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.

Staff

Dr Agnieszka Borowska : Research Assistant

Member of other research groups: Statistical Methodology, Scholarship of Learning and Teaching in Statistics, Environmental Statistics
Supervisor: Dirk Husmeier

  • Dr Christina A Cobbold : Reader

    Bayesian methods; inference and statistical methods for dynamical systems with applications to genetic data

    Member of other research groups: Mathematical Biology
    Research students: David Ewing, Cameline Orlendo

  • Personal Website
  • Publications
  • Dr Mayetri Gupta : Reader

    Bayesian methodology for gene regulation;  Statistical analysis of microarray, tiling array and deep sequencing data; Phylogenetic analysis;  Analysis of GWAS

    Member of other research groups: Statistical Methodology

  • Personal Website
  • Publications
  • Dr Dimitra Kosta : LKAS Fellowship

    Algebraic statistics; Phylogenetic algebraic geometry; group-based tree models.

    Member of other research groups: Statistical Methodology, Environmental Statistics, Geometry and Topology, Algebra

  • Personal Website
  • Dr Duncan Lee : Reader

    Spatiotemporal modelling; Bayesian methods; environmental epidemiology and disease mapping

    Member of other research groups: Environmental Statistics
    Research staff: Gary Napier
    Research students: Eilidh Jack, Kamol Sanittham, Yoana Borisova, Cillian Doherty
    Postgraduate opportunities: Mapping disease risk in space and time, Estimating the effects of air pollution on human health

  • Personal Website
  • Publications
  • Dr Vincent Macaulay : Reader

    Statistical genetics; population genetics; Bayesian methods; phylogenetics

    Member of other research groups: Statistical Methodology
    Research students: Flynn Gewirtz-O'Reilly, Suzy Whoriskey

  • Personal Website
  • Publications
  • Prof John McColl : Professor of Learning and Teaching in Statistics

    Missing values; medical statistics

    Member of other research groups: Scholarship of Learning and Teaching in Statistics
    Research student: Andrew Smith

  • Personal Website
  • Publications
  • Dr Gary Napier : Research Assistant

    Member of other research groups: Environmental Statistics
    Supervisor: Duncan Lee

  • Publications
  • Dr Tereza Neocleous : Lecturer

    Forensic statistics; quantile regression; semiparametric models; biostatistics applications

    Research students: Dimitra Eleftheriou, Craig Alexander

  • Personal Website
  • Publications
  • Dr Surajit Ray : Senior lecturer

    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 , Salihah Alghamdi, Bader Lafi Q Alruwaili, Flynn Gewirtz-O'Reilly
    Postgraduate opportunities: Analysis of Spatially correlated functional data objects.

  • Personal Website
  • Publications
  • Dr Liberty Vittert : Mitchell Lecturer

  • Personal Website
  • Dr Vlad Vyshemirsky : Lecturer

    Member of other research groups: Statistical Methodology
    Research students: Randa Alharbi, Lida Mavrogonatou

  • Publications

  • Postgraduates

    Craig Alexander : PhD Student

    Research Topic: Recovering the dynamics of talk: tracking temporal dependence in multilevel models for speech
    Member of other research groups: Statistical Methodology
    Supervisors: Ludger Evers, Tereza Neocleous

  • Salihah Alghamdi : PhD Student

    Research Topic: Analysis of Spatially correlated functional data objects.
    Member of other research groups: Statistical Methodology, Scholarship of Learning and Teaching in Statistics
    Supervisor: Surajit Ray

  • Randa Alharbi : PhD Student

    Research Topic: Bayesian Inference Over Markov Chain Models
    Supervisor: Vlad Vyshemirsky

  • Katie Stewart : PhD Student

    Member of other research groups: Environmental Statistics
    Supervisors: Marian Scott OBE, Dirk Husmeier

  • Ashwini Venkatasubramaniam : PhD Student

    Supervisors: Konstantinos Ampountolas, Ludger Evers, Vonu Thakuriah, Jinhyun Hong


  • Postgraduate opportunities

    Estimating the effects of air pollution on human health (PhD)

    Supervisors: Duncan Lee
    Relevant research groups: Biostatistics and Statistical Genetics, Environmental Statistics

    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)

    Supervisors: Duncan Lee
    Relevant research groups: Biostatistics and Statistical Genetics, Environmental Statistics

    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.