bio-data-visualization-manhattan-qq-locuszoom
Build Manhattan, Miami, QQ, and locuszoom-style regional plots from GWAS, TWAS, PWAS, and QTL summary statistics with correct genomic-inflation diagnostics, multi-trait overlays, lead-SNP labeling, and LD-aware regional rendering. Use when visualizing association results across the genome, comparing two traits, computing genomic inflation lambda, or zooming into a locus with LD coloring.
npx skills add BioTender-max/awesome-bio-agent-skills --skill manhattan-qq-locuszoom --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: qqman 0.1.9 (R), CMplot 4.5+ (R), matplotlib 3.8+, pandas 2.2+, scipy 1.12+, plinkQC 0.3+. For locuszoom-style: locuszoomr 0.3+ (R) or pyranges + matplotlib. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Manhattan, QQ, and Locuszoom Plots **"Plot my GWAS results"** -> Render per-variant -log10(p) across the genome (Manhattan), compare expected vs observed p quantiles (QQ + λGC), overlay two traits with mirrored axes (Miami), and zoom into a locus with LD-colored points + recombination rate + gene track (locuszoom). The choices that matter: significance thresholds, axis truncation for ultra-significant peaks, lead-SNP labeling, and LD reference selection for regional plots. - R: `qqman::manhattan` / `qqman::qq` (Turner 2018), `CMplot::CMplot`, `locuszoomr::locus_plot` - Python: `matp
- Version Compatibility
- The Single Most Important Modern Insight -- The Threshold Is Always Conditional
- Decision Tree by Analysis
- Genomic Inflation (λGC) -- The Mandatory QC Step
- Small-N and Rare-Variant Regimes -- When Standard Asymptotics Break
- Manhattan Plot -- Canonical Layout
- Extreme Tail Handling -- The Y-Axis Cap
- Miami Plot -- Two-Trait Comparison
- Locuszoom-Style Regional Plot
- Per-Method Failure Modes
- Inflation diagnosed as polygenic signal
- Wrong significance threshold for the analysis
- Cap with no indication
- Manhattan with random chromosome colors
What does the bio-data-visualization-manhattan-qq-locuszoom skill do?
Build Manhattan, Miami, QQ, and locuszoom-style regional plots from GWAS, TWAS, PWAS, and QTL summary statistics with correct genomic-inflation diagnostics, multi-trait overlays, lead-SNP labeling, and LD-aware regional rendering. Use when visualizing association results across the genome, comparing two traits, computing genomic inflation lambda, or zooming into a locus with LD coloring.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill manhattan-qq-locuszoom --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.