Biology subjects

Sanderson, E.

Publications and source records attributed to Sanderson, E..

9 recordsLinked to original sources

bioRxiv2022

DNA methylation at the suppressor of cytokine signaling 3 (SOCS3) gene influences height in childhood

Human height is strongly influenced by genetics but the contribution of modifiable epigenetic factors is under-explored, particularly in low and middle-income countries (LMIC). We investigated links between blood DNA methylation and child height in four LMIC cohorts (n=1927) and identified a robust association at three CpGs in the suppressor of cytokine signalling 3 (SOCS3) gene which replicated in a high-income country cohort (n=879). SOCS3 methylation (SOCS3m) - height associations were independent of genetic effects. Mendelian randomization analysis confirmed a causal effect of SOCS3m on height. In longitudinal analysis in a LMIC cohort, SOCS3m explained a maximum 9.5% of height variance in mid-childhood while the variance explained by height polygenic risk score increased from birth to 21 years (2% to 18%). Childrens SOCS3m was associated with prenatal maternal folate and socio-economic status. In-vitro characterization confirmed a regulatory effect of SOCS3m on gene expression. Our findings suggest that epigenetic modifications may play an important role in driving child height in LMIC.

genomics↗

bioRxiv2020

The use of negative control outcomes in Mendelian Randomisation to detect potential population stratification or selection bias.

A key assumption of Mendelian randomisation (MR) analysis is that there is no association between the genetic variants used as instruments and the outcome other than through the exposure of interest. Two ways in which this assumption can be violated are through population stratification and selection bias which can introduce confounding of the relationship between the genetic variants and the outcome and so induce an association between them. Negative control outcomes are increasingly used to detect unobserved confounding in observational epidemiological studies. Here we consider the use of negative control outcomes in MR studies. As a negative control outcome in an MR study we propose the use of phenotypes which are determined before the exposure and outcome but which are likely to be subject to the same confounding as the exposure or outcome of interest. We illustrate our method with a two-sample MR analysis of a preselected set of exposures on self-reported tanning ability and hair colour. Our results show that, of the 33 exposures considered, GWAS studies of adiposity and education related traits are likely to be subject to population stratification and/or selection bias that is not controlled for through adjustment and so any MR study including these traits may be subject to bias that cannot be identified through standard pleiotropy robust methods.

genetics↗

bioRxiv2020

Testing and Correcting for Weak and Pleiotropic Instruments in Two-Sample Multivariable Mendelian Randomisation

Multivariable Mendelian Randomisation (MVMR) is a form of instrumental variable analysis which estimates the direct effect of multiple exposures on an outcome using genetic variants as instruments. Mendelian Randomisation and MVMR are frequently conducted using two-sample summary data where the association of the genetic variants with the exposures and outcome are obtained from separate samples. If the genetic variants are only weakly associated with the exposures either individually or conditionally, given the other exposures in the model, then standard inverse variance weighting will yield biased estimates for the effect of each exposure. Here we develop a two-sample conditional F-statistic to test whether the genetic variants strongly predict each exposure conditional on the other exposures included in a MVMR model. We show formally that this test is equivalent to the individual level data conditional F-statistic, indicating that conventional rule-of-thumb critical values of F > 10, can be used to test for weak instruments. We then demonstrate how reliable estimates of the causal effect of each exposure on the outcome can be obtained in the presence of weak instruments and pleiotropy, by re-purpousing a commonly used heterogeneity Q-statistic as an estimating equation. Furthermore, the minimised value of this Q-statistic yields an exact test for heterogeneity due to pleiotropy. We illustrate our methods with an application to estimate the causal effect of blood lipid fractions on age related macular degeneration.

genetics↗

bioRxiv2020

Cross-platform genetic discovery of small molecule products of metabolism and application to clinical outcomes

Circulating levels of small molecules or metabolites are highly heritable, but the impact of genetic differences in metabolism on human health is not well understood. In this cross-platform, genome-wide meta-analysis of 174 metabolite levels across six cohorts including up to 86,507 participants (70% unpublished data), we identify 499 (362 novel) genome-wide significant associations (p<4.9×10-10) at 144 (94 novel) genomic regions. We show that inheritance of blood metabolite levels in the general population is characterized by pleiotropy, allelic heterogeneity, rare and common variants with large effects, non-linear associations, and enrichment for nonsynonymous variation in transporter and enzyme encoding genes. The majority of identified genes are known to be involved in biochemical processes regulating metabolite levels and to cause monogenic inborn errors of metabolism linked to specific metabolites, such as ASNS (rs17345286, MAF=0.27) and asparagine levels. We illustrate the influence of metabolite-associated variants on human health including a shared signal at GLP2R (p.Asp470Asn) associated with higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes risk, and demonstrate beta-arrestin signalling as the underlying mechanism in cellular models. We link genetically-higher serine levels to a 95% reduction in the likelihood of developing macular telangiectasia type 2 [odds ratio (95% confidence interval) per standard deviation higher levels 0.05 (0.03-0.08; p=9.5×10-30)]. We further demonstrate the predictive value of genetic variants identified for serine or glycine levels for this rare and difficult to diagnose degenerative retinal disease [area under the receiver operating characteristic curve: 0.73 (95% confidence interval: 0.70-0.75)], for which low serine availability, through generation of deoxysphingolipids, has recently been shown to be causally relevant. These results show that integration of human genomic variation with circulating small molecule data obtained across different measurement platforms enables efficient discovery of genetic regulators of human metabolism and translation into clinical insights.Competing Interest StatementA.S.B. has received grants from AstraZeneca, Biogen, Bioverativ, Merck, Novartis, and Sanofi. J. D. sits on the International Cardiovascular and Metabolic Advisory Board for Novartis (since 2010), the Steering Committee of UK Biobank (since 2011), the MRC International Advisory Group (ING) member, London (since 2013), the MRC High Throughput Science Omics Panel Member, London (since 2013), the Scientific Advisory Committee for Sanofi (since 2013), the International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis and the Astra Zeneca Genomics Advisory Board (2018). E.B.F. is an employee and stock holder of Pfizer.View Full Text

genetics↗

bioRxiv2019

Mendelian randomisation for mediation analysis: current methods and challenges for implementation

Mediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Mediation analysis experiences a number of methodological difficulties, including bias due to confounding and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable Mendelian randomisation (MVMR) and two-step Mendelian randomisation. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although Mendelian randomisation relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our examples demonstrate that it is unlikely to be affected by confounders of the exposure or mediator and the outcome, reverse causality and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR, and can improve causal inference in mediation analysis.

genetics↗

bioRxiv2019

Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases

Mendelian randomization (MR) is a widely-used method for causal inference using genetic data. Mendelian randomization studies of unrelated individuals may be susceptible to bias from family structure, for example, through dynastic effects which occur when parental genotypes directly affect offspring phenotypes. Here we describe methods for within-family Mendelian randomization and through simulations show that family-based methods can overcome bias due to dynastic effects. We illustrate these issues empirically using data from 61,008 siblings from the UK Biobank and Nord-Trondelag Health Study. Both within-family and population-based Mendelian randomization analyses reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while MR estimates from population-based samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects largely disappeared in within-family MR analyses. We found differences between population-based and within-family based estimates, indicating the importance of controlling for family effects and population structure in Mendelian randomization studies.

genetics↗

bioRxiv2019

Using genetic instruments to estimate interactions in Mendelian Randomization studies

BACKGROUNDThe interactive effect of two exposures on an outcome can be confounded. We demonstrate the use of Mendelian Randomization (MR) to estimate unconfounded additive interactions. METHODSUsing simulation, we test an extension to multivariable MR using two-stage least squares to estimate the additive interaction between two continuous exposures on a continuous outcome, including scenarios where one exposure has a causal effect on the other (mediation). The interaction parameters were set to be one third of the main effects parameters to impose a limit on the variance explained by interaction terms. We compare the performance of the two-stage least squares estimator to a Factorial MR design, in which genetic risk scores for each exposure are dichotomised to create four groups, akin to a factorial randomized controlled trial. As an illustrative example, we apply factorial MR and the 2SLS estimator to the interactive effect of education and BMI on systolic blood pressure in UK Biobank. RESULTSOur simulations demonstrate that factorial MR has very low statistical power; 5-7% at N=50,000 and 8-23% at N=500,000 across the range of parameters tested. The two-stage least squares estimator had higher power to detect interactions than factorial MR and a lower type I error. For N=500,000, the two-stage least squares estimator had a power ranging from 29.7-92.9% and a type I error ranging from 4-6%. 95% Monte Carlo confidence intervals suggested that the estimator was unbiased to a reasonable degree of accuracy at this sample size. In comparison, the power at N=50,000 was 7-55%. CONCLUSIONSA two-stage least squares estimator using genetic risk scores for each exposure is a more powerful alternative for detecting an unconfounded additive interaction of two exposures on an outcome than existing approaches that rely on dichotomising genetic risk scores, but it requires a large sample size and instruments of adequate strength.

epidemiology↗

bioRxiv2018

An examination of multivariable Mendelian randomization in the single sample and two-sample summary data settings.

BackgroundMendelian Randomisation (MR) is a powerful tool in epidemiology which can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to Multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome.\n\nMethods/ResultsWe use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK biobank to estimate the effect of education and cognitive ability on body mass index.\n\nConclusionMVMR analysis consistently estimates the effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual or summary level data.

epidemiology↗

bioRxiv2018

The effect of education and general cognitive ability on smoking: A Mendelian randomisation study

Recent analyses have shown educational attainment to be associated with a number of health outcomes. This association may, in part, be due to an effect of educational attainment on smoking behaviour. In this study we apply a multivariable Mendelian randomisation design to determine whether the effect of educational attainment on smoking behaviour could be due to educational attainment or general cognitive ability. We use individual data from the UK Biobank study (N = 120,050) and summary data from large GWAS studies of educational attainment, cognitive ability and smoking behaviour. Our results show that more years of education are associated with a reduced likelihood of smoking which is not due to an effect of general cognitive ability on smoking behaviour. Given the considerable physical harms associated with smoking, the effect of educational attainment on smoking is likely to contribute to the health inequalities associated with differences in educational attainment.

epidemiology↗