The paper introduces COAST-SS, a method to identify allelic series using summary statistics, showing it is effective and robust to LD matrix misspecification.

The American Journal of Human Genetics · Oct 13, 2025Peer-reviewedGenetics

Leveraging multimodal neuroimaging and GWAS for identifying modality-level causal pathways to Alzheimer's disease

Yuan Tian, Daniel Felsky, Jessica L. Gronsbell, Jun Young Park

The paper proposes a method to test causality between genetic variants and brain imaging modalities for Alzheimer's disease using summary statistics from GWAS data.

Imaging Neuroscience · Jan 1, 2025Peer-reviewedGenetic & Genomic Medicine

Leveraging a machine learning derived surrogate phenotype to improve power for genome-wide association studies of partially missing phenotypes in population biobanks

Zachary Ryan McCaw, Jianhui Gao, Xihong Lin, Jessica L. Gronsbell

This paper introduces SynSurr, a method that improves GWAS power for partially missing phenotypes by jointly analyzing the target phenotype and a synthetic surrogate predicted by machine learning, without requiring a correctly specified imputation model.

Nature Genetics · Jun 13, 2024Peer-reviewedGenetics

High-throughput Multimodal Automated Phenotyping (MAP) with Application to PheWAS

Katherine P. Liao, Jiehuan Sun, Tianrun A. Cai, Nicholas Link, Chuan Hong, Jie Huang, Jennifer E. Huffman, Jessica L. Gronsbell, Yichi Zhang, Yuk-Lam Anne Ho, Victor Manuel Castro, Vivian S. Gainer, Shawn N. Murphy, Christopher J. O’Donnell, J. Michael Gaziano, , with the VA Million Veteran Program

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

Journal of the American Medical Informatics Association · Apr 26, 2019Peer-reviewedBioinformatics