Genome-Wide Association Studies (GWAS) for Evolutionary Biology (GWAS01) – Applications for Marine Mammal Research
https://prstats.org/course/genome-wide-association-studies-gwas-for-evolutionary-biology-gwas01/ Dates: 9–13 November 2026 Duration: 25 hours (5 days) Instructor: Dr Nikolay Oskolkov Format: Live online, with all sessions recorded Registration fee: £400 PR Stats is pleased to announce an upcoming practical workshop on Genome-Wide Association Studies (GWAS), with applications to evolutionary biology, population genetics and conservation genomics. As genomic datasets become increasingly available for marine mammal populations, GWAS provides a powerful framework for investigating the genetic architecture of complex traits, identifying genetic variants associated with phenotypic variation, and exploring the genomic basis of adaptation. These approaches are particularly relevant to marine mammal researchers investigating population-level genetic variation, evolutionary responses to environmental conditions, and the relationship between genotype and phenotype in cetaceans, pinnipeds and other marine mammals. What participants will learn * Principles of GWAS and the genetic architecture of complex traits * Genotype phasing and imputation using PHASEIT2 and IMPUTE2 * Genomic quality control, SNP filtering and data preparation * Genome-wide association testing using PLINK and SNPTEST * GWAS meta-analysis using GWAMA and METAL * Next-generation sequencing data processing and variant calling using GATK * Population structure analysis using PCA and correction for ancestry-related confounding * Permutation testing and conditional association analysis * Visualisation of association results using Manhattan plots and LocusZoom * Construction and validation of polygenic risk scores * Understanding missing heritability and the contribution of rare variants, structural variation and gene–environment interactions Relevance to marine mammal research Marine mammals provide important opportunities to investigate the relationships between genomic variation, phenotypic diversity and evolutionary adaptation. GWAS methodologies can potentially be applied to questions concerning: * Genetic associations with morphological, physiological and life-history traits * Genomic variation associated with environmental adaptation * Population differences in complex phenotypic characteristics * Genetic contributions to disease susceptibility and other health-related traits * The influence of population structure and relatedness on genotype–phenotype associations * The genetic architecture of traits relevant to marine mammal conservation The course also addresses methodological challenges particularly relevant to wildlife genomics, including population structure, genomic data quality and the interpretation of complex trait associations. Course format * Five-day live, instructor-led online workshop * 25 hours of teaching combining theory and practical exercises * Hands-on analysis using genomic datasets * Practical training in established GWAS software and bioinformatics workflows * Course code, datasets and presentation materials provided * All sessions recorded, with 30 days of access * 30 days of post-course instructor email support Who should attend? Marine mammal biologists, conservation geneticists, evolutionary biologists, population geneticists, bioinformaticians, postgraduate students and researchers interested in genomic approaches to complex traits and adaptation. The workshop is suitable for researchers with a basic understanding of genetics and statistics, together with some familiarity with R and command-line computing. Previous experience conducting GWAS is not required. Full course details and registration https://prstats.org/course/genome-wide-association-studies-gwas-for-evolutionary-biology-gwas01/ Please feel free to share this announcement with colleagues, laboratories, research groups, institutions and wider marine mammal research and conservation networks. Best wishes, PR Stats https://prstats.org<https://prstats.org/> Sent from Outlook for Mac
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