Tae-Hwi Linus Schwantes-An

The paper develops and validates polygenic scores for substance use disorders across multiple ancestries, finding clinically relevant performance in European and Latinx populations but not in African populations.

The paper identifies Bmp signaling pathway mutations as ethanol-sensitive contributors to jaw malformations in zebrafish and humans.

The paper identifies NCOA7's role in lysosomal function and inflammatory sterol metabolism in PAH, showing its deficiency exacerbates disease through oxysterol/bile acid generation.

This paper introduces a deep learning framework, REGLE, that improves genomic discovery and risk prediction for respiratory and circulatory diseases by learning low-dimensional embeddings from high-dimensional clinical data.

This paper introduces M-REGLE, a multimodal deep learning method that improves genetic analysis of cardiovascular traits by integrating multiple clinical data modalities and outperforms unimodal approaches in locus discovery and risk prediction.

The paper identifies a gene co-expression module, the pink module, associated with PAH severity, genetic risk, vascular cell types, and therapeutic targets through multiomics integration.

This paper trains a deep neural network on noisy COPD labels to predict COPD status from spirograms, improving genetic discovery by identifying new loci and replicating existing ones.

This study identifies genetic modifiers of LRRK2 mutations in Parkinson's disease, finding a significant variant in CORO1C and a polygenic risk score effect on penetrance.