Education
In addition to conducting groundbreaking research in our field, we are committed to fostering knowledge and skills development by offering a variety of educational courses. These courses are designed to provide learners with the latest insights, methodologies, and practical skills that are emerging from our research.
EL 016 – Linux for Scientists
EL 016 – Linux for Scientists
next addition: 31-01-2025 to 24-01-2025
This course is designed to teach users how to work with the Linux/UNIX command line interface. It covers basic commands for file and directory management, process handling, input/output redirection, and text processing tools like ‘sed’ and ‘gawk’. The second half focuses on writing Bash shell scripts to automate tasks, with practical applications such as using the Sun Grid Engine job queue system on the epib-genstat servers.
The course is hands-on, allowing experienced users to skip basic content and dive into advanced topics like regular expressions, version control, and advanced text editing.
Objectives
By the end, students will be able to:
• Manage project data from the command line
• Reformat output for subsequent analyses
• Write scripts to automate tasks
• Run complex analyses efficiently without overloading the server.
Prerequisites
No prior Linux knowledge is required, though experience with Linux-based analyses is recommended for maximum benefit.
Faculty
Lennart C. Karssen, PhD- l.c.karssen@polyomica.com
More information: EL016 Linux for Scientists – NIHES
EL 017 - Intro to Genome-Wide Association Studies
EL 017 – Intro to Genome-Wide Association Studies
next addition: 27-01-2025 to 31-01-2025
This 5-day introductory course aims to give an overview of the field of Genome Wide Association Studies (GWAS). During the first half of the course we focus on the biological knowledge required to understand GWAS as well as the design of GWAS studies and basic skills required to perform GWAS. In the second half of the course you will perform a GWAS as well as post-GWAS analyses to help you understand the field as a whole. GWAS is a skill one can only master by practice, which is why we give you ample opportunity to practice what we have taught you and learn the skills needed to perform your own GWAS.
Objectives
To get a broad understanding of GWAS and to learn the basics of performing your own GWAS.
Prerequisites
Understanding of genetic epidemiology and statistics (regression analysis and maximum likelihood estimation); experience in working with the Linux command line interface, e.g. at the level of the “Linux for Scientists” course (EL016 or previously SC09).
Faculty
Linda Broer (course coordinator)
Fernando Rivadeneira (associate program director) Cindy Boer
Carolina Medina Gomez
Vid Prijatelj
Katerina Trajanoska
More information: EL017 Introduction to Genome-Wide Association Studies – NIHES
El 018 - Mendelian Randomization
El 018 – Mendelian Randomization
next addition: 17-02-2025 to 19-02-2025
With the growth of genetic databases, opportunities for Mendelian randomization (MR) studies are expanding. MR uses genetic variants as instrumental variables to assess the causal effect of non-genetic risk factors on diseases or outcomes. When assumptions hold, MR can overcome limitations of observational studies and help identify targets for interventions.
This 3-day course covers the principles of causal inference in MR, from basic concepts to advanced statistical methods for both one- and two-sample MR frameworks. Students will apply these concepts through hands-on sessions using R-libraries, learn to select and assess instrumental variables, and critically appraise MR studies. The course also explores novel MR applications.
Objectives
• Understand the distinction between causation and association
• Learn MR study design, execution, and interpretation
• Get hands-on experience with MR methods commonly used in consortia
Prerequisites
Basic knowledge of genetic epidemiology and statistics (probability and regression).
Faculty
Katerina Trajanoska – McGill University, Erasmus MC (course coordinator)
Jeremy Labrecque – Erasmus MC
Fernando Rivadeneira – Erasmus MC (associate program director)
Carolina Borges – Bristol University
More information: EL018 Mendelian Randomisation – NIHES
EL019 - Next-Generation Sequencing Data
EL019 – Next-Generation Sequencing Data
next addition: 03-03-2025 to 07-03-2025
This course provides an introduction to working with Next-Generation Sequencing (NGS) data. It targets individuals who have access to NGS data and want to learn how to work with this data and what the possibilities and limitations of NGS are. Lectures will be complemented with practical sessions in which the student will gain hands-on experience with various tools and techniques.Subjects that will be covered include:
• NGS: an introduction to methodology and techniques;
• Basic statistics of NGS data, e.g. coverage;
• Aligning the sequence reads;
• Calling sequence and structural variants;
• Dealing with various file formats (samtools, VCFtools, GATK);
• Annotating sequence and structural variants;
• Evaluating functional effects of the genetic variants on proteins;
• Conversion to other formats;
• Single variant and Collapsed genotype analyses with various tools (e.g. seqMeta, RAREMETAL and RVtest);
• Finding variants with recessive effects and compound heterozygosity;
• Search for rare variants in families and population based studies for complex phenotypes;
• Search for rare variants in Mendelian disorders, and
• Imputation of sequence variants.
Faculty
Jeroen van Rooij (course coordinator)
Robert Kraaij
Haojie Lu
Carolina Medina Gomez
Vid Prijatelj
Costanza Vallerga
Joost Verlouw
Dina Vojinovic
More information: EL019 An Introduction to the Analysis of the Next-generation Sequencing Data – NIHES
EL 020 - Analysis of Proteomics & Metabolomics
EL 020 – Analysis of Proteomics & Metabolomics
next addition:12-05-2025 to 16-05-2025
This course introduces the analysis of proteomics and metabolomics data, two emerging fields that enhance our understanding of molecular pathways and aid in identifying novel biomarkers for complex diseases. It combines theoretical lectures with practical assignments, offering participants the opportunity to analyze example datasets.
The course is designed for students, epidemiologists, clinicians, and molecular biologists with limited background in molecular epidemiology. Participants will learn the principles of protein and metabolite profiling, association analyses, quality control, normalization, and analytical approaches.
Objectives
By the end of the course, participants will:
• Understand key concepts in proteomics and metabolomics analysis.
• Learn technical aspects, quality control, and applications of these fields.
• Use resources and technologies for proteomics and metabolomics.
• Analyze and interpret population-based datasets.
Prerequisites
This is a basic course open to a broad audience. While no specific prerequisites are required, familiarity with molecular epidemiology and basic R is recommended.
Faculty
Prof. dr. Joyce van Meurs (Proteomics part)
Dr. Mohsen Ghanbari (Metabolomics part)
More information: EL020 Introduction to the Analysis of Population Proteomics & Metabolomics – NIHES
EL 034 - Introduction to the Analysis of Population Epigenomics & Transcriptomics
EL 034 – Introduction to the Analysis of Population Epigenomics & Transcriptomics
next addition: 12-05-2025 to 16-05-2025
This course aims to give an introduction into the analysis and interpretation of epigenomics and transcriptomics data in the setting of population-based studies. We will introduce both types of omics and discuss their technical background, quality control and normalization, analytical approaches, interpretation of results and follow-up analyses.
The course will include short practical sessions during which course participants can learn to with epigenomic and transcriptomic data using R.
See ‘how to apply’ for the course registration period.
Objectives
To familiarize participants with the basic concepts relevant for analyzing and interpreting epigenomic and transcriptomic data in population studies.
Participant profile
Prerequisites
Basic understanding of genetic epidemiology and statistics.
Basic familiarity with R is useful for the practicals, but not needed for the course.
Faculty
Prof. Dr. Joyce van Meurs
Dr. Mohsen Ghanbari
Dr. Janine Felix
More information: EL034 Introduction to the Analysis of Population Epigenomics & Transcriptomics – NIHES
ESP74 - Advanced Analysis of Genome-wide Association Studies
ESP74 – Advanced Analysis of Genome-wide Association Studies
next addition: to be decided
Genome-wide association studies (GWAS) are a powerful tool for exploring the genetic basis of complex traits and disorders. This course combines lectures on quality control, genotype imputation, stratification correction, meta-analysis, and genomic annotation with hands-on exercises.
Participants will learn state-of-the-art GWAS procedures, including QC (PLINK2), GWAS analysis (SAIGE, BOLT, Regenie), meta-analysis (EasyQC, METAL), and post-GWAS follow-up (GCTA-CoJo, FINEMAP, colocalization, and FUMA). Breakout sessions provide opportunities for discussions and expert guidance on GWAS workflows and collaborative research.
Objectives
• Understand GWAS principles and study design
• Perform QC and GWAS analysis with advanced tools
• Interpret GWAS results and integrate findings using bioinformatics resources
Prerequisites
Basic knowledge of genetic epidemiology, statistics (regression analysis, maximum likelihood), Linux, and R programming. Prior completion of EL016 and EL019 courses is recommended.
Faculty
Prof. Fernando Rivadeneira, MD PhD
Carolina Medina Gomez, PhD
Katerina Trajanoska, MD PhD
Linda Broer, PhD
Cindy Boer, PhD
Jeroen van Rooij, PhD
Vid Prijatel, MSc
More information: Erasmus Summer Programme Courses – Erasmus Summer Programme (ESP)
ESP81 - Microbiome Data Analysis
ESP81 – Microbiome Data Analysis
next addition: to be decided
This course explores the expanding link between the human gut microbiome and health outcomes, highlighting the role of microbial communities in physiology and disease processes. Participants will learn to analyze microbiome data, including quality control, diversity assessment, clustering, and association analysis, using real datasets.
Study design examples for gut, oral, and skin microbiome research will also be covered. The program includes theoretical lectures and interactive practical sessions.
