dbSNP Database

Overview

NCBI dbSNP is the primary public repository for short human genetic variants, cataloguing over 1 billion SNPs, indels, and MNVs with allele frequencies, functional annotations, and cross-references to ClinVar, gnomAD, and 1000 Genomes. Variants are identified by stable rsIDs (reference SNP cluster IDs). Access is free via two APIs: the legacy NCBI E-utilities and the newer NCBI Variation Services REST API, which returns structured JSON.

When to Use

• Looking up allele frequencies and variant class for a known rsID
• Searching all dbSNP variants in a gene or chromosomal region by name or coordinates
• Resolving rsIDs to genomic coordinates (GRCh38/GRCh37) and HGVS notation
• Checking whether a variant of interest has clinical significance links to ClinVar entries
• Batch-fetching hundreds of rsIDs efficiently using epost+efetch history server
• Cross-referencing a list of variant positions to dbSNP rsIDs for downstream annotation
• For clinical pathogenicity classifications use clinvar-database; dbSNP provides IDs and frequency but not curated clinical significance
• For population frequency stratified by ancestry use gnomad-database; dbSNP MAF is a single aggregate frequency

Prerequisites

• Python packages: requests, pandas, matplotlib, xml.etree.ElementTree (stdlib)
• Data requirements: rsIDs (rs80357906), gene symbols, or chromosomal coordinates
• Environment: internet connection; NCBI Entrez email required for E-utilities (set email parameter)
• Rate limits: 3 requests/second without API key; 10 requests/second with free NCBI API key. Register at https://www.ncbi.nlm.nih.gov/account/ — add &api_key=YOUR_KEY to all requests

pip install requests pandas matplotlib
# xml.etree.ElementTree is part of Python stdlib — no additional install needed

Quick Start

import requests
import json

EMAIL = "[email protected]"      # required by NCBI policy
BASE_EUTILS = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"
BASE_VARIATION = "https://api.ncbi.nlm.nih.gov/variation/v0"

def fetch_snp_by_rsid(rsid: str) -> dict:
    """Fetch a dbSNP record by rsID using the NCBI Variation Services API (structured JSON)."""
    rs_num = str(rsid).lstrip("rs")
    r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15)
    r.raise_for_status()
    return r.json()

record = fetch_snp_by_rsid("rs1800497")  # DRD2 Taq1A
print(f"rsID: rs{record['refsnp_id']}")
print(f"Variant type: {record['primary_snapshot_data'].get('variant_type')}")
# Top-level keys: citations, create_date, dbsnp1_merges, last_update_build_id,
# last_update_date, lost_obs_movements, mane_select_ids, present_obs_movements,
# primary_snapshot_data, refsnp_id. (No top-level `organism` field.)
# rsID: rs1800497
# Variant type: snv

Core API

Query 1: rsID Lookup via E-utilities

Fetch the full SNP record for a single rsID using efetch with db=snp. Returns an XML document with alleles, placements, and frequency data.

import requests
import xml.etree.ElementTree as ET

EMAIL = "[email protected]"
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"

def efetch_snp_xml(rsid: str) -> ET.Element:
    """Fetch dbSNP XML record for a single rsID via the docsum rettype.
    Note: rettype="xml" returns a namespaced ExchangeSet; rettype="docsum"
    returns the simpler eSummaryResult/DocumentSummary tree without namespaces."""
    rs_num = str(rsid).lstrip("rs")
    r = requests.get(f"{BASE}/efetch.fcgi",
                     params={"db": "snp", "id": rs_num,
                             "rettype": "docsum", "retmode": "xml",
                             "email": EMAIL},
                     timeout=20)
    r.raise_for_status()
    return ET.fromstring(r.text)

root = efetch_snp_xml("rs80357906")

# Parse the DocumentSummary record (MAF/MAFALLELE were removed in 2024;
# GLOBAL_MAFS is a sub-tree — use ESummary JSON below for easier access)
for docsum in root.iter("DocumentSummary"):
    rs_id = docsum.get("uid")
    snp_class = docsum.findtext("SNP_CLASS", "Unknown")
    chr_pos = docsum.findtext("CHRPOS", "N/A")
    clin_sig = docsum.findtext("CLINICAL_SIGNIFICANCE", "N/A")
    print(f"rs{rs_id} | Class: {snp_class} | Position: {chr_pos}")
    print(f"  ClinSig: {clin_sig}")
# rs80357906 | Class: delins | Position: 17:43057062
#   ClinSig: pathogenic,risk-factor,uncertain-significance

# Fetch using ESummary for structured JSON (preferred for batch)
def esummary_snp(rsid: str) -> dict:
    rs_num = str(rsid).lstrip("rs")
    r = requests.get(f"{BASE}/esummary.fcgi",
                     params={"db": "snp", "id": rs_num,
                             "retmode": "json", "email": EMAIL},
                     timeout=15)
    r.raise_for_status()
    result = r.json()["result"]
    return result.get(rs_num, {})

rec = esummary_snp("rs80357906")
print(f"rs{rec.get('snp_id')}:")
print(f"  Class       : {rec.get('snp_class')}")            # e.g., 'delins'
# `maf`/`mafallele` were removed from ESummary in 2024 — use `global_mafs`
# (list of {study, freq}) and pick a study (e.g., 'GnomAD_genomes') or the
# global aggregate ('TOPMED'/'1000Genomes').
for m in rec.get('global_mafs', [])[:4]:
    print(f"  MAF[{m['study']:18s}]: {m['freq']}")
print(f"  ChrPos      : {rec.get('chrpos')}")               # 17:43057062
print(f"  ClinSig     : {rec.get('clinical_significance')}")
print(f"  FxnClass    : {rec.get('fxn_class')}")

Query 2: Gene Variant Search

Search dbSNP for all variants in a gene using esearch. Returns a list of rsIDs matching the gene.

import requests

EMAIL = "[email protected]"
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"

def esearch_snp(query: str, retmax: int = 100) -> tuple[list, int]:
    """Search dbSNP using a query string. Returns (id_list, total_count)."""
    r = requests.get(f"{BASE}/esearch.fcgi",
                     params={"db": "snp", "term": query,
                             "retmax": retmax, "retmode": "json",
                             "email": EMAIL},
                     timeout=15)
    r.raise_for_status()
    result = r.json()["esearchresult"]
    return result["idlist"], int(result["count"])

# All variants in BRCA1
ids, total = esearch_snp("BRCA1[gene] AND human[orgn]", retmax=20)
print(f"BRCA1 variants in dbSNP: {total:,} total")
print(f"First 5 rsIDs: {['rs' + i for i in ids[:5]]}")

# Only clinical variants (linked to ClinVar)
ids_clin, total_clin = esearch_snp(
    "BRCA1[gene] AND human[orgn] AND clinsig[filter]", retmax=50)
print(f"BRCA1 variants with clinical significance: {total_clin:,}")

Query 3: Chromosomal Region Search

Search for all variants in a genomic region using chromosome coordinates.

import requests

EMAIL = "[email protected]"
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"

def search_region(chrom: str, start: int, stop: int,
                  assembly: str = "GRCh38", retmax: int = 200) -> tuple[list, int]:
    """Find all dbSNP variants in a chromosomal region."""
    query = f"{chrom}[CHR] AND {start}:{stop}[CHRPOS37]" if assembly == "GRCh37" else \
            f"{chrom}[CHR] AND {start}:{stop}[CHRPOS]"
    r = requests.get(f"{BASE}/esearch.fcgi",
                     params={"db": "snp", "term": query,
                             "retmax": retmax, "retmode": "json",
                             "email": EMAIL},
                     timeout=20)
    r.raise_for_status()
    result = r.json()["esearchresult"]
    return result["idlist"], int(result["count"])

# PCSK9 exon 4 region (GRCh38)
ids, total = search_region("1", 55039700, 55040200)
print(f"Variants in chr1:55039700-55040200: {total:,} total")
print(f"Retrieved {len(ids)} rsIDs: {['rs' + i for i in ids[:5]]}")

Query 4: Variant Summary — MAF, Alleles, Clinical Significance

Retrieve structured summary data for variant records using ESummary, extracting MAF, alleles, and database cross-links.

import requests, json

EMAIL = "[email protected]"
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"

def fetch_snp_summaries(rsids: list) -> dict:
    """Fetch ESummary records for a list of rsIDs. Returns dict keyed by rs number."""
    ids_str = ",".join(str(r).lstrip("rs") for r in rsids)
    r = requests.post(f"{BASE}/esummary.fcgi",
                      data={"db": "snp", "id": ids_str,
                            "retmode": "json", "email": EMAIL},
                      timeout=20)
    r.raise_for_status()
    return r.json()["result"]

rsids = ["rs80357906", "rs80357220", "rs28897672", "rs1801133"]
result = fetch_snp_summaries(rsids)

for rs_num in rsids:
    uid = str(rs_num).lstrip("rs")
    rec = result.get(uid, {})
    # `global_mafs` is a list of {study, freq}; pick the first or filter by study
    gmafs = rec.get("global_mafs", [])
    maf_str = gmafs[0]["freq"] if gmafs else "N/A"
    maf_study = gmafs[0]["study"] if gmafs else ""
    print(f"\n{rs_num}:")
    print(f"  Class        : {rec.get('snp_class', 'N/A')}")
    print(f"  MAF          : {maf_str} (from {maf_study})")
    print(f"  Location     : {rec.get('chrpos', 'N/A')}")
    print(f"  ClinSig      : {rec.get('clinical_significance', 'N/A')}")
    print(f"  Function     : {rec.get('fxn_class', 'N/A')}")

Query 5: Batch rsID Query with EPost+EFetch

Efficiently upload hundreds of rsIDs to the NCBI history server using EPost, then retrieve them in batches with EFetch.

import requests, time, pandas as pd

EMAIL = "[email protected]"
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"

def epost_ids(id_list: list) -> tuple[str, str]:
    """Upload rsIDs to NCBI history server. Returns (WebEnv, query_key)."""
    ids_str = ",".join(str(i).lstrip("rs") for i in id_list)
    r = requests.post(f"{BASE}/epost.fcgi",
                      data={"db": "snp", "id": ids_str, "email": EMAIL},
                      timeout=30)
    r.raise_for_status()
    import xml.etree.ElementTree as ET
    root = ET.fromstring(r.text)
    webenv = root.findtext("WebEnv")
    query_key = root.findtext("QueryKey")
    return webenv, query_key

def efetch_history(webenv: str, query_key: str,
                   retstart: int = 0, retmax: int = 100) -> dict:
    """Retrieve records from NCBI history server using ESummary."""
    r = requests.get(f"{BASE}/esummary.fcgi",
                     params={"db": "snp", "WebEnv": webenv,
                             "query_key": query_key, "retstart": retstart,
                             "retmax": retmax, "retmode": "json",
                             "email": EMAIL},
                     timeout=30)
    r.raise_for_status()
    return r.json()["result"]

rsid_batch = ["rs80357906", "rs80357220", "rs28897672", "rs1801133",
              "rs429358", "rs7412", "rs1800497"]

webenv, query_key = epost_ids(rsid_batch)
print(f"Posted {len(rsid_batch)} IDs | WebEnv: {webenv[:40]}...")

records = []
for start in range(0, len(rsid_batch), 100):
    result = efetch_history(webenv, query_key, retstart=start, retmax=100)
    for uid in result.get("uids", []):
        rec = result[uid]
        # global_mafs replaced maf/mafallele in 2024 — flatten the first entry
        gmafs = rec.get("global_mafs", [])
        records.append({
            "rsid": f"rs{uid}",
            "snp_class": rec.get("snp_class"),
            "maf": gmafs[0]["freq"] if gmafs else None,
            "maf_study": gmafs[0]["study"] if gmafs else None,
            "chrpos": rec.get("chrpos"),
            "clinical_sig": rec.get("clinical_significance"),
        })
    time.sleep(0.5)

df = pd.DataFrame(records)
print(f"\nRetrieved {len(df)} records:")
print(df.to_string(index=False))

Query 6: NCBI Variation Services API

The newer REST API returns structured JSON with detailed allele placements, frequencies, and variant type annotations. Preferred for programmatic rsID resolution.

import requests
import pandas as pd

BASE_VARIATION = "https://api.ncbi.nlm.nih.gov/variation/v0"

def fetch_refsnp(rsid: str) -> dict:
    """Fetch structured JSON from NCBI Variation Services API."""
    rs_num = str(rsid).lstrip("rs")
    r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15)
    r.raise_for_status()
    return r.json()

def parse_allele_frequencies(record: dict) -> list:
    """Extract allele frequencies from a Variation Services record.
    freq[i] keys: study_name, study_version, local_row_id, observation, allele_count, total_count.
    The asserted allele identifier lives in freq.observation.inserted_sequence (NOT at ann.allele)."""
    freqs = []
    snapshot = record.get("primary_snapshot_data", {})
    for ann in snapshot.get("allele_annotations", []):
        for f in ann.get("frequency", []):
            obs = f.get("observation", {})
            freqs.append({
                "allele": obs.get("inserted_sequence"),
                "study": f.get("study_name"),
                "allele_count": f.get("allele_count"),
                "total_count": f.get("total_count"),
                "freq": (f["allele_count"] / f["total_count"]
                         if f.get("total_count") else None),
            })
    return freqs

record = fetch_refsnp("rs1800497")   # DRD2 Taq1A variant
print(f"rsID: rs{record['refsnp_id']}")
print(f"Variant type: {record['primary_snapshot_data'].get('variant_type')}")  # 'snv'

placements = record["primary_snapshot_data"].get("placements_with_allele", [])
for placement in placements[:2]:
    seq_id = placement.get("seq_id")
    for allele in placement.get("alleles", []):
        spdi = allele.get("allele", {}).get("spdi", {})
        print(f"  Placement: {seq_id} | SPDI: {spdi.get('inserted_sequence')}")

freqs = parse_allele_frequencies(record)
if freqs:
    df_freq = pd.DataFrame(freqs[:5])
    print(f"\nAllele frequencies ({len(freqs)} entries):")
    print(df_freq.to_string(index=False))

Key Concepts

rsID vs. ss ID (Submitted SNP)

dbSNP uses two IDs: rs IDs (Reference SNP cluster IDs) are stable public identifiers assigned after clustering submitted variants. ss IDs (Submitted SNP IDs) are assigned to individual laboratory submissions before clustering. Use rs IDs for all queries — ss IDs are internal and submission-specific. A single rs ID may cluster multiple ss IDs from different submissions.

MAF vs. Clinical Significance

• MAF (Minor Allele Frequency): The aggregate frequency of the minor allele across all dbSNP submissions. This is a population-level statistic aggregated from 1000 Genomes, gnomAD, TOPMED, and other studies. It does not indicate whether the variant is pathogenic.
• Clinical significance: A link field pointing to ClinVar classifications (Pathogenic, VUS, Benign, etc.). A variant can have a very low MAF (rare) yet be classified as Benign, or a moderate MAF yet be Pathogenic in a specific context (e.g., founder variants). Use clinvar-database for the full pathogenicity record.

Variant Classes in dbSNP

Common Workflows

Workflow 1: Batch rsID Annotation from a Variant List

Goal: Given a list of rsIDs from a variant call pipeline, retrieve MAF, position, and clinical significance for all variants in one run.

import requests, time, pandas as pd

EMAIL = "[email protected]"
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"

def epost_snp(rsids):
    ids = ",".join(str(r).lstrip("rs") for r in rsids)
    r = requests.post(f"{BASE}/epost.fcgi",
                      data={"db": "snp", "id": ids, "email": EMAIL}, timeout=30)
    r.raise_for_status()
    import xml.etree.ElementTree as ET
    root = ET.fromstring(r.text)
    return root.findtext("WebEnv"), root.findtext("QueryKey")

def esummary_history(webenv, query_key, start, retmax=100):
    r = requests.get(f"{BASE}/esummary.fcgi",
                     params={"db": "snp", "WebEnv": webenv,
                             "query_key": query_key, "retstart": start,
                             "retmax": retmax, "retmode": "json", "email": EMAIL},
                     timeout=30)
    r.raise_for_status()
    return r.json()["result"]

# Example: VCF post-processing — annotate a list of called variants
variant_rsids = [
    "rs80357906", "rs80357220", "rs28897672", "rs1801133",
    "rs429358", "rs7412", "rs1800497", "rs2230199",
]

print(f"Posting {len(variant_rsids)} rsIDs to NCBI history server...")
webenv, query_key = epost_snp(variant_rsids)

records = []
for start in range(0, len(variant_rsids), 100):
    result = esummary_history(webenv, query_key, start=start, retmax=100)
    for uid in result.get("uids", []):
        rec = result[uid]
        gmafs = rec.get("global_mafs", [])  # 2024 schema (replaced maf/mafallele)
        records.append({
            "rsid": f"rs{uid}",
            "snp_class": rec.get("snp_class"),
            "maf": gmafs[0]["freq"] if gmafs else None,
            "maf_study": gmafs[0]["study"] if gmafs else None,
            "chrpos_grch38": rec.get("chrpos"),
            "gene": rec.get("genes", [{}])[0].get("name") if rec.get("genes") else None,
            "clinical_significance": rec.get("clinical_significance"),
            "fxn_class": rec.get("fxn_class"),
        })
    time.sleep(0.5)

df = pd.DataFrame(records)
df.to_csv("variant_annotations.csv", index=False)
print(f"Annotated {len(df)} variants → variant_annotations.csv")
print(df[["rsid", "snp_class", "maf", "chrpos_grch38", "clinical_significance"]].to_string(index=False))

Workflow 2: Gene Variant Class Distribution Visualization

Goal: Search all dbSNP variants in a gene and plot their variant class distribution.

import requests, time
import pandas as pd
import matplotlib.pyplot as plt

EMAIL = "[email protected]"
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"

def search_gene_variants(gene: str, retmax: int = 500) -> list:
    r = requests.get(f"{BASE}/esearch.fcgi",
                     params={"db": "snp", "term": f"{gene}[gene] AND human[orgn]",
                             "retmax": retmax, "retmode": "json", "email": EMAIL},
                     timeout=20)
    r.raise_for_status()
    return r.json()["esearchresult"]["idlist"]

def fetch_summaries_batch(ids: list, batch_size: int = 100) -> list:
    records = []
    for i in range(0, len(ids), batch_size):
        batch = ids[i:i+batch_size]
        ids_str = ",".join(batch)
        r = requests.post(f"{BASE}/esummary.fcgi",
                          data={"db": "snp", "id": ids_str,
                                "retmode": "json", "email": EMAIL},
                          timeout=30)
        r.raise_for_status()
        result = r.json()["result"]
        for uid in result.get("uids", []):
            rec = result[uid]
            records.append({"rsid": f"rs{uid}", "snp_class": rec.get("snp_class", "unknown")})
        time.sleep(0.5)
    return records

gene = "CFTR"
print(f"Searching dbSNP for {gene} variants...")
ids = search_gene_variants(gene, retmax=300)
print(f"Found {len(ids)} IDs; fetching summaries...")

records = fetch_summaries_batch(ids)
df = pd.DataFrame(records)
class_counts = df["snp_class"].value_counts()

# Plot variant class distribution
fig, ax = plt.subplots(figsize=(8, 5))
colors = ["#4472C4", "#ED7D31", "#A9D18E", "#FF0000", "#FFC000", "#7030A0"]
bars = ax.bar(class_counts.index, class_counts.values,
              color=colors[:len(class_counts)], edgecolor="white")
ax.bar_label(bars, padding=3, fontsize=9)
ax.set_xlabel("Variant Class")
ax.set_ylabel("Count")
ax.set_title(f"dbSNP Variant Classes in {gene} (n={len(df)})")
plt.tight_layout()
plt.savefig(f"{gene}_variant_classes.png", dpi=150, bbox_inches="tight")
print(f"Saved {gene}_variant_classes.png")
print(class_counts.to_string())
# snv                 241
# indel                38
# mnv                  12
# del                   7
# ins                   2

Key Parameters

Best Practices

1. Register for a free NCBI API key: Adds api_key=YOUR_KEY to requests and triples your rate limit (3 → 10 req/sec) with no other changes. Register at https://www.ncbi.nlm.nih.gov/account/.

1. Use epost+esummary for batches of more than 10 rsIDs: Avoid looping individual efetch calls. EPost uploads all IDs in one request to the history server; subsequent ESummary calls retrieve them in configurable batches of up to 500.

1. Prefer NCBI Variation Services API for structured JSON: The /variation/v0/refsnp/{rs_num} endpoint returns a fully structured JSON with SPDI allele representations, placements, and frequency tables. Easier to parse than E-utilities XML for modern applications.

1. Check `snp_class` before interpreting MAF: Indels and MNVs use different allele counting conventions than SNVs. Treat multi-allelic sites carefully — the reported MAF may refer to one allele among several.

1. Combine dbSNP with ClinVar lookups: dbSNP records the clinical_significance field as a string (e.g., "pathogenic") but does not contain submitter details, review status, or HGVS details. Use clinvar-database for the full pathogenicity record when clinical interpretation is required.

Common Recipes

Recipe: Quick rsID Existence and Class Check

When to use: Check whether a variant is registered in dbSNP before downstream annotation.

import requests

EMAIL = "[email protected]"

def check_rsid(rsid: str) -> dict:
    """Check if an rsID exists in dbSNP and return basic info."""
    rs_num = str(rsid).lstrip("rs")
    r = requests.get(
        "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi",
        params={"db": "snp", "id": rs_num, "retmode": "json", "email": EMAIL},
        timeout=10
    )
    result = r.json().get("result", {})
    rec = result.get(rs_num, {})
    if not rec or "error" in rec:
        return {"rsid": rsid, "found": False}
    gmafs = rec.get("global_mafs", [])
    return {
        "rsid": rsid,
        "found": True,
        "snp_class": rec.get("snp_class"),
        "chrpos": rec.get("chrpos"),
        "maf": gmafs[0]["freq"] if gmafs else None,
        "maf_study": gmafs[0]["study"] if gmafs else None,
        "clinical_significance": rec.get("clinical_significance"),
    }

for rsid in ["rs80357906", "rs9999999999", "rs1800497"]:
    info = check_rsid(rsid)
    if info["found"]:
        print(f"{rsid}: {info['snp_class']} | pos={info['chrpos']} | MAF={info['maf']} ({info['maf_study']})")
    else:
        print(f"{rsid}: NOT FOUND in dbSNP")
# rs80357906: delins | pos=17:43057062 | MAF=G=0.0008929/4 (Estonian)
# rs9999999999: NOT FOUND in dbSNP
# rs1800497: snv | pos=11:113400106 | MAF=G=0.42... (study varies)

Recipe: Resolve Gene Variants to rsIDs and Coordinates

When to use: Convert a gene name to a list of rsIDs for use in downstream tools (PLINK, ANNOVAR, etc.).

import requests, time, pandas as pd

EMAIL = "[email protected]"
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"

def gene_to_rsids(gene: str, max_variants: int = 200) -> pd.DataFrame:
    """Search dbSNP for a gene and return rsIDs with GRCh38 coordinates."""
    # Step 1: search
    r = requests.get(f"{BASE}/esearch.fcgi",
                     params={"db": "snp", "term": f"{gene}[gene] AND human[orgn]",
                             "retmax": max_variants, "retmode": "json", "email": EMAIL},
                     timeout=15)
    r.raise_for_status()
    ids = r.json()["esearchresult"]["idlist"]
    if not ids:
        return pd.DataFrame()
    time.sleep(0.4)

    # Step 2: fetch summaries
    r2 = requests.post(f"{BASE}/esummary.fcgi",
                       data={"db": "snp", "id": ",".join(ids),
                             "retmode": "json", "email": EMAIL},
                       timeout=30)
    r2.raise_for_status()
    result = r2.json()["result"]
    rows = []
    for uid in result.get("uids", []):
        rec = result[uid]
        gmafs = rec.get("global_mafs", [])
        rows.append({
            "rsid": f"rs{uid}",
            "chrpos_grch38": rec.get("chrpos"),
            "snp_class": rec.get("snp_class"),
            "maf": gmafs[0]["freq"] if gmafs else None,
        })
    return pd.DataFrame(rows)

df = gene_to_rsids("APOE", max_variants=50)
print(f"APOE variants retrieved: {len(df)}")
print(df.head(8).to_string(index=False))
df.to_csv("APOE_rsids.csv", index=False)

Troubleshooting

Related Skills

• clinvar-database — ClinVar pathogenicity classifications for variants identified by rsID (complement to dbSNP)
• gnomad-database — Population allele frequencies by ancestry group (more detailed than dbSNP MAF)
• gwas-database — GWAS Catalog for SNP-trait associations from published GWAS studies
• ensembl-database — Ensembl REST API for variant consequences and gene annotations
• snpeff-variant-annotation — Annotate VCF files with SnpEff and SnpSift, which adds dbSNP rsIDs and functional predictions

References

• NCBI E-utilities for dbSNP — E-utilities field tags and query syntax specific to dbSNP
• NCBI Variation Services API — REST API documentation and interactive Swagger UI
• Sherry et al., Nucleic Acids Research 2001 — dbSNP original description paper
• NCBI E-utilities Reference — Full E-utilities API reference (applies to all NCBI databases)