onekgpd

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).
• 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. --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.

# 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

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.

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]
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

# Step 1. NCBI Gene 672, GRCh38.p14 / NC_000017.11, RS_2025_08:
# BRCA1 spans chr17:43044295-43170327 (1-based inclusive).
# Source: https://www.ncbi.nlm.nih.gov/gene/672 ; re-resolve for your analysis.
# 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 43044295 --end 43170327 \
  --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 43044295 --end 43170327 \
  --consequence MISSENSE_VARIANT \
  --alpha-missense-class AM_LIKELY_PATHOGENIC \
  --output /tmp/samples.json
# Step 4. Count then select variants for actual returned sample IDs.
# HG03169,NA20506 below are illustrative IDs; substitute the Step 3 results.
uv run scripts/onekgpd_api.py count-variants-in-samples \
  --chrom chr17 --start 43044295 --end 43170327 \
  --samples HG03169,NA20506 \
  --consequence MISSENSE_VARIANT --alpha-missense-class AM_LIKELY_PATHOGENIC \
  --output /tmp/variant_count.json
uv run scripts/onekgpd_api.py select-variants-in-samples \
  --chrom chr17 --start 43044295 --end 43170327 \
  --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 uses 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. These interpretations require
  result_incomplete=false; otherwise variant_present is null. No variant
  record is not evidence that all 3,202 individuals have callable 0/0 genotypes.
• 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

The following is an illustrative template; replace all angle-bracket placeholders.

# 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: count variants for those individuals before selecting.
uv run scripts/onekgpd_api.py count-variants-in-samples \
  --chrom <chr> --start <start> --end <end> \
  --samples <name1,name2,...> \
  --consequence MISSENSE_VARIANT --alpha-missense-class AM_LIKELY_PATHOGENIC \
  --output /tmp/variant_count.json
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

Illustrative template; replace the placeholders with verified coordinates.

# 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).
• Current API contract and live-check scope: the
  command reference.
• 1000 Genomes Project / IGSR: https://www.internationalgenome.org/
• 1000 Genomes Project dataset online: https://dnaerys.org/online/