Novel Statistical Methods for Multi-omics Data Integration in Alzheimer's Disease

Novel Statistical Methods for Multi-omics Data Integration in Alzheimer's Disease Principal Investigators: Chong Wu, Ph.D. (contact); Jonathan Bradley, Ph.D.

Summary A fundamental public health need is to understand the genetic basis of Alzheimer?s disease (AD) to enable enhanced screening and preventive therapies. To date, genome-wide association studies (GWAS) have identified more than 30 late-onset AD risk loci.

These identified risk loci only explain a modest proportion of the late-onset AD heritability, which motivates the development of transcriptome-wide association studies (TWASs).

TWASs test for predicted gene expression-trait associations by leveraging individual-level gene expression reference data and successfully enhanced the discovery of genetic risk loci for many complex traits, including late-onset AD. Even though many TWASs related methods have been proposed and investigated, critical gaps remain.

First, existing TWAS methods predominantly require expression reference panels, which can be limited in sample size and create a challenge when developing prediction models.

Second, while many expression prediction models have been built, no statistical method has been proposed to build a new expression prediction model that combines/uses existing expression prediction models.

These critical gaps in knowledge deter the statistical power of detecting gene-trait associations by TWAS, which is ultimately needed to gain potentially transformative insight into the genetic basis of AD.

In response to PAS-19-391, this project?s overall objective is to develop statistical methods and software for improving the power of TWAS and offering biological insights into AD.

Our central hypothesis is to maximize the power of TWAS either by using eQTL summary data with much larger sample sizes as the expression reference panel or by leveraging existing expression prediction models.

To test our central hypothesis, we will 1) develop expression prediction models by leveraging eQTL summary data, 2) develop expression models by integrating existing expression prediction models, and 3) develop open-source, cross-platform, publicly available, easy-to-use software to implement the proposed methods.

The overarching aim of this study not only enhances the power of the widely used method TWASs but also offers biological insights into AD pathology. The proposed new methods can also be applied to other complex diseases.