WELCOME TO QUANTITATIVE GENETICS AND STATISTICAL LEARNING LAB

We are dedicated to bridging the gap between quantitative genetics and advanced statistical learning to drive the next generation of plant breeding. Our research focuses on developing robust analytical frameworks and computational tools to decipher complex biological systems and optimize genetic gain.

What we do

• Genotype by Environment Interaction (G×E): Investigating environmental drivers and modeling stability to improve selection accuracy across diverse landscapes.
• Omics Prediction: Leveraging machine learning, mixed models and Bayesian statistics to enhance the predictive ability of breeding pipelines.
• Statistical Genetics: Developing specialized statistical methods for multi-environment trials, inbreeding depression, genetic competition and other genetics challenges.
• Open Software Development: Crafting high-performance tools (see products session) to make complex genetic analyses accessible and efficient.