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- Introduced a comprehensive RNA velocity analysis pipeline using scVelo, including data loading, preprocessing, velocity estimation, and visualization. - Added a script for running RNA velocity analysis with customizable parameters and output options. - Created detailed documentation for IQ-TREE 2 phylogenetic inference, covering command syntax, model selection, bootstrapping methods, and output interpretation. - Included references for velocity models and their mathematical framework, along with a comparison of different models. - Enhanced the scVelo skill documentation with installation instructions, use cases, and best practices for RNA velocity analysis.
396 lines
12 KiB
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396 lines
12 KiB
Markdown
---
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name: gnomad-database
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description: Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance. Essential for variant pathogenicity interpretation, rare disease genetics, and identifying loss-of-function intolerant genes.
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license: CC0-1.0
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metadata:
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skill-author: Kuan-lin Huang
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---
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# gnomAD Database
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## Overview
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The Genome Aggregation Database (gnomAD) is the largest publicly available collection of human genetic variation, aggregated from large-scale sequencing projects. gnomAD v4 contains exome sequences from 730,947 individuals and genome sequences from 76,215 individuals across diverse ancestries. It provides population allele frequencies, variant consequence annotations, and gene-level constraint metrics that are essential for interpreting the clinical significance of genetic variants.
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**Key resources:**
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- gnomAD browser: https://gnomad.broadinstitute.org/
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- GraphQL API: https://gnomad.broadinstitute.org/api
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- Data downloads: https://gnomad.broadinstitute.org/downloads
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- Documentation: https://gnomad.broadinstitute.org/help
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## When to Use This Skill
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Use gnomAD when:
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- **Variant frequency lookup**: Checking if a variant is rare, common, or absent in the general population
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- **Pathogenicity assessment**: Rare variants (MAF < 1%) are candidates for disease causation; gnomAD helps filter benign common variants
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- **Loss-of-function intolerance**: Using pLI and LOEUF scores to assess whether a gene tolerates protein-truncating variants
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- **Population-stratified frequencies**: Comparing allele frequencies across ancestries (African/African American, Admixed American, Ashkenazi Jewish, East Asian, Finnish, Middle Eastern, Non-Finnish European, South Asian)
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- **ClinVar/ACMG variant classification**: gnomAD frequency data feeds into BA1/BS1 evidence codes for variant classification
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- **Constraint analysis**: Identifying genes depleted of missense or loss-of-function variation (z-scores, pLI, LOEUF)
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## Core Capabilities
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### 1. gnomAD GraphQL API
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gnomAD uses a GraphQL API accessible at `https://gnomad.broadinstitute.org/api`. Most queries fetch variants by gene or specific genomic position.
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**Datasets available:**
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- `gnomad_r4` — gnomAD v4 exomes (recommended default, GRCh38)
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- `gnomad_r4_genomes` — gnomAD v4 genomes (GRCh38)
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- `gnomad_r3` — gnomAD v3 genomes (GRCh38)
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- `gnomad_r2_1` — gnomAD v2 exomes (GRCh37)
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**Reference genomes:**
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- `GRCh38` — default for v3/v4
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- `GRCh37` — for v2
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### 2. Querying Variants by Gene
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```python
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import requests
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def query_gnomad_gene(gene_symbol, dataset="gnomad_r4", reference_genome="GRCh38"):
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"""Fetch variants in a gene from gnomAD."""
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url = "https://gnomad.broadinstitute.org/api"
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query = """
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query GeneVariants($gene_symbol: String!, $dataset: DatasetId!, $reference_genome: ReferenceGenomeId!) {
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gene(gene_symbol: $gene_symbol, reference_genome: $reference_genome) {
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gene_id
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gene_symbol
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variants(dataset: $dataset) {
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variant_id
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pos
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ref
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alt
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consequence
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genome {
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af
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ac
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an
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ac_hom
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populations {
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id
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ac
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an
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af
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}
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}
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exome {
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af
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ac
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an
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ac_hom
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}
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lof
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lof_flags
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lof_filter
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}
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}
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}
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"""
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variables = {
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"gene_symbol": gene_symbol,
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"dataset": dataset,
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"reference_genome": reference_genome
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}
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response = requests.post(url, json={"query": query, "variables": variables})
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return response.json()
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# Example
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result = query_gnomad_gene("BRCA1")
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gene_data = result["data"]["gene"]
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variants = gene_data["variants"]
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# Filter to rare PTVs
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rare_ptvs = [
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v for v in variants
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if v.get("lof") == "LC" or v.get("consequence") in ["stop_gained", "frameshift_variant"]
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and v.get("genome", {}).get("af", 1) < 0.001
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]
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print(f"Found {len(rare_ptvs)} rare PTVs in {gene_data['gene_symbol']}")
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```
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### 3. Querying a Specific Variant
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```python
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import requests
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def query_gnomad_variant(variant_id, dataset="gnomad_r4"):
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"""Fetch details for a specific variant (e.g., '1-55516888-G-GA')."""
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url = "https://gnomad.broadinstitute.org/api"
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query = """
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query VariantDetails($variantId: String!, $dataset: DatasetId!) {
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variant(variantId: $variantId, dataset: $dataset) {
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variant_id
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chrom
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pos
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ref
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alt
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genome {
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af
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ac
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an
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ac_hom
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populations {
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id
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ac
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an
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af
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}
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}
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exome {
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af
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ac
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an
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ac_hom
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populations {
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id
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ac
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an
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af
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}
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}
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consequence
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lof
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rsids
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in_silico_predictors {
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id
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value
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flags
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}
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clinvar_variation_id
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}
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}
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"""
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response = requests.post(
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url,
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json={"query": query, "variables": {"variantId": variant_id, "dataset": dataset}}
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)
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return response.json()
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# Example: query a specific variant
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result = query_gnomad_variant("17-43094692-G-A") # BRCA1 missense
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variant = result["data"]["variant"]
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if variant:
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genome_af = variant.get("genome", {}).get("af", "N/A")
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exome_af = variant.get("exome", {}).get("af", "N/A")
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print(f"Variant: {variant['variant_id']}")
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print(f" Consequence: {variant['consequence']}")
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print(f" Genome AF: {genome_af}")
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print(f" Exome AF: {exome_af}")
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print(f" LoF: {variant.get('lof')}")
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```
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### 4. Gene Constraint Scores
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gnomAD constraint scores assess how tolerant a gene is to variation relative to expectation:
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```python
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import requests
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def query_gnomad_constraint(gene_symbol, reference_genome="GRCh38"):
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"""Fetch constraint scores for a gene."""
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url = "https://gnomad.broadinstitute.org/api"
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query = """
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query GeneConstraint($gene_symbol: String!, $reference_genome: ReferenceGenomeId!) {
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gene(gene_symbol: $gene_symbol, reference_genome: $reference_genome) {
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gene_id
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gene_symbol
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gnomad_constraint {
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exp_lof
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exp_mis
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exp_syn
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obs_lof
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obs_mis
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obs_syn
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oe_lof
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oe_mis
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oe_syn
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oe_lof_lower
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oe_lof_upper
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lof_z
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mis_z
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syn_z
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pLI
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}
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}
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}
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"""
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response = requests.post(
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url,
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json={"query": query, "variables": {"gene_symbol": gene_symbol, "reference_genome": reference_genome}}
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)
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return response.json()
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# Example
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result = query_gnomad_constraint("KCNQ2")
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gene = result["data"]["gene"]
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constraint = gene["gnomad_constraint"]
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print(f"Gene: {gene['gene_symbol']}")
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print(f" pLI: {constraint['pLI']:.3f} (>0.9 = LoF intolerant)")
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print(f" LOEUF: {constraint['oe_lof_upper']:.3f} (<0.35 = highly constrained)")
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print(f" Obs/Exp LoF: {constraint['oe_lof']:.3f}")
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print(f" Missense Z: {constraint['mis_z']:.3f}")
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```
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**Constraint score interpretation:**
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| Score | Range | Meaning |
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|-------|-------|---------|
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| `pLI` | 0–1 | Probability of LoF intolerance; >0.9 = highly intolerant |
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| `LOEUF` | 0–∞ | LoF observed/expected upper bound; <0.35 = constrained |
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| `oe_lof` | 0–∞ | Observed/expected ratio for LoF variants |
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| `mis_z` | −∞ to ∞ | Missense constraint z-score; >3.09 = constrained |
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| `syn_z` | −∞ to ∞ | Synonymous z-score (control; should be near 0) |
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### 5. Population Frequency Analysis
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```python
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import requests
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import pandas as pd
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def get_population_frequencies(variant_id, dataset="gnomad_r4"):
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"""Extract per-population allele frequencies for a variant."""
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url = "https://gnomad.broadinstitute.org/api"
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query = """
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query PopFreqs($variantId: String!, $dataset: DatasetId!) {
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variant(variantId: $variantId, dataset: $dataset) {
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variant_id
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genome {
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populations {
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id
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ac
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an
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af
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ac_hom
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}
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}
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}
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}
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"""
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response = requests.post(
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url,
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json={"query": query, "variables": {"variantId": variant_id, "dataset": dataset}}
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)
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data = response.json()
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populations = data["data"]["variant"]["genome"]["populations"]
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df = pd.DataFrame(populations)
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df = df[df["an"] > 0].copy()
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df["af"] = df["ac"] / df["an"]
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df = df.sort_values("af", ascending=False)
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return df
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# Population IDs in gnomAD v4:
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# afr = African/African American
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# ami = Amish
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# amr = Admixed American
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# asj = Ashkenazi Jewish
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# eas = East Asian
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# fin = Finnish
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# mid = Middle Eastern
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# nfe = Non-Finnish European
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# sas = South Asian
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# remaining = Other
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```
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### 6. Structural Variants (gnomAD-SV)
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gnomAD also contains a structural variant dataset:
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```python
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import requests
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def query_gnomad_sv(gene_symbol):
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"""Query structural variants overlapping a gene."""
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url = "https://gnomad.broadinstitute.org/api"
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query = """
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query SVsByGene($gene_symbol: String!) {
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gene(gene_symbol: $gene_symbol, reference_genome: GRCh38) {
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structural_variants {
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variant_id
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type
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chrom
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pos
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end
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af
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ac
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an
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}
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}
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}
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"""
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response = requests.post(url, json={"query": query, "variables": {"gene_symbol": gene_symbol}})
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return response.json()
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```
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## Query Workflows
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### Workflow 1: Variant Pathogenicity Assessment
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1. **Check population frequency** — Is the variant rare enough to be pathogenic?
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- Use gnomAD AF < 1% for recessive, < 0.1% for dominant conditions
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- Check ancestry-specific frequencies (a variant rare overall may be common in one population)
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2. **Assess functional impact** — LoF variants have highest prior probability
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- Check `lof` field: `HC` = high-confidence LoF, `LC` = low-confidence
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- Check `lof_flags` for issues like "NAGNAG_SITE", "PHYLOCSF_WEAK"
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3. **Apply ACMG criteria:**
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- BA1: AF > 5% → Benign Stand-Alone
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- BS1: AF > disease prevalence threshold → Benign Supporting
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- PM2: Absent or very rare in gnomAD → Pathogenic Moderate
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### Workflow 2: Gene Prioritization in Rare Disease
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1. Query constraint scores for candidate genes
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2. Filter for pLI > 0.9 (haploinsufficient) or LOEUF < 0.35
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3. Cross-reference with observed LoF variants in the gene
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4. Integrate with ClinVar and disease databases
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### Workflow 3: Population Genetics Research
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1. Identify variant of interest from GWAS or clinical data
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2. Query per-population frequencies
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3. Compare frequency differences across ancestries
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4. Test for enrichment in specific founder populations
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## Best Practices
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- **Use gnomAD v4 (gnomad_r4)** for the most current data; use v2 (gnomad_r2_1) only for GRCh37 compatibility
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- **Handle null responses**: Variants not observed in gnomAD are not necessarily pathogenic — absence is informative
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- **Distinguish exome vs. genome data**: Genome data has more uniform coverage; exome data is larger but may have coverage gaps
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- **Rate limit GraphQL queries**: Add delays between requests; batch queries when possible
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- **Homozygous counts** (`ac_hom`) are relevant for recessive disease analysis
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- **LOEUF is preferred over pLI** for gene constraint (less sensitive to sample size)
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## Data Access
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- **Browser**: https://gnomad.broadinstitute.org/ — interactive variant and gene browsing
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- **GraphQL API**: https://gnomad.broadinstitute.org/api — programmatic access
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- **Downloads**: https://gnomad.broadinstitute.org/downloads — VCF, Hail tables, constraint tables
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- **Google Cloud**: gs://gcp-public-data--gnomad/
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## Additional Resources
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- **gnomAD website**: https://gnomad.broadinstitute.org/
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- **gnomAD blog**: https://gnomad.broadinstitute.org/news
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- **Downloads**: https://gnomad.broadinstitute.org/downloads
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- **API explorer**: https://gnomad.broadinstitute.org/api (interactive GraphiQL)
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- **Constraint documentation**: https://gnomad.broadinstitute.org/help/constraint
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- **Citation**: Karczewski KJ et al. (2020) Nature. PMID: 32461654; Chen S et al. (2024) Nature. PMID: 38conservation
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- **GitHub**: https://github.com/broadinstitute/gnomad-browser
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