A structured genome-wide association analysis system for reproducible variant association analysis, interpretation, and reporting.
The GWAS System is included as a planned expansion within the CDI Omics Systems Architecture. It demonstrates how genotype data can be transformed into statistically and biologically meaningful insights through a structured and reproducible analytical framework.
Biological Focus
Genome-wide association studies (GWAS) enable the study of:
genetic variation
genotype–phenotype relationships
population structure
trait-associated loci
variant prioritization
biological interpretation
The goal is not simply to identify statistically significant variants, but to understand how genetic variation contributes to biological traits, disease processes, and biological mechanisms.
Why GWAS?
The GWAS System serves as the expansion architecture for population-scale genetic association analysis within the Omics Systems framework.
While RNA-Seq focuses on gene expression, microbiome analysis focuses on microbial communities, and proteomics focuses on protein-level biological changes, GWAS focuses on identifying associations between genetic variants and phenotypic traits.
As a result, the GWAS System introduces analytical concepts such as sample-level genotype quality control, variant filtering, population structure correction, large-scale association testing, variant prioritization, and genetic interpretation while retaining the same principles of reproducibility, statistical reasoning, and biological interpretation.
Relationship to the Omics Systems Architecture
All Omics System Builds share a common analytical foundation.
Biological Question
↓
Experimental Design
↓
Data Generation
↓
Omics Data Processing
↓
Quality Control
↓
Feature Generation
↓
Domain-Specific Analysis
↓
Statistical Inference
↓
Biological Interpretation
↓
Reproducible Reporting
The GWAS System extends this architecture by transforming genotype and phenotype data into association evidence that can be statistically evaluated, biologically interpreted, and reported within a reproducible analytical framework.
GWAS System Architecture
Code
flowchart TD A[Genotype Data] B[Sample QC] C[Variant QC] D[Population Structure] E[Association Testing] F[Manhattan and QQ Plots] G[Variant Prioritization] H[Biological Interpretation] I[Reproducible Reporting] A --> B B --> C C --> D D --> E E --> F F --> G G --> H H --> I style A fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#0f172a style B fill:#e0f2fe,stroke:#0284c7,stroke-width:2px,color:#0f172a style C fill:#ecfeff,stroke:#0891b2,stroke-width:2px,color:#0f172a style D fill:#ede9fe,stroke:#7c3aed,stroke-width:2px,color:#0f172a style E fill:#f3e8ff,stroke:#9333ea,stroke-width:2px,color:#0f172a style F fill:#fae8ff,stroke:#c026d3,stroke-width:2px,color:#0f172a style G fill:#fef3c7,stroke:#d97706,stroke-width:2px,color:#0f172a style H fill:#ecfccb,stroke:#65a30d,stroke-width:2px,color:#0f172a style I fill:#f0fdf4,stroke:#16a34a,stroke-width:2px,color:#0f172a
flowchart TD
A[Genotype Data]
B[Sample QC]
C[Variant QC]
D[Population Structure]
E[Association Testing]
F[Manhattan and QQ Plots]
G[Variant Prioritization]
H[Biological Interpretation]
I[Reproducible Reporting]
A --> B
B --> C
C --> D
D --> E
E --> F
F --> G
G --> H
H --> I
style A fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#0f172a
style B fill:#e0f2fe,stroke:#0284c7,stroke-width:2px,color:#0f172a
style C fill:#ecfeff,stroke:#0891b2,stroke-width:2px,color:#0f172a
style D fill:#ede9fe,stroke:#7c3aed,stroke-width:2px,color:#0f172a
style E fill:#f3e8ff,stroke:#9333ea,stroke-width:2px,color:#0f172a
style F fill:#fae8ff,stroke:#c026d3,stroke-width:2px,color:#0f172a
style G fill:#fef3c7,stroke:#d97706,stroke-width:2px,color:#0f172a
style H fill:#ecfccb,stroke:#65a30d,stroke-width:2px,color:#0f172a
style I fill:#f0fdf4,stroke:#16a34a,stroke-width:2px,color:#0f172a
System Components
Genotype and Phenotype Inputs
GWAS begins with genotype data linked to phenotype and sample metadata.
Common input elements include:
genotype files
sample identifiers
phenotype tables
covariates
population or ancestry information
study design metadata
Sample Quality Control
Sample-level quality control evaluates data completeness, relatedness, ancestry, sex checks, heterozygosity, contamination signals, and overall sample integrity before association testing.
Variant Quality Control
Variant-level quality control evaluates genotype quality and filtering criteria.
Common assessments include:
call rate
minor allele frequency
Hardy–Weinberg equilibrium
variant missingness
allele coding consistency
Population Structure
Population structure analysis identifies genetic stratification that could confound association results.
Common approaches include:
principal component analysis (PCA)
ancestry estimation
relatedness assessment
covariate adjustment
Association Testing
Association testing evaluates relationships between genetic variants and phenotypic traits.
The choice of statistical model depends on the study design, phenotype type, population structure, relatedness, and available covariates.
Manhattan and QQ Plots
Visualization helps evaluate association signals and assess potential inflation or systematic bias.
Common visualizations include:
Manhattan plots
QQ plots
Variant Prioritization
Variant prioritization identifies loci that warrant further investigation.
Common considerations include:
effect size
statistical significance
biological relevance
genomic context
nearby genes
prior evidence
Biological Interpretation
Biological interpretation translates association signals into biological understanding.
Common activities include:
gene mapping
pathway analysis
functional annotation
literature integration
trait biology review
Reproducible Reporting
Reproducible reporting connects analytical decisions, quality control thresholds, association results, visualizations, interpretation, and conclusions within a transparent analytical document.
Typical tools include:
Quarto
GitHub
reproducible computational environments
Core Technologies
Examples of technologies commonly used within the GWAS System include:
PLINK
R
Python
PCA and population structure tools
locus visualization tools
Quarto
GitHub
These technologies support the workflow, but the primary focus of the GWAS System is genetic reasoning, variant interpretation, and reproducibility.
Expected Outputs
A complete GWAS System should produce:
sample quality control summaries
variant quality control summaries
filtered genotype datasets
population structure outputs
association result tables
Manhattan plots
QQ plots
prioritized variant or locus tables
biological interpretation summaries
reproducible analytical reports
Status
Planned expansion
The GWAS System is included as an expansion architecture for population-scale genetic association analysis within the CDI Omics Systems framework.
The GWAS System illustrates the Omics Systems approach to genetic association analysis.
Rather than treating quality control, population structure assessment, association testing, variant prioritization, interpretation, and reporting as separate activities, the system connects them into a unified analytical framework.
The result is a workflow that links:
genotype data
↓
association evidence
↓
biological interpretation
↓
reproducible reporting
in a transparent, reproducible, and scientifically defensible manner.