Biology subjects

Bowden, J.

Publications and source records attributed to Bowden, J..

27 records · Page 2Linked to original sources

bioRxiv2017

Improving the accuracy of two-sample summary data Mendelian randomization: moving beyond the NOME assumption

BackgroundTwo-sample summary data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated.\n\nMethodsCausal estimation and heterogeneity assessment in MR requires an approximation for the variance, or equivalently the inverse-variance weight, of each ratio estimate. We show that the most popular 1st order weights can lead to an inflation in the chances of detecting heterogeneity when in fact it is not present. Conversely, ostensibly more accurate 2nd order weights can dramatically increase the chances of failing to detect heterogeneity, when it is truly present. We derive modified weights to mitigate both of these adverse effects.\n\nResultsUsing Monte Carlo simulations, we show that the modified weights outperform 1st and 2nd order weights in terms of heterogeneity quantification. Modified weights are also shown to remove the phenomenon of regression dilution bias in MR estimates obtained from weak instruments, unlike those obtained using 1st and 2nd order weights. However, with small numbers of weak instruments, this comes at the cost of a reduction in estimate precision and power to detect a causal effect compared to 1st order weighting. Moreover, 1st order weights always furnish unbiased estimates and preserve the type I error rate under the causal null. We illustrate the utility of the new method using data from a recent two-sample summary data MR analysis to assess the causal role of systolic blood pressure on coronary heart disease risk.\n\nConclusionsWe propose the use of modified weights within two-sample summary data MR studies for accurately quantifying heterogeneity and detecting outliers in the presence of weak instruments. Modified weights also have an important role to play in terms of causal estimation (in tandem with 1st order weights) but further research is required to understand their strengths and weaknesses in specific settings.

epidemiology↗

bioRxiv2017

Investigating the role of insulin in increased adiposity: Bi-directional Mendelian randomization study

Insulin may serve as a key causal agent which regulates fat accumulation in the body. Here we assessed the causal relationship between fasting insulin and adiposity using publicly-available results from two large-scale genome-wide association studies for body mass index and fasting insulin levels in a two-sample, bidirectional Mendelian Randomized approach. This approach is only valid on the condition that the two instruments are independent of one another. In analysis excluding overlapping loci, there was an increase of 0.20 (0.17, 0.23) log pmol/L fasting insulin per SD increase in BMI (P= 2.80 x 10-36), while there was a null effect of fasting insulin on BMI, with a 0.01 (-0.39, 0.38) SD decrease in BMI per log pmol/L increase in fasting insulin (P= 0.98). Furthermore, a high degree of heterogeneity in the causal estimates was obtained from the insulin-related variants, which may be attributed to varying mechanisms of action of the insulin-associated variants. Results were largely consistent when an Egger regression technique and weighted median and mode estimators were applied. Findings suggest that the positive correlation between adiposity and fasting insulin levels are at least in part explained by the causal effect of adiposity on increasing insulin, rather than vice versa.

epidemiology↗

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

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

Testing the principles of Mendelian randomization: Opportunities and complications on a genomewide scale

BackgroundMendelian randomization (MR) uses genetic variants as instrumental variables to assess whether observational associations between exposures and disease reflect causal relationships. MR requires genetic variants to be independent of factors that confound observational associations.\n\nMethodsUsing data from the Avon Longitudinal Study of Parents and Children, associations within and between 121 phenotypes and 13,720 genetic variants (from the NHGRI-EBI GWAS catalog) were examined to assess the validity of MR assumptions.\n\nResultsAmongst 7,260 pairwise comparisons between the 121 phenotypes, 2,188 (30%) provided evidence of association, where 363 were expected at the 5% level (observed:expected ratio=6.03; 95% CI: 5.42, 6.70; {chi}2=9682.29; d.f. =1, P[≤]1x10-50). Amongst 1,660,120 pairwise associations between phenotypes and genotypes, 86,748 (5.2%) gave evidence of association at the same threshold, where 83,006 were expected (observed:expected ratio=1.05; 95% CI: 1.04, 1.05; {chi}2=117.57; d.f. =1, P=2.15x10-27). Amongst 1,171,764 pairwise associations between the phenotypes and LD pruned independent genetic variants, 60,136 (5.1%) gave evidence of association, where 58,588 were expected (observed:expected ratio=1.03; 95% CI: 1.03, 1.08; {chi}2= 43.05; d.f. = 1, P=5.33x10-11).\n\nConclusionThese results confirm previously observed patterns of phenotypic correlation. They also provide evidence of a substantially lower level of association between genetic variants and phenotypes, with residual inflation the likely product of indistinguishable real genetic association, multiple variables measuring the same biological phenomena, or pleiotropy. These results reflect the favorable properties of genetic instruments for estimating causal relationships, but confirm the need for functional information or analytical methods to account for pleiotropic events.

epidemiology↗

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↗

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↗

bioRxiv2016

Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits

Chronic sleep disturbances, associated with cardio-metabolic diseases, psychiatric disorders and all-cause mortality1,2, affect 25-30% of adults worldwide3. While environmental factors contribute importantly to self-reported habitual sleep duration and disruption, these traits are heritable4-9, and gene identification should improve our understanding of sleep function, mechanisms linking sleep to disease, and development of novel therapies. We report single and multi-trait genome-wide association analyses (GWAS) of self-reported sleep duration, insomnia symptoms including difficulty initiating and/or maintaining sleep, and excessive daytime sleepiness in the UK Biobank (n=112,586), with discovery of loci for insomnia symptoms (near MEIS1, TMEM132E, CYCL1, TGFBI in females and WDR27 in males), excessive daytime sleepiness (near AR/OPHN1) and a composite sleep trait (near INADL and HCRTR2), as well as replication of a locus for sleep duration (at PAX-8). Genetic correlation was observed between longer sleep duration and schizophrenia (rG=0.29, p=1.90x10-13) and between increased excessive daytime sleepiness and increased adiposity traits (BMI rG=0.20, p=3.12x10-09; waist circumference rG=0.20, p=2.12x10-07).

genetics↗