Cost-Effectiveness of Remote Cognitive-Behavioral-Based Therapy for Chronic Pain Among People With High-Impact Chronic Pain.

Polygenic scores (PGSs) aggregate the effects of multiple genetic variants, derived from independent genome-wide association studies (GWAS), to estimate inherited susceptibility to complex traits. Each PGS model is defined by the specific variants it includes and corresponding effect sizes. Applied in a research context, PGSs have demonstrated substantial utility for risk stratification, disease prediction, and exploration of shared genetic etiology across phenotypes through phenome-wide association studies (PheWASs). The Polygenic Score Catalog is a centralized repository of 3688 PGS models with associated metadata and contributor-supplied performance metrics, spanning 1450 traits. PGS Catalog has been pivotal in advancing the field by promoting standardized reporting practices. However, most published models are accompanied by heterogeneous validation metrics derived from completely different cohorts, complicating model selection even within a single disorder. In the absence of standardized benchmarking that puts models on a common performance scale, choosing an optimal model for a given disease remains challenging. To date, large-scale external validation has been conducted only in a cohort of non-European ancestry, but because most PGSs were originally developed in European cohorts, such assessments provide only partial insight. Unlike individual pathogenic variants, which can be directly linked to disease risk, the absolute value of a PGS has no inherent meaning and must be interpreted relative to a population-based reference distribution. Such interpretation requires large-scale genetic datasets and careful adjustment for confounding factors, particularly population structure. Pipelines have been developed to automate reproducible PGS calculation and reference-based interpretation, but by default they rely on small public datasets such as the 1000 Genomes and Human Genome Diversity Project, whose limited size and lack of clinical information constrain their value as population references. This gap underscores the need for advanced, biobank-linked, reference frameworks to enable more informative interpretation of individual PGSs.