HoloSimR: a comprehensive framework for simulating breeding programs including the hologenome scenario.

Area of Science:

• Animal breeding
• Microbiome research
• Computational biology

Background:

• Integrating microbiome data into livestock breeding schemes can enhance selection response.
• Microbiome complexity poses challenges for multi-species and multi-trait modeling.
• Accurate estimation of microbiability and species identification depends heavily on methodology.

Purpose of the Study:

• To develop an R package, HoloSimR, for simulating hologenome evolution under selection.
• To enable exploration of microbiota effects on phenotypic selection response.
• To investigate environmental, host genetic, and microbial symbiotic influences on microbiota composition.

Main Methods:

• Developed HoloSimR, an R package for simulating coevolution of genome and microbiota.
• Integrated genetic, microbiota, and hologenome-based phenotypic models.
• Simulated divergent selection over ten generations across three scenarios with real data structures.

Main Results:

• HoloSimR demonstrated strong computational performance in complex simulations.
• The package accurately reproduced real microbiota distributions, heritability, and interspecies correlations.
• Simulations on a standard laptop completed within 68.42 minutes.

Conclusions:

• HoloSimR is a scalable and robust platform for in silico research in animal breeding.
• It facilitates hypothesis testing and development of novel breeding approaches.
• The package advances understanding of host-microbiota interactions for improved livestock breeding programs.