The University of Exeter and the University of Queensland are seeking exceptional students to join a world-leading, cross-continental research team tackling major challenges facing the world’s population in global sustainability and wellbeing as part of the QUEX Institute. The joint PhD programme provides a fantastic opportunity for the most talented doctoral students to work closely with world-class research groups and benefit from the combined expertise and facilities offered at the two institutions, with a lead supervisor within each university. This prestigious programme provides full tuition fees, stipend, travel funds and research training support grants to the successful applicants. The studentship provides funding for up to 42 months (3.5 years).
Ten generous, fully-funded studentships are available for the best applicants, five offered by the University of Exeter and five by the University of Queensland. This select group will spend at least one year at each University and will graduate with a joint degree from the University of Exeter and the University of Queensland.
Find out more about the PhD studentships http://www.exeter.ac.uk/quex/phds
Successful applicants will have a strong academic background and track record to undertake research projects based in one of the three themes of: Physical Activity and Nutrition; Healthy Ageing; and Environmental Sustainability.
The closing date for applications is midnight on 19 May 2019 (BST), with interviews taking place week commencing 8 July 2019. The start date will be January 2020.
Please note that of the seven Exeter led projects advertised, we expect that up to five studentships will be awarded to Exeter based students.
Exeter Academic Lead: Dr Rachel Freathy, Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter [email protected]
Queensland Academic Lead: Proffessor David Evans, Statistical Genetics, University of Queensland Diamantina Institute.
There is a well-documented observational relationship between low birthweight infants and a higher risk of cardiometabolic disease in later life (e.g. type 2 diabetes, hypertension, cardiovascular disease). The “Developmental Origins of Health and Disease” (DOHaD) hypothesis proposes that an adverse intrauterine environment (e.g. due to poor maternal nutrition) might explain this relationship because it would lead not only to reduced fetal growth, but also to higher later-life disease risk through physiological adaptations made during fetal development. The DOHaD hypothesis has been one of the preeminent paradigms in life-course epidemiology over the last thirty years.
In a recent ground-breaking study (Horikoshi et al. 2016 Nature), we showed that the inverse correlation between birthweight and cardiometabolic disease may in fact be predominantly mediated by genetic rather than environmental factors. However, maternal and offspring genotypes are correlated, meaning that dissecting the genetic and environmental contributions to this relationship is fraught with difficulties, including the possibility that any genetic effects may be mediated through the mother’s (rather than the offspring’s) genotype operating on the intrauterine environment. There is a pressing need to develop novel approaches to reduce the prevalence and burden of cardiometabolic diseases. The use of novel statistical methods and large datasets represents a unique opportunity to extend our understanding of the biology underlying DOHAD, and will help identify genes that underpin key pathways important for the development of disease.
The aim of this PhD is to dissect the maternal and fetal contributions to the relationship between offspring birthweight and risk of cardiometabolic disease. The successful candidate will perform analyses on a number of large datasets including (but not limited to) the UK Biobank (a large cohort of 500,000 participants who have been genome-wide genotyped and have relevant phenotype data including own/offspring’s birth weight and later life diseases), and studies in the Early Growth Genetics (EGG) consortium (more than 40 international pregnancy and birth cohorts with genetic data, including studies with data on mother/offspring pairs).
The successful candidate will gain experience across a wide range of advanced statistical genetics methodologies including Mendelian randomization (a way of using genetic variants to investigate putatively causal relationships), genome-wide association analysis (GWAS), and genetic restricted maximum likelihood (G-REML) analysis of genome-wide data which can be used to partition variation in phenotypes into genetic and environmental sources of variation. The candidate will also assist in the development of new statistical genetics and causal modelling methods.