Computing Skills in Plant Breeding (CSinPB)

Organized by North Carolina State University, Plant Breeding Consortium.

computing skills in plant breeding workshop

Registration opens: August 1, 2024

Computing skills are essential for plant breeders in various aspects of their work. These skills enable them to manage large genetic, phenotypic, and environmental datasets and conduct complex statistical analyses for decision-making. In essence, computing skills enhance efficiency, accuracy, and innovation in plant breeding, ultimately leading to the development of improved crop varieties.

  • Foster understanding of theoretical concepts.
  • Develop practical skills through hands-on experience.
  • Encourage collaboration and teamwork among participants.

Exploring the Power of Linear Mixed Models with ASReml-R: Theory and Application in Plant Breeding: Understanding the potential of linear mixed models as a cornerstone in the analysis of diverse datasets within plant and animal breeding. This module delves into the theoretical underpinnings and practical applications of linear mixed models, specifically leveraging ASReml-R software. By focusing on plant breeding contexts, participants gain essential skills that serve as a foundation for advanced topics such as multi-environmental and multi-year trials, as well as genomic selection models. 

Analysis of Multi-Environmental Trials for Predictions of Genetic Merit: This module provides comprehensive coverage of analyzing multi-environmental and multi-year data to predict breeding and genetic values crucial for selection. It starts with linear mixed models theory and follows practical demonstrations for both continuous and binary traits. 

Quality Control, and Parentage Assignment in Plant Breeding Using DNA Markers: This module employs DNA markers to address essential quality control aspects within breeding programs. Examples range from cultivar identification through fingerprinting to pedigree correction and population structure analysis, offering tools for enhancing breeding efficiency.

Image Analysis for High-throughput Phenotyping and AI: High-throughput phenotyping generates complex data, including images captured through various methods. This module equips participants with essential software skills for streamlining image analysis in plant breeding, enhancing efficiency in phenotypic evaluations.

Marker-Aided-Selection: Exploring marker-aided selection, this module delves into crucial aspects relevant to disease resistance and other traits controlled by Mendelian inheritance. Participants learn to integrate validated QTLs from QTL mapping and GWAS into routine selection processes applicable across diploid and polyploid species.

Imputation: Routine application of genomics requires the imputation of missing genotypes or imputation from a low-density panel to a medium-density genotyping panel to save resources. In this module, pedigree-free software tools, such as Beagle for imputation, will be covered with real demos. 

Genomic selection: This module delves into the theory and practical applications of genomic selection, providing a comprehensive understanding of its differences from conventional linear mixed model predictions. Real-world examples from various crops and trees will enhance the learning experience.

Data Management in Plant Breeding: Databases are essential in plant breeding for managing, analyzing, and making decisions based on vast amounts of genetic, environmental and phenotypic data. They facilitate collaboration, provide historical reference, and support informed breeding strategies, ultimately enhancing the efficiency and effectiveness of breeding programs. The module covers the foundation of data management using well-known database examples in plant breeding.

Introduction and Theory:

  • Research teams present the foundational concepts.
  • Cover key theories, principles, and methodologies.
  • Provide context and objectives for the session.


  • Present demonstrations to illustrate theoretical concepts.
  • Use practical examples and applications to enhance understanding.
  • Encourage interactive engagement and ask questions from participants.

Break (15 minutes)

Practice Session:

  • Participants work in small teams to apply concepts learned.
  • Provide sample data sets and practical exercises.
  • Encourage collaboration, problem-solving, and critical thinking.
  • Faculty, post-docs, and students provide guidance and support as needed.
  • Chair: Fikret Isik 
  • Members: Carlos Iglesias, Gina Brown-Guedira, Jeffrey Dunne, Marcelo Mollinari, Joseph Gage, Grant Billings, Jaswinder Kaur.