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Studying evolutionary processes with polymorphism-aware phylogenetic models

  • Full or part time
  • Application Deadline
    Sunday, December 01, 2019
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

The recent sequencing of genomes of closely related species and of many individuals from the same species enables the study of speciation and the inference of the history of populations. Standard phylogenetic methods reduce entire populations to single points in genotypic space by modelling evolution as a process in which a single gene mutates along the branches of a phylogeny. In this project, we envisage developing new theory and software to tackle the problem of species tree estimation and molecular dating genome-wide.
In our group, we have developed a new method called POlymorphisms-aware phylogenetic MOdel (PoMo). It extends any DNA substitution model and additionally accounts for polymorphisms in the present and in the ancestral population by expanding the state space to include polymorphic states in a continuous-time Markov process (DeMaio et al., 2016). PoMo performs well incomplete lineage sorting because ancestral populations can be in a polymorphic state. We have estimated species trees from neutrally evolving sites of genomes of recently diverged populations such as baboon species (Rogers et al., 2019)
However, including sites under selection is less well understood (Borges et al., 2019). The project will investigate the influence of sites under selection on the PoMo approach. Extensions to other types of data such as modelling the evolutionary process of copy numbers in a population, or combinations with trait evolution are possible. The group is also interested in analysing data from evolutionary processes at very short time-scales as they are observed in experimental evolution studies.
The successful candidate should have a strong interest in applying quantitative methods and modelling to Biology. They will have a degree in Bioinformatics, Computer Science, Statistics, Mathematics, Physics or a related field. Prior experience with either population genetics, phylogeny or comparative genomics is a benefit. Preferably the candidate will have experience in programming language such as C, C++, Java and a scripting language such as Python or Perl.

Funding Notes

Eligibility requirements: Upper second-class degree in Biology or a related area.
Funding: Fees and stipend is provided for 3.5 years.

References

De Maio, N., Schrempf, D., and Kosiol, C. (2015). PoMo: An allele frequency-based approach for species tree estimation. Systematic Biology, 64(6):1018–1031
Borges, R., Szöllősi, G. J., and Kosiol, C. (2019). Quantifying GC-Biased Gene Conversion in Great Ape
Genomes Using Polymorphism-Aware Models. Genetics, 212(4):1321–1336.
Rogers et al.(2019)The comparative genomics and complex population history of Papio baboons. Science Advances, 5(1):eaau6947.

How good is research at University of St Andrews in Biological Sciences?

FTE Category A staff submitted: 50.45

Research output data provided by the Research Excellence Framework (REF)

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