23 Those retrospective findings could have been identified prospectively with easy open access to complete summary data of everything tested in all RCTs. This issue is not discussed by Gao et al. Mendelian randomization (MR) is one of such approaches whose reliability has been established in epidemiology and is gaining popularity among health economists in testing causal research statements and obtaining consistent evidence in a cost effective manner. Consider whether measurement error and/or survivor bias (where predominantly prevalent cases are used) might have influenced findings. found that testosterone increased the density of bone mineral and decreased body fat. NJ Two-sample MR exploits the fact that it is not necessary to obtain the effect of the instrumental variable-risk factor association (ratio denominator) and instrumental variable-outcome association (ratio numerator) from the same sample of participants. Figure 1 a and c both illustrate the three key assumptions of IV analyses: i. that the IV ‘Z’ (randomization to statins in Figure 1 a and genetic variants related to LDLc in Figure 1 c) is (or is plausibly) causally related to the risk factor (LDLc in all figures); ii. that confounding factors for the risk factor-outcome ‘X’-’Y’ association (here LDLc on CHD in all figures) are not related to the instrumental variable; iii. that the instrumental variable ‘Z’ only affects the outcome ‘Y’ (CHD) through its effect on the risk factor ‘X’ (LDLc). G SG 9 In the case of childhood and adult BMI, we know that is unlikely to be the case. Design Mendelian randomisation study. Thus, it is concluded BMI reduces breast cancer risk {odds ratio[OR] 0.66 [95% confidence interval (CI): 0.57, 0.77)]}, but the same is not concluded for WHR [0.73 (0.53, 1.00)]. EP-I Croteau-Chonka C . L Humphries MRC Integrative Epidemiology Unit at the University of Bristol and School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK. Joshi Gao and colleagues examine the effects of childhood BMI and adult BMI, but they are not really able to determine effects at different life stages because of the correlation between BMI assessed at different ages and because of the nature of MR. Horikoshi The Mendelian randomization analysis made it possible to examine the effects of lifelong naturally elevated testosterone levels on 469 traits and diseases. . Bradfield Describe any key additional analyses that would have been important to conduct, such as of sub-phenotypes or interactions, that were not possible because of the summary data. 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. S Locke RM If this overlap is large, then some of the advantages of two-sample over one-sample MR are potentially lost, but the disadvantages of using summary data are maintained. Hardy with respect to gender, sex, age, ethnicity etc. This will require searching of the original publications and/or the consortia website. • Not relevant if individual level data on both samples, as can then decide what to adjust for, • If using summary data from published GWAS have to accept the adjustments that have been made in those GWAS, but should comment on the likely impact of this, • Methods available for testing this, though have additional assumptions and require large sample sizes 24,25, • Might be possible to apply the methods that have been developed for this, 24,25 if individual participant data available for the two samples. Over the past few years, several methodological advances have been made. Typically, for small sample sizes these effect sizes are going to overestimate the true effect size (i.e. Mendelian randomization (MR) Use inherited genetic variants to infer causal relationship of an exposure and a disease outcome. The dashed lines represent the parameters that need to be estimated, which are equal to the multiplication of the respective effects represented by the solid lines. Wurtz Lawlor Yaghootkar PMID: 23500241 Board Certified or Board Eligible AP/CP Full-Time or Part-Time Pathologist, Chief of ID, VA Ann Arbor Healthcare System, Instrumental variable is related to risk factorÂ, Confounders of the risk factor-outcome association are not related to the genetic instrumentÂ, Genetic instrument only related to the outcome through its effect on the risk factorÂ, Copyright © 2020 International Epidemiological Association. TW DA Comment on J Am Coll Cardiol. ), • Weak instrument biases towards the null, • Can (and should) check this for measured confounders, • If individual participant data are available for the two-samples can (and should) check this for measured confounders, • When using summary data from publicly available GWAS results, will often not be possible to check this, • Directional (horizontal) pleiotropy can be explored through use of different genetic instruments, multivariable instrumental variable analyses and MR-Egger 8,9, • Directional (horizontal) pleiotropy can be explored through use of different genetic instruments and MR-Egger 9, • In general. Davey Smith Taylor Day She declares no other conflicts of interest. BJ Abstract Background In observational studies of the general population, higher body mass index (BMI) has been associated with increased incidence When using summary GWAS data in what might be considered to be true two-sample MR, it is possible that the two samples overlap because of some cohort studies contributing to both GWAS (for example many adult cohort studies have contributed both to GWAS of adiposity measurements and also of disease outcomes such as CHD and type 2 diabetes). N Thus, it is impossible to know whether the assumption of no sex differences holds for these two risk factors. 14,18–20 For birthweight and child BMI, there seemed to have been no attempt to explore sex differences, which likely reflects the low power in those studies to do that. In this volume of the IJE , Gao and colleagues explore the causal effect of adiposity on several cancers using two-sample Mendelian randomization (MR), and find some evidence that greater adult body mass index (BMI) causally reduces the risk of breast cancer while increasing ovarian, lung and colorectal cancer. Exaggeration of the true effect sizes due to ‘winners curse’ may be present, and it will be important for future studies to better estimate the true effect in both sexes. In Figure 1 c and d, the IV is genetic variants that are robustly related to LDLc (i.e. et al.  Kahali a difference of -0.2 in this case ). 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 Silverwood NM works in a unit that receives funding from the University of Bristol and UK Medical Research Council (grant ref: MC_UU_12013/5) and she is a National Institute of Health Research Senior Investigator (NF-SI-0611‐10196). Biobank If that is not possible, consider possible biases, undertake sensitivity analyses and/or consider whether it is appropriate to undertake the analyses. Harbord The authors note that whereas their MR results suggest a protective effect of greater adult BMI on breast cancer, many observational studies have reported a protective effect of greater BMI on premenopausal breast cancer but a detrimental effect on postmenopausal breast cancer. A related issue is whether the WHR findings could have been biased towards the null more than BMI findings. Harbord ... (also called ‘winner’s curse’). Tilling For full access to this pdf, sign in to an existing account, or purchase an annual subscription. 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. Mendelian randomisation (MR) is an epidemiological technique that uses genetic variants as proxies for exposures in an attempt to determine whether there is a causal link between an exposure and an outcome. What is more surprising is that they seem to have also used sex-combined results for determining effects of WHR adjusted for BMI, despite the fact that it is clear from the title of the original GWAS paper that sex differences were examined and found 19 ( Table 2 ). In addition to money from public or charity grant funding bodies for her research, D.A.L. The WHR variants used by Gao and colleagues were adjusted for BMI, which the authors do not seem to acknowledge. Furthermore the MR-Egger test, 9 which the authors used to test violation of the exclusion restriction criteria, cannot be used to differentiate effects of adult from child BMI, as Gao and colleagues acknowledge. Yeung, Shiu Lun [corrected to Au Yeung, Shiu Lun]. This is because MR-Egger is only valid if the effect of the genetic instrument on the risk factor of interest is independent of its effect on any other phenotypes that might violate that assumption. . T There are 3 assumptions that must be satisfied to obtain suitable results: 1) The genetic variant is strongly associated with the exposure, 2) The genetic variant is independent of the outcome, given the exposure and all confounders (measured and unmeasured) of the exposure … If there have been adjustments, ensure that presentation and interpretation of results take this into account. The winner's curse is the phenomenon that the association estimate of the variant with the strongest association from a GWA study tends to be overestimated [Göring et al., 2001]. #Imagine that we have two populations that differ by 20%. . Computationally that is difficult, but a recent GWAS of BMI trajectories from age 1 to 17 years shows some potential for future studies to be able to explore such possibilities. 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. On the other hand, the known “winner’s curse” in GWAS studies might also lead to an underestimation of causal effect estimates using Mendelian randomization . H Seven of the 14 WHR adjusted for BMI variants used by Gao and colleagues were stronger in females compared with males ( Table 2 ), with 19 of the 44 variants in the more up-to-date GWAS being stronger in females (and one stronger in males). Mendelian randomization (MR) overcomes some of the limitations of causal interpretation in observational studies. The views expressed in this commentary are those of the author and not necessarily of anyone acknowledged here. comment on the ‘strong’ assumptions of MR, but rarely do we see such statements about the equally strong, and untestable, assumptions of conventional multivariable regression analyses. DA G Patel R 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… Felix Methods and findings. et al.  . P has received support from industry (Medtronic and Roche Diagnostics) in relation to her biomarker research. AE However, some of the point estimates for BMI and WHR are not that dissimilar. S A gene-based association method for mapping traits using reference transcriptome data. Egger Timpson ADAC Mendelian randomization estimates may be inflated. Kraft Typically, GWA studies report the single variant from each gene region showing the strongest association with the trait of interest. NJ J ... or Winner's Curse) (Goring et al., 2001; Ioannidis, 2008; Burgess et al., 2011). S For example, UK Biobank will soon release GWAS data on all 500 000 participants and has already amassed large numbers of incident cases of cardiovascular disease and common cancers such as breast cancer. • Not relevant as can decide within the one sample with genetic instrument, risk factor and outcome, what to adjust for. Mendelian randomisation (MR) is an epidemiological technique that uses genetic variants as proxies for exposures in an attempt to determine whether there is a causal link between an exposure and an outcome. Pathways to cognitive decline and dementia involve a combination of vascular and et al.  Davies Oxford University Press is a department of the University of Oxford. Heron 1The authors conclude that the study provides ‘…additional understanding of the complex relationship between adiposity and cancer risks’. et al.  . A gene-based association method for mapping traits using reference transcriptome data. . 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Similarly, an odds ratio of 1.27 (1.09, 1.49) for the effect of adult BMI on all lung cancers is declared as a positive result but the same conclusion is not made for an odds ratio of 1.33 (95%CI: 0.75, 2.36) for the MR effect of WHR on squamous lung cancer. Wasserstein Although it seems unlikely that this is an issue in the study undertaken by Gao and colleagues, methods to explore this ought to be included and their results discussed in any two-sample MR paper using summary data. Soininen Winner's curse. Participants 156 848 women in the multivariable regression and one sample mendelian randomisation (MR) analysis in UK … Columns are ‘hgnc_symbol’: HUGO Gene Nomenclature … It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide, This PDF is available to Subscribers Only. Heid Allen Those developing MR as a method have from the start been very open about its limitations and have worked at developing methods to test and limit sources of bias. #If we did a t-test from a random sample drawn from population 1 and population 2 using a sample size of 3 we are underpowered to detect a difference and so won't usually get a p value < 0.05. JF 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. Can we really use MR to test effects of adiposity on (breast) cancer at different life stages? MR G 12,13 From the original GWAS, 12 of the 15 child BMI overlapped with known adult BMI variants, 14 which illustrates the difficulty of distinguishing these two. But this study does illustrate some of the pitfalls of using summary GWAS data and methods that might be used to limit these. SI J One disadvantage of using summary data is that you have to take the results as analysed in the original study. AL A Such studies exploit what is known as Mendel’s DC Winner’s curse, replication and meta-analysis Winner’s curse, replication and meta-analysis. Lau The Mendelian Randomization (MR) approach is a method that enables causal inference in observational studies. . Thus, as the paper by Gao et al. Greater adult BMI, but not waist-hip ratio (WHR), is concluded to decrease breast cancer and increase ovarian, lung and colorectal cancer risk. G I This is a special case of \Mendelian randomization" where genetic variation is used as IV and typically X is an epidemiological risk factor (more downstream). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, Evidence for familial clustering in breast cancer age of onset, Cohort profile: HABITAT—a longitudinal multilevel study of physical activity, sedentary behaviour and health and functioning in mid-to-late adulthood, Plant foods, dietary fibre and risk of ischaemic heart disease in the European prospective investigation into cancer and nutrition (EPIC) cohort, Cohort Profile: The Care Trajectories—Enriched Data (TorSaDE) Cohort, Cohort profile: the China Multi-Ethnic cohort (CMEC) study, About International Journal of Epidemiology, About the International Epidemiological Association, Overlapping samples and the use of summary or individual participant data.

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