Neda Barghi

January 04, 2024

Neda Barghi will join the MPI-EB in autumn 2024.

Please refer to or contact Neda <> for further information on the project after having read the publications stated below.

If you wish to apply for the position, please contact Neda Barghi by email providing a short motivational statement, names of two referees and a short CV (biosketch).

1. Genetic and adaptive architecture of polygenic traits

This project is part of a joint research program (SFB) on 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.


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

2. Patterns of genomic and phenotypic changes during adaptation of complex traits in small and large populations

In molecular population genetics, adaptation is typically thought to occur via selective sweeps, where targets of selection have independent effects on the phenotype and rise to fixation (1). In quantitative genetics, many loci contribute to the phenotype and subtle frequency changes occur at many loci during polygenic adaptation after a shift in trait optimum. Polygenic adaptation is probably the prevalent mode of adaptation for many traits (2), but we are still lacking a solid understanding of the selection signatures under this model. Furthermore, recent theoretical and empirical studies have shown that both selective sweep and polygenic adaptation models could result in a sweep-like genomic signature (3,4) (i.e. large allele frequency change); therefore, additional criteria are needed to distinguish the two models.

In a computer simulation study (5) we identified several distinct patterns for selective sweep and trait optimum models in experimental populations of different sizes. These features include the temporal changes in allele frequencies and phenotype, haplotype structure and (non)-parallelism among replicates. Our results showed that the combination of large and small replicate populations uncovers some distinctive patterns that can be used for developing test statistics to discriminate between the two models. Building on the results of the computer simulations we have performed an experimental evolution where 20 replicates of small (800 individuals) populations and 6 replicates of large populations (100,000 individuals) of Drosophila simulans were adapted to a high protein diet for more than 70 generations. The proposed project aims to test the predictions of computer simulations with empirical data.

The doctoral researcher will have access to a large dataset consisting of time-series genomic, gene expression and fitness data for these experimentally evolved populations. Thus, the researcher should have strong programming skills (Python, R, etc) and experience with handling large data sets. Background is population genetic is essential, prior experience with machine learning is a plus. The project is a great opportunity for doctoral researchers who are interested in analyzing time-series data and combining bioinformatics methods with population genetic theory.


1. Hermisson J, Pennings PS. Soft Sweeps: Molecular Population Genetics of Adaptation From Standing Genetic Variation. Genetics. 2005;169(4):2335–52.

2. Barton NH, Etheridge AM, Véber A. The infinitesimal model: Definition, derivation, and implications. Theor Popul Biol. 2017;118:50–73.

3. Barghi N, Tobler R, Nolte V, Jakšić AM, Mallard F, Otte KA, et al. Genetic redundancy fuels polygenic adaptation in Drosophila. PLOS Biol. 2019;17(2):e3000128.

4. Höllinger I, Pennings PS, Hermisson J. Polygenic adaptation: From sweeps to subtle frequency shifts. PLOS Genet. 2019;15(3):e1008035.

5. Barghi N, Schlötterer C. Distinct patterns of selective sweep and polygenic adaptation in evolve and re-sequence studies. Genome Biol Evol. 2020;12(6):890–904

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