AI Insight
The authors developed OmicsPred, a centralised online platform that aggregates and distributes genetic prediction models for multi-omic traits including transcriptomics, proteomics, and metabolomics, currently hosting over 3.3 million individual models. The platform standardises metadata, ensures compatibility with widely used analytical tools such as PGS Catalog Calculator and MetaXcan, and consolidates previously fragmented resources including PredictDB. To demonstrate its utility, the authors conducted a multi-omic phenome-wide association study using data from the Million Veterans Program, illustrating how the resource can support systematic molecular target discovery for disease research.
Why it matters
Centralising genetic prediction models in a findable and interoperable repository reduces redundant computational work and lowers barriers for researchers seeking cost-effective alternatives to direct omics profiling in large biobank studies. This infrastructure could accelerate the identification of molecular targets relevant to complex diseases across diverse research settings.
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Genetic prediction of multi-omic data has emerged as a cost-effective alternative to direct omics profiling, particularly useful for identifying molecular features associated with disease susceptibility. However, despite its popularity, multi-omic imputation models are fragmented across studies, hindering findability, accessibility, interoperability and re-use. To address this, we developed OmicsPred (https://www.omicspred.org), a centralised platform for the deposition and dissemination of genetic prediction models of multi-omic traits. OmicsPred unifies the most commonly used molecular imputation models (e.g. from PredictDB) and other published studies totalling 3,339,469 prediction models spanning transcriptomic, proteomic, and metabolomic traits (as of May 2026). Each model is accompanied by metadata describing score development and predictive performance, and distributed in formats compatible with popular analytic tools, such as PGS Catalog Calculator and MetaXcan. To demonstrate the utility of the resource for systematic target discovery, we perform a multi-omic phenome-wide association analysis in Million Veterans Program data.
Source: OmicsPred as a centralised resource for genetic prediction of multi-omic traits