Manhattan Plot
Chart overview
The Manhattan plot is the canonical visualization for genome-wide association studies (GWAS).
Key points
- Each point represents a single nucleotide polymorphism (SNP) positioned on the x-axis by chromosomal coordinate and on the y-axis by -log10 of its association p-value with a phenotype of interest.
- The alternating color scheme across chromosomes helps distinguish adjacent chromosome boundaries.
- Two horizontal reference lines are universally used: a suggestive threshold at p = 1e-5 (yellow) and a genome-wide significant threshold at p = 5e-8 (red), the latter correcting for the approximately one million independent tests in a typical array-based GWAS.
Example Visualization
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"Create a Manhattan plot from my GWAS summary statistics file with columns: CHR, BP, SNP, P. Plot -log10(P) on the y-axis and chromosomal position on the x-axis. Alternate blue and navy colors between chromosomes. Add a red dashed line at genome-wide significance (p=5e-8) and a grey dashed line at suggestive significance (p=1e-5). Highlight and label the top significant SNPs at each locus. Format for Nature Genetics publication standards at 300 DPI."
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Python Code Example
Console Output
Figure saved: plotivy-manhattan-plot.png
Common Use Cases
- 1Identifying genetic loci associated with complex traits such as height, BMI, or disease risk
- 2Pharmacogenomics: finding SNPs that predict drug response or adverse reactions
- 3Population genetics: detecting signatures of natural selection from differentiation statistics
- 4eQTL mapping: associating genetic variants with gene expression levels in GTEx-style studies
Pro Tips
Pre-filter SNPs to MAF > 1% and imputation quality > 0.8 before plotting to reduce noise
Always accompany the Manhattan plot with a Q-Q plot to assess genomic inflation
Use a regional association plot (LocusZoom-style) to zoom into significant loci for LD structure
Clump or prune correlated SNPs so that each independent signal has only one representative point
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.