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Divide and conquer strategies for population genomics

  • Full or part time
  • Application Deadline
    Monday, January 06, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

DNA sequences contain a wealth of information about the history of a population. Typical genomes contain about 3 X 109bases, of which around 107arevariable in typical samples,reflecting 10 million mutations in the ancestry of the sample. The patterns of these mutations –the number of copies in a sample, their geographic distribution, and their relationships to each other –can tell us a great deal about changes in past population size, the structure of populations and migration rates between subpopulations, as well as the effects of mutation, recombination, and natural selection. Although many complex models of the evolution of DNA sequences in populations have been proposed, it is still very challenging to efficiently estimate the parameters of these models, and to compare models and test hypotheses.

Funding Notes

This is a competition funded project through the NERC GW4+ DTP. There is a competitive selection process. This studentship will cover fees, stipend and research costs for UK students and UK residents for 3.5 years.

Candidates should have an interest in computational biology, and be able to demonstrate experience in previous projects that require some level of programming and/or scripting. Suitable candidates from all STEM backgrounds will be considered. It is important to have an interest in biological problems, but no formal training in biology or genomics is required.


Beaumont, M. A. (2019). Approximate Bayesian computation. Annual review of statistics and its application, 6, 379-403.Heine, K., Beskos, A., Jasra, A., Balding, D., & De Iorio, M. (2018). Bridging trees for posterior inference on ancestral recombination graphs. Proceedings of the Royal Society A, 474(2220).Mondal, M., Bertranpetit, J., & Lao, O. (2019). Approximate Bayesian computation withdeep learning supports a third archaic introgression in Asia and Oceania. Nature communications, 10(1), 246.Schraiber, J. G., & Akey, J. M. (2015). Methods and models for unravelling human evolutionary history. Nature Reviews Genetics, 16(12), 727.Vehtari, A., Gelman, A., Sivula, T., Jylänki, P., Tran, D., Sahai, S., Blomstedt, P., Cunningham, J.P., Schiminovich, D. and Robert, C., 2019. Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data. arXiv:1412.4869.

Related Subjects

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