Lesson 1: Preface and Setup
Why This Guide Exists
Genome-wide association studies are technically straightforward to run.
Large genotype matrices are analyzed. Millions of p-values are generated. Significant hits are reported.
Yet interpretation is rarely straightforward.
Small modeling decisions can inflate signals. Population structure can create false positives. Unclear phenotype definitions can distort conclusions.
This guide focuses on reasoning discipline, not just computation.
The GWAS Reasoning Chain
Every GWAS result sits at the end of a structured chain:
Study Design → Phenotype Definition → Genotype QC → Population Structure → Association Testing → Calibrated Biological Claims
Each layer constrains what can be claimed confidently.
If one layer is weak, downstream conclusions become fragile.
This guide walks through each layer deliberately.
What This Free Track Covers
This free track covers:
- Conceptual foundations of GWAS
- Study design and trait clarity
- Genotype quality control
- Population structure and stratification
- Association testing using linear models
- Signal visualization and interpretation discipline
Advanced topics such as fine-mapping, polygenic risk scores, and replication strategies are addressed in the premium guide.
Simulated Data for Clarity
To keep focus on interpretation rather than file formats, this guide uses fully simulated GWAS data in R.
The dataset includes:
- Genotypes simulated under Hardy-Weinberg equilibrium
- Population structure via principal components
- Controlled missingness
- Known causal variants (for evaluation)
To generate the dataset:
Rscript scripts/R/generate-demo-data.RAll files are written to the /data directory.
Reproducibility
Rendered output is produced using Quarto.
From the project root:
quarto renderOutput is written to /docs.
The public version of this guide is deployed at:
https://gwas.complexdatainsights.com
Interpretation Discipline
A statistically significant association is not automatically a biological insight.
Association does not imply causation. Population structure can mimic genetic effects. Multiple testing inflates false positives. Effect size matters as much as p-value.
Throughout this guide, results will be interpreted cautiously and explicitly.
The goal is not to produce hits.
The goal is to produce defensible claims.