OneKGPd: Individual-Level Queries over the 1000 Genomes Project

Scope

This skill queries the 1000 Genomes Project dataset — the extended high-coverage cohort of 3,202 whole-genome-sequenced individuals, on the GRCh38 assembly. All results are drawn from this cohort, and sample names returned by the skill (for example HG00096 or NA21130) identify its participants.

Queries resolve against the cohort's per-individual genotype data. This supports two complementary classes of question: selecting variants carried within a region (across the whole cohort or within a specified set of individuals), and selecting the individuals who carry variants matching given criteria. Variant selection can be filtered by allele frequency, predicted consequence, clinical significance, AlphaMissense classification, and the other annotation axes listed below. Relatedness between two named individuals is also available.

The genotype state in which a variant is carried — heterozygous or homozygous — is a criterion that queries may specify; results are returned as variants or as sample names, not as raw genotypes.

The public service is TLS gRPC at db.dnaerys.org:443, accessed with dnaerys 0.2.1 (Python 3.11+); it is not a REST base URL. The maintained service snapshot advertises VEP 115 / GENCODE 49, ClinVar 202502, and gnomAD 4.1. These are the service's annotation releases, not the latest release of each upstream resource. Record them when interpreting results.

When to Use

Use this skill when you need to:

• Find variants carried in a region or set of regions matching some criteria across the whole cohort (select-variants).
• Find variants carried in a region or set of regions matching some criteria in specific set of individuals (select-variants-in-samples).
• Find which 1000 Genomes individuals carry variants matching some criteria in a region or set of regions (select-samples).
• Count how many individuals carry specific variants (count-samples).
• Restrict any variant query to heterozygous-only or homozygous-only carriage, or query both together (default).
• Identify which individuals are homozygous reference at a single position (select-samples-hom-ref).
• Determine the relatedness between two named 1000 Genomes individuals — both the degree (twin / 1st / 2nd / 3rd / unrelated) and the KING kinship coefficient (kinship).
• Get dataset totals — sample count, sex split, variant count, assembly (dataset-info).
• Variant selection can be specified by KGP allele frequency, gnomAD 4.1 exome and gnomAD 4.1 genome allele frequency, AlphaMissense Score and AlphaMissense Class, ClinVar significance (202502), and VEP annotations (impact, biotype, feature type, variant class, consequences).

Do NOT use this skill for:

• Resolving a gene symbol, rsID, or transcript to coordinates, or fetching reference sequence. Resolve coordinates first (see Coordinate Provenance below), then query this skill with the resolved GRCh38 region.
• Any cohort other than the 1000 Genomes Project — this skill serves only that dataset.

Prerequisites

1. uv: This skill's script is run with uv run, which reads the script's inline dependency metadata and provisions an ephemeral environment. Ensure uv is installed and on PATH (https://docs.astral.sh/uv/).
2. Data use terms: The 1000 Genomes Project data is open; users should be aware of the 1000 Genomes Project / IGSR data-use terms (https://www.internationalgenome.org/data).
3. Access constraints: There is no API key, no .env file, and no rate-limit token to configure.
4. Timeouts: network commands use --timeout 30 seconds per RPC by default. A positive finite override is allowed. Pagination makes several RPCs and retryable failures retry the whole fetch up to three times, so this is not a deadline for the whole command.

Core Rules

• Use the Wrappers: ALWAYS execute the provided helper scripts rather than constructing your own client calls or network requests. Use scripts/onekgpd_api.py for variant/sample/kinship queries (it handles the connection, streaming, pagination, and JSON serialization), and scripts/onekgpd_meta.py for sample/population metadata (offline, see Sample & population metadata).

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