Biostatistics and Statistical Genetics
Biostatistics and Statistical Genetics
This group researches into design, and analysis of quantitative investigations in human health and genetics with a particular emphasis on applying advanced methods of statistical inference.
Member of other research groups: Statistical Methodology, Scholarship of Learning and Teaching in Statistics, Environmental Statistics
Supervisor: Dirk Husmeier
Bayesian methods; inference and statistical methods for dynamical systems with applications to genetic data
Bayesian methodology for gene regulation; Statistical analysis of microarray, tiling array and deep sequencing data; Phylogenetic analysis; Analysis of GWAS
Algebraic statistics; Phylogenetic algebraic geometry; group-based tree models.
Spatiotemporal modelling; Bayesian methods; environmental epidemiology and disease mapping
Member of other research groups: Environmental Statistics
Research staff: Gary Napier
Research students: Kamol Sanittham, Yoana Napier, Xueqing Yin
Postgraduate opportunities: Mapping disease risk in space and time, Estimating the effects of air pollution on human health
Statistical genetics; population genetics; Bayesian methods; phylogenetics
Missing values; medical statistics
Member of other research groups: Scholarship of Learning and Teaching in Statistics
Forensic statistics; quantile regression; semiparametric models; biostatistics applications
Research student: Dimitra Eleftheriou
Functional Data Analysis; Analysis of mixture models; high-dimensional data; medical image analysis; analysis of earth systems data; immunoinformatics
Member of other research groups: Statistical Methodology, Environmental Statistics
Research students: Maryam Al Alawi , Flynn Gewirtz-O'Reilly
Postgraduate opportunities: Analysis of Spatially correlated functional data objects.
Estimating the effects of air pollution on human health (PhD)
The health impact of exposure to air pollution is thought to reduce average life expectancy by six months, with an estimated equivalent health cost of 19 billion each year (from DEFRA). These effects have been estimated using statistical models, which quantify the impact on human health of exposure in both the short and the long term. However, the estimation of such effects is challenging, because individual level measures of health and pollution exposure are not available. Therefore, the majority of studies are conducted at the population level, and the resulting inference can only be made about the effects of pollution on overall population health. However, the data used in such studies are spatially misaligned, as the health data relate to extended areas such as cities or electoral wards, while the pollution concentrations are measured at individual locations. Furthermore, pollution monitors are typically located where concentrations are thought to be highest, known as preferential sampling, which is likely to result in overly high measurements being recorded. This project aims to develop statistical methodology to address these problems, and thus provide a less biased estimate of the effects of pollution on health than are currently produced.
Mapping disease risk in space and time (PhD)
Disease risk varies over space and time, due to similar variation in environmental exposures such as air pollution and risk inducing behaviours such as smoking. Modelling the spatio-temporal pattern in disease risk is known as disease mapping, and the aims are to: quantify the spatial pattern in disease risk to determine the extent of health inequalities, determine whether there has been any increase or reduction in the risk over time, identify the locations of clusters of areas at elevated risk, and quantify the impact of exposures, such as air pollution, on disease risk. I am working on all these related problems at present, and I have PhD projects in all these areas.