This is an exciting opportunity to join a multidisciplinary team to develop models of BMI trajectories leading to childhood obesity. The project builds on cutting edge research developed by the University of Surrey aimed at modelling BMI dynamics using Bayesian semi-mechanistic models.
Childhood obesity, a risk factor for adult cardio-metabolic disease, is defined as a body mass index (BMI) above the 95th percentile. Currently, over 20% of children are obese, and the prevalence is rising. Despite the relatively large heritability (40-86%), the genes controlling the increase and decrease of BMI at different periods of infancy and childhood remain largely unknown.
Recently, we showed that distinct genetic factors control infant and child BMI . This finding led us to hypothesise that genetic effects are dynamic, varying throughout life. We, therefore, aim to investigate whether BMI trajectories associated with normal, overweight and obese children are controlled by different genes and whether these genes have varying genetic effects.
Despite the significant progress of growth modelling, BMI remains a challenging phenotype because the BMI trajectory is highly nonlinear and controlled by multiple environmental and genetic factors. Current approaches have focused on data-driven models, while semi-mechanistic models  and the incorporation of domain-specific knowledge has been left unexplored.
The focus of this project is to evaluate and develop probabilistic models of BMI trajectories, and apply them to real data to identify genes controlling normal and obese BMI trajectories. We intend to develop statistical software for the application of these models in the Comprehensive R Archive Network (CRAN) and make it publicly available.
The successful candidate will receive comprehensive research training including bioinformatics and statistical genetics, hands-on practice with several studies of growth data, in addition to an extensive training programme in technical, personal and professional skills at the Doctoral College of the University of Surrey. The candidate will have multiple opportunities to present his work in conferences, workshops and seminars and this way develop his communication skills.
Supervisory Committee and Research Team
The candidate will join a supervisory team composed of:
· Prof. John Holloway from Southampton University.
· Dr Alex Couto Alves from the University of Surrey.
· Dr Naratip Santitissadeekorn from the University of Surrey.
The project is a collaboration with:
Prof. John Wright, Director of the Born in Bradford (BiB) Study; and
Ms Gillian Santorelli, Principal Statistician at Born in Bradford (BiB) Study.
Applicants should have completed an MSc degree by the time the PhD starts. We will consider applicants with an MSc. or BSc degree in statistics, physics, mathematics, data science, machine learning, bioinformatics, computer science, or related degrees. Candidates that do not hold any of the above degrees but have strong research potential may also be considered.
A solid background in any of the programming languages R, Python or C is required.
Experience with Bayesian statistics, longitudinal models, or genetics data analysis is highly desired.
English language requirements: IELTS Academic: 6.5 or above (or equivalent) with 6 in each individual category.
How to apply
Applications must be submitted via the Biosciences and Medicine PhD programme page on the ‘Apply’ tab. State clearly the project and supervisor.
Please prepare to submit your CV; degree certificates and transcripts; names of 2 referees (ideally uploading 2 references at time of application also); and research proposal (including examples of previous project work).