Neda Barghi
Please refer to https://www.vetmeduni.ac.at/populationsgenetik/forschung/gruppe-barghi or contact Neda Barghi <barghi@evolbio.mpg.de> for further information on the project after having read the recommended literature.
Please submit your application documents via our online portal.
Genetic and adaptive architecture of polygenic traits
This project is part of a joint research program (SFB) on polygenic adaptation (https://www.vetmeduni.ac.at/sfb-polygenic-adaptation) and faculty in this program will be co-supervisors of the project.
The genetic architecture of quantitative traits comprises of all the contributing alleles and their effect sizes, i.e. genetic architecture. However, only a subset of the underlying alleles responds to selection, these alleles comprise the adaptive architecture (1). Factors such as the distance to the new trait optimum, starting frequencies and pleiotropy determine which alleles are potentially adaptive. While the genetic architecture has been the focus of many quantitative trait loci (QTL) and genome-wide association (GWA) studies, the adaptive architecture of polygenic traits is not well characterized.
The aim of this doctoral project is to compare the genetic and adaptive architectures of a polygenic traits, Drosophila simulans female body size. We will determine the genetic architecture of female body size using GWAS with 1000 individuals. In a parallel evolve and re-sequence (E&R) project Drosophila simulans populations are experimentally evolved for larger body size. Availability of this dataset allows us to distinguish alleles with adaptive potential from alleles with constraints, and to compare the adaptive and genetic architectures of female body size. Moreover, A total of 936 phenotypes (2) are available for Drosophila Genetic Reference Panel. A meta-analysis of these GWAS data, would facilitate the identification of pleiotropic alleles which can be used to corroborate the alleles under constraints identified in this study.
The doctoral researcher will have access to a large dataset consisting of GWAS and time-series genomic data from E&R experiments. The researcher should have strong programming skills (Python, R, etc) and experience with handling large data sets. Background in quantitative genetic is essential, and background in population genetics is a plus.
References
1. Barghi N, Hermisson J, Schlötterer C. Polygenic adaptation: a unifying framework to understand positive selection. Nat Rev Genet. 2020;21(12),769–781.
2. Gardeux V, Bevers R.P.J., David F.P.A., Rosschaert E, Rochepeau R, Deplancke B. DGRPool: A web tool leveraging harmonized Drosophila Genetic Reference Panel phenotyping data for the study of complex traits. 2023, eLife 12:RP88981