# Introduction

<figure><img src="https://2442862337-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FUJXTPxJsaKahj9yvWoaZ%2Fuploads%2Fa2aSZxwceVMuDvTt9t8K%2FScreenshot%202024-12-29%20at%203.35.48%E2%80%AFPM.png?alt=media&#x26;token=16bdba63-2cc7-42f7-8d76-e2cd79136ac9" alt=""><figcaption></figcaption></figure>

[PennPRS](https://pennprs.org/) offers a cloud-based ecosystem for PRS applications, including pseudo-training pipelines, data resources, and cloud computing capabilities to support large-scale PRS model training.&#x20;

PennPRS enables efficient online applications of pseudo-training methods, allowing users to upload or query GWAS summary statistics, submit jobs, and download trained PRS models. It provides a user-friendly framework for the global genetic research community, aiming to enhance the accessibility of PRS applications and address disparities in computational resources. Specifically, we develop end-to-end pipelines to support both [single-ancestry](https://pennprs.gitbook.io/pennprs/user-manual/single-ancestry-analysis) and [multi-ancestry](https://pennprs.gitbook.io/pennprs/user-manual/multi-ancestry-analysis) data analyses. More details of these pipelines are provided in the User Manual.&#x20;

The PennPRS Team&#x20;
