Resolving the role of seasonally fluctuating balancing selection in maintaining adaptive genetic diversity in wild butterfly populations

Our PhD students become part of Queen Mary’s Doctoral College which provides training and development opportunities, advice on funding, and financial support for research. Our students also have access to a Researcher Development Programme designed to help recognise and develop key skills and attributes needed to effectively manage research, and to prepare and plan for the next stages of their career.

This project's main method are computational analyses of large DNA sequencing datasets. Full training in evolutionary genomics, bioinformatics, programming, and statistics will be provided by supervisors and collaborators, by attending specialised courses, and through collaboration with other lab members.

This project applies deep learning tools to whole-genome resequencing population genetic data, embedded in a phylogenetic and ecological framework, in order to test key theoretical predictions from evolutionary models. You will learn and apply advanced analytical techniques to identify signatures of balancing selection, and compare these with theoretical predictions.

There is ample funding for PhD student career development opportunities (visits to collaborators, conferences, courses, etc).

Standing genetic variation is a key fuel for rapid adaptation. Understanding forces that maintain variation is therefore crucial to predict adaptation, for instance to climate change or animal breeding. Balancing selection potentially plays a fundamental role, countering the effects of drift and directional selection (1,2).

Seasonally fluctuating balancing selection is widespread yet poorly characterised. Here, populations experience contrasting selection pressures during the year (3). However, how such selection contributes to maintaining variation is poorly understood (4). Recent models predict that it should increase diversity close to selected sites and decrease it further away (4). The net effect however is unclear, and we lack suitable model systems to test predictions.

Supervisor Oostra has an established model system of African butterflies where some species inhabit seasonal savannahs (fluctuating selection) and others inhabit aseasonal rainforests (constant selection). Invasion of savannahs by rainforest species has independently occurred multiple times, allowing replicated tests of the effect of seasonally fluctuating balancing selection on diversity (5). This system combines excellent genomic resources with deep understanding of seasonal selection pressures (3).

A related challenge is paucity of analytical techniques for detecting balancing selection at short timescales. Co-supervisor Fumagalli has developed new tools targeting recent and transient balancing selection in non-model species (2). Co-supervisor Andrés has an established research programme in quantifying balancing selection from large-scale genomic data, both in humans and non-model species (1).

You will test if seasonal environments promote balancing selection, and what effect this has on genetic diversity. You will:

• a) Identify genomic regions under seasonally fluctuating balancing selection, applying new analytical tools to whole-genome resequencing data (year 1-2)
• b) Test whether seasonal environments promote balanced polymorphism (year 3)
• c) Resolve the role of balancing selection in driving patterns of diversity across the genome, and test how this depends on recombination (year 4).

Applications are invited from outstanding candidates with or expecting to receive a first or upper-second class honours degree or masters degree in a relevant area such as Bioinformatics or Evolutionary Genomics.

We seek applicants who are curious, willing to learn, and able to work independently and in a team. Quantitative skills (e.g. statistics, handling large datasets), and/or experience in bioinformatics (e.g. working with Illumina data, computer programming in R/bash/Python) are highly desirable.

An interest or experience in population genetics, phylogenetics or evolutionary genomics are essential.

We value diversity and encourage candidates with unique backgrounds and skills to apply, especially candidates from underrepresented backgrounds, or who may have faced particular barriers. We are conscious that candidates may bring skills or experience that we have not listed here.

1. Bitarello BD, et al. Genome Biology and Evolution. 2023 Mar 1;15(3):evad032.

2. Isildak U et al. Molecular Ecology Resources. 2021;21(8):2706–18.

3. Oostra V et al. Nature Communications. 2018 Mar 8;9(1):1005.

4. Wittmann MJ et al. 2023 Apr 1;223(4):iyad022.

5. Aduse-Poku K et al. Systematic Biology. 2022 May 1;71(3):570–88.