Population genetic methods for inferring adaptation in populations with complex demography

• Antragstellerinnen / Antragsteller: Professor Dr. Dirk Metzler; Professorin Dr. Laura Rose
• Fachliche Zuordnung: Ökologie und Biodiversität der Tiere und Ökosysteme, Organismische Interaktionen
• Förderung: Förderung von 2008 bis 2016
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 64631874

During the first funding period, we developed Jaatha, a statistical method to reconstruct the population history of two closely related species or populations based on population genetic data. During the development of Jaatha, we were guided by a dataset of two closely related species of wild tomatoes, Solanum peruvianum and S. chilense, for which seven genes were available. Since Jaatha is a composite-likelihood based method, it is computationally faster than other methods (including ABC) and at least as accurate as computationally intense methods if recombination rates are high. However, our simulation studies show that precise estimates of demographic parameters are only possible with much larger datasets. Novel sequencing technologies allow the collection of such datasets, but also carry new analytical challenges. Because of its integrated scaling properties, Jaatha offers an excellent starting point for the development of analytical methods to keep pace with technological advances. In the upcoming funding period, we propose to use next-generation sequencing technologies to generate a very large dataset for this pair of wild tomato species. In combination with the development of improved statistical methods, we will investigate the form and strength of selection in natural populations on a genome-wide scale.

• DFG-Verfahren: Forschungsgruppen
• Teilprojekt zu: FOR 1078: Natural selection in structered populations