Development of a cloud-based analytical tool for polygenic risk score and its implication in heart failure research.

University Of Texas Hlth Sci Ctr Houston, Houston TX

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Abstract

ABSTRACT This application is an administrative supplement in response to NIH Notice of Special Interest (NOT-OD-23-070): Administrative Supplements to Support the Exploration of Cloud in NIH-supported Research. The parent project aims to elucidate the genetic architecture of cardiac structure and function and to improve heart failure (HF) prediction by using polygenic risk scores (PRS) constructed for cardiac structure and function. HF increases with age markedly and is associated with a 50% 5-year mortality. It is heritable; genome-wide association studies (GWAS) have identified scores of genetic loci for HF, suggesting polygenic inheritance. PRS quantifies chronic disease susceptibility stemming from multiple genetic variants, which has demonstrated the potential to tailor clinical intervention for cardiovascular disease. Few studies have explored PRS for HF. Although packages of PRS construction have been developed, the effort to transit resources to cloud platforms to facilitate cloud-based analyses, where large-scale genomic data are hosted, is limited. For this administrative supplement, we therefore aim to 1) develop a PRS construction and analyses tool on BioData Catalyst (BDC), a cloud computing platform designated by the TOPMed program, and 2) assess the use of PRS for predicting HF development, in a collaborative cloud environment. It is distinct from the parent award as the parent award focuses on PRS of cardiac structure and function, not HF, and the parent award doesn’t include effort to develop a cloud-based analytical tool. This supplement is also complementary to the parent award as the analytical tool built could facilitate the analyses of the parent award and the construction of HF PRS could advance of knowledge of HF etiology, in line with the parent award. The completion of this supplement will provide valuable experience for tools development on BDC, deliver a user-friendly tool for BDC users, and demonstrate the advantage of incorporating genetic susceptibility to early-detect and to prevent HF, an essential step in decreasing HF- associated morbidity and mortality.