GWAS for Beginners-unlocking the basis of genetics
Master the language of the genome and bridge the gap between Big Data and Biological Insight. Harness AI-driven genomic analytics to identify disease-linked variants with precision and speed.
Course Description
In an era where precision medicine and AI-driven genomics are transforming healthcare, understanding the genetic architecture of complex traits is essential. This intensive workshop provides a foundational dive into Genome-Wide Association Studies (GWAS), designed specifically for beginners in bioinformatics and computational biology. We go beyond theory, guiding you through the end-to-end pipeline: from raw genotype quality control to the identification of Single Nucleotide Polymorphisms (SNPs) associated with specific phenotypes. You will explore how machine learning and statistical genetics are leveraged to handle massive datasets, correcting for population structure and ensuring robust discovery. By the end of this workshop, you will be equipped to navigate the GWAS Catalog, utilize industry-standard tools like PLINK and R, and interpret genetic associations that pave the way for breakthrough drug discovery and personalized treatments.
What You'll Learn
"Fundamentals of Linkage Disequilibrium (LD) and the Hardy-Weinberg Equilibrium (HWE).
Techniques for Genotype Imputation using reference panels like the 1000 Genomes Project.
How to account for Population Stratification using Principal Component Analysis (PCA).
The role of Polygenic Risk Scores (PRS) in predicting disease susceptibility.
Methods for Post-GWAS analysis, including functional annotation and pathway analysis."
Curriculum
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Module 1: Foundations of Genetics & Statistics – Introduction to SNPs, Haplotypes, and basic probability.
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Module 2: The GWAS Workflow – Study design, power calculations, and case-control vs. quantitative traits.
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Module 3: Data Preprocessing & QC – Filtering for Minor Allele Frequency (MAF), missingness, and heterozygosity.
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Module 4: Running the Association – Executing GWAS using PLINK and interpreting p-values.
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Module 5: Population Structure & Covariates – Correcting for bias and using PCA to handle ancestry.
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Module 6: Advanced Topics & AI – Introduction to Machine Learning in Genomics and Meta-analysis.
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Module 7: Hands-on Capstone – Conduct your own GWAS on a provided real-world dataset.
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