ChromaX: a fast and scalable breeding program simulator

Plant breeding programs are often complex and require breeders to make strategic decisions to effectively allocate available resources. In recent years, several breeding tools have been developed to increase the rate of genetic gain, such as genomic selection, high throughput phenotyping or rapid generation advance. However, the efficient incorporation of these tools into plant breeding programs is still an active area of research, as it is highly species- and breeding program-dependent. To address this, computer simulations provide a rapid and inexpensive means of assessing the potential for integrating these tools into a breeding program.

In collaboration with leading scientists in the field of computer science, we developed ChromaX. ChromaX is a Python library that enables the simulation of genetic recombination, genomic estimated breeding values and selection processes. This offers breeders and scientists new opportunities to simulate genetic gain and optimize breeding schemes. By utilizing GPU processing, it can perform these simulations up to two orders of magnitude faster than existing tools with standard hardware.

This makes ChromaX a fast and scalable solution for simulating and optimizing breeding programs across various plant and animal species. The paper provides insights into ChromaX's features, implementation, and benchmarks against state-of-the-art solutions, showcasing its speed advantages on GPU hardware.

Citation:
Younis OG, Turchetta M, Ariza Suarez D, Yates S, Studer B, Athanasiadis IN, Krause A, Buhmann JM and Corinzia L (2023) ChromaX: a fast and scalable breeding program simulator. Bioinformatics 39,btad691.
external page https://academic.oup.com/bioinformatics/article/39/12/btad691/7441500