'Beating the odds': Early life experiences influencing the association between genetic prediction and language development in mid-childhood

Summary This project will (1) investigate the association between this polygenic score and language development (vocabulary) at 11-12 years, and (2) investigate the mediation of this genetic-outcome correlation by a range of early-life exposures, including home environment, extra-curricular activities, parental involvement, social support and family demographics. The results may help to identify key lifestyle factors that contribute to resilience for language development in children with poorer genetic prediction.

Project Details

Language skills are important determinants of daily functioning and health, and are closely linked to academic and employment outcomes. Recent evidence has identified a range of genetic factors that influence educational attainment, but genetics is clearly only one part of a complex interplay of factors. This project aims to investigate the interaction between genetic predisposition for educational attainment, and early life exposures, in determining childhood language development in a large cohort of Australian 11-12 year old children.     The Longitudinal Study of Australian Children (LSAC), is a national, population-derived cohort of Australian children, collecting data every two years since birth. We recently undertook the Child Health CheckPoint study, a cross sectional physical and biospecimens module of LSAC at 11-12 years of age. We are currently applying an adult-derived polygenic score for educational attainment to the CheckPoint cohort. This project will (1) investigate the association between this polygenic score and language development (vocabulary) at 11-12 years, and (2) investigate the mediation of this genetic-outcome correlation by a range of early-life exposures, including home environment, extra-curricular activities, parental involvement, social support and family demographics. The results may help to identify key lifestyle factors that contribute to resilience for language development in children with poorer genetic prediction. This in turn may inform future targeted policy development and intervention strategies.     This project will suit a student with an interest in population-based health, developing statistical experience and Stata skills. Some prior experience in basic statistical techniques or analytical packages (such as Stata, R, MPlus or MATLAB) would be an advantage. The broader research team includes expertise in longitudinal and high-throughput genetic data analyses, statistical support, and access to Stata software and expertise to conduct the project. Given the large, high quality data available, findings are likely to be published in a quality journal.