Tianjie Gu

This study evaluates two sequence-to-function models for predicting regulatory variant effects across ancestries using blood eQTL data, finding better performance for high-confidence variants in African American populations.

This study performs a multi-ancestry transcriptome-wide association study (TWAS) to identify shared and population-specific genetic effects in Alzheimer's disease using RNA-seq and genotype data from multiple cohorts.

The paper develops a large-scale blood transcriptomic resource for Alzheimer's disease, identifies sex-specific differences, and models insulin resistance biomarkers using machine learning.