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