Registration and Deadlines

The registration cost includes two coffee breaks, five lunches (Monday through Friday), one dinner (Wednesday), and electronic copies of handouts of lectures. The registration is limited to 80 people. A maximum of 30 students are accepted to the workshop.

Prerequisites: You will need to bring your laptop each day and have familiarity with linear mixed models and R programming.

Save money when you register by Friday, April 2, 2027. Early registration for graduate students is $500 and for others, $1250.

The regular registration deadline is Monday, May 3, 2027. For graduate students, it is $600 and for others, $1500.

The cancellation deadline is Monday, May 3, 2027, or 10 business days before the event and includes a $100 fee; otherwise, no refund will be issued.

Locations

• Witherspoon Student Center, Sankofa Room 126, 2810 Cates Ave, Raleigh, NC 27606 (directions)

Overnight Lodging

Ramada Raleigh
(approximately 4 miles or a 10-minute drive from Witherspoon Student Center)
1520 Blue Ridge Rd, Raleigh, NC 27607
Phone: (919) 832-4100
ramadaraleigh.com
$89 per night plus taxes; be sure to mention that you are participating in an NC State workshop to get the discounted rate.

Aloft Raleigh
(approximately 2 miles or 10-minute drive from Witherspoon Student Center)
2100 Hillsborough Street, Raleigh, North Carolina, USA, 27607
Phone: (919) 828-9900
$179 per night plus taxes on reservations made by April 17 (30-days prior)
Book your room

Parking on Campus

Everyone who parks on campus (including faculty and staff) is required to have a parking permit. If you will require a parking permit, please contact Janice Sitzes at [email protected].

Parking recommendations for Witherspoon.

Why should you attend the workshop?

Computing skills are essential for plant breeders in various aspects of your work, enabling you to manage large genetic, phenotypic, and environmental datasets and conduct complex statistical analyses for decision-making. During this five-day, hands-on workshop, you will master computing skills that enhance efficiency, accuracy, and innovation in plant breeding, ultimately leading to the development of improved crop varieties.

• Gain an understanding of theoretical concepts.
• Develop practical skills through hands-on experience.
• Enhance collaboration and teamwork with your fellow participants.

Teaching Format:

Introduction and Theory (1 hour):

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

Demos (1 hour):

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

Practice Session (2 hours):

• Participants work in small teams to apply the concepts they have 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.

Software:

A free ASReml-R license will be available to all participants. Any additional software and computing requirements will be communicated two weeks before the workshop.

Workshop Modules:

Module 1: Experimental Designs in Plant Breeding

Instructor: Jonathan Stallrich
Monday, May 17, 2027, 13:00-17:00

This module provides an overview of proper randomization and analysis of classical experimental designs used in plant breeding. It covers experiments with one or multiple treatment factors, including both fixed and random effects. Blocking factors are introduced, with a discussion of how blocking influences randomization and statistical efficiency. The module concludes with an introduction to split-plot designs. The R package agricolae will be used throughout to demonstrate practical approaches to experimental layout and randomization. 

Module 2: Exploring Linear Mixed Models

Instructor: Christian Maltecca
Tuesday, May 18, 2027, 8:30-12:00

This module introduces linear mixed models as a cornerstone of data analysis in plant and animal breeding. Participants will learn both the theoretical foundations and practical applications of mixed models using ASReml. The focus is on plant breeding datasets, with skills that provide a foundation for advanced topics such as multi-environment and multi-year trials and genomic selection models.

Module 3: Analysis of Multi-Environment Trials (MET)

Instructor: James Holland
Tuesday, May 18, 2027, 13:00-17:00

This module provides comprehensive coverage of analyzing multi-environment and multi-year trial data to predict the genetic merit of individuals for selection. It will describe fitting a range of variance-covariance structures to capture complex patterns in GxE interactions and within-environment residual effects. Participants will learn how to estimate heritability and obtain predictions from complex genotype-by-environment (G×E) covariance structures using ASReml.

Modules 4 & 5: Image Analysis for High-Throughput Phenotyping and AI

Instructors: Jing Zhang, Nick Garrity, Alexis Suero Mirabal, Caiwang Zheng
Wednesday, May 19, 2027, 8:00 – 17:00

High-throughput phenotyping generates large and complex datasets, including images collected from field and controlled environments. These modules introduce software tools and workflows for efficient image processing and trait extraction in plant breeding. Participants will gain practical skills to streamline phenotypic data collection and integrate image-based traits into breeding and selection pipelines, including applications of machine learning and AI.

Module 6: Marker-Assisted Selection

Instructors: Jeffrey Dunne, Ryan Andres
Thursday, May 20, 2027, 8:00-12:00

This module covers the principles and applications of marker-assisted selection, with emphasis on traits controlled by major genes, such as disease resistance. Participants will learn how to integrate validated major-effect markers identified through linkage mapping or GWAS into routine breeding and selection decisions. This module will introduce Python protocol development for the Opentrons OT-2 robot and developing custom web-based breeding applications using R Shiny and Plotly Dash.

Modules 7 & 8: Genomic Selection-Theory and Practice

Instructors: Fikret Isik, Roberto Fritsche Neto, Nasir Shalizi
Thursday, May 20, 2027, 13:00-17:00
Friday,  May 21, 2027, 8:30-12:00

These modules provide an in-depth treatment of the theory and practical implementation of genomic selection, highlighting how it differs from conventional mixed-model prediction based on pedigree alone. Topics include the construction and scaling of pedigree- and marker-based relationship matrices, the integration of genomic and pedigree information in single-step and two-step GBLUP models, and the evaluation of prediction accuracy and bias. Real-world examples from both crop and forest tree breeding programs will be used to illustrate best practices and common challenges.

Sponsorship Opportunities

Questions

For questions regarding registration and logistics, contact Janice Sitzes ([email protected]). For all other questions, contact Dr. Fikret Isik ([email protected]).

Organizing Committee

• Fikret Isik, Chair, Co-Director NCSU Tree Improvement Program, College of Natural Resources
• Carlos Iglesias, Director of the Plant Breeding Consortium, College of Agriculture & Life Sciences
• Kathleen Denya, Director of Innovation Partnerships, Plant Sciences Initiative
• Gina Brown-Guedira, USDA Professor, Department of Crop and Soil Sciences
• Jeffrey Dunne, Associate Professor, Department of Crop and Soil Sciences
• Grant Billings, Bioinformatics doctoral student; Genomic Sciences Program