From Mendelian Inheritance to Machine Learning: The Evolution of Genetic Improvement in Rabbits: A Review
Abstract
Rabbits’ genetic improvement has evolved from traditional phenotypic selection and crossbreeding to a more sophisticated genomic and gene-editing technologies. The comparative efficacy and effectiveness of the current genomic methodologies is crucial for sustainable animal agriculture. As global demand for animal protein continues to widen, efficient genetic im-provement strategies have become increasingly necessary to meet consumption needs. This review evaluates traditional and modern approaches to rabbit genetic improvement, comparing their efficacy, implementation challenges, and future prospects in sustainable rabbit production systems. The findings show that traditional breeding, while foundational, is of-ten constrained by the low heritability of key traits, long generation intervals, and environmental influences. The advent of molecular genetics has introduced more precise tools, such as microsatellites markers, SNPs markers and Whole genome sequencing techniques, which promise to accelerate genetic gain for economically important traits like growth rate, feed efficiency, litter size, disease resistance among others. The review also found that gene editing technologies including Zinc-Finger Nucleases, Transcription Activator-Like Endonucleases and Clustered Regularly Interspaced Short Palin-dromic Repeats-Cas9 play vital role in the improvement of rabbit genetics. However, implementation success depends heavily on population structure, economic resources, and the availability of technical infrastructures especially in developing countries. Integrating the strengths of all the technologies synergistically offers the greatest potential for sustainable rabbit genetic improvement.
Fields of Study & Subjects
Genetic and phenotypic traits in livestock
Rabbits: Nutrition
Reproduction
Health
Genetic Mapping and Diversity in Plants and Animals
Publication details
- Journal / source
- Preprints.org
- Publication date
- 2 February 2026
- Document type
- Preprint