Tree breeding programs often operate with small populations and long juvenile periods, making it difficult to apply conventional genomic approaches such as Genome-Wide Association Studies (GWAS).
Traditional methods typically require hundreds of individuals to reliably identify marker-trait associations, which is often impractical for tree crops.
As a result, breeders face limitations in understanding the genetic basis of important production traits, slowing the development of improved varieties and reducing the effectiveness of emerging technologies such as marker-assisted selection and gene editing.
This project will develop advanced bioinformatic and computational methods specifically designed for small tree breeding populations.
Using simulations and empirical data from multiple fruit tree species, researchers will create a novel analytical approach that combines individual segregant analysis with discriminant analysis of local genome regions.
This method is designed to significantly improve the power, precision and reliability of detecting marker-trait associations in breeding populations of fewer than 100 individuals.
The project aims to deliver practical, industry-ready tools and knowledge that will:
