Mendelian Randomisation Study of Childhood BMI and Early Menarche

To infer the causal association between childhood BMI and age at menarche, we performed a mendelian randomisation analysis using twelve established “BMI-increasing” genetic variants as an instrumental variable (IV) for higher BMI. In 8,156 women of European descent from the EPIC-Norfolk cohort, height was measured at age 39–77 years; age at menarche was self-recalled, as was body weight at age 20 years, and BMI at 20 was calculated as a proxy for childhood BMI. DNA was genotyped for twelve BMI-associated common variants (in/nearFTO, MC4R, TMEM18, GNPDA2, KCTD15, NEGR1, BDNF, ETV5, MTCH2, SEC16B, FAIM2andSH2B1), and for each individual a “BMI-increasing-allele-score” was calculated by summing the number of BMI-increasing alleles across all 12 loci. Using this BMI-increasing-allele-score as an instrumental variable for BMI, each 1 kg/m2increase in childhood BMI was predicted to result in a 6.5% (95% CI: 4.6–8.5%) higher absolute risk of early menarche (before age 12 years). While mendelian randomisation analysis is dependent on a number of assumptions, our findings support a causal effect of BMI on early menarche and suggests that increasing prevalence of childhood obesity will lead to similar trends in the prevalence of early menarche.

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Association of timing of menarche with depressive symptoms and depression in adolescence: Mendelian randomisation study

The British Journal of Psychiatry ◽

10.1192/bjp.bp.115.168617 ◽

2017 ◽

Vol 210 (1) ◽

pp. 39-46 ◽

Cited By ~ 34

Author(s):

Maija-Eliina Sequeira ◽

Sarah J. Lewis ◽

Carolina Bonilla ◽

George Davey Smith ◽

Carol Joinson

Keyword(s):

Depressive Symptoms ◽

Risk Score ◽

Causal Effect ◽

Genetic Risk Score ◽

Mendelian Randomisation ◽

Age At Menarche ◽

Genetic Score ◽

Early Menarche ◽

Timing Of Menarche ◽

Effect Of Age

BackgroundObservational studies report associations between early menarche and higher levels of depressive symptoms and depression. However, no studies have investigated whether this association is causal.AimsTo determine whether earlier menarche is a causal risk factor for depressive symptoms and depression in adolescence.MethodThe associations between a genetic score for age at menarche and depressive symptoms at 14, 17 and 19 years, and depression at 18 years, were examined using Mendelian randomisation analysis techniques.ResultsUsing a genetic risk score to indicate earlier timing of menarche, we found that early menarche is associated with higher levels of depressive symptoms at 14 years (odds ratio per risk allele 1.02, 95% CI 1.005–1.04,n=2404). We did not find an association between the early menarche risk score and depressive symptoms or depression after age 14.ConclusionsOur results provide evidence for a causal effect of age at menarche on depressive symptoms at age 14.

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Estimation of causal effects of a time-varying exposure at multiple time points through Multivariable Mendelian randomization

10.1101/2022.01.04.22268740 ◽

2022 ◽

Author(s):

Eleanor Sanderson ◽

Tom G Richardson ◽

Tim T Morris ◽

Kate Tilling ◽

George Davey Smith

Keyword(s):

Direct Effect ◽

Genetic Variants ◽

Causal Effect ◽

Smoking Behaviour ◽

Causal Effects ◽

Mendelian Randomisation ◽

Multiple Time ◽

Time Points ◽

Time Periods ◽

Multiple Time Points

Mendelian Randomisation (MR) is a powerful tool in epidemiology to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants as instrumental variables (IVs) for the exposure. The effects obtained from MR studies are often interpreted as the lifetime effect of the exposure in question. However, the causal effects of many exposures are thought to vary throughout an individual's lifetime and there may be periods during which an exposure has more of an effect on a particular outcome. Multivariable MR (MVMR) is an extension of MR that allows for multiple, potentially highly related, exposures to be included in an MR estimation. MVMR estimates the direct effect of each exposure on the outcome conditional on all of the other exposures included in the estimation. We explore the use of MVMR to estimate the direct effect of a single exposure at different time points in an individual's lifetime on an outcome. We use simulations to illustrate the interpretation of the results from such analyses and the key assumptions required. We show that causal effects at different time periods can be estimated through MVMR when the association between the genetic variants used as instruments and the exposure measured at those time periods varies, however this estimation will not necessarily identify exact time periods over which an exposure has the most effect on the outcome. We illustrate the method through estimation of the causal effects of childhood and adult BMI on smoking behaviour.

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Association of genetic variants for plasma LRG1 with rapid decline in kidney function in patients with type 2 diabetes

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/clinem/dgab268 ◽

2021 ◽

Author(s):

Resham Lal Gurung ◽

Rajkumar Dorajoo ◽

M Yiamunaa ◽

Jian-Jun Liu ◽

Sharon Li Ting Pek ◽

...

Keyword(s):

Type 2 Diabetes ◽

Glomerular Filtration ◽

Kidney Function ◽

Genetic Variants ◽

Genome Wide Association Study ◽

Causal Effect ◽

Mendelian Randomisation ◽

Rapid Decline ◽

A Genome

Abstract Context Elevated levels of plasma Leucine Rich α-2-Glycoprotein 1 (LRG1), a component of TGF-ß signalling, are associated with development and progression of chronic kidney disease in patients with type 2 diabetes (T2D). However, whether this relationship is causal is uncertain. Objectives To identify genetic variants associated with plasma LRG1 levels and determine whether genetically predicted plasma LRG1 contributes to a rapid decline in kidney function (RDKF) in patients with T2D. Design and participants We performed a genome-wide association study (GWAS) of plasma LRG1 among 3,694 T2D individuals [1,881(983 Chinese, 420 Malay and 478 Indian) discovery from SMART2D cohort and 1,813 (Chinese) validation from DN cohort]. One- sample Mendelian randomization analysis was performed among 1,337 T2D Chinese participants with preserved glomerular filtration function (baseline estimated glomerular filtration rate (eGFR) >60ml/min/1.73m 2). RDKF was defined as an eGFR decline of 3 mL/min/1.73 m 2/year or greater. Results We identified rs4806985 variant near LRG1 locus robustly associated with plasma LRG1 levels (MetaP=6.66x10 -16). Among 1,337 participants, 344 (26%) developed RDKF and the rs4806985 variant was associated with higher odds of RDKF (meta odds ratio =1.23, P=0.030 adjusted for age and sex). Mendelian randomisation analysis provided evidence for a potential causal effect of plasma LRG1 on kidney function decline in T2D (P<0.05). Conclusion We demonstrate that genetically influenced plasma LRG1 increases the risk of RDKF in T2D patients suggesting plasma LRG1 as a potential treatment target. However, further studies are warranted to elucidate underlying pathways to provide insight into DKD prevention.

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Power calculator for instrumental variable analysis in pharmacoepidemiology

10.1101/084541 ◽

2016 ◽

Author(s):

Venexia M Walker ◽

Neil M Davies ◽

Frank Windmeijer ◽

Stephen Burgess ◽

Richard M Martin

Keyword(s):

Instrumental Variable ◽

Web Application ◽

Statistical Power ◽

Causal Effect ◽

Mendelian Randomisation ◽

List Type ◽

Continuous Outcome ◽

Instrumental Variable Analysis ◽

Research Questions ◽

Variable Analysis

ABSTRACTBackgroundInstrumental variable analysis, for example with physicians’ prescribing preferences as an instrument for medications issued in primary care, is an increasingly popular method in the field of pharmacoepidemiology. Existing power calculators for studies using instrumental variable analysis, such as Mendelian randomisation power calculators, do not allow for the structure of research questions in this field. This is because the analysis in pharmacoepidemiology will typically have stronger instruments and detect larger causal effects than in other fields. Consequently, there is a need for dedicated power calculators for pharmacoepidemiological research.Methods and resultsThe formula for calculating the power of a study using instrumental variable analysis in the context of pharmacoepidemiology is derived before being validated by a simulation study. The formula is applicable for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome. A web application is provided for the implementation of the formula by others.ConclusionsThe statistical power of instrumental variable analysis in pharmacoepidemiological studies to detect a clinically meaningful treatment effect is an important consideration. Research questions in this field have distinct structures that must be accounted for when calculating power.FUNDING STATEMENTThis work was supported by the Perros Trust and the Integrative Epidemiology Unit. The Integrative Epidemiology Unit is supported by the Medical Research Council and the University of Bristol [grant number MC_UU_12013/9]. Stephen Burgess is supported by a post-doctoral fellowship from the Wellcome Trust [100114].Key MessagesResearch questions using instrumental variable analysis in pharmacoepidemiology have distinct structures that have previously not been catered for by instrumental variable analysis power calculators.Power can be calculated for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome in the context of pharmacoepidemiology using the presented formula and online power calculator.The use of this power calculator will allow investigators to determine whether a pharmacoepidemiology study is likely to detect clinically meaningful treatment effects prior to the study’s commencement.

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Deciphering the complex interplay between pancreatic cancer, diabetes mellitus subtypes and obesity/BMI through causal inference and mediation analyses

Gut ◽

10.1136/gutjnl-2019-319990 ◽

2020 ◽

pp. gutjnl-2019-319990 ◽

Cited By ~ 1

Author(s):

Esther Molina-Montes ◽

Claudia Coscia ◽

Paulina Gómez-Rubio ◽

Alba Fernández ◽

Rianne Boenink ◽

...

Keyword(s):

Diabetes Mellitus ◽

Pancreatic Cancer ◽

Causal Effect ◽

Glycated Haemoglobin ◽

Mendelian Randomisation ◽

P Value ◽

Causal Association ◽

Related Factors ◽

Logistic Regression Models ◽

Mediation Analyses

ObjectivesTo characterise the association between type 2 diabetes mellitus (T2DM) subtypes (new-onset T2DM (NODM) or long-standing T2DM (LSDM)) and pancreatic cancer (PC) risk, to explore the direction of causation through Mendelian randomisation (MR) analysis and to assess the mediation role of body mass index (BMI).DesignInformation about T2DM and related factors was collected from 2018 PC cases and 1540 controls from the PanGenEU (European Study into Digestive Illnesses and Genetics) study. A subset of PC cases and controls had glycated haemoglobin, C-peptide and genotype data. Multivariate logistic regression models were applied to derive ORs and 95% CIs. T2DM and PC-related single nucleotide polymorphism (SNP) were used as instrumental variables (IVs) in bidirectional MR analysis to test for two-way causal associations between PC, NODM and LSDM. Indirect and direct effects of the BMI-T2DM-PC association were further explored using mediation analysis.ResultsT2DM was associated with an increased PC risk when compared with non-T2DM (OR=2.50; 95% CI: 2.05 to 3.05), the risk being greater for NODM (OR=6.39; 95% CI: 4.18 to 9.78) and insulin users (OR=3.69; 95% CI: 2.80 to 4.86). The causal association between T2DM (57-SNP IV) and PC was not statistically significant (ORLSDM=1.08, 95% CI: 0.86 to 1.29, ORNODM=1.06, 95% CI: 0.95 to 1.17). In contrast, there was a causal association between PC (40-SNP IV) and NODM (OR=2.85; 95% CI: 2.04 to 3.98), although genetic pleiotropy was present (MR-Egger: p value=0.03). Potential mediating effects of BMI (125-SNPs as IV), particularly in terms of weight loss, were evidenced on the NODM-PC association (indirect effect for BMI in previous years=0.55).ConclusionFindings of this study do not support a causal effect of LSDM on PC, but suggest that PC causes NODM. The interplay between obesity, PC and T2DM is complex.

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Inference about causation from examination of familial confounding (ICE FALCON): a model for assessing causation analogous to Mendelian randomization

International Journal of Epidemiology ◽

10.1093/ije/dyaa065 ◽

2020 ◽

Vol 49 (4) ◽

pp. 1259-1269

Author(s):

Shuai Li ◽

Minh Bui ◽

John L Hopper

Keyword(s):

Genetic Variants ◽

Instrumental Variable ◽

Mendelian Randomization ◽

Causal Effect ◽

A Priori ◽

Genetic Data ◽

Regression Coefficients ◽

Genetic Knowledge ◽

Wide Range ◽

Related Individuals

Abstract Background We developed a method to make Inference about Causation from Examination of FAmiliaL CONfounding (ICE FALCON) using observational data for related individuals and considering changes in a pair of regression coefficients. ICE FALCON has some similarities to Mendelian randomization (MR) but uses in effect all the familial determinants of the exposure, not just those captured by measured genetic variants, and does not require genetic data nor make strong assumptions. ICE FALCON can assess tracking of a measure over time, an issue often difficult to assess using MR due to lack of a valid instrumental variable. Methods We describe ICE FALCON and present two empirical applications with simulations. Results We found evidence consistent with body mass index (BMI) having a causal effect on DNA methylation at the ABCG1 locus, the same conclusion as from MR analyses but providing about 2.5 times more information per subject. We found evidence that tracking of BMI is consistent with longitudinal causation, as well as familial confounding. The simulations supported the validity of ICE FALCON. Conclusions There are conceptual similarities between ICE FALCON and MR, but empirically they are giving similar conclusions with possibly more information per subject from ICE FALCON. ICE FALCON can be applied to circumstances in which MR cannot be applied, such as when there is no a priori genetic knowledge and/or data available to create a valid instrumental variable, or when the assumptions underlying MR analysis are suspect. ICE FALCON could provide insights into causality for a wide range of public health questions.

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The causal effect of adiposity on hospital costs: Mendelian Randomization analysis of over 300,000 individuals from the UK Biobank

10.1101/589820 ◽

2019 ◽

Cited By ~ 1

Author(s):

Padraig Dixon ◽

William Hollingworth ◽

Sean Harrison ◽

Neil M Davies ◽

George Davey Smith

Keyword(s):

Genetic Variants ◽

Instrumental Variable ◽

Healthcare Costs ◽

Mendelian Randomization ◽

Causal Effect ◽

Hospital Costs ◽

Overweight And Obesity ◽

Effect Sizes ◽

Marginal Effect ◽

Omitted Variables

AbstractEstimates of the marginal effect of measures of adiposity such as body mass index (BMI) on healthcare costs are important for the formulation and evaluation of policies targeting adverse weight profiles. Many existing estimates of this association are affected by endogeneity bias caused by simultaneity, measurement error and omitted variables. The contribution of this study is to avoid this bias by using a novel identification strategy – random germline genetic variation in an instrumental variable analysis – to identify the presence and magnitude of the causal effect of BMI on inpatient hospital costs. We also use data on genetic variants to undertake much richer testing of the sensitivity of results to potential violations of the instrumental variable assumptions than is possible with existing approaches. Using data on over 300,000 individuals, we found effect sizes for the marginal unit of BMI more than 50% larger than multivariable effect sizes. These effects attenuated under sensitivity analyses, but remained larger than multivariable estimates for all but one estimator. There was little evidence for non-linear effects of BMI on hospital costs. Within-family estimates, intended to address dynastic biases, were null but suffered from low power. This paper is the first to use genetic variants in a Mendelian Randomization framework to estimate the causal effect of BMI (or any other disease/trait) on healthcare costs. This type of analysis can be used to inform the cost-effectiveness of interventions and policies targeting the prevention and treatment of overweight and obesity, and for setting research priorities.

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Constrained Instruments and their Application to Mendelian Randomization with Pleiotropy

10.1101/227454 ◽

2017 ◽

Cited By ~ 3

Author(s):

Lai Jiang ◽

Karim Oualkacha ◽

Vanessa Didelez ◽

Antonio Ciampi ◽

Pedro Rosa ◽

...

Keyword(s):

Instrumental Variables ◽

Genetic Variants ◽

Instrumental Variable ◽

Genetic Variant ◽

Mendelian Randomization ◽

Causal Effect ◽

Naive Method ◽

Effect Estimation ◽

Selection Of ◽

Better Than

AbstractIn Mendelian randomization (MR), genetic variants are used to construct instrumental variables, which enable inference about the causal relationship between a phenotype of interest and a response or disease outcome. However, standard MR inference requires several assumptions, including the assumption that the genetic variants only influence the response through the phenotype of interest. Pleiotropy occurs when a genetic variant has an effect on more than one phenotype; therefore, a pleiotropic genetic variant may be an invalid instrumental variable. Hence, a naive method for constructing instrumental variables may lead to biased estimation of the causality between the phenotype and the response. Here, we present a set of intuitive methods (Constrained Instrumental Variable methods [CIV]) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists, focusing particularly on the situation where pleiotropic phenotypes have been measured. Our approach includes an automatic and valid selection of genetic variants when building the instrumental variables. We also provide details of the features of many existing methods, together with a comparison of their performance in a large series of simulations. CIV methods performed consistently better than many comparators across four different pleiotropic violations of the MR assumptions. We analyzed data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Mueller et al. (2005) to disentangle causal relationships of several biomarkers with AD progression. The results showed that CIV methods can provide causal effect estimates, as well as selection of valid instruments while accounting for pleiotropy.

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Robust inference in summary data Mendelian randomisation via the zero modal pleiotropy assumption

10.1101/126102 ◽

2017 ◽

Cited By ~ 14

Author(s):

Fernando Pires Hartwig ◽

George Davey Smith ◽

Jack Bowden

Keyword(s):

Instrumental Variable ◽

Causal Effect ◽

Association Studies ◽

Mendelian Randomisation ◽

Effect Estimate ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Weighted Median ◽

Summary Data ◽

Genetic Instruments

AbstractBackgroundMendelian randomisation (MR) is being increasingly used to strengthen causal inference in observational studies. Availability of summary data of genetic associations for a variety of phenotypes from large genome-wide association studies (GWAS) allows straightforward application of MR using summary data methods, typically in a two-sample design. In addition to the conventional inverse variance weighting (IVW) method, recently developed summary data MR methods, such as the MR-Egger and weighted median approaches, allow a relaxation of the instrumental variable assumptions.MethodsHere, a new method –the mode-based estimate (MBE) – is proposed to obtain a single causal effect estimate from multiple genetic instruments. The MBE is consistent when the largest number of similar (identical in infinite samples) individual-instrument causal effect estimates comes from valid instruments, even if the majority of instruments are invalid. We evaluate the performance of the method in simulations designed to mimic the two-sample summary data setting, and demonstrate its use by investigating the causal effect of plasma lipid fractions and urate levels on coronary heart disease risk.ResultsThe MBE presented less bias and type-I error rates than other methods under the null in many situations. Its power to detect a causal effect was smaller compared to the IVW and weighted median methods, but was larger than that of MR-Egger regression, with sample size requirements typically smaller than those available from GWAS consortia.ConclusionsThe MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in a sensitivity analysis.Key MessagesSummary data Mendelian randomisation, typically in a two-sample setting, is being increasingly used due to the availability of summary association results from large genome- wide association studies.Mendelian randomisation analyses using multiple genetic instruments are prone to bias due to horizontal pleiotropy, especially when genetic instruments are selected based solely on statistical criteria.A causal effect estimate robust to horizontal pleiotropy can be obtained using the mode- based estimate (MBE).The MBE requires that the most common causal effect estimate is a consistent estimate of the true causal effect, even if the majority of instruments are invalid (i.e., the ZEro Modal Pleiotropy Assumption, or ZEMPA).Plotting the smoothed empirical density function is useful to explore the distribution of causal effect estimates, and to understand how the MBE is determined.

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Is vitamin D a modifiable risk factor for dental caries?

Wellcome Open Research ◽

10.12688/wellcomeopenres.16369.1 ◽

2020 ◽

Vol 5 ◽

pp. 281

Author(s):

Serena A. Dodhia ◽

Nicola X. West ◽

Steven J. Thomas ◽

Nicholas J. Timpson ◽

Ingegerd Johansson ◽

...

Keyword(s):

Vitamin D ◽

Genetic Variants ◽

Primary Teeth ◽

Causal Effect ◽

Permanent Teeth ◽

Childhood And Adolescence ◽

Causal Association ◽

Standard Deviation Increase ◽

Proxy Measure ◽

Tooth Surfaces

Background: Prior observational studies have reported that higher levels of vitamin D are associated with decreased caries risk in children. However, these studies are prone to bias and confounding so do not provide causal inference. Genetic variants associated with a risk factor of interest can be used as proxies, in a Mendelian randomization (MR) analysis, to test for causal association with an outcome. The objective was to estimate the causal association between serum 25-hydroxyvitamin D (25(OH)D) (the commonly measured vitamin D metabolite in blood) and dental caries using a MR approach which estimates the causal effect of an exposure on an outcome. Methods: A total of 79 genetic variants reliably associated with 25(OH)D were identified from genome-wide association studies and used as a proxy measure of 25(OH)D. The association of this proxy measure with three outcome measures was tested; specifically: caries in primary teeth (n=17,035, aged 3-12 years), caries in permanent teeth in childhood and adolescence (n=13,386, aged 6-18 years), and caries severity in adulthood proxied by decayed, missing and filled tooth surfaces (DMFS) counts (n=26,792, aged 18-93 years). Results: The estimated causal effect of a one standard deviation increase in natural log-transformed 25(OH)D could be summarized as an odds ratio of 1.06 (95%CI: 0.81, 1.31; P=0.66) for caries in primary teeth and 1.00 (95%CI: 0.76, 1.23; P=0.97) for caries in permanent teeth in childhood and adolescence. In adults, the estimated casual effect of a one standard deviation increase in natural log-transformed 25(OH)D was 0.31 fewer affected tooth surfaces (95%CI: from 1.81 fewer DMFS to 1.19 more DMFS; P=0.68) Conclusions: The MR-derived effect estimates for these three measures are small in magnitude with wide confidence intervals and do not provide evidence against the null hypothesis of no effect.

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