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New methods for detecting and quantifying genetic adaptation

   School of Biological Sciences

  Dr M Hartfield, Dr K Lohse  Wednesday, January 19, 2022  Competition Funded PhD Project (Students Worldwide)

About the Project

Understanding how populations adapt to their environment is a major focus of evolution research. Falling sequencing costs means genome data is more abundant than ever, but we still lack efficient methods for using it to infer and quantify the effects of natural selection. In particular, there has been great interest in identifying how beneficial mutations go to fixation in populations; are they instantly favourable after arising as a new mutation, or are other mechanisms at play, including recurrent mutation or selection on existing genetic variation? Despite many advances in this field, it has still proved tricky to determine the evolutionary origins of beneficial mutations. This is especially true if we wish to infer the nature of adaptation in wild populations, especially plants, that uniparentally reproduce to some degree (that is, via self-fertilisation and/or clonal reproduction) to test evolutionary theories regarding how species with different reproductive modes adapt to their environment.

 The goals of this exciting project is to develop novel methods for detecting the prevalence of different types of favourable mutations along a genome, and then apply them to genome data to determine the how adaptation proceeds in species with different reproductive modes. The student will first work with the supervisor to develop new methods for classifying genetic regions with favourable mutations; testing these methods using computer simulations; and determining how these methods are affected by different types of uniparental reproductions. They will subsequently be applied to plant genome data to answer questions, including: How does the nature of adaptive evolution differ in species with different reproductive modes? Are these differences predicted to affect the long-term viability of natural populations?

 There will be potential to investigate data from a wide range of organisms, including highly self-fertilising plants (Arabidopsis thaliana; Capsella rubella; Medicago truncatula) and those whose occurrence of self-fertilisation varies throughout its range (Arabis alpina). Training in bioinformatics and genome sequence analyses will be provided through Edinburgh Genomics. Further training in statistical analyses, population genetics, and computational programming will be provided by the lead supervisor. There will also be scope for students to become involved with the management and direction of the project, and to instigate new fieldwork studies to further investigate how selection is realised in wild plant populations.


 The School of Biological Sciences is committed to Equality & Diversity:


Funding Notes

The “Institution Website” button on this page will take you to our Online Application checklist. Please carefully complete each step and download the checklist which will provide a list of funding options and guide you through the application process. From here you can formally apply online. Application for admission to the University of Edinburgh must be submitted by 5th January 2022.


Hartfield, M et al. (2017) “The Evolutionary Interplay between Adaptation and Self-Fertilization”. Trends in Genetics 33: 420–431.
Hermisson, J and Pennings, PS (2017) “Soft sweeps and beyond: understanding the patterns and probabilities of selection footprints under rapid adaptation”. Methods Ecol. Evol. 8:700–716.
Huber, CD et al. (2014) “Keeping It Local: Evidence for Positive Selection in Swedish Arabidopsis thaliana”. Mol. Biol. Evol. 31: 3026-3039.
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