Project ID NS-MH2024_53


Co Supervisor 1A Institute of Psychiatry, Psychology & Neuroscience, School of Mental Health & Psychological Sciences, Social, Genetic & Developmental Psychiatry CentreWebsite

Co Supervisor 1B Institute of Psychiatry, Psychology & Neuroscience, School of Mental Health & Psychological Sciences, Social, Genetic & Developmental Psychiatry CentreWebsite

Genetic insights into learning: Using polygenic scores to predict school performance independent of cognitive ability

Children differ dramatically in how easily they learn skills such as reading and mathematics and in how well they use these skills to learn more generally. These skills are among the most heritable behavioural traits and the DNA revolution is making it possible to predict these traits from DNA using polygenic scores that aggregate thousands of DNA variants to predict target traits. However, there is a growing need for specificity within polygenic scores which capture trait-specific genetic variation.
The novel aim of the proposed project is to maximise the power of polygenic scores to predict key educational outcomes independent of general cognitive ability. In addition to learning highly transferable skills for ‘big data’ genomic analysis, which are in great demand in academic and industry, the student will create DNA predictors of key educational outcomes, controlling for general cognitive ability, which could transform education.
In Year 1 the student will learn general genomic analytic techniques and compare the predictive power of methods for creating polygenic scores. In Year 2 the student will use machine-learning approaches to create multi-polygenic scores that maximize prediction of school achievement independent of general cognitive abilities, using GWAS-by-subtraction. In Year 3 the student will use a discordant MZ-twin design to study so-called ‘non-shared’ environmental influences on educational outcomes, in a genetically sensitive design. In Year 4, the student will analyse newly obtained data on ‘quads’ (two parents, two children) to separate ‘direct’ and ‘indirect’ genetic effects.
The supervisors and their datasets (TEDS and GLAD) are based at the internationally renowned Social, Genetic and Developmental Psychiatry (SGDP) Centre.

Representative Publications

Plomin, R. Blueprint: How DNA Makes Us Who We Are. Allen Lane (2018)/Penguin Books (2019).

Procopio, F., Zhou, Q., Wang, Z., Gidziela, A., Rimfeld, K., Malanchini, M., & Plomin, R. (2022). The genetics of specific cognitive abilities. Intelligence, 95. doi: 10.1016/j.intell.2022.101689

Gidziela, A., Rimfeld, K., Malanchini, M., Allegrini, A. G., McMillan, A., Selzam, S., Ronald, A., Viding, E., von Stumm, S., Eley, T. C., & Plomin, R. (2021). Using DNA to predict behaviour problems from preschool to adulthood. Journal of Child Psychology and Psychiatry, 63, 781-792. doi: 10.1111/jcpp.13519

Vinsland, E., Baskaran, P., Mihaylov, S., Hobbs, C., Wood, H., Shah, K., Bouybayoune, I., Houart, C., Tee, A., Murn, J., Fernandes, C., Bateman, J. (2021) The zinc finger/RING domain protein Unkempt regulates cognitive flexibility. Sci Rep 11: 16299.

Armstrong, E.C., Caruso, A., Servadio, M., Andreae, L.C., Trezza, V., Scattoni, M.L., Fernandes, C. (2019) Assessing the developmental trajectory of mouse models of neurodevelopmental disorders: Social and communication deficits in mice with Neurexin 1? deletion. Genes, Brain and Behavior 19(4):e12630. doi: 10.1111/gbb.12630.

Janecka, M., Mill, J., Basson, M.A., Goriely, A., Spiers, H., Reichenberg, A., Schalkwyk, L., Fernandes, C. (2017) Advanced paternal age effects in neurodevelopmental disorders-review of potential underlying mechanisms. Transl Psychiatry 7(1):e1019. doi: 10.1038/tp.2016.294.