GWAS
Genome-Wide Association Studies (GWAS)
Identify SNPs significantly associated with a phenotype across the genome, with proper multiple-testing correction.
How it works
Our GWAS module fits a mixed linear model (MLM) at each marker, optionally correcting for population structure via principal components. We report both Bonferroni and Benjamini–Hochberg FDR-controlled thresholds, and provide a permutation max-T option that gives empirical family-wise error rates (FWER) without distributional assumptions. LD-pruned independent hits separate true causal regions from linked passengers.
Formula
y = Xβ + Zα + Wm·b + e, where m is the marker tested, b its effect, and Z encodes random polygenic background variance.
What you get
- ▸Manhattan plot of −log₁₀(p) by marker position
- ▸QQ plot of observed vs expected p-values (genomic inflation λ)
- ▸Bonferroni- and BH-FDR-corrected significance thresholds
- ▸LD-pruned independent significant hits with effect sizes
When to use it
- ▸You have genotype data (SNPs) and a measured phenotype across the same individuals
- ▸You want to discover the genomic regions driving a trait
- ▸You plan to follow up with QTL mapping or functional annotation
References
Run GWAS on your data
Open the module and upload a CSV.