Thesis topics

Note that all thesis topics are described in English but you can of course write your thesis in German as well if nothing else is stated in the description. All our theses require that you have basic knowledge in R programming from our core modules. Please contact the potential supervisor for more information on the topics. You can start anytime if not stated otherwise.

Optimization of Family Size Allocation in Recurrent Selection Breeding Programs (Master thesis)

Recurrent selection is a strategy in plant breeding programs, aimed at improving populations through repeated cycles of selection and recombination. In such programs, multiple crosses between parental lines generate families that differ in their expected mean performance and segregation variance. How breeding resources are allocated across these families, in terms of family size could influence long-term genetic gain and the maintenance of genetic diversity.

This thesis aims to investigate the long-term impact of different family size optimization in recurrent selection using simulation-based approaches. The study will assess how alternative allocation strategies affect genetic gain, genetic variance, and population structure over multiple selection cycles.

You will be provided with relevant datasets and will implement simulations of recurrent selection schemes under different family size allocation strategies.

Target group: Master students of Nutzpflanzenwissenschaften (Crop Science), Informationstechnologie, or Agrobiotechnology.

Supervisor: Joshua Okoye (uche.okoye[at]agrar.uni-giessen.de).

Requirements: MK-002 or MK-002-EN and either MK-119-EN, MP-236-EN (would be ideal), or MP-240-EN completed successfully.

Focus: R programming, breeding theory.

Comparing Genome-wide association study methods for tetraploid carnation (Bachelor or Master thesis)

Genome-wide association studies (GWAS) have become a central tool for dissecting the genetic basis of complex traits. However, the large majority of methods developed to implement GWAS have been designed with only diploid organisms in mind. The dissection of complex traits in polyploids is equally relevant, as they account for a substantial proportion of agronomic and ornamental crop production. The aim of this thesis is to compare GWAS methods specifically developed for polyploid species alongside relatively novel diploid-based models, using a tetraploid carnation dataset. Tetraploid carnation represents a particularly interesting system for this comparison, as its mode of inheritance remains poorly characterised, making the choice of an appropriate GWAS model non-trivial. Performance will be evaluated based on the ability of each method to detect marker-trait associations for key ornamental traits.

Target group: Bachelor students of Agricultural Science, and master students of Crop Sciences (Nutzpflanzenwissenschaften), Informationstechnologie, or Agrobiotechnology.

Supervisor: Hugo H. Tavera (hugo.tavera[at]agrar.uni-giessen.de).

Requirements: Bachelor: BP-041 Biostatistics, Master: MK-002 or MK-002-EN and either MK-119-EN, or MP-240-EN completed successfully.

Focus: Data management, R programming.

Literature to start with: Chen et al. 2025 Nature Plants 11.9: 1714-1728

Validating the accuracy of published Deep Learning architectures for genomic prediction (Master thesis)

In the recent years, a considerable number of papers have been published presenting novel ideas and architectures for applying Neural Networks to genomic prediction in different plant species. However, validation of these models by extensive cross validation is often lacking. In this thesis, the objective will be to recreate a neural network as described in a paper that will be provided to you. Then, a cross validation study based on this Neural Network will be conducted to compare it to an industry standard method, GBLUP.

Target group: Master students of Crop Sciences (Nutzpflanzenwissenschaften), Informationstechnologie, or Agrobiotechnology.

Supervisor: Philipp Heilmann (philipp.g.heilmann[at]agrar.uni-giessen.de).

Requirements: MK-002 or MK-002-EN and either MK-119-EN, MP-163-EN-DI, MP-236-EN, or MP-240-EN completed successfully. Ideally a solid background in programming (Python).

Focus: Programming.

Impact of base model diversity on the success of stacked models (Bachelor thesis)

In plant breeding, we often use statistical models to predict the traits of interest of certain genotypes based on their genetic composition, rather than growing them in the field. One possible approach is to combine several statistical models to create a larger model, known as a stacked ensembles or model stacking.

The aim of this thesis is to establish a model stacking procedure and test the hypothesis that a more diverse composition of base models results in better ensemble models, as some authors claim. Thesis can be in English or German.

Target group: Bachelor students of any programme.

Supervisor: Philipp Heilmann (philipp.g.heilmann[at]agrar.uni-giessen.de).

Requirements: BK-005 and BP-041 completed successfully. Ideally, you find programming intuitive and pick up new concepts quickly.

Focus: Programming in R or Python, Machine Learning.

Literature to start with: Heilmann et al. 2023

Estimation of marker effects from two-stage field trial analyses (Bachelor or Master thesis)

The R package StageWise (Endelman 2023) provides different methods for the two-stage analysis of field trials: First, each environment (year/location combination) is analyses separately. In the second stage, the resulting adjusted entry means are taken as the observations of a model with the environments as an independent variable in the model. The objective of the thesis is to conduct a simulation study that uses pre-defined marker effects. Based on these, field trials are simulated. The goal is then to investigate which of the two-stage evaluation methods for the field trials results in estimates of marker effects that resemble the original values as closely as possible.

Target group: Bachelor students of Agrarwissenschaften, Master students of Crop Sciences (Nutzpflanzenwissenschaften), Informationstechnologie, or Agrobiotechnology.

Supervisor: Carola Zenke-Philippi (carola.zenke-philippi[at]uni-giessen.de).

Requirements: For Bachelor students: BK-005 and BP-041 completed successfully. For master students: MK-002 or MK-002-EN and either MK-119-EN (would be ideal), MP-236-EN, or MP-240-EN completed successfully.

Focus: R programming, theory of population genetics.

Literature to start with: Endelman 2023 Theoretical and Applied Genetics (2023) 136:65

Facilitating selection with automated data analysis (Master thesis)

The master thesis has two main parts:

(1) Development of a shiny (https://shiny.posit.co/) app that allows for automatic combination of information on selection candidates and calculation of selection indices, including cross-selection criteria.

(2) Simulation study for selection for different traits with different selection indices using the app. You will use a real-life wheat dataset for both parts of the thesis. The thesis is conducted in cooperation with a breeding company so you will have to attend online meetings with the company and you will have to present your final results to them.