When polygenic scores (PGS) are used to inform research or policy (education, health, screening), agencies and journals should require a short, standardized provenance statement: sample ancestry composition, GWAS training sample size, expected variance explained in the target population, and known confounders (e.g., SES correlation). This would make PGS use transparent, limit overclaiming, and allow policymakers to weigh predictive value against ethical risks.
— Standardizing how PGS predictive power and limits are reported would prevent misinterpretation in debates over schooling, screening, and resource allocation and would make policy interventions evidence‑aware rather than hype‑driven.
Davide Piffer
2026.01.07
78% relevant
The article underscores the need for transparent phenotype construction and population‑covariance provenance (which GWAS produced the summary stats, how structure was modeled)—precisely the governance measures proposed in the provenance idea to prevent misapplication of polygenic claims in policy or medicine.
2018.01.08
100% relevant
The article quantifies prediction gains (≈4% from intelligence GWAS, >10% when combined with education GWAS) and stresses that PGS predict from birth; those concrete claims create the immediate need for standardized provenance when scores enter policy or clinical contexts.
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