One popular approach to disease-gene association testing is a transcriptome-wide association study (TWAS). Conceptually, TWAS is a test for the genetic correlation between cis-regulated gene expression and disease. However, only half of the genetic regulation of gene expression is expected to be in cis, e.g. by genetic variation within 1 Mb of the gene. The goal of this project is to develop a novel statistical method that leverages priors on SNP-gene regulatory links beyond the cis-window to improve our understanding of the genetic regulation of gene expression. As a result, our ability to identify disease-associated genes via TWAS should substantially improve due to (1) the enhanced identification of genes regulated by genetic variation and (2) the increased accuracy with which we can predict an individual's gene expression.

One approach to infer causal genes in disease is a transcriptome-wide association study (TWAS). However, TWAS is not powerful in non-European populations due to poor trans-ancestry portability of gene expression prediction models and smaller genome-wide association study (GWAS) sample sizes. The purpose of this project is to develop a novel machine learning approach to mitigate the issue of trans-ancestry portability. This approach will allow for powerful TWAS in non-European populations by simultaneously modeling genetic and genomic data from different populations, which has previously been challenging due to population-specific differences in genetic architecture such as linkage disequilibrium.

We developed a novel tau propagation model using 3D spheroid model that rapidly develop tau pathology and neurodegeneration in just three weeks. Single cell transcriptomics of the model reveals cell type specific changes that resemble transcriptomic signatures from Alzheimer’s disease postmortem brain.

To understand cell type specific vulnerability of Alzheimer’s disease, we utilize snRNA-seq to characterize human brain tissues from Alzheimer’s disease patients across different brain regions.