xGBLUP: Smooth Transition to Complex Genomic-BLUP analyses with ASReml-R (Webinar)

Short breakdown of the key takeaways from the webinar with Dr. Salvador Gezan:
Advanced Statistical Techniques: The webinar delved into the application of advanced statistical techniques, particularly linear mixed models (LMMs), in the context of plant and animal breeding.
Beyond GLUP: Attendees learned how to move beyond traditional Genomic-BLUP (GLUP) analyses, exploring the versatility and power of LMMs in breeding.
Diverse Datasets: Dr. Gezan showcased a variety of breeding datasets, which included replicated data, multi-environment trials, and multi-trait analyses. This demonstrated the broad applicability of LMMs across different scenarios.
ASReml-R Usage: The analyses were conducted using ASReml-R, a robust statistical software package known for its ability to handle complex variance-covariance structures in large datasets.
Data Scale: Participants got a glimpse of the scale of modern breeding programs, working with datasets consisting of hundreds of genotyped individuals and thousands of phenotypic records.
Complex Variance-Covariance Structures: The importance of specifying complex variance-covariance structures within LMMs was emphasized to capture the nuances present in breeding data effectively.
Efficient Computation: Dr. Gezan shared valuable recommendations and "tricks" to efficiently perform these complex analyses, ensuring that genomic predictions can be obtained successfully without overwhelming computational resources.
Genomic Predictions: The ultimate goal of these analyses was to generate genomic predictions for specific genotypes of interest, which is crucial for selecting superior individuals or lines in breeding programs.
Interactive Learning: Attendees had the opportunity to engage with the presenter, asking questions and seeking clarification, enhancing their understanding of the material.
In conclusion, the webinar provided attendees with a comprehensive understanding of how to apply advanced statistical techniques and LMMs to complex breeding datasets. It equipped them with the knowledge and tools needed to make informed decisions in plant and animal breeding, ultimately contributing to more successful and efficient breeding programs.