Biology subjects

Davey-Smith, G.

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

5 recordsLinked to original sources

bioRxiv2020

Genomic evaluation of circulating proteins for drug target characterisation and precision medicine

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. By mapping and replicating protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, we identified 467 pQTLs for 85 proteins. The pQTLs were used in combination with other sources of information to evaluate known drug targets, and suggest new target candidates or repositioning opportunities, underpinned by a) causality assessment using Mendelian randomization, b) pathway mapping using trans-pQTL gene assignments, and c) protein-centric polygenic risk scores enabling matching of plausible target mechanisms to sub-groups of individuals enabling precision medicine.

genetics↗

bioRxiv2020

Genetic predictors of participation in optional components of UK Biobank

Large studies (e.g. UK Biobank) are increasingly used for GWAS and Mendelian randomization (MR) studies. Selection into and dropout from studies may bias genetic and phenotypic associations. We examine genetic factors affecting participation in four optional components in up to 451,306 UK Biobank participants. We used GWAS to identify genetic variants associated with participation, MR to estimate effects of phenotypes on participation, and genetic correlations to compare participation bias across different studies. 32 variants were associated with participation in one of the optional components (P<6x10-9), including loci with known links to intelligence and Alzheimers disease. Genetic correlations demonstrated that participation bias was common across studies. MR showed that longer educational duration, older menarche and taller stature increased participation, whilst higher levels of adiposity, dyslipidaemia, neuroticism, Alzheimers and schizophrenia reduced participation. Our effect estimates can be used for sensitivity analysis to account for selective participation biases in genetic or non-genetic analyses.

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

Haplotype genetic score analysis in 10,734 mother/infant pairs reveals complex maternal and fetal genetic effects underlying the associations between maternal phenotypes, birth outcomes and adult phenotypes

Many maternal traits are associated with a neonates gestational duration, birth weight and birth length. These birth outcomes are subsequently associated with late onset health conditions. Based on 10,734 mother/infant duos of European ancestry, we constructed haplotype genetic scores to dissect the maternal and fetal genetic effects underlying these observed associations. We showed that maternal height and fetal growth jointly affect the duration of gestation - maternal height positively influences the gestational duration, while faster fetal growth reduces gestational duration. Fetal growth is influenced by both maternal and fetal effects and can reciprocally influence maternal phenotypes: tall maternal stature and higher blood glucose causally increase birth size; in the fetus, the height and metabolic risk increasing alleles can lead to increased and decreased birth size respectively; birth weight-raising alleles in fetus may reduce gestational duration and increase maternal blood pressure. These maternal and fetal genetic effects can largely explain the observed associations between the studied maternal phenotypes and birth outcomes as well as the life-course associations between these birth outcomes and adult phenotypes.

genetics↗

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↗