable Development Goals (SDG, 2015) has specified NCDs as one of their important health related targets (Target-3.4) for improving overall wellbeing of human populations (2). Antman T A copy of the book "Mendelian randomization: Methods for using genetic variants in causal estimation" is included in the course fees for in-person courses (not for online courses). use the most up-to-date BMI GWAS data, 20 they do not do the same for WHR, despite the most recent GWAS for WHR adjusted for BMI identifying 33 additional variants (as well as confirming the 14 used here from the earlier GWAS) and being published around the same time as the most up-to-date BMI GWAS. 2014 Jun 17;63(23):2642. JJ Davey Smith AE Kahali Davies For two-sample MR to be valid, the two samples have to be from the same underlying population, but for the sex-specific cancers in the paper by Gao et al. One disadvantage of using summary data is that you have to take the results as analysed in the original study. A further potential explanation for why most of the emphasized (based on statistical testing) MR results are seen for adult BMI, rather than any of the other adiposity risk factors, is that the genetic instrument for adult BMI is stronger than for the other traits. Figure shows DAGs of instrumental variable (IV) analyses to test the causal effect of low-density lipoprotein cholesterol (LDLc) on CHD. ; Consortium . In particular ‘winners’ curse’, which can underestimate true causal effects in one-sample MR, 10 is unlikely to happen in two-sample MR, and unlike the impact of weak instrument bias in one-sample MR (which biases effects towards the confounded multivariable regression result), in two-sample MR weak instrument bias is towards the null. In the GWAS there was strong statistical evidence that each association had a low probability of being due to chance, particularly in women ( Pwomen only 1.55 × 10 −6 to 3.84 × 10 −34 ; Pmen only 0.043 to 9.41 × 10 −13 ; Pcombined 1.9 × 10 −9 to 1.8 × 10 −40 ). Report on the extent of any overlap between the two samples. Fine-Needle Aspiration Cytology in Preoperative Diagnosis of Bone Lesions: A Three-Year Study in a Tertiary Care Hospital. a difference of -0.2 in this case ). Paré et al. I B To test this using MR requires establishing different (independent) genetic variants related to early-life BMI and subsequent change in weight. Indeed, there are some advantages to obtaining them from two different sets of participants. the outcome) can be done on separate sets of studies; so called ‘two-sample Mendelian randomization’ • Know the basics of how to undertake a two-sample Mendelian randomization study Future availability A Mendelian randomization short course will run as a … . Lau ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? The basic assumption—that genetic variants which can proxy for a potentially modifiable exposure are essentially unrelated to confounding factors—has been demonstrated to have widespread plausibility.25 The connection between the standard Mendelian randomization experiment and the theory of instrumental variables has been elaborated upon.26,27 Extensions to use multiple genetic variants for increasing power and investigatin… regression coefficient of adiposity measure per allele of combined adiposity genetic variants. DAG of instrumental variable analyses in an RCT and MR study exploring the effect of LDLc on CHD. She declares no other conflicts of interest. BP indicates blood pressure. Did you miss the ISSLS Prize webinar or wish to view it again? Thompson S That funding is not related to the comments in this paper. pQTL for both cohorts are included, however, in order to avoid a ‘winner’s curse’, MR was conducted using data from the secondary protein cohort (ORCADES). Course tutors. Smith Whereas one of the noted advantages of MR is that it generally assesses the cumulative effects of a risk factor over a long period of the life course (potentially from conception) without requiring repeat risk factor assessment and with little chance of regression dilution bias or reverse causation (confounding by prevalent disease), 7 this also brings a disadvantage in that MR is limited in the extent to which it can explore different life course models, such as whether exposure effects differ at different points in the life course. . Differences in just two of the 77 variants might not have been sufficient to bias the results for adult BMI with the sex-specific outcomes, but it is disappointing that the authors did not use the sex-specific beta values for each variant with the sex-specific outcome nor clarified in the paper that the denominators combined data from both sexes. Dale The authors speculate that the protective effect of adult BMI on breast cancer (including postmenopausal) might represent a complex interplay between early life BMI and later weight gain. M Davey Smith 21. Davey Smith Schooling CM, Au Yeung SL, Freeman G. Erratum in J Am Coll Cardiol. et al.  Mendelian randomization methods, which use genetic variants as instrumental variables for exposures of interest to overcome problems of confounding and reverse causality, are becoming widespread for assessing causal relationships in epidemiological studies. Mook-Kanamori . Timpson RA NE Davey Smith 11 Collection of unique imaging data on a subsample of 100 000 of those participants has begun, and thus MR to determine the causal effect of novel imaging biomarkers on common chronic disease outcomes, in which the genetic instrument-disease outcome association in 500 000 participants is divided by the genetic instrument-imaging biomarker association in the 100 000 subgroup, will soon be possible. Thus, as the paper by Gao et al. Tilling Shungin Egger If that is not possible, consider possible biases, undertake sensitivity analyses and/or consider whether it is appropriate to undertake the analyses. Consider whether measurement error and/or survivor bias (where predominantly prevalent cases are used) might have influenced findings. So far, MR studies in this area have focussed solely on Alzheimer’s dementia, with all three reporting no impact of diabetes [4–6]. Winner’s curse, replication and meta-analysis Winner’s curse, replication and meta-analysis. 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