Multi-Omics Integrative Risk Model for Alzheimer’s Disease in a Large-Scale Biobank

Poster Number: P160
Presentation Time: 05:00 PM - 06:30 PM

Abstract Keywords: Omics (genomics, metabolomics, proteomics, transcriptomics, etc.) and Integrative Analyses, Bioinformatics, Computational Biology, Machine Learning
Primary Track: Foundations
Programmatic Theme: Translational Bioinformatics

Alzheimer’s Disease (AD) is a neurodegenerative disease characterized by genetic heterogeneity, which complicates prediction of genetic risk associated with the condition. We constructed a multi-omics integrative risk model for AD, leveraging imputed transcriptomic and proteomic expression information from the Alzheimer’s Disease Sequencing Project (ADSP). Gene-disease and protein-disease associations were identified in ADSP and validated in the UK Biobank. This study highlights the importance of integrating multi-omics data to predict genetic risk for complex diseases.

Speaker(s):
Rasika Venkatesh, B.S.
University of Pennsylvania Perelman School of Medicine

Author(s):
Anni Moore, Graduate Student - University of Pennsylvania; Rachit Kumar, B.S. - University of Pennsylvania; Yuki Bradford, MS - University of Pennsylvania; Marylyn Ritchie, PhD - University of Pennsylvania, Perelman School of Medicine;