Tianxi Cai

The paper uses Mendelian Randomization to predict adverse drug reactions by linking genetic variants to gene expression and protein abundance across large datasets.

This study conducts a multi-ancestry GWAS of rheumatoid arthritis using the VA Million Veteran Program, identifying 152 genome-wide significant loci and improving fine-mapping resolution.

This study uses large biobanks to identify gene-trait pairs with causal evidence, rediscover approved drug targets, and develop a predictive model for drug development.

This study performs a genome-wide association study for Long COVID, identifying a significant association with the FOXP4 locus and highlighting the role of pulmonary dysfunction and COVID-19 severity.

Population-stratified GWAS of 2,068 traits in 635,969 Million Veteran Program participants, 29% genetically non-European, yields 98,485 associations and 57,601 fine-mapped signals with largely shared cross-population architecture.

The paper examines differential associations of the IL6R variant across genetic ancestries and identifies trait differences that may inform targeted therapies.

SCORNET estimates survival curves using EHR data with noisy event times and limited current status labels, achieving efficiency similar to Weibull regression with non-parametric flexibility.

sureLDA automates phenotype labeling in EHR data using topic modeling, achieving high accuracy and robustness across various phenotypes.

This paper develops an automated high-throughput phenotyping method (MAP) that improves phenotype accuracy and scalability for PheWAS studies.

This paper validates EHR-based BD phenotyping algorithms by showing their genetic correlation with traditional BD cases, suggesting they identify genetically similar cases.