A new wave of increasingly large scale genetic information is being produced by large population cohorts and next generation sequencing technologies. This opens up novel possibilities in understanding the contribution of genetic variation to many human traits and diseases and their effect on human lifespan, which has hitherto been understudied due to lack of statistical power. This project will focus on the analysis of evidence for genetic associations with complex diseases such as heart disease, type 2 diabetes, Alzheimer’s disease and cancer and examine the impact on human lifespan and healthy ageing. Work will employ 500k subjects genotyped at UK biobank and population cohorts run by us and our collaborators: Generation Scotland, ORCADES(Orkney), EGCUT(Estonia) to examine the nature of the genomic architectures, to explore novel association signals and to follow up confirmed signals using bioinformatic analysis.
Aims & Objectives
To accurately measure the genetic influence on these complex traits. To understand what lifespan has in common with and how it differs from common diseases, for example answering longstanding questions as to whether there are frailty genes which predispose or protect people from diseases in general, or if each disease has its own genetic basis. To understand how disease, lifespan and healthy lifespan inter-relate, genetically.
Genome-wide array data available on UK biobank and other cohorts will be used to perform analysis of the genetic contribution lifespan using leading genomic software (LD Score regression, GCTA, polygenic risk scores and association analyses using PLINK and R).
There is further potential to develop the use of these data for population genetic inference, the analysis of coding and other functional variation, Mendelian randomization and bi-variate and multi-variate phenotypic analyses, looking at lifespan and health outcomes simultaneously.
Depending on the student’s background, it may be appropriate to attend courses in the The University’s world leading Master’s programme: Quantitative Genetics and Genome Analysis
Development of computational skills including scripting language and R programming and use of major computational packages (PLINK, GenABEL, GCTA, etc.) and experience with high-performance and parallel computation.
Experience in complex mixed linear model statistical analyses and interpretation of results.
Knowledge of large scale genetic data analysis (association analyses, heritability estimation).
Understanding and analysis of questionnaire data.
Analysis of genetic risk scores and other health predictors.
Interpretation and communication of complex research outcomes to scientific and general audiences.
Jim Wilson and Peter Joshi [email protected]
Please contact Peter in the first instance, if you are interested.
• Personal statement indicating how you meet the criteria for this studentship
• marks for your degree(s)
• 2 academic references
Email to: [email protected]
Interviews will be held in Edinburgh (interviews may be conducted by videoconference or Skype).
The studentship will ideally begin by October 2016, or earlier.
1. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine. 2015;12(3)
2. Deelen J, Beekman M, Uh HW, Broer L, Ayers KL, Tan Q, et al. Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age. Human molecular genetics. 2014;23(16):4420-32.