Genome-Wide Association Studies (GWAS) for Evolutionary Biology (GWAS01)
GWAS for Evolutionary Biology: learn genome-wide association studies, quantitative trait analysis & genomic methods for evolutionary data.
Duration: 25 hours
Next Date: November 9-13, 2026
Format: Live Online Format
Sweden (GMT+1) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.
£400Registration Fee
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- Overview
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Course Description
Genome-Wide Association Studies (GWAS) have become a cornerstone of modern genetics, enabling researchers to identify genetic variants associated with complex traits, diseases, adaptation, and evolutionary processes. Advances in large-scale genotyping and sequencing projects have created unprecedented opportunities to investigate the genetic architecture of phenotypic variation across populations and species. This course provides a practical introduction to GWAS methodologies, from data generation and quality control to association testing, meta-analysis, and polygenic prediction.
Participants will learn how to process and analyze genome-wide genotype data, perform association studies using widely adopted software tools, visualize and interpret association results, and construct polygenic risk scores. The course also covers modern genomic data processing workflows, population structure analysis, and the challenges associated with missing heritability. Through practical examples and real-world datasets, participants will gain the skills required to design, execute, and critically evaluate GWAS analyses in evolutionary biology and related fields.
What You’ll Learn
- The principles of genetic variation and genome-wide association studies.
- The goals and contributions of major genomic reference resources, including HapMap, the 1000 Genomes Project, HGDP, and ERGA.
- How genotype phasing and imputation improve genomic coverage and statistical power.
- How to phase and impute chip genotyping data using PHASEIT2 and IMPUTE2.
- Best practices for genotype quality control, including Hardy-Weinberg equilibrium filtering, SNP call-rate filtering, and minor allele frequency and count thresholds.
- How to perform genome-wide association testing using PLINK and SNPTEST.
- The principles of GWAS meta-analysis using GWAMA and METAL.
- How to process next-generation sequencing data using GATK best-practice workflows.
- Quality control, alignment, duplicate handling, base quality score recalibration, indel realignment, and variant calling using HaplotypeCaller.
- How to perform permutation testing and conditional association analyses.
- Strategies for parallelizing large-scale GWAS computations.
- How to visualize GWAS results using Manhattan plots and LocusZoom.
- The role of PCA and population structure correction in association studies.
- How to construct and validate polygenic risk scores using independent datasets.
- The concept of missing heritability and current approaches for addressing it.
- Practical skills for conducting complete GWAS analyses from raw genomic data to biological interpretation.
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Course Format
Flexible Learning Structure
Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace, ideal for busy professionals or those balancing multiple commitments.
Access Anytime, Anywhere
All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.
Independent Exploration with Support
Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.
Comprehensive Learning Resources
Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.
Work With Your Own Data, On Your Terms
Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.
Continued Guidance and Resource Access
Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.
Who Should Attend / Intended Audiences
This course is intended for evolutionary biologists, population geneticists, bioinformaticians, medical geneticists, quantitative biologists, postgraduate students, and early-career researchers interested in genome-wide association studies and complex trait genetics.
Participants are expected to have a basic understanding of genetics, molecular biology, and statistics, together with some familiarity with command-line computing and R. Prior experience with genomic datasets or population genetics is beneficial but not required. Familiarity with next-generation sequencing data and statistical analysis workflows would be advantageous, although key concepts and methodologies will be introduced during the course.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R / Rstudio and Python / Jupyter, which are free tools and can be installed from https://posit.co/download/rstudio-desktop/ and Jupyter https://jupyter.org/install, resepctively. During the course, the Google Colab, https://colab.research.google.com/, and Posit Cloud, https://posit.cloud, will be used for practical session, which require a Google account.
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. Nikolay Oskolkov
Nikolay is a bioinformatician, computational biologist, and data scientist working at the intersection of biology, medicine, statistics, and artificial intelligence. His research focuses on applying mathematical statistics, machine learning, and deep learning methods to complex biological and biomedical datasets, including genomics, transcriptomics, microbiome research, single-cell data, metagenomics, and multi-omics integration.
Nikolay has a PhD in theoretical physics from 2007, he transition to the Life Sciences in 2011. He currently leads the Metabolic Research Group (MRG) within the TARGETWISE project at the National Institute of Research and Innovation in Latvia, and having a teaching position at Lund University, Sweden, he has previously held research positions at the Danish Technical University, University of North Carolina, Lund University and the National Bioinformatics Infrastructure Sweden (NBIS/SciLifeLab).
Nikolay has more than 20 years of teaching experience and is widely recognised for his ability to communicate advanced statistical and computational methods to researchers from diverse scientific backgrounds. His expertise spans both frequentist and Bayesian statistics, machine learning, dimensionality reduction, clustering, bioinformatics, and scientific programming in R and Python. He has delivered numerous international workshops, summer schools, and professional training courses in computational biology, genomics, and AI-driven biomedical research.
Education & Career
- PhD in Theoretical Physics (2007)
• Transitioned from theoretical physics to bioinformatics and computational biology in 2011
• Group Leader (PI), Metabolic Research Group, TARGETWISE Project, Latvia
• Former researcher and bioinformatician at Lund University and NBIS/SciLifeLab, Sweden
• Author of more than 60 peer-reviewed scientific publications with extensive international collaborations in computational biology and biomedical research
Research Focus
Nikolay’s work centres on extracting biological insight from large-scale, high-dimensional datasets using advanced statistical and machine learning approaches. His research interests include:
- Machine learning and deep learning for biomedical and omics data
• Multi-omics integration and systems biology
• Single-cell transcriptomics and dimensionality reduction methods
• Population genomics and evolutionary biology
• Microbiome, environmental DNA, and ancient DNA analysis
• Statistical modelling and Bayesian approaches for complex biological systems
• AI applications in precision medicine and drug discovery
Current Projects
- Development of machine learning methods for multi-omics data integration and drug discovery in metabolic diseases
• AI-driven approaches for genomics and computational biology
• Statistical and computational methods for ancient and environmental DNA research
• Machine learning analysis workflows for single-cell and population genomics datasets
• Research on metabolic diseases through integrative bioinformatics and systems biology approaches
Professional Consultancy
Nikolay provides expert consultancy in biological and biomedical data analysis, supporting academic researchers, healthcare scientists, and industry teams. His consultancy expertise includes:
- Bioinformatics and computational biology
• Medical genomics and precision medicine
• Single-cell and multi-omics data analysis
• Metagenomics and population genomics
• Frequentist and Bayesian statistical modelling
• Machine learning and deep learning applications
• Scientific programming in R, Python, Bash, and C++
• Study design, data analysis pipelines, and reproducible research workflows
Teaching & Skills
- More than 20 years of teaching experience in statistics, machine learning, and computational biology
• Teaches topics including machine learning, deep learning, Bayesian statistics, dimensionality reduction, clustering, single-cell analysis, genomics, and bioinformatics
• Instructor for international courses and workshops through organisations including Instats, Physalia, NBIS SciLifeLab, TARGETWISE, and RaukR
• Strong advocate for rigorous statistical thinking, reproducible research, and accessible scientific education
• Experienced in translating advanced computational methods into practical tools for life scientists and healthcare researchers
Links
Session 1 – 02:30:00 – Introduction to Genetic Variation and GWAS
Overview of genome-wide association studies; genetic variation; major genomic reference resources including the International HapMap Project, 1000 Genomes Project, Human Genome Diversity Project, and European Reference Genome Atlas.
Break – 01:00:00
Session 2 – 02:30:00 – Genotype Phasing and Imputation
Principles of haplotype phasing and genotype imputation; practical workflows using PHASEIT2 and IMPUTE2; improving genomic coverage and statistical power.
Session 3 – 02:30:00 – Quality Control of Genotype Data
Filtering genotyping array data; Hardy-Weinberg equilibrium tests, SNP call-rate thresholds, minor allele frequency filters, and identification of problematic samples.
Break – 01:00:00
Session 4 – 02:30:00 – Association Testing with PLINK and SNPTEST
Linear and logistic association models; covariate adjustment; interpretation of association statistics; genome-wide significance thresholds.
Session 5 – 02:30:00 – Meta-Analysis of GWAS Results
Combining association results across studies using GWAMA and METAL; fixed-effect and random-effect models; heterogeneity assessment.
Break – 01:00:00
Session 6 – 02:30:00 – Genomic Data Processing with GATK
GATK best-practice workflow including quality control, sequence alignment, duplicate handling, base quality score recalibration, indel realignment, and variant calling with HaplotypeCaller.
Session 7 – 02:30:00 – Advanced Association Testing and Computational Strategies
Permutation testing, conditional association analyses, fine-mapping concepts, and strategies for parallelizing large-scale GWAS computations.
Break – 01:00:00
Session 8 – 02:30:00 – Visualization and Population Structure Analysis
Visualization of GWAS results using Manhattan plots and LocusZoom; population structure correction using PCA; identification of confounding due to ancestry.
Session 9 – 02:30:00 – Polygenic Risk Scores
Construction, optimization, and validation of polygenic risk scores; evaluation on independent datasets; applications and limitations.
Break – 01:00:00
Session 10 – 02:30:00 – Missing Heritability and Future Directions in GWAS
The missing heritability problem; rare variants, structural variation, gene-gene interactions, gene-environment interactions, multi-omics integration, and future directions in complex trait genetics and evolutionary biology.
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
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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
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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.
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Courses attended
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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?
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