Methods and findings. Participants 156 848 women in the multivariable regression and one sample mendelian randomisation (MR) analysis in UK … 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.
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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. regression coefficient of adiposity measure per allele of combined adiposity genetic variants. NCDs include chronic diseases like cardiovascular disease (CVDs), cancer, diabetes, and chronic respiratory diseases, etc. et al.Â
. Mendelian randomization has emerged as a valuable approach in investigating whether an association of a biomarker with CAD is casual or not.
Already, the evidence points to several long-held candidates (plasma HDL cholesterol level, C-reactive protein) as not being causal. 22 In 1997, Egger and Davey Smith showed the same with respect to beta-blockers and mortality after acute myocardial infarction.
The Mendelian randomization analysis made it possible to examine the effects of lifelong naturally elevated testosterone levels on 469 traits and diseases.
found that testosterone increased the density of bone mineral and decreased body fat. A gene-based association method for mapping traits using reference transcriptome data.
Mendelian randomization: genetic anchors for causal inference in epidemiological studies, Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors, Randomised by (your) god: robust inference from an observational study design, Mendelian randomization: using genes as instruments for making causal inferences in epidemiology, Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects, Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression, Mendelian randomization in health research: using appropriate genetic variants and avoiding biased estimates, Model selection of life course hypotheses involving continuous exposures, Model selection of the effect of binary exposures over the life course, Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index, A genome-wide association study of body mass index across early life and childhood, The ASAâs statement on p-values: context, process, and purpose, Adjusting for heritable covariates can bias effect estimates in genome-wide association studies, New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism, Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution, Genetic studies of body mass index yield new insights for obesity biology, New genetic loci link adipose and insulin biology to body fat distribution, Cumulative meta-analysis of therapeutic trials for myocardial infarction, Testing for non-linear causal effects using a binary genotype in a Mendelian randomization study: application to alcohol and cardiovascular traits, Instrumental variable analysis with a nonlinear exposure-outcome relationship, Metabolomic profiling of statin use and genetic inhibition of HMG-CoA reductase, Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology.
. 2014 Jun 17;63(23):2642. 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… Davey Smith
. Winner's curse. Thompson
), â¢âWeak instrument biases towards the null, â¢âCan (and should) check this for measured confounders, â¢âIf individual participant data are available for the two-samples can (and should) check this for measured confounders, â¢âWhen using summary data from publicly available GWAS results, will often not be possible to check this, â¢âDirectional (horizontal) pleiotropy can be explored through use of different genetic instruments, multivariable instrumental variable analyses and MR-Egger 8,9, â¢âDirectional (horizontal) pleiotropy can be explored through use of different genetic instruments and MR-Egger 9, â¢âIn general.
What strikes me in watching (and participating in) the development of GWAS and MR over the past decade is how slow those of us largely working in epidemiology, including in intervention research, have been to do what we all know is good science. 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 ).
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. J
14,18â20 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. RJ
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.
21 In both GWAS, the results of the per allele effect of the genetic instrument on WHR adjusted for BMI is notably stronger in females than males. A related issue is whether the WHR findings could have been biased towards the null more than BMI findings. . Hardy
They are unable with the summary data available to test differences in effect between pre- and postmenopausal breast cancer, as GWAS separated by these sub-phenotypes are not presented by the breast cancer consortia. I
For full access to this pdf, sign in to an existing account, or purchase an annual subscription. 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. Schooling CM, Au Yeung SL, Freeman G. Erratum in J Am Coll Cardiol.
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.
Mendelian randomization estimates may be inflated.
The extent to which bias towards the null as a possible result of weak instrument bias and adjustment of WHR for BMI (discussed above) is balanced by possible exaggeration of the true effect as a result of not using sex-specific data for the genetic instrument-WHR association in the female cancers, is impossible to tell. . Further information can be obtained by mousing over the Course tab above, including: Course outline and timetable.
Mendelian randomization analysis depends on a number of assumptions.
Oxford University Press is a department of the University of Oxford. Taylor
Greater adult BMI, but not waist-hip ratio (WHR), is concluded to decrease breast cancer and increase ovarian, lung and colorectal cancer risk. Egger
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. 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. .
3 The main advantage of using summary data from GWAS consortia in two-sample MR is the increased statistical power, particularly in relation to testing effects on binary disease outcomes.
found that testosterone increased the density of bone mineral and decreased body fat. 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 … Heid
has received support from industry (Medtronic and Roche Diagnostics) in relation to her biomarker research.
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.
with summary data from large GWAS consortia, likely to have more power for these analyses which tend to be statistically inefficient, â¢âPossible if large sample sizes and data on the relevant stratifying risk factors (and genetic instruments for these) available, â¢âPossible if individual participant data on the two samples and large sample sizes and data on the relevant stratifying risk factors (and genetic instruments for these) available, â¢âIn general, with summary data from large GWAS consortia, it is unlikely to be able to test these. Setting UK Biobank prospective cohort study and Breast Cancer Association Consortium (BCAC) case-control genome-wide association study. 21. et al.Â
17 Thus, the MR estimate of the effect of unadjusted WHR on cancer would be to bias it towards the null because the denominator of the ratio (the genetic instrument-WHR association) will be exaggerated due to adjustment for BMI. et al.Â
... or Winner's Curse) (Goring et al., 2001; Ioannidis, 2008; Burgess et al., 2011). Humphries
According to data presented by Gao in their Supplementary Table 1, it seems that the association of the genetic instrumental variable with each adiposity trait has been taken from samples that combine females and males, whereas for the association of the genetic instrument with breast and ovarian cancer, females only are included and with prostate cancer males only are included. Day
9 In the case of childhood and adult BMI, we know that is unlikely to be the case. . C
. Mendelian randomization (MR) overcomes some of the limitations of causal interpretation in observational studies.
Fine-Needle Aspiration Cytology in Preoperative Diagnosis of Bone Lesions: A Three-Year Study in a Tertiary Care Hospital. Our genetic colleagues have led the way in ensuring replication in large collaborations where âteam scienceâ is appreciated and for the large part appropriately rewarded. To test this using MR requires establishing different (independent) genetic variants related to early-life BMI and subsequent change in weight. Timpson
If this is not the case (as in this paper for the sex-specific cancers), check the original paper publications and/or contact the original authors to see if it is possible to obtain results from the same population (here sex-specific results). Thus, as the paper by Gao etÂ al.
As a result the one-sample MR effect estimate will be an underestimate of the true causal effect 10, â¢âUsing two non-overlapping samples avoids this. comment on the âstrongâ assumptions of MR, but rarely do we see such statements about the equally strong, and untestable, assumptions of conventional multivariable regression analyses. The comments made in this paper are those of the author and not necessarily of the MRC or NIHR.
Design Mendelian randomisation study. Indeed, there are some advantages to obtaining them from two different sets of participants. This will require searching of the original publications and/or the consortia website.
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. Evidence from a two-sample Mendelian randomization analysis, High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease.
The combined per-allele effect in women was stronger than in men, specifically; for those marked with an asterisk (*), there was strong statistical evidence of a sex difference ( Psex difference 1.9âÃâ10 â3 to 1.2âÃâ10 â13 ). 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. Typically, for small sample sizes these effect sizes are going to overestimate the true effect size (i.e. Figure 1 a and c both illustrate the three key assumptions of IV analyses: i.âthat the IV âZâ (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.âthat confounding factors for the risk factor-outcome âXâ-âYâ association (here LDLc on CHD in all figures) are not related to the instrumental variable; iii.âthat the instrumental variable âZâ only affects the outcome âYâ (CHD) through its effect on the risk factor âXâ (LDLc). S
Objective To examine whether sleep traits have a causal effect on risk of breast cancer. 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. E-I
... or Winner's Curse) (Goring et al., 2001; Ioannidis, 2008; Burgess et al., 2011). Heron
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.
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 … 3 Gao and colleagues do not provide any information on the strength of the different instrumental variables, such as the F-statistic or R 2 for the genetic instrument-adiposity trait associations. M
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. Vilhjalmsson
... (also called ‘winner’s curse’). C
Pathways to cognitive decline and dementia involve a combination of vascular and
Can we really use MR to test effects of adiposity on (breast) cancer at different life stages? Ensure that the two samples are from the same populations.
Setting UK Biobank prospective cohort study and Breast Cancer Association Consortium (BCAC) case-control genome-wide association study.
This issue is not discussed by Gao etÂ al. Winkler
11 Collection of unique imaging data on a subsample of 100â000 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 500â000 participants is divided by the genetic instrument-imaging biomarker association in the 100â000 subgroup, will soon be possible. et al.Â
All variants combined explained 1.34% and 0.46% of the variation in WHR adjusted for BMI in women and men, respectively. . S
This two-sample Mendelian randomization study aimed to delve into the effects of genetically predicted adipokine levels on OA.Methods. et al.Â
âMendelian randomizationâ: can genetic epidemiology contribute to understanding environmental determinants of disease? 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).
Mendelian randomization (MR) Use inherited genetic variants to infer causal relationship of an exposure and a disease outcome. Per allele effect magnitude of GWAS significant SNPs with waist-hip ratio (adjusted for body mass index) by sex from the original GWAS and used in two-sample MR of cancer effects by Gao and colleagues, All values are the per allele difference in waist-hip ratio (WHR) adjusted for body mass index (BMI). TheÂ 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. 13, 27 In the context of this analysis, examining whether urate has a causal effect on BMD, the first assumption is that the genetic urate score genotype is associated with the serum urate concentration phenotype and is an instrumental variable of adequate strength. Winner’s curse, replication and meta-analysis Winner’s curse, replication and meta-analysis. Davey Smith
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. Report on the extent of any overlap between the two samples.
Contrary to IJE author recommendations and recent guidance from the American Statistics Society, 16 Gao etÂ al. 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.
â¢âIf the same sample is used for GWAS discovery of the instrumental variables (i.e. Ebrahim
Diagram adapted from Relton & Davey Smith, Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease, International Journal of Epidemiology, 2012, 41, 161–176. Abstract Background In observational studies of the general population, higher body mass index (BMI) has been associated with increased incidence
Mendelian randomization analysis depends on a number of assumptions. G
with respect to gender, sex, age, ethnicity etc. . SG
illustrates (see below), one has to use the summary results presented, even when these are not idea, for example because they have been adjusted for co-variables that you would rather they had not been adjusted for or the sample used is not idea for your question.
4 Gamazon, E. et al.
Nature Genetics, 47(9). a difference of -0.2 in this case ). Munafo
If there have been adjustments, ensure that presentation and interpretation of results take this into account. Beyond Mendelian randomizationâwhat can we learn from genetic epidemiology?
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.
Webinar – 2020 ISSLS Prize Winners. If that is not possible, consider possible biases, undertake sensitivity analyses and/or consider whether it is appropriate to undertake the analyses.
Mendelian Randomization results from GeneAtlas. Over the past few years, several methodological advances have been made.
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).
14,18â20 Interestingly, although Gao etÂ al. Kahali
Course tutors. Heron
4 Gamazon, E. et al.
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. Smartphone education improves embarrassment, bowel preparation, and satisfaction with care in patients receiving colonoscopy: A randomized controlled trail. Beyond the study findings themselves, this paper is int… I looked at the four original GWAS papers to explore whether there was any difference in the GWAS of the adiposity traits by sex.
Typically, GWA studies report the single variant from each gene region showing the strongest association with the trait of interest. Davey Smith
MR could be undertaken in one âsampleâ of participants with genetic instrument and outcome data on all participants, and data on the risk factor in a (random) subsample. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide, This PDF is available to Subscribers Only.