Genomic Prediction of Complex Traits (GPCT01)
Genomic Prediction of Complex Traits: learn predictive modelling, polygenic prediction, and genomic methods for analysing complex traits.
Duration: 22.5 hours
Next Date: September 21-25, 2026
Format: Live Online Format
Spain (GMT+2) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.
£400Registration Fee
Register NowLike what you see? Click and share!
- Overview
- Instructors
- Schedule
Course Description
Genomic selection is a powerful tool in plant and animal breeding that enables the prediction of untested individuals, shortens selection cycles, and improves selection accuracy. Its implementation relies on genome-wide marker data and phenotypic information from a training population to calibrate prediction models, allowing the genetic merit of untested individuals to be estimated using marker data alone. Genomic selection is particularly well suited for quantitative traits, where the complex genetic architecture often makes approaches such as marker-assisted selection impractical.
In this course, participants will learn the fundamental principles of genomic selection and gain the practical skills needed to implement a genomic selection pipeline. We will develop an intuitive understanding of the mathematical framework underlying genomic prediction while illustrating its application across different breeding programs and modeling strategies. The course will also explore how genomic prediction models can be used to disentangle the genetic and environmental factors influencing phenotypes, highlighting the biological interpretation of different modeling approaches. Finally, we will introduce more advanced topics, including multi-trait genomic prediction and genotype-by-environment interaction models.
What You’ll Learn
- What is genomic selection and how it mostly bypasses the missing heritability problem
- Factors influencing genomic prediction accuracy
- Advantages and disadvantages of genomic selection over other selection methodologies in breeding programs
- Overview of linear regression, fixed vs random effects and covariance structures
- Genomic prediction models, with a focus on linear mixed models (GBLUP, rrBLUP) and an introduction on other alternatives (Bayesian and machine learning models)
- Genomic relationship matrices and differences with the pedigree-based numerator relationship matrix
- Heritability estimation methods
- Estimation of additive and non-additive genetic effects
- Interpretability of the models under the breeding and genotypic parameterizations
- Single and multi-stage modeling
- Genomic selection modeling in different breeding schemes: line, clonal, hybrid and animal breeding.
- Introduction to more complex topics: multi-trait and genotype by environment interaction.
.
Course Format
Interactive Learning Format
Each day features a well-balanced combination of lectures and hands-on practical exercises, with dedicated time for discussing participants’ own data, time permitting.
Global Accessibility
All live sessions are recorded and made available on the same day, ensuring accessibility for participants across different time zones.
Collaborative Discussions
Open discussion sessions provide an opportunity for participants to explore specific research questions and engage with instructors and peers.
Comprehensive Course Materials
All code, datasets, and presentation slides used during the course will be shared with participants by the instructor.
Personalized Data Engagement
Participants are encouraged to bring their own data for discussion and practical application during the course.
Post-Course Support
Participants will receive continued support via email for 30 days following the course, along with on-demand access to session recordings for the same period.
Who Should Attend / Intended Audiences
This course is intended for breeders, biologists, quantitative geneticists, postgraduate students and early-career researchers interested in genomic selection. The aim is preparing participants for implementing genomic selection in real breeding scenarios, as well as understanding the intuition behind its core concepts.
Participants are expected to have a basic understanding of quantitative and population genetics, molecular biology, statistics and matrix algebra, although key concepts and methodologies will be introduced throughout the course. Some familiarity with R programming is also required.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R / Rstudio, which are free tools and can be installed from https://www.r-project.org/ and https://posit.co/download/rstudio-desktop/.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary packages will be introduced and installed during the workshop. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.
Dr. Javier Fernández-González
Javier is a quantitative geneticist and biostatistician specialising in statistical genomics and computational methods for plant breeding. His research focuses on developing and improving genomic selection methods to enhance breeding decisions and accelerate genetic gain. His expertise spans genomic prediction, genomic mating, training population optimisation, variance component estimation, field trial simulation, and the statistical analysis of complex traits. Through collaborations with private breeding companies, he has developed practical statistical solutions tailored to real-world commercial breeding programmes.
Javier holds a Bachelor’s degree in Biotechnology from the University of León, a Master’s degree in Computational Biology from the Polytechnic University of Madrid, and a PhD in Quantitative Genetics and Biostatistics from the Polytechnic University of Madrid. His doctoral research was conducted at the Centre for Biotechnology and Genomics of Plants (CBGP). Since 2025, he has worked as an independent quantitative genetics consultant, supporting the development and application of statistical and genomic methods for breeding programmes.
Javier has developed several software tools for genomic analyses, including MateR, an R package implementing advanced genomic mating strategies, and has contributed to the development of TrainSel, a tool for training population optimisation. He has published extensively in quantitative genetics and statistical genomics and regularly presents his work at international scientific conferences.
Education & Career
PhD in Quantitative Genetics and Biostatistics
- Bachelor’s degree in Biotechnology, University of León
- Master’s degree in Computational Biology, Polytechnic University of Madrid
- PhD in Quantitative Genetics and Biostatistics, Polytechnic University of Madrid
- Doctoral research at the Centre for Biotechnology and Genomics of Plants (CBGP)
- Independent Quantitative Genetics Consultant (since 2025)
- Collaborator with commercial plant breeding companies, developing statistical methods for modern breeding programmes
- Developer of the MateR R package and contributor to TrainSel for training population optimisation
- Author of multiple peer-reviewed publications in quantitative genetics and statistical genomics
- Research Focus
Javier’s research centres on applying quantitative genetics, statistical genomics, and computational methods to improve breeding strategies and accelerate genetic gain. His research interests include:
- Genomic prediction and genomic selection
- Quantitative genetics and statistical genomics
- Genomic mating strategies
- Training population optimisation
- Variance component estimation
- Field trial simulation and experimental design
- Complex trait analysis
- Statistical modelling for plant breeding
- Computational methods for breeding programmes
Professional Consultancy
Javier provides consultancy to academic researchers and commercial breeding organisations, supporting the design and implementation of statistical and genomic workflows. His consultancy expertise includes:
- Genomic prediction and genomic selection
- Quantitative genetics and breeding programme optimisation
- Statistical genomics and genomic data analysis
- Experimental design and field trial analysis
- Training population optimisation
- Genomic mating strategies
- Statistical modelling in R
- Reproducible genomic analysis workflows
Teaching & Skills
Javier has extensive teaching experience in quantitative genetics, genomic prediction, and statistical genomics. He is an instructor on the annual Genomic Selection Course at the Polytechnic University of Madrid, where he delivers lectures and practical sessions covering quantitative genetics, genomic prediction, and statistical methods for breeding. He has also delivered specialist training for the commercial breeding sector and supervised Master’s and PhD students.
- Instructor on the annual Genomic Selection Course, Polytechnic University of Madrid
- Teaches quantitative genetics, genomic prediction, and statistical genomics
- Delivers specialist training for commercial breeding organisations
- Supervisor and mentor of Master’s and PhD students
- Experienced in developing practical, reproducible genomic analysis workflows in R
- Passionate about making advanced quantitative genetics and statistical methods accessible to researchers and plant breeders through hands-on, applied training
Links
Session 1 – 01:00:00 – Genomics: the promise, the reality and the solution
Introduction on the challenges when working with quantitative traits, the limitations of genomics and missing heritability, as well as how genomic selection bypasses most of them and its advantages and disadvantages in the context of breeding.
Session 2 – 01:00:00 – Introduction to linear models
Build intuition behind the mathematics in simple linear models and introduce matrix notation
Break – 00:30:00
Session 3 – 02:00:00 – Introduction to linear models (continuation)
Fixed vs random effects. Solving the p>n issue, shrinkage and philosophical differences. REML and shrinkage factor.
Session 4 – 02:00:00 – Basic genomic selection models
Linear mixed models: rrBLUP, relationship matrices and GBLUP. Calculate heritability and make predictions on untested genotypes.
Break – 00:30:00
Session 5 – 02:00:00 – Non-additive effects: usage and interpretation
Inclusion of dominance under the breeding and genotypic parameterizations, and explanation on how their interpretation differs.
Session 6 – 02:00:00 – Multi-stage analysis and alternatives to linear mixed models
Explain how predictive models can be built in a single vs several stages. Introduce Bayesian and machine learning models and compare them to the classical linear mixed models.
Break – 00:30:00
Session 7 – 02:00:00 – Data preparation and Cross-validation
Basic preparation of phenotypic and genotypic data. Explain how cross-validation allows to evaluate model performance in a realistic setting.
Session 8 – 02:00:00 – Practical implementation of genomic selection
Showcase genomic selection in realistic datasets. Line, clonal and animal breeding programs.
Break – 00:30:00
Session 9 – 02:00:00 – Practical implementation of genomic selection (continuation)
Theory and practical examples for hybrid breeding.
Session 10 – 02:00:00 – Introduction to multi-trait analysis
Basic multi-trait modeling and index selection
Break – 00:30:00
Session 11 – 02:00:00 – Introduction of genotype by environment modeling
Basic overview of genotype by environment interactions and how to model them within the framework of genomic selection.
Testimonials
PR Stats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PR Stats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PR Stats provided excellent training in stable isotope analysis through the SIMMPR course, which was incredibly valuable for my research. I was fortunate to attend the course through a generous fee waiver, which directly supported my work and enabled me to develop skills that contributed to my recent publication on reservoir food webs in Sri Lanka. I’m very grateful for the opportunity and support, and would highly recommend their courses to others working in ecological research.
Subodha Silva
Aquatic Ecology Researcher
Courses attended
SIMMPR
Testimonials
PR Stats has become an invaluable part of developing my skills in advanced statistical and spatial analysis. Through training in areas such as Bayesian statistics and Species Distribution Modelling, I’ve gained both practical expertise and exposure to leading experts in the field. The impact on my research has been significant with at least four of my published papers have been directly influenced by PR Stats courses. My most recent work benefitted from modelling advice on sample design and model accuracy evaluation and can be seen here.
Carlos P.E. Bedson
Quantitative Spatial Ecology, Ecology and Environment Research Centre, Manchester Metropolitan University, United Kingdom
Courses attended
ADVR08, ENMR03, BMIN02, ISBD01, BADA01, SDMB06
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
Still have questions?
Can’t find the answer you’re looking for? Please chat to our friendly team.
