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The Effect of Genetic Predisposition to Alzheimer’s Disease and Related Traits on Recruitment Bias in a Study of Cognitive Aging

Published online by Cambridge University Press:  21 July 2023

Lina M. Gomez Open the ORCID record for Lina M. Gomez [Opens in a new window] ,
Brittany L. Mitchell ,
Kerrie McAloney ,
Jessica Adsett ,
Natalie Garden ,
Madeline Wood ,
Santiago Diaz-Torres ,
Luis M. Garcia-Marin ,
Michael Breakspear  and
Nicholas G. Martin Open the ORCID record for Nicholas G. Martin [Opens in a new window]
...Show all authors
Lina M. Gomez*
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Brittany L. Mitchell
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Kerrie McAloney
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Jessica Adsett
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Natalie Garden
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Madeline Wood
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Santiago Diaz-Torres
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
Luis M. Garcia-Marin
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
Michael Breakspear
Affiliation:
School of Psychological Sciences, The University of Newcastle, Newcastle, New South Wales, Australia
Nicholas G. Martin
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Michelle K. Lupton
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Queensland, Australia
*
Corresponding author: Lina Gomez; Email: lina.gomez@qimrberghofer.edu.au
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Abstract

The recruitment of participants for research studies may be subject to bias. The Prospective Imaging Study of Ageing (PISA) aims to characterize the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer’s disease (AD). Participants approached to take part in PISA were selected from existing cohort studies with available genomewide genetic data for both successfully and unsuccessfully recruited participants, allowing us to investigate the genetic contribution to voluntary recruitment, including the genetic predisposition to AD. We use a polygenic risk score (PRS) approach to test to what extent the genetic risk for AD, and related risk factors predict participation in PISA. We did not identify a significant association of genetic risk for AD with study participation, but we did identify significant associations with PRS for key causal risk factors for AD, IQ, household income and years of education. We also found that older and female participants were more likely to take part in the study. Our findings highlight the importance of considering bias in key risk factors for AD in the recruitment of individuals for cohort studies.

Keywords

Alzheimer’s diseaserecruitmentbiasgeneticspolygenicPRS
Type
Article
Information
Twin Research and Human Genetics , Volume 26 , Issue 3 , June 2023 , pp. 209 - 214
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of International Society for Twin Studies

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References

Andrews, S. J., Fulton-Howard, B., O’Reilly, P., Marcora, E., Goate, A. M., & Collaborators of the Alzheimer’s Disease Genetics Consortium. (2021). Causal associations between modifiable risk factors and the Alzheimer’s phenome. Annals of Neurology, 89, 54–65. https://doi.org/10.1002/ana.25918 CrossRefGoogle ScholarPubMed
Baker, L., Lavender, T., & Tincello, D. (2005). Factors that influence women’s decisions about whether to participate in research: An exploratory study. Birth, 32, 60–66. https://doi.org/10.1111/j.0730-7659.2005.00346.x CrossRefGoogle ScholarPubMed
Beard, C. M., Lane, A. W., O’Fallon, W. M., Riggs, B. L., & Melton, L. J., 3rd. (1994). Comparison of respondents and nonrespondents in an osteoporosis study. Annals of Epidemiology, 4, 398–403. https://doi.org/10.1016/1047-2797(94)90075-2 CrossRefGoogle Scholar
Bellenguez, C., Küçükali, F., Jansen, I. E., Kleineidam, L., Moreno-Grau, S., Amin, N., Naj, A. C., Campos-Martin, R., Grenier-Boley, B., Andrade, V., Holmans, P. A., Boland, A., Damotte, V., van der Lee, S. J., Costa, M. R., Kuulasmaa, T., Yang, Q., de Rojas, I., Bis, J. C., … Lambert, J.-C. (2022). New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nature Genetics, 54, 412–436. https://doi.org/10.1038/s41588-022-01024-z CrossRefGoogle ScholarPubMed
Benfante, R., Reed, D., MacLean, C., & Kagan, A. (1989). Response bias in the Honolulu Heart Program. American Journal of Epidemiology, 130, 1088–1100. https://doi.org/10.1093/oxfordjournals.aje.a115436 CrossRefGoogle ScholarPubMed
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015). Second-generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience, 4, 7. https://doi.org/10.1186/s13742-015-0047-8 CrossRefGoogle Scholar
Clark, K., Leung, Y. Y., Lee, W.-P., Voight, B., & Wang, L.-S. (2022). Polygenic Risk scores in Alzheimer’s disease genetics: Methodology, applications, inclusion, and diversity. Journal of Alzheimer’s Disease, 89, 1–12. https://doi.org/10.3233/JAD-220025 CrossRefGoogle ScholarPubMed
Elwood, J. M. (2013). Commentary: On representativeness. International Journal of Epidemiology, 42, 1014–1015. https://doi.org/10.1093/ije/dyt101 CrossRefGoogle ScholarPubMed
Enzenbach, C., Wicklein, B., Wirkner, K., & Loeffler, M. (2019). Evaluating selection bias in a population-based cohort study with low baseline participation: the LIFE-Adult-Study. BMC Medical Research Methodology, 19, 135. https://doi.org/10.1186/s12874-019-0779-8 CrossRefGoogle Scholar
Galea, S., & Tracy, M. (2007). Participation rates in epidemiologic studies. Annals of Epidemiology, 17, 643–653. https://doi.org/10.1016/j.annepidem.2007.03.013 CrossRefGoogle ScholarPubMed
Ganguli, M., Lee, C.-W., Hughes, T., Snitz, B. E., Jakubcak, J., Duara, R., & Chang, C.-C. H. (2015). Who wants a free brain scan? Assessing and correcting for recruitment biases in a population-based sMRI pilot study. Brain Imaging and Behavior, 9, 204–212. https://doi.org/10.1007/s11682-014-9297-9 CrossRefGoogle Scholar
Ghanbari, R., Lovasi, G. S., & Bader, M. D. M. (2023). Exploring potential for selection bias in using survey data to estimate the association between institutional trust and depression. Annals of Epidemiology, 77, 61–66. https://doi.org/10.1016/j.annepidem.2022.11.004 CrossRefGoogle ScholarPubMed
Haring, R., Alte, D., Völzke, H., Sauer, S., Wallaschofski, H., John, U., & Schmidt, C. O. (2009). Extended recruitment efforts minimize attrition but not necessarily bias. Journal of Clinical Epidemiology, 62, 252–260. https://doi.org/10.1016/j.jclinepi.2008.06.010 CrossRefGoogle Scholar
Heath, A. C., Whitfield, J. B., Martin, N. G., Pergadia, M. L., Goate, A. M., Lind, P. A., McEvoy, B. P., Schrage, A. J., Grant, J. D., Chou, Y.-L., Zhu, R., Henders, A. K., Medland, S. E., Gordon, S. D., Nelson, E. C., Agrawal, A., Nyholt, D. R., Bucholz, K. K., Madden, P. A. F., & Montgomery, G. W. (2011). A quantitative-trait genome-wide association study of alcoholism risk in the community: Findings and implications. Biological Psychiatry, 70, 513–518. https://doi.org/10.1016/j.biopsych.2011.02.028 CrossRefGoogle ScholarPubMed
Hill, W. D., Davies, N. M., Ritchie, S. J., Skene, N. G., Bryois, J., Bell, S., Di Angelantonio, E., Roberts, D. J., Xueyi, S., Davies, G., Liewald, D. C. M., Porteous, D. J., Hayward, C., Butterworth, A. S., McIntosh, A. M., Gale, C. R., & Deary, I. J. (2019). Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income. Nature Communications, 10, 5741. https://doi.org/10.1038/s41467-019-13585-5 CrossRefGoogle ScholarPubMed
Howe, L. D., Tilling, K., Galobardes, B., & Lawlor, D. A. (2013). Loss to follow-up in cohort studies: Bias in estimates of socioeconomic inequalities. Epidemiology, 24, 1–9. https://doi.org/10.1097/EDE.0b013e31827623b1 CrossRefGoogle ScholarPubMed
Jack, C. R., Jr, Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., Liu, E., Molinuevo, J. L., Montine, T., Phelps, C., Rankin, K. P., Rowe, C. C., Scheltens, P., Siemers, E., Snyder, H. M., … Contributors. (2018). NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia, 14, 535–562. https://doi.org/10.1016/j.jalz.2018.02.018 CrossRefGoogle Scholar
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Nguyen-Viet, T. A., Bowers, P., Sidorenko, J., Karlsson Linnér, R., Fontana, M. A., Kundu, T., Lee, C., Li, H., Li, R., Royer, R., Timshel, P. N., Walters, R. K., Willoughby, E. A., … Cesarini, D. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50, 1112–1121. https://doi.org/10.1038/s41588-018-0147-3 CrossRefGoogle ScholarPubMed
Li, M., Kim, H., Sereika, S. M., Nissley, T. J., & Lingler, J. H. (2022). Willingness to participate in clinical research among individuals with cognitive impairment. Research in Gerontological Nursing, 15, 76–84. https://doi.org/10.3928/19404921-20220131-01 CrossRefGoogle ScholarPubMed
Lissner, L., Skoog, I., Andersson, K., Beckman, N., Sundh, V., Waern, M., Edin Zylberstein, D., Bengtsson, C., & Björkelund, C. (2003). Participation bias in longitudinal studies: Experience from the population study of women in Gothenburg, Sweden. Scandinavian Journal of Primary Health Care, 21, 242–247. https://doi.org/10.1080/02813430310003309-1693 CrossRefGoogle ScholarPubMed
Liu, K. A., & Dipietro Mager, N. A. (2016). Women’s involvement in clinical trials: Historical perspective and future implications. Pharmacy Practice, 14. https://doi.org/10.18549/PharmPract.2016.01.708 CrossRefGoogle Scholar
Loh, P.-R., Danecek, P., Palamara, P. F., Fuchsberger, C., Reshef, A, Y., K Finucane, H., Schoenherr, S., Forer, L., McCarthy, S., Abecasis, G. R., Durbin, R., Price, A. L. (2016). Reference-based phasing using the Haplotype Reference Consortium panel. Nature Genetics, 48, 1443–1448. https://doi.org/10.1038/ng.3679 CrossRefGoogle ScholarPubMed
Lupton, M. K., Medland, S. E., Gordon, S. D., Goncalves, T., MacGregor, S., Mackey, D. A., Young, T. L., Duffy, D. L., Visscher, P. M., Wray, N. R., Nyholt, D. R., Bain, L., Ferreira, M. A., Henders, A. K., Wallace, L., Montgomery, G. W., Wright, M. J., & Martin, N. G. (2018). Accuracy of Inferred APOE genotypes for a range of genotyping arrays and imputation reference panels. Journal of Alzheimer’s Disease, 64, 49–54. https://doi.org/10.3233/JAD-171104 CrossRefGoogle ScholarPubMed
Lupton, M. K., Robinson, G. A., Adam, R. J., Rose, S., Byrne, G. J., Salvado, O., Pachana, N. A., Almeida, O. P., McAloney, K., Gordon, S. D., Raniga, P., Fazlollahi, A., Xia, Y., Ceslis, A., Sonkusare, S., Zhang, Q., Kholghi, M., Karunanithi, M., Mosley, P. E., … Breakspear, M. (2021). A prospective cohort study of prodromal Alzheimer’s disease: Prospective Imaging Study of Ageing: Genes, Brain and Behaviour (PISA). NeuroImage. Clinical, 29, 102527. https://doi.org/10.1016/j.nicl.2020.102527 CrossRefGoogle ScholarPubMed
Medland, S. E., Nyholt, D. R., Painter, J. N., McEvoy, B. P., McRae, A. F., Zhu, G., Gordon, S. D., Ferreira, M. A. R., Wright, M. J., Henders, A. K., Campbell, M. J., Duffy, D. L., Hansell, N. K., Macgregor, S., Slutske, W. S., Heath, A. C., Montgomery, G. W., & Martin, N. G. (2009). Common variants in the trichohyalin gene are associated with straight hair in Europeans. American Journal of Human Genetics, 85, 750–755. https://doi.org/10.1016/j.ajhg.2009.10.009 CrossRefGoogle ScholarPubMed
Milani, S. A., Cottler, L. B., & Striley, C. W. (2021). Perceptions of research participation among a sample of Florida residents aged 50 and over reporting dementia. Ageing International. https://doi.org/10.1007/s12126-021-09441-x CrossRefGoogle Scholar
Oswald, L. M., Wand, G. S., Zhu, S., & Selby, V. (2013). Volunteerism and self-selection bias in human positron emission tomography neuroimaging research. Brain Imaging and Behavior, 7, 163–176. https://doi.org/10.1007/s11682-012-9210-3 CrossRefGoogle ScholarPubMed
Patten, S. B. (2000). Selection bias in studies of major depression using clinical subjects. Journal of Clinical Epidemiology, 53, 351–357. https://doi.org/10.1016/s0895-4356(99)00215-2 CrossRefGoogle ScholarPubMed
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., & Sham, P. C. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81, 559–575. https://doi.org/10.1086/519795 CrossRefGoogle ScholarPubMed
Rothman, K. J., Gallacher, J. E. J., & Hatch, E. E. (2013). Why representativeness should be avoided. International Journal of Epidemiology, 42, 1012–1014. https://doi.org/10.1093/ije/dys223 CrossRefGoogle ScholarPubMed
Savage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., de Leeuw, C. A., Nagel, M., Awasthi, S., Barr, P. B., Coleman, J. R. I., Grasby, K. L., Hammerschlag, A. R., Kaminski, J. A., Karlsson, R., Krapohl, E., Lam, M., Nygaard, M., Reynolds, C. A., Trampush, J. W., … Posthuma, D. (2018). Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nature Genetics, 50, 912–919. https://doi.org/10.1038/s41588-018-0152-6 CrossRefGoogle ScholarPubMed
Stuber, J. M., Middel, C. N. H., Mackenbach, J. D., Beulens, J. W. J., & Lakerveld, J. (2020). Successfully recruiting adults with a low socioeconomic position into community-based lifestyle programs: A qualitative study on expert opinions. International Journal of Environmental Research and Public Health, 17, 2764. https://doi.org/10.3390/ijerph17082764 CrossRefGoogle ScholarPubMed
Thorp, J. G., Mitchell, B. L., Gerring, Z. F., Ong, J.-S., Gharahkhani, P., Derks, E. M., & Lupton, M. K. (2022). Genetic evidence that the causal association of educational attainment with reduced risk of Alzheimer’s disease is driven by intelligence. Neurobiology of Aging, 119, 127–135. https://doi.org/10.1016/j.neurobiolaging.2022.07.011 CrossRefGoogle ScholarPubMed
Tyrrell, J., Zheng, J., Beaumont, R., Hinton, K., Richardson, T. G., Wood, A. R., Davey Smith, G., Frayling, T. M., & Tilling, K. (2021). Genetic predictors of participation in optional components of UK Biobank. Nature Communications, 12, 1–13. https://doi.org/10.1038/s41467-021-21073-y CrossRefGoogle ScholarPubMed
Van Loon, A. J. M., Tijhuis, M., Picavet, H. S. J., Surtees, P. G., & Ormel, J. (2003). Survey non-response in the Netherlands: Effects on prevalence estimates and associations. Annals of Epidemiology, 13, 105–110. https://doi.org/10.1016/s1047-2797(02)00257-0 CrossRefGoogle ScholarPubMed
Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., Goddard, M. E., & Visscher, P. M. (2010). Common SNPs explain a large proportion of the heritability for human height. Nature Genetics, 42, 565–569. https://doi.org/10.1038/ng.608 CrossRefGoogle ScholarPubMed
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011). GCTA: A tool for genome-wide complex trait analysis. American Journal of Human Genetics, 88A, 76–82. https://doi.org/10.1016/j.ajhg.2010.11.011 CrossRefGoogle Scholar
Zheng, X., Levine, D., Shen, J., Gogarten, S. M., Laurie, C., & Weir, B. S. (2012). A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics, 28, 3326–3328. https://doi.org/10.1093/bioinformatics/bts606 CrossRefGoogle ScholarPubMed