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.

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/).

1. 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).

1. Access constraints: There is no API key, no .env file, and no

rate-limit token to configure.

1. No credentials required

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).

• Coordinates MUST be resolved against an authoritative source first — see

Coordinate Provenance. 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.

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), and query with those resolved coordinates. 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.

bash
# Resolve gene symbol -> GRCh38 region with an authoritative source FIRST,
# then pass the verified coordinates to the OneKGPd query below.

> !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

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; 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] > Allele-frequency fields use 0.0 to mean "not present in that source." So > --gnomad-exomes-af-gt 0 selects variants that are in gnomAD exomes; a > returned gnomad_exomes_af of 0.0 means the variant is absent from gnomAD > exomes. The same convention for gnomAD genomes AF. > Conversely, --gnomad-exomes-af-lt / --gnomad-genomes-af-lt bounds include unannotated variants: "AF < X in gnomAD" includes variants with gnomAD AF = 0, i.e. unannotated; pair it with --gnomad-*-af-gt 0 to require presence in gnomAD.

> !NOTE] > am_score of 0.0 means not scored or not annotated by AlphaMissense - it does not mean benign. > A real AlphaMissense score is always greater than 0.

Quick Start

bash
# Step 1. Resolve coordinates against an authoritative source — see Coordinate Provenance.
#    example: BRCA1: chr17:43044292-43170245
# Step 2. Size the result set: how many individuals carry predicted likely-pathogenic
#    missense variants in this region?
uv run scripts/onekgpd_api.py count-samples \
  --chrom chr17 --start 43044292 --end 43170245 \
  --consequence MISSENSE_VARIANT \
  --alpha-missense-class AM_LIKELY_PATHOGENIC \
  --output /tmp/count.json
# Step 3. If the count is manageable, list those individuals.
uv run scripts/onekgpd_api.py select-samples \
  --chrom chr17 --start 43044292 --end 43170245 \
  --consequence MISSENSE_VARIANT \
  --alpha-missense-class AM_LIKELY_PATHOGENIC \
  --output /tmp/samples.json
# Step 4: For that set of individuals, see the actual variants they carry.
uv run scripts/onekgpd_api.py select-variants-in-samples \
  --chrom chr17 --start 43044292 --end 43170245 \
  --samples HG03169,NA20506 \
  --consequence MISSENSE_VARIANT --alpha-missense-class AM_LIKELY_PATHOGENIC \
  --output /tmp/variants.json

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_commands.md.

Variant-returning commands

select-* return matching variants; count-* return an integer count.

• count-variants — count variants in a region, cohort-wide.
• select-variants — select variants in a region, cohort-wide. Use --limit N

(hard cap, default 200) or --page-size N (retrieve the full set in pages); the two are mutually exclusive. The summary flags truncated when the cap is reached.

• count-variants-in-samples — as count-variants, restricted to

--samples NAME1,NAME2,... (required).

• select-variants-in-samples — as select-variants, restricted to

--samples NAME1,NAME2,... (required).

Each returned variant carries these 22 keys: chr, start, end, ref, alt, af, ac, an, hom_samples, het_samples, mis_samples, hom_samples_fx, het_samples_fx, mis_samples_fx, hom_samples_mxy, het_samples_mxy, mis_samples_mxy, gnomad_exomes_af, gnomad_genomes_af, am_score, amino_acids, biallelic. ClinVar significance and VEP consequence are filter criteria only and are not returned. Full schema: references/onekgpd_commands.md.

Sample-returning commands

• count-samples — count individuals carrying a matching variant in a region.
• select-samples — list the names of individuals carrying a matching variant.

Supports --skip N and --limit N. Returns names only; to see which variants qualified an individual, feed the names into select-variants-in-samples.

Homozygous-reference commands

Single position via --chrom + --position (not a region).

• count-samples-hom-ref — count individuals with a 0/0 call at the position.

The count is a sentinel: -1 = no variant exists at that position at all; 0 = a variant exists but no individual is homozygous reference; >0 = the number of homozygous-reference individuals. The summary states which case.

• select-samples-hom-ref — list the individuals with a 0/0 call at the position.

Relatedness command

• kinship --sample1 NAME --sample2 NAME — relatedness between two named

individuals: the degree (TWINS_MONOZYGOTIC / FIRST_DEGREE / SECOND_DEGREE / THIRD_DEGREE / UNRELATED) and the KING kinship coefficient (phi_bwf).

Dataset metadata command

• dataset-info — dataset totals: samples_total (3,202), female/male split,

variants_total, assembly (GRCh38), and the cohort breakdown. No region required; doubles as a connectivity check.

Sample & population metadata (offline)

Population, sex, pedigree, and superpopulation questions are answered by a second script, scripts/onekgpd_meta.py, from a data file bundled in the skill — no network, no credentials, no coordinates. The sample IDs are the same names the variant commands use, so the two layers compose (e.g. pick a cohort by population, then query its variants). Run uv run scripts/onekgpd_meta.py <command>.

The cohort has 5 superpopulations (AFR, AMR, EAS, EUR, SAS) and 26 populations. Population/superpopulation values match case-insensitively by short code or full name; sample IDs are case-sensitive.

• sample-metadata --samples NA19240,HG00096 — family, gender, parents,

children, population, superpopulation, and phase3 status for the given samples.

• list-populations — all 26 populations with superpopulation and sample count

(use to discover valid values).

• list-superpopulations — the 5 superpopulations with sample count and

constituent populations.

• population-stats --populations YRI [--populations CHS …] — per-population sex

split, phase3 count, and trio membership. Repeat --populations for multiple values (full names contain commas, so they are not comma-separated).

• superpopulation-summary --superpopulations EAS [--superpopulations EUR …] —

per-superpopulation totals with a per-population breakdown.

• select-samples-by-population --population YRI and/or --superpopulation AFR,

with optional --skip/--limit (default 0 / 50, max 3202) — the sample IDs in a population and/or superpopulation; both given intersects. Feed the names into select-variants-in-samples to see their variants.

See references/onekgpd_commands.md for full argument tables and JSON output schemas.

Typical Workflows

Which individuals, then which variants they carry

bash
# Step 1: resolve gene -> verified GRCh38 region (authoritative source).
# Step 2: count individuals carrying a qualifying variant in the region.
uv run scripts/onekgpd_api.py count-samples \
  --chrom <chr> --start <start> --end <end> \
  --consequence MISSENSE_VARIANT --alpha-missense-class AM_LIKELY_PATHOGENIC \
  --output /tmp/n.json
# Step 3: list those individuals.
uv run scripts/onekgpd_api.py select-samples \
  --chrom <chr> --start <start> --end <end> \
  --consequence MISSENSE_VARIANT --alpha-missense-class AM_LIKELY_PATHOGENIC \
  --output /tmp/who.json
# Step 4: for that set of individuals, see the actual variants they carry.
uv run scripts/onekgpd_api.py select-variants-in-samples \
  --chrom <chr> --start <start> --end <end> \
  --samples <name1,name2,...> \
  --consequence MISSENSE_VARIANT --alpha-missense-class AM_LIKELY_PATHOGENIC \
  --output /tmp/variants.json

Homozygous-reference carriers at a position of interest

bash
# After identifying a position of interest (verified coordinate):
uv run scripts/onekgpd_api.py count-samples-hom-ref \
  --chrom <chr> --position <pos> --output /tmp/homref_n.json
uv run scripts/onekgpd_api.py select-samples-hom-ref \
  --chrom <chr> --position <pos> --output /tmp/homref.json

Common Mistakes

• Mistake: Querying with an unverified coordinate.

Fix: Always resolve gene/feature → GRCh38 against an authoritative source first. A misplaced region returns results for an unintended location without error.

• Mistake: Calling a selection command before its counting command.

Fix: Count first; selection result sets can be large.

• Mistake: Assuming a GRCh37 coordinate will work.

Fix: The dataset is GRCh38 only.

References

• references/onekgpd_commands.md — full

per-command argument tables and the returned-variant output schema.

• references/annotation_vocabularies.md

— the controlled-vocabulary terms accepted by the CSV filter flags (consequence, impact, biotype, feature type, ClinVar significance, AlphaMissense class, variant class).

• 1000 Genomes Project / IGSR: https://www.internationalgenome.org/
• 1000 Genomes Project dataset online: https://dnaerys.org/online/