The Evolution of Redundancy During Parallel Polygenic Adaptation
Theme: Ecology genetics and evolution
Description: We invite an enthusiastic honours student to join an exciting research project exploring the evolutionary dynamics of parallel evolution and polygenic adaptation. Exploring redundancy during parallel evolution is a fascinating area of research that investigates whether similar genetic mechanisms underlie the same adaptive trait in different populations. This kind of research can reveal the predictability of evolution and the extent to which genetic diversity influences adaptive outcomes.
Additional Information:
Roles and Responsibilities:
• Perform a Genome-Wide Association Study (GWAS) on each population pair to identify loci associated with the adaptive trait.
• Calculate genetic divergence (e.g., Fst or Dxy) at GWAS loci and compare it to the background level of divergence across the genome.
• Use statistical tests (e.g., permutations or bootstrapping) to determine if GWAS loci are more divergent than expected by chance.
• Identify GWAS loci in multiple independent population pairs that have adapted to similar environmental pressures.
• Compare the list of loci across these populations to see if the same loci are implicated in the adaptive trait.
• Use statistical analyses to test for non-random association patterns, suggesting consistent recruitment of genetic loci.
• Estimate the breeding value of GWAS loci by assessing their effect size and direction on the phenotype within each population.
• Develop genomic prediction models using the effect sizes of GWAS loci to predict the phenotype in one population based on the genotype data from another population.
• Validate the models using cross-validation techniques or by applying the model to independent datasets to assess predictive accuracy.
Qualifications:
• Currently enrolled in an honours program in Genetics, Biology, Computational Biology, or a related field.
• A strong foundation in genetics and evolutionary biology.
• Experience or a keen interest in computational modelling and data analysis.
• Proficiency in a programming language such as Python or R.
• Excellent communication skills and the ability to work independently as well as part of a team.
What We Offer:
• The opportunity to be part of a pioneering research project at the intersection of genetics and evolutionary biology.
• Mentorship from experienced researchers and opportunities for professional development.
• Access to state-of-the-art computational resources and datasets.
• The potential to contribute to scientific publications and present at conferences.
Application Process: Please submit your CV, a brief statement of research interests, and any relevant work or project samples to d.ortizbarrientos@uq.edu.au. The ideal candidate will start honours in RQ3 2024 or RQ1 2025.
Contact: Professor Daniel Ortiz-Barrientos