Publication Details
Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation
Schwarzerová Jana, Ing. et Ing., MSc (FEEC BUT)
Nagele Thomas (LMU)
Weckwerth Wolfram (UNIVIE)
Provazník Valentine, prof. Ing., Ph.D. (DBME FEEC BUT)
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY FIT BUT)
Polygenic risk score, genetic variations, computational biology, genetic programming
The polygenic risk score has proven to be a valuable tool for assessing an individual's genetic predisposition to phenotype (disease) within biomedicine in recent years. However, traditional regression-based methods for polygenic risk scores calculation have limitations that can impede their accuracy and predictive power. This study introduces an innovative approach to enhance polygenic risk scores calculation through the application of genetic programming. By harnessing the power of genetic programming, we aim to overcome the limitations of traditional regression techniques and improve the accuracy of polygenic risk scores predictions. Specifically, we showed that a polygenic risk score generated through Cartesian genetic programming yielded comparable or even more robust statistical distinctions between groups that we evaluated within three independent case studies.
@INPROCEEDINGS{FITPUB13077, author = "Martin Hurta and Jana Schwarzerov\'{a} and Thomas Nagele and Wolfram Weckwerth and Valentine Provazn\'{i}k and Luk\'{a}\v{s} Sekanina", title = "Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation", pages = "3782--3787", booktitle = "2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2023)", year = 2023, location = "Istanbul, TR", publisher = "Institute of Electrical and Electronics Engineers", ISBN = "979-8-3503-3748-8", doi = "10.1109/BIBM58861.2023.10385615", language = "english", url = "https://www.fit.vut.cz/research/publication/13077" }