The Illingworth lab is based in the Department of Genetics at the University of Cambridge. We develop and make use of statistical techniques in population genetics to understand the adaptation of rapidly evolving systems. Current topics of interest include:
Within-host viral evolution and transmission
We have developed techniques for analysing short-read sequence data describing the within-host evolution of the influenza virus. Where data has been collected multiple times during the course of an infection, changes in the genetic composition of the population can be used to identify signatures of natural selection, for example, caused by the host immune response. Building upon these techniques, we are investigating the within-host diversity and evolution of the influenza virus.
Within-host dynamics of the HA gene of an influenza virus, observed within a pig (original data from Murcia et al, PLoS Pathogens, 8(5), e1002730, 2012). The G allele at locus 553, observed in the green haplotype, encodes a variant amino acid in a region of the virus known to be targeted by the immune system; the death of this haplotype may correspond to an immune response. Figure from Illingworth et al., PLoS Computational Biology, 10(7), e1003755, 2014.
Evolution in laboratory settings
Evolutionary experiments provide a unique window into processes of adaptation, as both the initial genetic composition and the subsequent environment of a population can be controlled. We are working on techniques to improve the interpretation of data from such experiments, so as to maximise what may be learnt from an experimental setup. A particular topic of interest is crossing experiments, where individuals of different strains are mated and evolved under selection in order to identify quantitative trait loci.
Overview of an evolutionary experiment involving a genetic cross. The underlying genetic structure of the population used for the experiment may be exploited to draw more accurate inferences of the location of beneficial variants. Figure taken from Illingworth and Mustonen, J. Stat. Mech, P1004, 2013