Genomic Prediction of Complex Traits (GPCT01) – Applications for Evolutionary Biology
https://prstats.org/course/genomic-prediction-of-complex-traits-gpct01/
Delivered by Dr. Javier Fernández-González, a quantitative geneticist specialising in statistical genomics, genomic prediction, and computational methods for breeding and quantitative genetics. 
Learn how to build genomic prediction models for complex traits, with methods that are highly applicable to evolutionary biology, population genetics, and evolutionary genomics.
Understanding the genetic basis of complex traits is central to modern evolutionary biology. Advances in genome sequencing now allow researchers to investigate how thousands of genetic variants collectively influence phenotypes, adaptation, fitness, and evolutionary potential. Although genomic prediction was originally developed for plant and animal breeding, these statistical approaches are increasingly being used to study natural populations, the evolution of quantitative traits, genotype-phenotype relationships, and responses to environmental change. 
What you'll gain
- A strong understanding of genomic prediction and quantitative genetics
- Practical experience building genomic prediction models using modern statistical methods
- Skills in predicting complex traits from genome-wide marker data
- Understanding of model evaluation, validation, and prediction accuracy
- Confidence in interpreting genomic prediction models within an evolutionary context
Course format
- Live, instructor-led online training
- Hands-on coding with real-world genomic datasets
- Interactive practical exercises throughout
- Strong focus on applied, research-ready workflows
Who is this course for?
- Evolutionary biologists
- Population and quantitative geneticists
- Evolutionary genomic researchers
- PhD students and postdoctoral researchers
- Anyone interested in applying statistical genomics to evolutionary research
Why take this course?
Many of the most important questions in evolutionary biology involve understanding how genetic variation contributes to phenotypic diversity, adaptation, and fitness. Genomic prediction provides a powerful statistical framework for modelling complex traits controlled by many loci, allowing researchers to investigate evolutionary potential, predict phenotypic variation, and better understand the genetic architecture of adaptive traits. These methods are increasingly being applied alongside GWAS, population genomics, and quantitative genetics to address fundamental evolutionary questions. 
This course equips you with the practical skills needed to develop, evaluate, and interpret genomic prediction models using modern statistical approaches. Whether you're studying adaptation, quantitative trait evolution, life-history variation, genotype-phenotype relationships, or evolutionary responses to changing environments, you'll gain the analytical toolkit needed to apply genomic prediction methods confidently in your own research.
Learn more & enrol
PR Stats course page for Genomic Prediction of Complex Traits (GPCT01)
https://prstats.org/course/genomic-prediction-of-complex-traits-gpct01/
Questions?
Email: oliver@prstats.org

Oliver Hooker
Managing Partner
Advanced Training for Researchers in the Life Sciences
coursesinfo@prstats.org | www.prstats.org

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