Query the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region,

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]( sample population metadata offline)). Coordinates MUST be resolved against an authoritative source first — see [Coordinate Provenance]( coordinate provenance mandatory first step). This is mandatory, not advisory. Count before you select : every variant and sample selection has a paired counting command. Call the count command FIRST to size the result set, then select only if the count is manageable. Zygosity defaults to both : selection and counting commands include both heterozygous and homozygous carriage by default. Narrow with het only or hom only when the question is specifically about one state. (You do not need to pass anything to get both.) Output : scripts write full JSON to a file ( output , default under /tmp/ ) and print a concise summary to stdout. Do not read large JSON files into context — use jq or a small disposable uv run python snippet to extract fields. page size retrieves every page but accumulates all variants in RAM; it is not a bounded memory export. Size the query first. Check completeness : result incomplete=true means results cannot support a definitive zero/absence claim. Re run after service recovery. For capped variant selections, truncated=true means the limit was reached and more records may exist, even if the cluster result itself was complete. Coordinate Provenance (MANDATORY FIRST STEP) Before any region based query, resolve the gene or feature to GRCh38 coordinates against an authoritative source (for example Ensembl or NCBI), and query with those resolved coordinates. Inputs are 1 based, inclusive : a BED interval [start0, end0) becomes start=start0+1, end=end0 . Record the source accession, annotation release, and retrieval date; gene boundaries can differ by annotation release even on the same assembly. The assembly must be explicit, and a gene range must be resolved to precise positions before use. This is structural, not advisory: there is no source side guardrail that would catch a misplaced region, so an unverified coordinate produces results for an unintended location with no error. [!CAUTION] The dataset is GRCh38. A GRCh37 coordinate, or any region that does not correctly correspond to the intended feature on GRCh38, will return results for an unintended location without raising an error. Verify the assembly and the resolved coordinates before querying. Command Selection Guide Match the question to the command. Counting commands are cheap and should precede their selection counterpart. Which individuals carry matching variants in a region → count samples then select samples Which variants are carried in a region, cohort wide → count variants then select variants Which variants are carried in a region, within a named set of individuals → count variants in samples then select variants in samples Who is homozygous reference at a single position → count samples hom ref then select samples hom ref Relatedness (degree + coefficient) between two named individuals → kinship Dataset totals (sample count, sex split, variant total, assembly) → dataset info Cohort interpretation The 3,202 sample cohort includes relatives: the additional 698 high coverage samples extend the original 2,504 sample panel. Carrier counts therefore are not counts of independent observations, and cohort AF is not a population prevalence estimate. For association or frequency comparisons, document the selected populations and relatedness policy; use the bundled pedigree metadata and kinship when choosing or auditing the analysis set. See the [IGSR cohort announcement](https://www.internationalgenome.org/announcements/3202 samples at high coverage from NYGC/). Annotation filters (shared across variant and sample selection/counting) All variant and sample selection commands ( count variants , select variants , their in samples forms, count samples , select samples ) accept the same annotation filters. Different filter fields are combined with AND ; multiple values within one field are combined with OR . Enum values are case insensitive (e.g. missense variant or MISSENSE VARIANT ). These are selection criteria applied on the server. The fields returned on a selected variant are listed under [Variant returning commands]( variant returning commands); a criterion used for filtering is not necessarily echoed back on the returned variant. af lt / af gt : 1000 Genomes dataset allele frequency bounds gnomad exomes af lt / gnomad exomes af gt : gnomAD v4.1 exome AF bounds gnomad genomes af lt / gnomad genomes af gt : gnomAD v4.1 genome AF bounds clin significance : ClinVar significance terms, CSV (e.g. PATHOGENIC,LIKELY PATHOGENIC ) consequence : Sequence Ontology consequence terms, CSV (e.g. MISSENSE VARIANT,STOP GAINED ) impact : VEP impact, CSV ( HIGH,MODERATE,LOW,MODIFIER ) variant type , feature type , bio type : SO variant class / VEP feature / VEP biotype, CSV alpha missense class : AM LIKELY BENIGN,AM LIKELY PATHOGENIC,AM AMBIGUOUS (CSV) alpha missense score lt / alpha missense score gt : AlphaMissense score bounds biallelic only / multiallelic only exclude males / exclude females min len bp / max len bp : alternate allele length bounds (bp) [!NOTE] alpha missense class and alpha missense score are mutually exclusive (the engine ignores the class when a score bound is set). biallelic only and multiallelic only are mutually exclusive. exclude males and exclude females are mutually exclusive. Setting a gt bound greater than or equal to its matching lt bound defines an empty range and will return nothing. [!NOTE] gnomad exomes af , gnomad genomes af , and am score use 0.0 for not annotated in this service snapshot . This does not establish absence from the current gnomAD release, biological rarity, or a benign prediction. The dataset's own af field is a different statistic, not this sentinel. [!CAUTION] A zero numeric filter is unset on the server , so gnomad exomes af gt 0 does not exclude missing annotations. The wrapper rejects zero, nonfinite, out of range, and float32 underflowing bounds. Choose an explicit positive threshold (for example gnomad exomes af gt 0.000001 means AF 1e 6, not merely annotation presence). For exact 0 , retrieve a complete variant set and post filter the returned AF locally. A < X filter alone includes unannotated zero values. Apply the same missing score caution to AlphaMissense. Categorical annotations are retained across transcripts. Combining consequence and impact filters does not establish that they describe the same transcript. amino acids may contain multiple HGVSp entries; the generic gRPC service places canonical annotations first, whereas the separate MCP layer trims its output. Preserve transcript identifiers, and do not treat a model's likely pathogenic class as a clinical diagnosis or a participant phenotype. Quick Start Commands Each command writes full JSON to a file ( output PATH , default a temp file) and prints a concise stdout summary. All region/sample commands share: the region input ( chrom / start / end with optional ref / alt , or one or more repeated region CHR:START END ), the zygosity flags ( het only / hom only , default both), and the annotation filters above. The full per flag tables live in [references/onekgpd comman