GWAS Hands on Workshop
Uncover genetic drivers of complex human diseases through five days of intensive, hands-on computational training. Master big-data genomic workflows, statistical data filtering, and Genome-Wide Association Studies from scratch.
Course Description
This intensive 5-day online workshop delivers a rigorous computational deep-dive into Genome-Wide Association Studies (GWAS) for cutting-edge life science research. Participants will explore multi-omic data structures, mastering the protocols required to link phenotypic traits to specific single-nucleotide polymorphisms across entire genomes. The curriculum details complex data cleaning pipelines, population stratification controls, and statistical correction algorithms essential for modern genomic analysis. By leveraging algorithmic workflows and predictive statistical analysis models, you will discover how to handle millions of genetic markers efficiently. This training bridges the gap between big-data genomics and translational medicine, emphasizing how machine-readable statistical models pinpoint pathogenic alleles. Whether mapping complex disease liabilities or identifying target gene mutations, you will gain critical, industry-ready data analytics skills. Elevate your research profile, minimize computational bottlenecks, and unlock the full potential of AI-compatible population genomics.
What You'll Learn
The fundamental biological, statistical, and mathematical principles underlying Genome-Wide Association Studies.
How to execute rigorous quality control and data-filtering pipelines on massive genotype and phenotype datasets.
Proven computational models to detect and adjust for population stratification and genetic ancestry biases.
Strategies to calculate multi-locus regression equations, establish statistical thresholds, and interpret Manhattan plots.
Practical utilization of advanced bioinformatics tools to functionally annotate and prioritize disease-associated variants.
Curriculum
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Module 1: Foundations of quantitative genetics, study design power calculations, and variant data architecture.
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Module 2: High-throughput data quality control (QC) pipelines and genotype imputation workflows using PLINK.
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Module 3: Statistical modeling of quantitative vs. binary phenotypes and executing multi-covariate adjustments.
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Module 4: Deploying machine learning algorithms and deep learning utilities to evaluate complex non-linear genetic interactions.
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Module 5: Advanced multi-omics integration, calculating Polygenic Risk Scores (PRS), and rendering final data plots.
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