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  • Training "Genome & epigenome enabled breeding in practice"

    3-4 March, 2026 | Wageningen Campus & Online
  • Course description:

    Within the framework of the GEroNIMO project , we offer a 2-day training on handling and using (epi)genetics data in population genomics and animal breeding, and using this data for variance partitioning and prediction of phenotypes and breeding values, existing of two 1-day modules, with hands-on training, at Wageningen Campus or online.

    Course objectives: 

    Experts in animal breeding and genomics will teach the latest insights and applications related to the handling and use of methylation information in breeding programs and population management. Real use cases will be discussed.

    Application to participate:

    Participation to the training modules is free of charge. It is possible to apply for separate modules only, but it is advisable to also follow Module 1 if you want to follow Module 2. Applications will be evaluated before being accepted.  

    Dates: 3-4 March, 2026

    Learning goals:

    • Analyze DNA methylation data in a population framework, 
    • Understand how methylation can be included in genomic prediction.

    Target Group: Practitioners in animal breeding, PhD students, postdocs and other researchers

    Group size: A maximum of 40 participants (priority given to on-site registrations)

    Training duration: 2 days

    Recommended prior knowledge:


    1) Basic knowledge on genome biology. Basic use of command line coding language (e.g. R, python) is a prerequisite

    2) Basic understanding of quantitative genetics and of mixed models.

    Language: English

    Teaching methods: The sessions will focus on teaching the background and theory, followed by hands-on training.

    Programme: 

    Module

    Topic Teachers
    1. March 3rd Handling of methylation data & applications in population genomics Marta Godia, Ole Madsen (WUR), Gwendal Restoux (INRAE)
    2. March 4th Using methylation data in breeding programs: inclusion in genomic prediction Mario Calus (WUR)
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