Computational statistics applied to population, evolutionary, medical and forensic genetics
- mathematical modelling of
- demographic history of populations,
- evolutionary processes such as mechanisms of selection;
- identifying sources of biological material (identification of individual and of tissue);
- measures of relatedness among two or more individuals and the role of relatedness in genomics analyses, including heritability analyses;
- predicting phenotypes from genotype and other data;
- association analysis, particularly in the presence of complex relatedness/population structure;
- analysis of other omics data (transcriptome, methylome, etc) in conjunction with genomics data.
Much of the above is informed by the coalescent-with-recombination model of population genetics. Performing statistical inference for large datasets under this model remains one of the major open problems in statistical genomics. Other statistical tools include mixed model (or penalised/shrinkage) regression for large numbers of predictors (p >> n) and various multivariate statistics techniques.
I work with collaborators in many different fields: forensic science, crop research, ancient DNA, pharmaceutical companies and diseases of humans, animals and plants. Much of this work involves stochastic modelling based on data generated by the collaborative partner or from public databases.
(Co-supervised with Stephen Harrap)
Ali Mahmoudi (PhD, 2017-) Main supervisor
Michael Silk (PhD, 2017-) Co-supervisor, with Slavé Petrovski
Lachlan Macintosh (PhD, 2017-) Co-supervisor, with Tony Pappenfuss
Yupei You (MSc Bioinformatics) Main supervisor
Tatiana Hessab (PhD student, Federal University of Rio De Janeiro, Jul 17 - Feb 18)
Shian Su (MSc Maths & Stats, 2016)
Maria Simonsen (Aarhus University, Denmark, 2016)
Tristan Mary-Huard (AgroParisTech, France, 2016)
Søren Vilsen (Aalborg University, Denmark, 2016)