An Explainable Unified AI Strategy for Efficient and Robust Integrative Analysis of Multi-omics Data from Highly Heterogeneous Multiple Studies

Jackson Laboratory, Bar Harbor ME

Investigators

Abstract

Healthy centenarians carry protective variants that counteract age-related disease risk variants, the former of which are mostly rare. Therefore, markers associated with exceptional longevity (EL) need be discovered through integrative multi-omics data analysis to improve detection power. However, existing integrative analysis method for multi-omics data do not model the relationships among markers in a modality and among studies, muddying the efficient use of pertinent information provided by multi-omics data from heterogeneous studies. We propose a unified AI strategy that models the relationships among markers, modalities, and studies, and learns nonlinear low-dimensional representations of data in a common space via graph neural networks (GNN). We achieve deep integration by enforcing the maximization of similarities between study representations and the phenotype prediction accuracy in a single GNN. The proposal has three specific aims: 1) Develop an explainable unified AI strategy and software for efficient and robust integrative analysis of multi-omics data from highly heterogeneous multiple studies. 2) Apply the methods developed in Aim 1 to Long-Life Family Study (LLFS) and Integrative Longevity Omics (ILO) data provided by the EL consortium to identify EL-associated pathways and biomarkers. 3) Apply the methods developed in Aim 1 to omics data from human and 100 species of diverse lifespan provided by the EL consortium to identify conserved and species-specific EL-associated pathways and markers. The outcome of this work will result in a publicly available integrative omics data analysis software which not only is able to identify robust longevity-associated pathways and biomarkers, but will also be applicable to any complex disease study with similar omics data analysis demands. Our work will contribute significantly to identify therapeutic interventions for improving human health.