Preface
What this guide is about
Genome-wide association studies sit at the intersection of genetics, statistics, and data science.
They are powerful — and easy to misuse.
This guide focuses on how GWAS actually works in practice:
- how studies are designed
- how genotype and phenotype data are structured
- why quality control decisions matter
- how population structure and relatedness affect inference
- how association results should be interpreted and reported
Rather than presenting GWAS as a checklist of commands, the emphasis is on reasoning, decision points, and reproducible analysis habits.
Who this guide is for
This guide is well suited for:
- Learners moving from genetics or genomics fundamentals into applied GWAS
- Researchers who want a clear, modern view of GWAS workflows
- Data scientists (R / Python users) seeking to understand GWAS beyond surface-level tools
- Anyone who wants to critically read, interpret, or reproduce GWAS results
You do not need to be an expert statistician to follow this guide — but you should be comfortable thinking carefully about data, assumptions, and uncertainty.
0.1 Note on data used in this guide
All analyses in this guide use a small, synthetic GWAS-style dataset created for instructional purposes.
The dataset is designed to illustrate standard GWAS workflows, diagnostics, and interpretation in a fully reproducible way, without requiring access to restricted real-world cohorts.
While effect sizes, signals, and sample sizes do not reflect any specific real study, the analytical principles and best practices demonstrated here transfer directly to real GWAS datasets.
How the guide is organized
The guide is organized into two sections:
- Foundational content, which focuses on concepts, study design, and interpretation
- Applied workflow content, which focuses on executing a full GWAS pipeline in a reproducible way
The boundary between these sections is intentional and explicit.
Foundational material builds the mental model needed to understand GWAS.
Applied material focuses on research-ready workflows and real analytical decisions.
Access level (Free or Premium) is handled at the platform level and does not affect how the guide is read or understood.
What you will gain from this guide
By working through this guide, you should be able to:
- Explain what GWAS can and cannot tell you
- Understand the full GWAS pipeline from raw data to interpretable results
- Identify common sources of confounding and bias
- Read and critique Manhattan and QQ plots with confidence
- Reason about multiple testing, power, and false positives
- Document and report GWAS analyses in a reproducible, research-aligned way
If you continue into the applied workflow section, you will also be able to run and document a complete GWAS analysis using modern tools and practices.
How to use this guide effectively
A recommended approach is:
- Read each lesson for conceptual understanding, not just commands
- Pay attention to why steps are performed, not only how
- Keep notes on:
- trait definitions
- covariates and confounders
- assumptions made at each stage
- trait definitions
- Treat figures and outputs as communication tools, not just diagnostics
GWAS is as much about interpretation and reporting as it is about computation.
Reproducibility and scientific responsibility
Throughout this guide, reproducibility is treated as a core principle:
- assumptions are stated explicitly
- decisions are justified
- outputs are structured for reporting and review
The goal is not only to run GWAS analyses, but to produce results that can be understood, questioned, and reproduced by others.
Support and updates
This guide will continue to evolve as tools, datasets, and best practices change.
For questions, feedback, or support, contact:
info@complexdatainsights.com
— Complex Data Insights (CDI)
Continue to → Lesson 01: Introduction to GWAS