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.
Python Tutorial
How to create a manhattan plot in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
Python Scatter Plot TutorialExample 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
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for manhattan-plot.
numpy
Useful in specialized workflows that complement core Python plotting libraries for manhattan-plot analysis tasks.
pandas
Good for quick exploratory drafts directly from DataFrame operations before polishing in matplotlib or plotly.
Scientific Chart Selection Cheat Sheet
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