Ryan Sun

Mendel is a foundation model trained on human whole genome sequences to prioritize regulatory variants and improve gene expression prediction.

PRIORITI uses language models to predict target-indication success in drug discovery, achieving high accuracy and robustness across different benchmarks.

This paper shows how data processing steps in GWAS affect reproducibility and stability of set-based analyses, and provides tools to address these issues.

This paper introduces a formal statistical replication analysis to reduce false positives in lung cancer GWAS, finding it significantly lowers false discovery rate and improves polygenic risk score efficiency.

The paper introduces MultiSTAAR, a statistical framework for multi-trait rare variant analysis in WGS studies, identifying new lipid trait associations in diverse populations.

The paper identifies twelve novel genetic risk loci for lung cancer subtypes through multi-ancestry GWAS and shows smoking-independent genetic risk.

The paper introduces a framework for detecting noncoding rare variant associations using whole-genome sequencing data, applying it to lipid traits in the TOPMed program.

The paper introduces a method to improve eQTL inference in undersampled tissues by jointly modeling a target outcome and a correlated surrogate outcome.

The paper introduces MACIE, a framework for predicting functional variants by integrating diverse annotations, and applies it to fine-mapping using GWAS data.

This paper introduces the Generalized Berk-Jones statistic for gene set analysis in GWAS, showing it increases power by incorporating multiple signals per gene and addressing confounding from gene set lists.