Biology subjects

Hartwig, F. P.

Publications and source records attributed to Hartwig, F. P..

10 recordsLinked to original sources

bioRxiv2020

Assortative mating and within-spouse pair comparisons

Comparisons between cohabitating spouses have been proposed as an aetiological design method to reduce confounding and evaluate effects of the shared adulthood environment. However, assortative mating, a phenomenon where individuals select phenotypically similar mates, could distort associations. We evaluated the use of spousal comparisons, as in the within-spouse pair (WSP) model, for aetiological epidemiological research. Using directed acyclic graphs and simulations, we demonstrated that the WSP model can reduce confounding if spouses are correlated for an unmeasured confounder, but that WSP comparisons are susceptible to collider bias induced by assortative mating. Empirical analyses using spouse pairs in UK Biobank found evidence that genetic association estimates from the WSP model are attenuated compared to random pairs for single nucleotide polymorphisms (SNPs) associated with height (shrinkage: 23%; 95% CI 20%, 25%), educational attainment (74%; 95% CI 66%, 81%) and body mass index (23%; 95% CI 14%, 32%) as well as for an alcohol consumption SNP (29%, 95% CI 5%, 46%). Some of these attenuations are likely to reflect effects of assortative mating because height and educational attainment are unlikely to be strongly influenced by the adulthood environment. In contrast, effect estimates of increasing age on coronary artery disease and systolic blood pressure were found to be concordant between random and spouse pairs. Assortative mating is likely to induce phenotypic and genetic structure between an individual and their spouse which complicates the interpretation of spousal comparisons in an aetiological context. A further consideration is that the joint participation of non-independent spouses in cohort studies could induce selection bias.

genetics↗

bioRxiv2019

Bias in two-sample Mendelian randomization by using covariable-adjusted summary associations

BackgroundTwo-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. MethodsWe performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. ResultsIn the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. ConclusionsOur findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution. Key messagesO_LISummary genetic associations from large genome-wide associations studies (GWAS) have been increasingly used in two-sample Mendelian randomization (MR) analyses. C_LIO_LIMany GWAS adjust for heritable covariates in an attempt to estimate direct genetic effects on the trait of interest. C_LIO_LIIn an extensive simulation study, we demonstrate that using covariable-adjusted summary associations may bias MR analyses. C_LIO_LIThe bias largely depends on the underlying causal structure, specially the presence of unmeasured common causes between the covariable and the outcome. C_LIO_LIOur findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. C_LI

genetics↗

bioRxiv2019

Association between breastfeeding and DNA methylation over the life course: findings from the Avon Longitudinal Study of Parents and Children (ALSPAC)

BackgroundBreastfeeding is associated with short and long-term health benefits. Long-term effects might be mediated by epigenetic mechanisms, yet a recent systematic review indicated that the literature on this topic is scarce. We performed the first epigenome-wide association study of infant feeding, comparing breastfed vs non-breastfed children. We measured DNA methylation in children from peripheral blood collected in childhood (age 7, N=640) and adolescence (age 15-17, N=709) within the Accessible Resource for Integrated Epigenomic Studies (ARIES) project, part of the larger Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Cord blood methylation (N=702) was used as a negative control for potential pre-natal residual confounding.\n\nResultsTwo differentially-methylated sites presented directionally-consistent associations with breastfeeding at ages 7 and 15-17, but not at birth. Twelve differentially-methylated regions in relation to breastfeeding were identified, and for three of them there was evidence of directional concordance between ages 7 and 15-17, but not between birth and age 7.\n\nConclusionsOur findings indicate that DNA methylation in childhood and adolescence may be predicted by breastfeeding, but further studies with sufficiently large samples for replication are required to identify robust associations.

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↗

bioRxiv2018

Evaluation of the causal effect of fibrinogen on incident coronary heart disease via Mendelian randomization

BackgroundFibrinogen is an essential hemostatic factor and cardiovascular disease risk factor. Early attempts at evaluating the causal effect of fibrinogen on coronary heart disease (CHD) and myocardial infraction (MI) using Mendelian randomization (MR) used single variant approaches, and did not take advantage of recent genome-wide association studies (GWAS) or multi-variant, pleiotropy robust MR methodologies.\n\nMethods and FindingsWe evaluated evidence for a causal effect of fibrinogen on both CHD and MI using MR. We used both an allele score approach and pleiotropy robust MR models. The allele score was composed of 38 fibrinogen-associated variants from recent GWAS. Initial analyses using the allele score incorporated data from 11 European-ancestry prospective cohorts to examine incidence CHD and MI. We also applied 2 sample MR methods with data from a prevalent CHD and MI GWAS. Results are given in terms of the hazard ratio (HR) or odds ratio (OR), depending on the study design, and associated 95% confidence interval (CI).\n\nIn single variant analyses no causal effect of fibrinogen on CHD or MI was observed. In multi-variant analyses using incidence CHD cases and the allele score approach, the estimated causal effect (HR) of a 1 g/L higher fibrinogen concentration was 1.62 (CI = 1.12, 2.36) when using incident cases and the allele score approach. In 2 sample MR analyses that accounted for pleiotropy, the causal estimate (OR) was reduced to 1.18 (CI = 0.98, 1.42) and 1.09 (CI = 0.89, 1.33) in the 2 most precise (smallest CI) models, out of 4 models evaluated. In the 2 sample MR analyses for MI, there was only very weak evidence of a causal effect in only 1 out of 4 models.\n\nConclusionsA small causal effect of fibrinogen on CHD is observed using multi-variant MR approaches which account for pleiotropy, but not single variant MR approaches. Taken together, results indicate that even with large sample sizes and multi-variant approaches MR analyses still cannot exclude the null when estimating the causal effect of fibrinogen on CHD, but that any potential causal effect is likely to be much smaller than observed in epidemiological studies.\n\nAuthor SummaryInitial Mendelian Randomization (MR) analyses of the causal effect of fibrinogen on coronary heart disease (CHD) utilized single variants and did not take advantage of modern, multivariant approaches. This manuscript provides an important update to these initial analyses by incorporating larger sample sizes and employing multiple, modern multi-variant MR approaches to account for pleiotropy. We used incident cases to perform a MR study of the causal effect of fibrinogen on incident CHD and the nested outcome of myocardial infarction (MI) using an allele score approach. Then using data from a case-control genome-wide association study for CHD and MI we performed two sample MR analyses with multiple, pleiotropy robust approaches. Overall, the results indicated that associations between fibrinogen and CHD in observational studies are likely upwardly biased from any underlying causal effect. Single variant MR approaches show little evidence of a causal effect of fibrinogen on CHD or MI. Multi-variant MR analyses of fibrinogen on CHD indicate there may be a small positive effect, however this result needs to be interpreted carefully as the 95% confidence intervals were still consistent with a null effect. Multi-variant MR approaches did not suggest evidence of even a small causal effect of fibrinogen on MI.

genetics↗

bioRxiv2018

The median and the mode as robust meta-analysis methods in the presence of small study effects

Meta-analyses based on systematic literature reviews are commonly used to obtain a quantitative summary of the available evidence on a given topic. Despite its attractive simplicity, and its established position at the summit of the evidence-based medicine hierarchy, the reliability of any meta-analysis is largely constrained by the quality of its constituent studies. One major limitation is small study effects, whose presence can often easily be detected, but not so easily adjusted for. Here, robust methods of estimation based on the median and mode are proposed as tools to increase the reliability of findings in a meta-analysis. By re-examining data from published meta-analyses, and by conducting a detailed simulation study, we show that these two simple methods offer notable robustness to a range of plausible bias mechanisms, without making any explicit modelling assumptions. In conclusion, when performing a meta-analysis with suspected small study effects, we recommend reporting the mean, median and modal pooled estimates as a simple but informative sensitivity analyses.

epidemiology↗

bioRxiv2017

Multiethnic Meta-analysis Identifies New Loci for Pulmonary Function

Nearly 100 loci have been identified for pulmonary function, almost exclusively in studies of European ancestry populations. We extend previous research by meta-analyzing genome-wide association studies of 1000 Genomes imputed variants in relation to pulmonary function in a multiethnic population of 90,715 individuals of European (N=60,552), African (N=8,429), Asian (N=9,959), and Hispanic/Latino (N=11,775) ethnicities. We identified over 50 novel loci at genome-wide significance in ancestry-specific and/or multiethnic meta-analyses. Recent fine mapping methods incorporating functional annotation, gene expression, and/or differences in linkage disequilibrium between ethnicities identified potential causal variants and genes at known and newly identified loci. Sixteen of the novel genes encode proteins with predicted or established drug targets, including KCNK2 and CDK12.

genetics↗

bioRxiv2017

Effect modification of FADS2 polymorphisms on the association between breastfeeding and intelligence: results from a collaborative meta-analysis

BackgroundAccumulating evidence suggests that breastfeeding benefits the childrens intelligence. Long-chain polyunsaturated fatty acids (LC-PUFAs) present in breast milk may explain part of this association. Under a nutritional adequacy hypothesis, an interaction between breastfeeding and genetic variants associated with endogenous LC-PUFAs synthesis might be expected. However, the literature on this topic is controversial.\n\nMethods and FindingsWe investigated this GenexEnvironment interaction in a de novo meta-analysis involving >12,000 individuals in the primary analysis, and >45,000 individuals in a secondary analysis using relaxed inclusion criteria. Our primary analysis used ever breastfeeding, FADS2 polymorphisms rs174575 and rs1535 coded assuming a recessive effect of the G allele, and intelligence quotient (IQ) in Z scores. Using random effects meta-analysis, ever breastfeeding was associated with 0.17 (95% CI: 0.03; 0.32) higher Z scores in IQ, or about 2.1 points. There was no strong evidence of interaction, with pooled covariate-adjusted interaction coefficients (i.e., difference between genetic groups of the difference in IQZ scores comparing ever with never breastfed individuals) of 0.12 (95% CI: -0.19; 0.43) and 0.06 (95% CI: -0.16; 0.27) for the rs174575 and rs1535 variants, respectively. Secondary analyses corroborated these results. In studies with >5.85 and <5.85 months of breastfeeding duration, pooled estimates for the rs174575 variant were 0.50 (95% CI: -0.06; 1.06) and 0.14 (95% CI: -0.10; 0.38), respectively, and 0.27 (95% CI: -0.28; 0.82) and -0.01 (95% CI: -0.19; 0.16) for the rs1535 variant. However, between-group comparisons were underpowered.\n\nConclusionsOur findings do not support an interaction between ever breastfeeding and FADS2 polymorphisms. However, our subgroup analysis raises the possibility that breastfeeding supplies LC-PUFAs requirements for cognitive development (if such threshold exists) if it lasts for some (currently unknown) time. Future studies in large individual-level datasets would allow properly powered subgroup analyses and would improve our understanding on the role of breastfeeding duration in the breastfeedingxFADS2 interaction.

epidemiology↗

bioRxiv2017

The genetic architecture of osteoarthritis: insights from UK Biobank

Osteoarthritis is a common complex disease with huge public health burden. Here we perform a genome-wide association study for osteoarthritis using data across 16.5 million variants from the UK Biobank resource. Following replication and meta-analysis in up to 30,727 cases and 297,191 controls, we report 9 new osteoarthritis loci, in all of which the most likely causal variant is non-coding. For three loci, we detect association with biologically-relevant radiographic endophenotypes, and in five signals we identify genes that are differentially expressed in degraded compared to intact articular cartilage from osteoarthritis patients. We establish causal effects for higher body mass index, but not for triglyceride levels or type 2 diabetes liability, on osteoarthritis.

genetics↗

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↗