Project detail

PGine: Py/Bioconda software package for calculation of polygenic risk score in plants

Duration: 01.03.2023 — 28.02.2024

Funding resources

Brno University of Technology - Vnitřní projekty VUT

- whole funder (2023-01-01 - 2024-12-31)

On the project

The most important scientific challenges connect to climate change. Prediction algorithms take an important role, including improving plant breeding according to new environmental conditions. Therefore, this project focuses on calculating human disease probability based on genotype information — polygenic risk score. This project's main goal is to adapt these algorithms to plant data in functional software. The software can be used to bring new insights into plant breeding and thus uncover possible solutions to the climate crisis.



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People responsible

Hurta Martin, Ing. - fellow researcher
Provazník Valentine, prof. Ing., Ph.D. - fellow researcher
Sekanina Lukáš, prof. Ing., Ph.D. - fellow researcher
Schwarzerová Jana, Ing. et Ing., MSc - principal person responsible


Faculty of Electrical Engineering and Communication
- (2023-01-01 - 2023-12-31)
Department of Biomedical Engineering
- (2023-01-01 - 2023-12-31)
Department of Computer Systems
- (2023-01-01 - 2023-12-31)
Faculty of Information Technology
- (2023-01-01 - 2023-12-31)


SCHWARZEROVÁ, J.; BARTOŇ, V.; WALTHER, D.; WECKWERTH, W. Comprehensive analysis of putrescine metabolism in A. thaliana using GWAS, genetic risk score, metabolic modelling and data mining. In Proceedings II of the 29th Conference STUDENT EEICT 2023 Selected Papers. 1. Brno: Brno University of Technology, Faculty of Elektronic Engineering and Communication, 2023. p. 151-155. ISBN: 978-80-214-6154-3.

HURTA, M.; SCHWARZEROVÁ, J.; NAGELE, T.; WECKWERTH, W.; PROVAZNÍK, V.; SEKANINA, L. Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation. In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2023). Istanbul: Institute of Electrical and Electronics Engineers, 2023. p. 3782-3787. ISBN: 979-8-3503-3748-8.

HURTA, M.; SCHWARZEROVÁ, J.; PROVAZNÍK, V.; WECKWERTH, W.; WALTHER, D.; SEKANINA, L. Utilizing Cartesian Genetic Programming for Efficient Polygenic Risk Score Calculation in Plants. Program and Abstract Book: Swedish Bioinformatics Workshop 2023. Stockholm: 2023. p. 49-49.

SCHWARZEROVÁ, J.; HURTA, M.; WECKWERTH, W.; WALTHER, D. Decoding the Hidden Secrets of SNP Data: Revealing Ancestral Origins, Genomic Predictions, and Polygenic Risk Score. Germany: 2023.