Cross-ancestry information transfer framework improves protein abundance prediction and protein-trait association identification.

ABSTRACT: Genetics-informed proteome-wide association studies (PWASs) provide an effective way to uncover proteomic mechanisms underlying complex diseases. PWAS relies on an ancestry-matched reference panel to model the impact of genetically determined protein expression on phenotype. However, reference panels from underrepresented populations remain relatively limited. We developed a multi-ancestry framework to enhance protein prediction in these populations by integrating diverse information-sharing strategies into a Multi-Ancestry Best-performing Model (MABM). Results indicated that MABM increased the prediction performance with higher performance observed in both cross-validation and an external dataset. Leveraging the Biobank Japan, we identified three times as many significant PWAS association

SUBMITTER: Zhai W 

PROVIDER: S-EPMC12777707 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature