Biology subjects

Davey Smith, G.

Publications and source records attributed to Davey Smith, G..

At least 73 records · Page 4Linked to original sources

bioRxiv2017

Partitioning Phenotypic Variance Due To Parent-Of-Origin Effects Using Genomic Relatedness Matrices

Introduction Introduction Methods Statistical Methods Results Discussion References Parent-of-origin effects (POEs) describe the phenomenon in which the effects of alleles depend upon their parental origin. POEs imply that heterozygote individuals have phenotypes which are distributed differently depending upon which of their alleles were maternally and paternally transmitted (Guilmatre and Sharp 2012; Lawson et al. 2013). The extreme case of POEs is polar overdominance, where the two heterozygotes' phenotypes differ in distribution but the two homozygotes share the same distribution (Hoggart et al. 2014). Imprinting, a phenomenon in which one parent's allele is not expressed, is probably the most widely studied example of POE (Peters 2 ...

genetics↗

bioRxiv2017

Genetic Epidemiology And Mendelian Randomization For Informing Disease Therapeutics: Conceptual And Methodological Challenges

The past decade has been proclaimed as a hugely successful era of gene discovery through the high yields of many genome-wide association studies (GWAS). However, much of the perceived benefit of such discoveries lies in the promise that the identification of genes that influence disease would directly translate into the identification of potential therapeutic targets (1-4), but this has yet to be realised at a level reflecting expectation. One reason for this, we suggest, is that GWAS to date have generally not focused on phenotypes that directly relate to the progression of disease, and thus speak to disease treatment.

genetics↗

bioRxiv2017

Causal epigenome-wide association study identifies CpG sites that influence cardiovascular disease risk

The extent to which genetic influences on complex traits and disease are mediated by changes in DNA methylation levels has not been systematically explored. We developed an analytical framework that integrates genetic fine mapping and Mendelian randomization with epigenome-wide association studies to evaluate the causal relationships between methylation levels and 14 cardiovascular disease traits.\n\nWe identified 10 genetic loci known to influence proximal DNA methylation which were also associated with cardiovascular traits (P < 3.83x10-08). Bivariate fine mapping suggested that the individual variants responsible for the observed effects on cardiovascular traits at the ABO, ADCY3, ADIPOQ, APOA1 and IL6R loci were likely mediated through changes in DNA methylation. Causal effect estimates on cardiovascular traits ranged between 0.109-0.992 per standard deviation change in DNA methylation and were replicated using results from large-scale consortia.\n\nFunctional informatics suggests that the causal variants and CpG sites identified in this study were enriched for histone mark peaks in adipose tissue and gene promoter regions. Integrating our results with expression quantitative trait loci data we provide evidence that variation at these regulatory regions is likely to also influence gene expression at these loci.

epidemiology↗

bioRxiv2017

Meffil: efficient normalisation and analysis of very large DNA methylation samples

AbstractO_ST_ABSBackgroundC_ST_ABSTechnological advances in high throughput DNA methylation microarrays have allowed dramatic growth of a new branch of epigenetic epidemiology. DNA methylation datasets are growing ever larger in terms of the number of samples profiled, the extent of genome coverage, and the number of studies being meta-analysed. Novel computational solutions are required to efficiently handle these data.\n\nMethodsWe have developed meffil, an R package designed to quality control, normalize and perform epigenome-wide association studies (EWAS) efficiently on large samples of Illumina Infinium HumanMethylation450 and MethylationEPIC BeadChip microarrays. We tested meffil by applying it to 6000 450k microarrays generated from blood collected for two different datasets, Accessible Resource for Integrative Epigenomic Studies (ARIES) and The Genetics of Overweight Young Adults (GOYA) study.\n\nResultsA complete reimplementation of functional normalization minimizes computational memory requirements to 5% of that required by other R packages, without increasing running time. Incorporating fixed and random effects alongside functional normalization, and automated estimation of functional normalisation parameters reduces technical variation in DNA methylation levels, thus reducing false positive associations and improving power. We also demonstrate that the ability to normalize datasets distributed across physically different locations without sharing any biologically-based individual-level data may reduce heterogeneity in meta-analyses of epigenome-wide association studies. However, we show that when batch is perfectly confounded with cases and controls functional normalization is unable to prevent spurious associations.\n\nConclusionsmeffil is available online (https://github.com/perishky/meffil/) along with tutorials covering typical use cases.

bioinformatics↗

bioRxiv2017

The causal effect of educational attainment on Alzheimer’s disease: A two-sample Mendelian randomization study

BackgroundObservational evidence suggests that higher educational attainment is protective for Alzheimers disease (AD). It is unclear whether this association is causal or confounded by demographic and socioeconomic characteristics. We examined the causal effect of educational attainment on AD in a two-sample MR framework.\n\nMethodsWe extracted all available effect estimates of the 74 single nucleotide polymorphisms (SNPs) associated with years of schooling from the largest genome-wide association study (GWAS) of educational attainment (N=293,723) and the GWAS of AD conducted by the International Genomics of Alzheimers Project (n=17,008 AD cases and 37,154 controls). SNP-exposure and SNP-outcome coefficients were combined using an inverse variance weighted approach, providing an estimate of the causal effect of each SD increase in years of schooling on AD. We also performed appropriate sensitivity analyses examining the robustness of causal effect estimates to the various assumptions and conducted simulation analyses to examine potential survival bias of MR analyses.\n\nFindingsWith each SD increase in years of schooling (3.51 years), the odds of AD were, on average, reduced by approximately one third (odds ratio= 0.63, 95% confidence interval [CI]: 0.48 to 0.83, p<0.001). Causal effect estimates were consistent when using causal methods with varying MR assumptions or different sets of SNPs for educational attainment, lending confidence to the magnitude and direction of effect in our main findings. There was also no evidence of survival bias in our study.\n\nInterpretationOur findings support a causal role of educational attainment on AD, whereby an additional [~]3.5 years of schooling reduces the odds of AD by approximately one third.

epidemiology↗

bioRxiv2017

Assessing the causal role of body mass index on cardiovascular health in young adults: Mendelian randomization and recall-by-genotype analyses

BackgroundMendelian randomization (MR) studies of body mass index (BMI) and cardiovascular health in mid-to-late life suggest causal relationships, but the nature of these has not been explored systematically at younger ages. Using complementary MR and recall-by-genotype (RbG) methodologies, our objective was to estimate the causal effect of BMI on detailed measures of cardiovascular health in a population of young healthy adults.\n\nMethods and FindingsData from the Avon Longitudinal Study of Parents and Children were used. For MR analyses, a genetic risk score (GRS) comprising 97 independent single nucleotide polymorphisms (SNPs) and constructed using external weighting was used as an instrument to test the causal effect of each unit increase in BMI (kg/m2) on selected cardiovascular phenotypes measured at age 17 (N=7909). An independent enriched sample from the same cohort participated in a RbG study at age 21, which enabled more detailed cardiovascular phenotyping (N=418; 191/227 from the lower/upper [~]30% of a genome-wide GRS distribution predicting variation in BMI). The causal effect of BMI on the additional cardiovascular phenotypes was assessed by comparing the two recalled groups. Difference in mean BMI between RbG groups was 3.85kg/m2 (95% CI: 2.53, 4.63; P=6.09x1011). In both MR and RbG analyses, results indicated that higher BMI causes higher blood pressure (BP) and left ventricular mass (indexed to height2.7, LVMI) in young adults (e.g. difference in LVMI per kg/m2 using MR: 1.07g/m2.7; 95% CI: 0.62, 1.52; P=3.87x10-06 and per 3.58kg/m2 using RbG: 1.65g/m2.7 95% CI: 0.83, 2.47; P=0.0001). Additionally, RbG results indicated a causal role of higher BMI on higher stroke volume (SV; difference per 3.58kg/m2: 1.49ml/m2.04; 95% CI: 0.62, 2.35; P=0.001) and cardiac output (CO; difference per 3.58kg/m2: 0.11l /min/m1.83; 95% CI: 0.03, 0.19; P=0.01). Neither analysis supported a causal role of higher BMI on heart rate.\n\nConclusionsComplementary MR and RbG causal methodologies, together with a range of appropriate sensitivity analyses, showed that higher BMI is likely to cause worse cardiovascular health, specifically higher BP and LVMI, even in youth. These consistent results support efforts to prevent or reverse obesity in the young.

epidemiology↗

bioRxiv2017

Association Between Genetically Elevated Levels Of Inflammatory Biomarkers And Risk Of Schizophrenia: A Two-Sample Mendelian Randomisation Study

BackgroundPositive associations between inflammatory biomarkers and risk of psychiatric disorders, including schizophrenia, have been reported in observational studies. However, conventional observational studies are prone to bias such as reverse causation and residual confounding.\n\nMethodsIn this study, we used summary data to evaluate the association of genetically elevated C reactive protein (CRP), interleukin-1 receptor antagonist (IL-1Ra) and soluble interleukin-6 receptor (IL-6R) levels with schizophrenia in a two-sample Mendelian randomisation design.\n\nResultsThe pooled odds ratio estimate using 18 CRP genetic instruments was 0.90 (95% CI: 0.84; 0.97) per two-fold increment in CRP levels; consistent results were obtained using different Mendelian randomisation methods and a more conservative set of instruments. The odds ratio for soluble IL-6R was 1.06 (95% CI: 1.01; 1.12) per two-fold increment. Estimates for IL-1Ra were inconsistent among instruments and pooled estimates were imprecise and centred on the null.\n\nConclusionUnder Mendelian randomisation assumptions, our findings suggest a protective causal effect of CRP and a risk-increasing causal effect of soluble IL-6R (potentially mediated at least in part by CRP) on schizophrenia risk.

epidemiology↗

bioRxiv2017

Robust Inference In Two-Sample Mendelian Randomisation Via The Zero Modal Pleiotropy Assumption

BackgroundMendelian 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.\n\nMethodsHere, 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.\n\nResultsThe 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.\n\nConclusionsThe MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in a sensitivity analysis.\n\nKey MessagesO_LISummary 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.\nC_LIO_LIMendelian 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.\nC_LIO_LIA causal effect estimate robust to horizontal pleiotropy can be obtained using the mode- based estimate (MBE).\nC_LIO_LIThe 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).\nC_LIO_LIPlotting the smoothed empirical density function is useful to explore the distribution of causal effect estimates, and to understand how the MBE is determined.\nC_LI

epidemiology↗

bioRxiv2017

DNA Methylation As A Marker For Prenatal Smoke Exposure In Adults

Prenatal cigarette smoke is an environmental stressor that has a profound effect on DNA methylation in the exposed offspring. We have previously shown that some of these effects persist throughout childhood and into adolescence. Of interest is whether these signals persist into adulthood.\n\nWe conducted an analysis to investigate associations between reported maternal smoking in pregnancy and DNA methylation in peripheral blood of women in the Avon Longitudinal Study of Parents and Children (ALSPAC) (n=754; mean age 30 years). We observed associations at 15 CpG sites in 11 gene regions, MYO1G, FRMD4A, CYP1A1, CNTNAP2, ARL4C, AHRR, TIFAB, MDM4, AX748264, DRD1, FTO (FDR < 5%). All but two of these CpG sites have previously been identified in relation to prenatal smoke exposure in the offspring at birth and the majority showed persistent hypermethylation among the offspring of smokers.\n\nWe confirmed that most of these associations were not driven by own smoking and that they were still present 18 years later (N = 656; mean age 48 years). In addition, we replicated findings of a persistent methylation signal related to prenatal smoke exposure in peripheral blood among men in the ALSPAC cohort (N = 230; mean age 53 years). For both participant groups, there was a strong signal of association above that expected by chance at CpG sites previously associated with prenatal smoke exposure in newborns (Wilcoxon rank sum p-value < 2.2 x 10-4). Furthermore, we found that a prenatal smoking score, derived by combining methylation values at these CpG sites, could predict whether the mothers of the ALSPAC women smoked during pregnancy with an AUC 0.69 (95% 0.67, 0.73).

epidemiology↗

bioRxiv2017

Sex-associated autosomal DNA methylation differences are wide-spread and stablethroughout childhood

Almost all species show sexual discordance in many traits and diseases. DNA methylation is known to contribute to these differences through well-established mechanisms including X-inactivation in females, imprinting and parent-of-origin effects. Here we investigate sex discordance in DNA methylation throughout childhood in a sample of 700 individuals from the Avon Longitudinal Study of Parents and Children. We show that autosomal sex-discordant methylation is widespread, affecting approximately 12,000 CpG sites at any given age, and stable; at least 8,500 sites are consistently different across all time points and a large proportion discordant in both the fetal and adult brain cortices. Just over 1,000 methylation differences change from birth to late adolescence, 90% of these between birth and around age seven. Sexually discordant CpG sites are enriched in genomic loci containing androgen but not estrogen targets and in genes involved in tissue development but not housekeeping functions. A methylation-derived sex score capturing the variance was calculated at each time point and found to be highly correlated between time points. This score is nominally associated with sex hormone levels in childhood as well as some phenotypes previously linked to sex hormone levels. These findings suggest that sex-discordant autosomal DNA methylation is widespread throughout the genome, likely due to the first androgen exposures in utero. It is then stably maintained from birth to late adolescence. Methylation variation at sex-discordant sites within the sexes, as summarized by the methylation sex score, likely reflects in utero androgen exposure which is relevant to human health.\n\nSignificance StatementAlthough we know that sex hormones are critical for establishing sexual discordance, less is known about how this discordance is achieved and maintained. Here we present evidence for widespread differences in DNA methylation between male and female children. We show that most of these differences are established prenatally, likely due to the first androgen exposures in utero, and then stably maintained throughout childhood, despite extreme fluctuations in the levels of these very same hormones. Our results support a role for DNA methylation as a means for recording and maintaining the effects of exposure to sex hormones and thus to better understand sexual variation and how it is driven by the prenatal environment.

epidemiology↗

bioRxiv2017

Orienting The Causal Relationship Between Imprecisely Measured Traits Using Genetic Instruments

Inference of the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT) and others. However, we show that measurement error in the phenotypes can lead to mediation-based approaches inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it is less susceptible to bias in the presence of measurement error; second, it is more statistically efficient; third, it can be performed using only summary level data from genome-wide association studies; and fourth, its sensitivity to measurement error can be evaluated. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality.

systems biology↗

bioRxiv2017

Distinct Blood DNA Methylation Profiles In Subtypes Of Orofacial Cleft

BackgroundThere is evidence that different subtypes of orofacial cleft have distinct aetiologies, although the precise molecular mechanisms underlying these are unknown. Given the key role of epigenetic processes such as DNA methylation in embryonic development, it is likely that aberrant DNA methylation may also play a part in the development of orofacial clefts.\n\nMethodsIn this study, we explored whether blood samples from children with different cleft subtypes showed distinct DNA methylation profiles.\n\nIn whole blood samples from 150 children from the Cleft Collective cohort study, we measured DNA methylation at over 450,000 sites on the genome. We then carried out epigenome-wide association studies (EWAS) to test the association between methylation at each site and cleft subtype (cleft lip only CLO n=50; cleft palate only CPO n=50; cleft lip and palate CLP n=50).\n\nResultsWe found four genomic regions differentially methylated in CLO compared to CLP, 17 in CPO compared to CLP and 294 in CPO compared to CLO. These regions included several mapping to genes that have previously been implicated in the development of orofacial clefts (for example, TBX1, COL11A2, HOXA2, PDGFRA) and over 250 novel associations.\n\nConclusionOur finding of distinct methylation profiles in different cleft subtypes might reflect differences in their aetiologies, with DNA methylation either playing a causal role in development of OFC subtypes or reflecting causal genetic or environmental factors.

epidemiology↗

bioRxiv2017

Do early life non-cognitive skills matter?A systematic review and meta-analysis of early life effects on academic achievement, psychosocial, language and cognitive, and health outcomes

BackgroundSuccess in school and the labour market is due to more than just high intelligence. Associations between traits such as attention, self-regulation, and perseverance in childhood, and later outcomes have been investigated by psychologists, economists, and epidemiologists. Such traits have been loosely referred to as \"non-cognitive\" skills. There has been no attempt to systematically assess the relative importance of non-cognitive skills in early life on later outcomes.\n\nMethodsThe systematic review protocol was registered with the International Prospective Register for Systematic Reviews (PROSPERO, CRD42013006566) in December 2013. We systematically reviewed electronic databases covering psychology, education, health and economics for articles published from database conception until September 2015. Titles and abstracts were screened for eligibility, and from eligible articles data was extracted on study design, sample type and size, age of participants at exposure and outcome, loss to follow up, measurement of exposure and outcome, type of intervention and comparison group, confounding adjustment and results. Where possible we extracted a standardised effect size. We reviewed all studies and rated their evidence quality as better, weak, or poor on the basis of study design and potential for confounding, selection and measurement bias.\n\nResultsWe reviewed 375 studies and provided interpretation of results from 142 (38%) better quality studies comprising randomised controlled trials, quasi-experimental, fixed effects including twin studies, longitudinal and some cross-sectional designs that made reasonable attempts to control confounding. In the academic achievement category outcomes were reported in 78 publications of better quality studies which were consistent with 0.1-0.2 SD effects.\n\nPsychosocial outcomes were reported in 65 better quality studies consistent with effects of 0.3-0.4 SD. For the language and cognitive category there were 42 publications reporting better quality studies consistent with effects of 0.3-0.4 SD. For physical health, results across only eight better quality studies were inconsistent but centred around zero. Analysis of funnel plots consistently showed asymmetric distributions, raising the potential of small study bias which may inflate these observed effects.\n\nConclusionsThe evidence under-pinning the importance of non-cognitive skills for life success is diverse and inconsistent. Nevertheless, there is tentative evidence from published studies that non-cognitive skills associate with modest improvements in academic achievement, psychosocial, and language and cognitive outcomes with effects in the range of 0.2-0.4 SD. The quality of evidence under-pinning this field is generally low with more than a third of studies making little or no attempt to control even the most basic confounding (endogeneity) bias. The evidence could be improved by adequately powering studies, and using procedures and tools that improve the conduct and reporting of RCTs and observational studies. Interventions designed to develop childrens non-cognitive skills could potentially improve opportunities, particularly for disadvantaged children. The inter-disciplinary researchers interested in these skills should take a more rigorous approach to determine which interventions are most effective.

epidemiology↗

bioRxiv2017

PHESANT: a tool for performing automated phenome scans in UK Biobank

MotivationEpidemiological cohorts typically contain a diverse set of phenotypes such that automation of phenome scans is non-trivial, because they require highly heterogeneous models. For this reason, phenome scans have to date tended to use a smaller homogeneous set of phenotypes that can be analysed in a consistent fashion. We present PHESANT (PHEnome Scan ANalysis Tool), a software package for performing comprehensive phenome scans in UK Biobank.\n\nGeneral featuresPHESANT tests the association of a specified trait with all continuous, integer and categorical variables in UK Biobank, or a specified subset. PHESANT uses a novel rule-based algorithm to determine how to appropriately test each trait, then performs the analyses and produces plots and summary tables.\n\nImplementationThe PHESANT phenome scan is implemented in R. PHESANT includes a novel Javascript D3.js visualization, and accompanying Java code that converts the phenome scan results to the required JavaScript Object Notation (JSON) format.\n\nAVAILABILITYPHESANT is available on GitHub at [https://github.com/MRCIEU/PHESANT]. Git tag v0.2 corresponds to the version presented here.

epidemiology↗

bioRxiv2017

Cigarette smoking increases coffee consumption: findings from a Mendelian randomisation analysis

BackgroundSmokers tend to consume more coffee than non-smokers and there is evidence for a positive relationship between cigarette and coffee consumption in smokers. Cigarette smoke increases the metabolism of caffeine, so this association may represent a causal effect of smoking on caffeine intake.\n\nMethodsWe performed a Mendelian randomisation analysis in 114,029 individuals from the UK Biobank, 56,664 from the Norwegian HUNT study and 78,650 from the Copenhagen General Population Study. We used a genetic variant in the CHRNA5 nicotinic receptor (rs16969968) as a proxy for smoking heaviness. Coffee and tea consumption were self-reported. Analyses were conducted using linear regression and meta-analysed across studies.\n\nResultsEach additional cigarette per day consumed by current smokers was associated with higher coffee consumption (0.10 cups per day, 95% CI:0.03,0.17). There was weak evidence for an increase in tea consumption per additional cigarette smoked per day (0.04 cups per day, 95% CI:-0.002,0.07). There was strong evidence that each additional copy of the minor allele of rs16969968 (which increases daily cigarette consumption) in current smokers was associated with higher coffee consumption (0.15 cups per day, 95% CI:0.11,0.20), but only weak evidence for an association with tea consumption (0.04 cups per day, 95% CI:- 0.01,0.09). There was no clear evidence that rs16969968 was associated with coffee or tea consumption in never or former smokers.\n\nConclusionThese findings suggest that higher cigarette consumption causally increases coffee intake. This is consistent with faster metabolism of caffeine by smokers, but may also reflect behavioural links between smoking and coffee.

genetics↗

bioRxiv2017

Education and coronary heart disease: a Mendelian randomization study

ObjectivesTo determine whether educational attainment is a causal risk factor in the development of coronary heart disease.\n\nDesignMendelian randomization study, where genetic data are used as proxies for education, in order to minimize confounding. A two-sample design was applied, where summary level genetic data was analysed from two publically available consortia.\n\nSettingIn the main analysis, we analysed genetic data from two large consortia (CARDIoGRAM and SSGAC), comprising of 112 cohorts from predominantly high-income countries. In addition, we also analysed genetic data from 7 additional large consortia, in order to identify putative causal mediators.\n\nParticipantsThe main analysis was of 589 377 men and women, predominantly of European origin.\n\nExposureA one standard deviation increase in the genetic predisposition towards higher education (i.e. 3.6 years of additional schooling). This was measured by 162 genetic variants that have been previously associated with education.\n\nMain outcomeCombined fatal and nonfatal coronary heart disease (63 746 events).\n\nResults3.6 years of additional education lowered the risk of coronary heart disease by a third (odds ratio = 0.67, 95% confidence interval [CI], 0.59 to 0.77, p=0.01). Equivalent increases in education were also causally associated with reductions in smoking, BMI and improvements in blood lipid profiles.\n\nConclusionsMore time spent in education is causally associated with a large reduction in the risk of coronary heart disease. This may be partly explained by changes to smoking, BMI and a blood lipids. These findings offer support for policy interventions that increase education, in order to also reduce the burden of cardiovascular disease.

epidemiology↗

bioRxiv2017

Mendelian randomization: a novel approach for the prediction of adverse drug events and drugrepurposing opportunities

Identification of unintended drug effects, specifically drug repurposing opportunities and adverse drug events, maximizes the benefit of a drug and protects the health of patients. However, current observational research methods are subject to several biases. These include confounding by indication, reverse causality, and missing data. We propose that Mendelian randomization (MR) offers a novel approach for the prediction of unintended drug effects. In particular, we advocate the synthesis of evidence from this method and other approaches, in the spirit of triangulation, to improve causal inferences concerning drug effects. MR overcomes some of the limitations associated with the existing methods in this field. Furthermore, it can be applied either pre- or post-approval of the drug and could therefore prevent the potentially harmful exposure of patients in clinical trials and beyond. The potential of MR as a pharmacovigilance and drug repurposing tool is yet to be realized and could both help prevent adverse drug events and identify novel indications for existing drugs in the future.

pharmacology and toxicology↗

bioRxiv2016

MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations

Published genetic associations can be used to infer causal relationships between phenotypes, bypassing the need for individual-level genotype or phenotype data. We have curated complete summary data from 1094 genome-wide association studies (GWAS) on diseases and other complex traits into a centralised database, and developed an analytical platform that uses these data to perform Mendelian randomization (MR) tests and sensitivity analyses (MR-Base, http://www.mrbase.org). Combined with curated data of published GWAS hits for phenomic measures, the MR-Base platform enables millions of potential causal relationships to be evaluated. We use the platform to predict the impact of lipid lowering on human health. While our analysis provides evidence that reducing LDL-cholesterol, lipoprotein(a) or triglyceride levels reduce coronary disease risk, it also suggests causal effects on a number of other non-vascular outcomes, indicating potential for adverse-effects or drug repositioning of lipid-lowering therapies.

epidemiology↗