Exploring the association of genetic factors with participation in the Avon Longitudinal Study of Parents and Children

Taylor, Amy E; Jones, Hannah; Sallis, Hannah M; Euesden, Jack; Stergiakouli, Evie; Davies, Neil M; Zammit, Stanley; Lawlor, Debbie A.; Munafò, Marcus R.; Smith, George Davey; Tilling, Kate

doi:10.1093/ije/dyy060

Cited by 193 publications

(200 citation statements)

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“…These include: lower intelligence and educational attainment; higher adiposity, and increased liability to ADHD, neuroticism and schizophrenia. Many of these were previously identified in the ALSPAC study 1,5 , previous UK Biobank study analyses 18 and Generation Scotland 18 . This implies that missingness of all the variables collected in the optional components of UK Biobank will be influenced by these underlying traits.…”

Section: Discussionmentioning

confidence: 93%

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Genetic predictors of participation in optional components of UK Biobank

Tyrrell

Zheng

Beaumont

et al. 2020

Preprint

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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 Alzheimer's 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, Alzheimer's 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.

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Section: Discussionmentioning

confidence: 93%

“…Initial participation and continued engagement in cohort studies may be influenced by an individual's social and lifestyle characteristics 1 . This selection has the potential to result in bias in estimating phenotypic and genotypic associations 2 .…”

Section: Introductionmentioning

confidence: 99%

Genetic predictors of participation in optional components of UK Biobank

Tyrrell

Zheng

Beaumont

et al. 2020

Preprint

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“…However, it is unlikely that our raw VA measure will suffer from heavy bias because the NPD key stage examinations represent the most accurate objective assessment of a child's educational ability. Finally, selection bias may influence our findings due to selective participation in the ALSPAC study (Munaf o et al, 2017;Taylor et al, 2018).…”

Section: Limitationsmentioning

confidence: 98%

Testing the validity of value‐added measures of educational progress with genetic data

Morris

Davies

Dorling

et al. 2018

British Educational Res J

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Value‐added measures of educational progress have been used by education researchers and policy‐makers to assess the performance of teachers and schools, contributing to performance‐related pay and position in school league tables. They are designed to control for all underlying differences between pupils and should therefore provide unbiased measures of school and teacher influence on pupil progress, however, their effectiveness has been questioned. We exploit genetic data from a UK birth cohort to investigate how successfully value‐added measures control for genetic differences between pupils. We use raw value‐added, contextual value‐added (which additionally controls for background characteristics) and teacher‐reported value‐added measures built from data at ages 11, 14 and 16. Sample sizes for analyses range from 4,600 to 6,518. Our findings demonstrate that genetic differences between pupils explain little variation in raw value‐added measures but explain up to 20% of the variation in contextual value‐added measures (95% CI = 6.06% to 35.71%). Value‐added measures built from teacher‐rated ability have a greater proportion of variance explained by genetic differences between pupils, with 36.3% of their cross‐sectional variation being statistically accounted for by genetics (95% CI = 22.8% to 49.8%). By contrast, a far greater proportion of variance is explained by genetic differences for raw test scores at each age of at least 47.3% (95% CI: 35.9 to 58.7). These findings provide evidence that value‐added measures of educational progress can be influenced by genetic differences between pupils, and therefore may provide a biased measure of school and teacher performance. We include a glossary of genetic terms for educational researchers interested in the use of genetic data in educational research.

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“…Although the LCV model is more robust than MR to biasing effects from sources such as horizontal pleiotropy and sample overlap, the LCV model can become biased by correlation between genetic variation and environmental factors which affect disease 17,35 . This aggregation might be due to factors such as ancient ancestry 36 , genetic nurture effects 37 or sampling phenomena 38 and is a concern in the UK Biobank sample 39 where much of the data used in this experiment were obtained. Interpreted in this light, it is possible that environmental factors which are more prevalent in groups of people with certain hair type or skin color are a cause of dental diseases.…”

Section: Discussionmentioning

confidence: 99%

Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis

Haworth

Kho

Holgerson

et al. 2019

Preprint

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BackgroundHypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments. MethodsWe developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed a causal architecture plot. We apply this process to body mass index and lipid traits as exemplars of traits where there is strong prior expectation for causal effects and dental caries and periodontitis as exemplars of traits where there is a need for causal inference. ResultsThe results for lipids and BMI suggest that these traits are best viewed as creating consequences on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health. ConclusionsThe automated process is available as part of the MASSIVE pipeline from the Complex-Traits Genetics Virtual Lab (https://vl.genoma.io) and results are available in (https://view.genoma.io). We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a way visualizing the overall causal map of the human phenome. Key messages1) The latent causal variable approach uses summary statistics from genome-wide association studies to estimate a parameter termed genetic causality proportion.2) Systematic estimation of genetic causality proportion for many pairs of traits provides an alternative method for phenome-wide causal inference with some theoretical and practical advantages compared to phenome-wide Mendelian randomization.3) Using this approach, we confirm that lipid traits are an upstream risk factor for other traits and diseases, and we identify that dental diseases are predominantly a downstream consequence of other traits rather than a cause of poor systemic health.4) The method assumes no bidirectional causality and no confounding by environmental correlates of genotypes, so care is needed when these assumptions are not met. 5)We developed an automated and accessible pipeline for estimating phenome-wide causal relationships and generating interactive visual summaries.

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Exploring the association of genetic factors with participation in the Avon Longitudinal Study of Parents and Children

Cited by 193 publications

References 43 publications

Genetic predictors of participation in optional components of UK Biobank

Genetic predictors of participation in optional components of UK Biobank

Testing the validity of value‐added measures of educational progress with genetic data

Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis

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