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Genetic predictors of participation in optional components of UK Biobank
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Abstract
Large studies such as UK Biobank are increasingly used for GWAS and Mendelian randomization (MR) studies. However, 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 < 6 × 10 −9 ), including loci with 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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“With individual level data and a continuous exposure, non-linear MR can be applied to estimate whether the causal effect of the exposure on the outcome varies across different levels of the exposure.30,108”
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Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882
An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255
Fry, A., Littlejohns, T. J., Sudlow, C., Doherty, N., Adamska, L., Sprosen, T., Collins, R., & Allen, N. E. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx246 · Cited by 3,979
23andMe Research Team, Lee, J. J., COGENT (Cognitive Genomics Consortium), Social Science Genetic Association Consortium, Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Nguyen-Viet, T. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0147-3 · Cited by 2,470
Review of inverse probability weighting for dealing with missing data
Seaman, S. R., & White, I. R. (2011) · Statistical Methods in Medical Research · doi:10.1177/0962280210395740 · Cited by 1,696
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Loh, P.-R., Tucker, G., Bulik-Sullivan, B. K., Vilhjálmsson, B. J., Finucane, H. K., Salem, R. M., Chasman, D. I., Ridker, P. M., Neale, B. M., Berger, B., et al. (2015) · Nature Genetics · doi:10.1038/ng.3190 · Cited by 1,507
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Anderson, E. L., Richmond, R. C., Jones, S. E., Hemani, G., Wade, K. H., Dashti, H. S., Lane, J. M., Wang, H., Saxena, R., Brumpton, B. M., et al. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyaa183 · Cited by 57
Self-citationCapalbo, A., de Wert, G. M. W. R., Mertes, H., Klausner, L., Coonen, E., Spinella, F., de Velde, H. V., Viville, S., Sermon, K., Vermeulen, N., et al. (2024) · Human Reproduction Update · doi:10.1093/humupd/dmae012 · Cited by 66
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Han, Y., Chien, C., Goel, P., He, K., Pinales, C., Buser, C., & Dickman, D. K. (2022) · eLife · doi:10.7554/elife.77924 · Cited by 38
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McAdams, T. A., Cheesman, R., & Ahmadzadeh, Y. I. (2022) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13720 · Cited by 38
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Davitte, J., Stott‐Miller, M., Ehm, M. G., Cunnington, M. C., & Reynolds, R. F. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2477 · Cited by 11
Duchen, D., Vergara, C., Thio, C. L., Kundu, P., Chatterjee, N., Thomas, D. L., Wojcik, G. L., & Duggal, P. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.12.013 · Cited by 10
Smart, S. E., Legge, S. E., Fenner, E., Pardiñas, A. F., Woolway, G., Lynham, A. J., Escott‐Price, V., Hall, J., Wilkinson, L. S., Holmans, P., et al. (2025) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.33037 · Cited by 9
Determining the stability of genome-wide factors in BMI between ages 40 to 69 years
Gillespie, N. A., Gentry, A. E., Kirkpatrick, R. M., MAES, H. H. M., Reynolds, C. A., Mathur, R., Kendler, K. S., Peterson, R. E., & Webb, B. T. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010303 · Cited by 9
Schoeler, T., Pingault, J., & Kutalik, Z. (2023) · medRxiv · doi:10.1101/2023.10.06.23296652 · Cited by 10
Kappel, D. B., Smart, S. E., Owen, M. J., O’Donovan, M. C., Pardiñas, A. F., & Walters, J. (2025) · The Lancet Psychiatry · doi:10.1016/s2215-0366(25)00123-3 · Cited by 9
Using instruments for selection to adjust for selection bias in Mendelian randomization
Gkatzionis, A., Tchetgen, E. J. T., Heron, J., Northstone, K., & Tilling, K. M. (2024) · Statistics in Medicine · doi:10.1002/sim.10173 · Cited by 8
Self-citationReed, Z. E., Sallis, H., Richmond, R. C., Attwood, A. S., Lawlor, D. A., & Munafo, M. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-90437-x · Cited by 12
Longer sleep improves cardiovascular outcomes: time to make sleep a priority
Sofer, T., Goodman, M. O., Bertisch, S. M., & Redline, S. S. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab248 · Cited by 7
Big data and the goal of personalized health interventions
Hindley, G., Smeland, O. B., Frei, O., & Andreassen, O. A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822201-0.00021-6 · Cited by 6
BMI and well-being in people of East Asian and European ancestry: a Mendelian randomisation study
O’Loughlin, J., Casanova, F., Hughes, A., Fairhurst-Hunter, Z., Li, L., Chen, Z., China Kadoorie Biobank Collaborative Group, Bowden, J., Watkins, E. R., Freathy, R. M., et al. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02539-7 · Cited by 6
Self-citationLewis, S. J., Dack, K., Relton, C. L., Munafo, M. R., & Smith, G. D. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-050656 · Cited by 6
Mignogna, G., Carey, C. E., Wedow, R., Baya, N. A., Cordioli, M., Pirastu, N., Bellocco, R., Nivard, M. G., Neale, B. M., Walters, R. K., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.11.480140 · Cited by 6
Life course plasma metabolomic signatures of genetic liability to Alzheimer’s disease
Compton, H., Smith, M. L., Bull, C. J., Korologou‐Linden, R., Ben‐Shlomo, Y., Bell, J. A., Williams, D. M., & Anderson, E. L. (2024) · Scientific Reports · doi:10.1038/s41598-024-54569-w · Cited by 7
Bøen, R., Kaufmann, T., Frei, O., van der Meer, D., Djurovic, S., Andreassen, O. A., Selmer, K. K., Alnæs, D., & Sønderby, I. E. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02358-w · Cited by 5
Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease Spread
Züfle, A. E., Salim, F. D., Anderson, T., Scotch, M., Xiong, L., Sokol, K., Xue, H., Kong, R., Heslop, D. J., Paik, H.-Y., et al. (2024) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3660631 · Cited by 5
D’Antona, S., Pathak, G. A., Koller, D., Porro, D., Cava, C., & Polimanti, R. A. (2023) · Human Genetics · doi:10.1007/s00439-023-02525-5 · Cited by 6
The brief resilience scale: a genome-wide association study in the UK Biobank
Cornelis, M. C., Caldwell, J. A., Vu, T. T., Hennigar, S. R., Berryman, C. E., & Lieberman, H. R. (2025) · BMC Medicine · doi:10.1186/s12916-025-04368-5 · Cited by 4
Use of genetic correlations to examine selection bias
Shapland, C. Y., Gkatzionis, A., Hemani, G., & Tilling, K. M. (2024) · Genetic Epidemiology · doi:10.1002/gepi.22584 · Cited by 4
Self-citationDaniore, P., Hurndall, K., Zavattaro, F., Leis, M., & Gille, F. (2025) · Data & Policy · doi:10.1017/dap.2024.95 · Cited by 4
Casanova, F., O’Loughlin, J., Lewis, C. M., Frayling, T. M., Wood, A. R., & Tyrrell, J. (2022) · International Journal of Epidemiology · doi:10.1093/ije/dyac052 · Cited by 4
Self-citationThe genetics of falling susceptibility and identification of causal risk factors
Smith, M. C., O’Loughlin, J., Karageorgiou, V., Casanova, F., Williams, G., Hilton, M. P., & Tyrrell, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-44566-w · Cited by 6
Self-citationKeyes, K. M., Pakserian, D., Rudolph, K. E., Salum, G. A., & Stuart, E. A. (2024) · Current topics in behavioral neurosciences · doi:10.1007/7854_2024_465 · Cited by 4
A cautionary tale for Alzheimer’s disease GWAS by proxy
Pedersen, E. M., Wimberley, T., & Vilhjálmsson, B. J. (2024) · Nature Genetics · doi:10.1038/s41588-024-02023-y · Cited by 4
Metadata
- Authors
- Tyrrell, J., Zheng, J., Beaumont, R., Hinton, K., Richardson, T. G., Wood, A. R., Davey Smith, G., Frayling, T. M., & Tilling, K.
- Affiliations
- University of Exeter (GB)
- University of Bristol (GB)
- Corresponding author
- Tyrrell, Jessica
- Tilling, Kate
- ORCID
- Venue
- Nature Communications
- Published
- 2021-02-09
- Volume
- 12
- Issue
- 1
- Pages
- 886-886
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7873270
- OpenAlex
- W3119247746
- Cited by
- 162
- References
- 43
- Citations by year
- 2026: 14 · 2025: 16 · 2024: 30 · 2023: 36 · 2022: 43 · 2021: 15
- FWCI
- 17.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Explore Bristol Research (acceptedVersion, cc-by)
- Bristol Research (University of Bristol) (acceptedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Open Research Exeter (University of Exeter) (submittedVersion, cc-by)
- Funders
- Academy of Medical Sciences (SBF004\1079)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Physical Performance
- Keywords
- research participation, UK Biobank, genetic correlation, Mendelian randomization, educational attainment, age at menarche, height, adiposity, dyslipidemia, neuroticism, Alzheimer's disease, schizophrenia
- MeSH terms
- Community Participation, Female, United Kingdom, Humans, Male, Middle Aged, Risk Factors, Genetic Variation, Selection Bias, Biological Specimen Banks, Genetic Predisposition to Disease, Genome-Wide Association Study, Genetic Loci, Mendelian Randomization Analysis
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