PRs

Polygenic Scores

Use this skill when the user asks about polygenic risk scores, PRS, PGS Catalog scores, common disease or trait risk from many variants, or applying a published scoring file to their genome.

Boundaries

• PRS/PGS here means applying published variant weights from a scoring file. Genomi does not train new PRS models from GWAS summary statistics.
• Default genome build is GRCh38 when omitted; use GRCh37 only when the Active Genome Index is GRCh37/hg19.
• Active Genome Index artifacts stay local. Public score metadata may use PGS Catalog, but private genotypes are not uploaded to external services.
• A raw PRS is common-risk or trait context, not a diagnosis, absolute disease risk, treatment recommendation, or clinical category.
• Only state standardized score context when valid score_mean and score_sd are supplied for the same score, build, cohort/reference distribution, and scoring convention.
• Do not use PRS output for ethnicity, identity, monogenic diagnosis, medication response, or rare-disease causality.

Workflow

1. Use prs.search_scores for public trait or score discovery. If the user already supplies a PGS ID, use that ID directly.
2. Use prs.fetch_score_metadata when the source publication, build, variant count, scoring-file URLs, licensing, or cohort/evaluation context matters.
3. Use prs.calculate_score with the chosen pgs_id and the user's genome source to get the raw weighted score plus overlap QC.
4. Use prs.check_score_overlap when you only need readiness and QC without a calculated score.
5. Use prs.list_imported_scores when the user asks what scores are already available locally.
6. Use prs.build_source_context when the user asks what PRS can or cannot tell them.

When published calibration is missing

PGS Catalog rarely publishes a reference cohort mean/SD, so a raw weighted score has units on an arbitrary scale. Deliver a defensible directional or quantitative answer for this specific question by combining capabilities that contribute orthogonal evidence — population allele frequencies feeding a closed-form z, direct effect-allele dosages at well-replicated lead loci, additional published scores derived by different methods, treatment-response context when the outcome is treatable, mechanism context from functional or pathway evidence, or whatever else Genomi currently exposes that fits. Disclose the assumptions of any closed-form estimate (HWE, variant independence, ancestry of the allele-frequency source).

Answering

When an Active Genome Index is scored or its overlap changes the result, report the score ID/source, genome build, overlap status, matched/missing/excluded variant counts, and whether the result is raw or calibrated. Do not add a routine Active Genome Index status line for public score metadata lookups.

Use careful language:

• "The raw weighted score was calculated from N matched score variants."
• "This is source-bound PRS context, not an absolute risk estimate."
• "Performance may not transfer across ancestry/evaluation cohorts."
• When grounded in an analytic z from gnomAD or a multi-score consensus: "Your analytic z relative to <population> under HWE is +X.X, ~Yth percentile. This is a closed-form estimate, not an empirical reference-cohort percentile."

Directional language ("leans above population average", "in the upper tertile of the analytic z distribution") is appropriate when grounded in the orthogonal evidence the synthesis combined.

Avoid:

• Clinical-risk category labels (high/elevated/low risk) unless a validated calibration and category threshold from the same source context is explicitly supplied.
• Absolute outcome probabilities ("X% chance of disease by age N") — these require an empirical risk-calibration model.
• "This diagnoses", "rules out", "predicts disease", or "determines origin".

Cross-Capability Synthesis

A scope-limited result from this capability is not a final user-facing answer when other Genomi capabilities can contribute orthogonal evidence to the same question. Returning "cannot answer" while applicable capabilities remain unexamined is a host-agent failure mode.

Tools

prs.build_source_context

Explain PGS Catalog provenance, local scoring workflow, genome-build defaults, calibration limits, and PRS risk boundaries.

Use when: The user asks what PRS can and cannot tell them, whether PRS means common risk analysis, or how Genomi applies published scores.

Why necessary: PRS answers require explicit boundaries around calibration, cohort portability, missing variants, and clinical non-diagnosis.

Not for: Calculating a personal score; use prs.calculate_score after Active Genome Index access approval.

Example prompts: Explain how Genomi implements PRS. Does PRS give common disease risk?

Result semantics: Returns public method context only; it does not read Active Genome Index.

prs.calculate_score

Apply a published polygenic score to an approved Active Genome Index and return raw weighted score plus QC.

Use when: The user asks to calculate or apply a published PRS/PGS score to their genome.

Why necessary: This keeps Active Genome Index local, applies only selected published weights, reports overlap and build defaults, and avoids unsupported risk-category claims.

Not for: Training a new PRS model. Diagnosis, monogenic disease interpretation, medication response, or absolute-risk prediction without a validated calibration model. Ancestry or identity inference.

Example prompts: Calculate PGS000001 for my Active Genome Index. Apply this local scoring file to my GRCh38 genome.

Result semantics: Output is a raw weighted score and QC unless explicit calibration parameters are supplied. Do not phrase it as diagnosis, absolute disease risk, ethnicity, or clinical actionability.