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*EASTBIO* What promotes rapid convergent adaptation under extreme selection? A population genomics approach in silent Hawaiian crickets

  • Full or part time
  • Application Deadline
    Sunday, January 05, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

BBSRC Thematic Group: Frontier Bioscience
PROJECT GOALS
Whether and how organisms adapt to extreme environmental selection pressures often determines population and species fates. This PhD project will test what promotes extremely rapid, adaptive evolution under such circumstances. The project will use a “natural laboratory” of multiple, independently evolving silent morphs of Hawaiian field crickets (Teleogryllus oceanicus). Males ordinarily sing to attract females for mating, but on Hawaii they also attract a lethal parasitoid fly. Under this selective force, multiple genetic morphs of silent male have arisen and spread convergently [1]. The project will make use of genomic resources developed for this species [2] to empirically test how selection, mutation and gene flow contribute to fast-paced adaptive evolutionary responses under strong selection. There is flexibility to tailor the research to emphasise your interests, and you will benefit from joining a well-resourced team that is newly funded by a UK Research Council standard grant to Bailey and Gaggiotti.

RESEARCH ACTIVITIES
- Characterise the genetic architecture of convergent adaptive phenotypes
- Detect and analyse genomic hotspots of adaptive evolution
- Using population genomics, infer processes and genomic features favouring rapid adaptive evolution

TRAINING
- Laboratory genetics (e.g. QTL crossing, RAD-mapping)
- Population genomics (next generation sequencing approaches and associated bioinformatics and statistics)

The student will perform bioinformatics analyses on the University of St Andrews’ computing cluster, and will access a variety of in-house workshops (e.g. Linux and the command line, NGS analysis). Support will be available to attend external workshops and training for additional topics (e.g. analysis of RADseq data, detecting signatures of selection, recombination variation, linkage disequilibrium, etc.)

The project is a collaboration involving the lead supervisor Dr. Nathan Bailey (St Andrews) and co-supervisors Prof. Oscar Gaggiotti (St Andrews) and Dr. Lesley Lancaster (Aberdeen). In addition to EASTBIO induction and annual symposia, the student will liaise with Edinburgh Genomics to acquire next-generation sequencing data. Yearly secondments to the Lancaster lab at Aberdeen will be used for training and collaboration on analyses, with opportunities to develop and test hypotheses about ecological and landscape factors influencing convergent adaptation [3].

Funding Notes

This project is eligible for the EASTBIO Doctoral Training Partnership: View Website

This opportunity is only open to UK nationals (or EU students who have been resident in the UK for 3+ years immediately prior to the programme start date) due to restrictions imposed by the funding body.

Apply by 5.00 pm on 5 January 2020 following the instructions on how to apply at: View Website

Please also upload the EASTBO Application Form as an additional document to the University of St Andrews online Application.

Informal inquiries to the primary supervisor are very strongly encouraged.

References

[1] Rayner JG, Aldridge S, Montealegre-Z F, Bailey NW (2019) A silent orchestra: convergent song loss in Hawaiian crickets is repeated, morphologically varied, and widespread. Ecology e02694.

[2] Pascoal S,…,Bailey NW (2018) Silent crickets reveal the genomic footprint of recent adaptive trait loss. bioRxiv doi:10.1101/489526

[3] Fitt RN, Lancaster LT (2019) Complex landscape topography can facilitate local adaptation during a range shift. PeerJ Preprints 7:e27899v1

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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