Characterization of the Recombination Landscape in Red-Breasted and Taiga Flycatchers

• Main Author: Vilhelmsson Sinclair, Bella
• Format: Bachelor Thesis
• Language: English
• Published: Uppsala universitet, Evolutionsbiologi 2019
• Subjects: recombination; Ficedula parva; Ficedula albicilla; flycatcher; statistical phasing; speciation; Bioinformatics (Computational Biology); Bioinformatik (beräkningsbiologi); taiga
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-397376

Between closely related species there are genomic regions with a higher level of differentiation compared to the rest of the genome. For a time it was believed that these regions harbored loci important for speciation but it has now been shown that these patterns can arise from other mechanisms, like recombination. The aim of this project was to estimate the recombination landscape for red-breasted flycatcher (Ficedula parva) and taiga flycatcher (F. albicilla) using patterns of linkage disequilibrium. For the analysis, 15 red-breasted and 65 taiga individuals were used. Scaffolds on autosomes were phased using fastPHASE and the population recombination rate was estimated using LDhelmet. To investigate the accuracy of the phasing, two re-phasings were done for one scaffold. The correlation between the rephases were weak on the fine-scale, and strong between means in 200 kb windows. 2,176 recombination hotspots were detected in red-breasted flycatcher and 2,187 in taiga flycatcher. Of those 175 hotspots were shared, more than what was expected by chance if the species were completely independent (31 hotspots). Both species showed a small increase in the rate at hotspots unique to the other species. The low number of shared hotspots might indicate that the recombination landscape is less conserved between red-breasted and taiga flycatchers than found between collared and pied flycatcher. However, the investigation of the phasing step indicate that the fine-scale estimation, on which hotspots are found, might not be reliable. For future analysis, it is important to use high-quality data and carefully chose methods.