Genomic basis of polygenic adaptation to high-altitude in Drosophila
The main goal of our research group is to understand the molecular mechanisms underlying adaptation in natural populations. Our case study are altitude gradients (i.e., changes in elevation), an ecological limit with important variations in temperature and humidity along very short distances, typically on a few kilometres. Our research model is Drosophila, specifically two species with large effective population sizes and high-quality genome sequences and annotations, two aspects that are determinants to find and characterize the footprint of positive selection at the molecular level. We are combining high-throughput sequencing data, powerful population genomics and bioinformatics inference, and computer modelling, in an innovative large-scale study across more than 2,500km at Colombian Andes Mountain ranges. We are focusing our study on determining the role of polygenic adaptation, i.e., the process in which a population adapts through subtle changes in allele frequencies at many functionally relevant sites across the genome (hundreds or thousands of genes) to altitude in Colombian populations and its environmental drivers. Tasks to be carried out by the student:Students will participate in the processing and analysis of NGS and third generation sequencing data. They will use population genomic data (Pool-seq data from multiple populations and various individually assembled genomes) and bioinformatics tools to perform raw reads quality filtering, reference guided alignment (mapping), variant calling and variation analyses. The data will be analyzed and interpreted under the theoretical framework of molecular population genetics and the point of view of ecological genomics. Most of these analyses will be carried out in a high-performance computer cluster (13 nodes, ¬~300 CPUs, 4TB RAM, 200 TB HD).
Expected student skills:Basic knowledge on evolutionary genetics, and/or on NGS data handling and analysis. Experience with Linux operating systems, and one of the programing languages commonly used in bioinformatics (Perl, Python, R) are desirable.
Project supervisor:Alejandro Sánchez-Gracia (firstname.lastname@example.org)
Software developed in the research group:
Publications of the EGB research group: