# 🧬 gnomAD Database

> Query gnomAD for population allele frequencies, constraint metrics, and loss-of-function intolerance. Use when interpreting variants, filtering common alleles, or prioritizing genes in rare disease workflows.

- Skill: `michaelschecht/gnomad-database` (Agent Skill)
- Install (CLI): `npx skillmds@latest add michaelschecht/gnomad-database`
- Raw SKILL.md: https://api.skillmd.com/api/skills/michaelschecht/gnomad-database/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: michaelschecht (https://skillmd.com/u/michaelschecht)
- Updated: 2026-08-19
- Page: https://skillmd.com/skills/michaelschecht/gnomad-database

---


# gnomAD Database

Use gnomAD when human genetic variant interpretation needs real population frequency and constraint context.

## When To Use

- Check whether a variant is rare, common, or absent in the general population
- Compare ancestry-specific allele frequencies
- Use gene constraint metrics like `pLI` and `LOEUF`
- Support ACMG or ClinVar-style variant interpretation
- Prioritize candidate genes in rare disease analysis

## Primary Resources

- Browser: `https://gnomad.broadinstitute.org/`
- GraphQL API: `https://gnomad.broadinstitute.org/api`
- Downloads: `https://gnomad.broadinstitute.org/downloads`

## Practical Workflow

1. Identify the variant or gene and confirm the reference build.
2. Prefer `gnomad_r4` for current GRCh38 work.
3. Check overall and ancestry-specific allele frequency.
4. Review consequence and loss-of-function annotations.
5. For gene-level work, examine `LOEUF` and related constraint metrics.
6. Combine gnomAD evidence with disease context rather than treating absence as proof of pathogenicity.

## Interpretation Notes

- Rare does not mean pathogenic, but common usually argues against severe Mendelian causality
- `LOEUF` is generally more useful than `pLI` for current constraint work
- Homozygous counts matter for recessive interpretation
- Exome and genome datasets have different coverage tradeoffs

## Best Practices

- Be explicit about `GRCh37` vs `GRCh38`
- Check population breakdowns before making rarity claims
- Handle null or missing responses cleanly
- Batch and rate-limit API calls when querying many variants