Grant Number:	3R01CA242929-04S1 Interpret this number
Primary Investigator:	Gao, Guimin
Organization:	University Of Chicago
Project Title:	Transcriptome-Wide Association Studies for Alzheimer’s Disease Integrating Rna Splicing and Gene Expression From Multiple Tissues
Fiscal Year:	2022

ABSTRACT Alzheimer’s disease (AD) is the most common form of neurodegenerative disorder with over 47 million people affected worldwide and a global economic impact estimated at about US $818 billion. Although genome-wide association studies (GWAS) have identified over 30 susceptibility loci associated with Alzheimer’s disease, the mechanisms of neurodegeneration are still poorly understood. To further explore the missing heritability, transcriptome-wide association studies (TWAS) using gene expression and splicing data have been performed to quantify associations of genetically predicted expression and splicing (as a linear combination of genetic variants) with AD. Splicing disruption is a widespread hallmark of AD and hundreds of aberrant pre-mRNA splicing (disruption) events have been reported to be reproducibly associated with AD. Recent studies showed that integrating expression or splicing information across multiple tissues into TWAS could significantly improve the chance of discovering genes susceptible for complex traits. Splicing events in a gene can be highly correlated. However, existing statistical methods for TWAS do not account for correlation among splicing events, and thus may result in loss of power in detecting disease genes. In addition, studies have shown that most splicing Quantitative Trait Loci (sQTL) acted independently from expression QTLs (eQTLs) when exerting influence on some complex diseases. Therefore, jointly analyzing the effect of eQTLs and sQTLs in a gene may increase power to detect disease genes. In our parent R01, we proposed joint expression and splicing TWAS methods that combine the effects of eQTLs and sQTLs in a gene across multiple tissues by a sampling method (sampling around 107 times), while accounting for the correlation among splicing events and between splicing and expression. However, when the number of tissues is very large the sampling method can be computationally intensive. Therefore, it is necessary to develop computationally efficient joint TWAS methods to handle a large number of tissues. The objective of this study is to develop computationally efficient TWAS methods to integrate splicing and gene expression information across multiple tissues and apply the methods to identify novel susceptibility genes for Alzheimer’s disease. Specifically, we will 1) develop computationally efficient TWAS methods that analyze joint effect of splicing and gene expression and leverage information from multiple tissues, and 2) apply the TWAS methods proposed in Aim 1 to the analysis of Alzheimer’s disease data. We will account for correlation among splicing events and between splicing and gene expression in a gene across multiple tissues. We expect that the proposed methods have higher power in detecting susceptibility genes for AD than existing methods. The proposed methods can also be applied to other complex diseases.





None. See parent grant details.