The Leverhulme Centre for Advanced Biological Modelling at the University of Sheffield are inviting applications for funded PhD studentships on the three projects below. For further details please use the corresponding email address or contact Dr Tim Heaton ([log in to unmask]) or Dr Miguel A. Juárez ([log in to unmask]).
Provisional deadline is 30 January 2015.
• Genomic basis of extra-pair paternity ([log in to unmask])
• Modelling population responses to climate: How populations respond and evolve in variable environments ([log in to unmask])
• Genomic Prediction in a wild bird ( [log in to unmask])
Genomic basis of extra-pair paternity
Supervisors: Prof Terry Burke and Dr Hannah Dugdale (Animal & Plant Sciences) , Dr Tim Heaton (School of Mathematics and Statistics)
Indirect genetic benefits are hypothesised to drive the evolution of extra-pair paternity (EPP), yet its genomic basis is unknown. This is important, as promiscuity has widespread impact on reproductive skew, gene flow and sexual selection. Studies are required to elucidate the genomic basis of EPP, and the consequences of this, to determine how and why this variation is maintained.
Most studies of natural populations cannot measure the evolutionary dynamics of EPP accurately. However, as Seychelles warblers almost never leave their resident islands, survival, lifetime fitness and EPP rates can be estimated accurately. Seychelles warblers have an unusually high rate of EPP (40%), which varies among individuals and has been linked to “good genes”. This provides an unusual opportunity to determine the genomic basis of EPP.
This PhD will use large-scale representational sequencing analysis from across the genome, combined with a genetic pedigree of >1500 individuals and detailed residency data, to quantify variation and identify genomic regions contributing to EPP, for example by testing for greater additive genetic variance of genomic regions in extra-pair than within-pair offspring.
The student will develop expertise in quantitative and evolutionary genetics, genomic analysis and statistical modelling. Prof Burke will supervise the genomic work, on which he has multiple projects in other species. Dr Dugdale will supervise data extraction and quantitative genetic analyses. Dr Heaton will supervise the statistical modelling.
The ideal applicant will have a background in Physics, Maths or Computer Science. Students with a Masters in biology must demonstrate advanced quantitative skills. Quantitative expertise will be one of the selection criteria used by the interview panel.
Genomic Prediction in a wild bird
Supervisors: Prof Jon Slate (Animal & Plant Sciences) , Dr Miguel A. Juárez (School of Mathematics and Statistics)
This project seeks to understand the genetic architecture of quantitative traits in a population of great tits (Parus major) that has been the focus of a long-term individual-based study, running since 1947. The student will utilise a recently collected dataset of 600,000 genetic markers, typed in approximately 2000 great tits, making it one of the largest genetic datasets for any natural population. One of the aims of the project will be to determine whether an individual’s genome can predict its phenotype. So-called genomic prediction or genomic selection underpins modern animal and plant breeding, as well as personalised medicine, but it has never been attempted in the context of environmental or ecological research. The project is ideally suited to a mathematics, physics or computer science graduate, interested in carrying out data analysis with genomics datasets. Informal enquiries to Professor Jon Slate ([log in to unmask]).
Modelling population responses to climate: How populations respond and evolve in variable environments
Supervisors: Prof Terry Burke and Dr Hannah Dugdale (Animal & Plant Sciences) , Dr Tim Heaton (School of Mathematics and Statistics)
Climate change is one of the most pressing environmental problems faced by humanity, and yet our understanding of how populations respond and evolve in fluctuating environments is limited. In this project the long-term dataset on house sparrows on Lundy Island will be used as a test bed for new statistical methods and models of population dynamics and evolution. The sparrow population is ideal for this as there is virtually no immigration or emigration, there is a pedigree, and we have complete life-history information on virtually all individuals since 2000 (although individuals were first banded in 1989). The population has experienced substantial variation in size and density due to extrinsic factors. The studentship will explore how individual-level variation driven by age, sex, and spatial location determines the demographic response of the population to climatic variation. Mathematical models using a wide range of approaches (individual-based simulations, matrix models and analytical approximations) will then be used to scale-up from the individual level data to the population. The models will be used to explore how climatic variation determines evolutionary trajectories for key traits such as clutch size.
A wide range of projects using the dataset are possible, depending on the student’s interests, but we anticipate they will work primarily in the following areas: statistical estimation of population parameters and/or population modelling.
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Dr Miguel A. Juárez
University of Sheffield
School of Mathematics & Statistics
Hicks Building I.13,
S3 7RH, Sheffield, UK
[log in to unmask]
http://majuarez.staff.shef.ac.uk/
Tel: +44 (0) 1142 223 908
Fax: +44 (0) 1142 223 809
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