Lida Wang

Bio

I am a Postdoctoral Research Fellow at the Dana-Farber Cancer Institute and Harvard Medical School, where I work with Prof. Alexander Gusev. My research lies at the intersection of statistical genetics, functional genomics, and data science.

I received my Ph.D. in Biostatistics from Penn State University, where I was advised by Prof. Dajiang Liu and collaborated closely with Prof. Laura Carrel. In the summer of 2023, I worked as a Research Data Scientist Intern at Calico Life Sciences, an Alphabet company.

Before beginning my doctoral studies, I earned an M.S. in Biostatistics from Georgetown University and a B.S. in Mathematics and Applied Mathematics from the Southern University of Science and Technology.

My CV is here.

Research Interests

Developing statistical models to characterize eQTL architecture at single-cell resolution and uncover context-specific genetic regulation of gene expression.
Integrating genetic and electronic health record (EHR) data to model and predict disease progression.
Investigating how somatic mutations influence cancer survival using genomic language models and machine learning.

Projects

Nature Communications

Axis-QTL: Bulk And Single cell eQTL Integration across Cell states (BASIC)

Project sc-eQTLs onto the PCs and calculate the “axis-QTLs”.

NC 2025 Abstract Package
Cell Genomics

JOint analysis of Bulk-eQTL and Sc-eQTL (JOBS)

Joint model of bulk-eQTLs as a weighted sum of sc-eQTLs from constituent cell types to improve power.

CG 2025 Abstract Package
Nature Communications

EXpression PREdiction with Summary Statistics Only (EXPRESSO)

Build gene expression prediction models using summary statistics only and propose novel variable selection methods.

NC 2024 Package Poster
Nature Communications

Prediction Using Models Informed by Chromatin conformations and Epigenomics (PUMICE)

Build gene expression prediction models by integrating epigenetic and 3D genomic information.

NC 2022 NC 2023 NC 2024 AJHG 2026 Package GitHub
Statistics in Medicine

Set-regression with applications to subgroup analysis

Develop a new type of regression: set-regression for a target subgroup.

SIM 2021

Selected Publication

[2025] Wang, L.†, Gao, S.†, et al. Integrating axis quantitative trait loci looks beyond cell types and offers insights into brain-related traits. Nat Commun 16, 10606 (2025)
[2025] Wang, L.†, Markus, H.†, et al. An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment. Cell Genomics (2025)
[2024] Wang, L.†, Khunsriraksakul, C.†, et al. Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes. Nat Commun 15, 4260 (2024)
[2022] Yuan, A., Wang, L., Tan, MT. Set-regression with applications to subgroup analysis. Statistics in Medicine. 2022;41(1):180-193.

Resources

[2025] Penn State News features our BASIC work.
[2025] Penn State News features our JOBS work.
[2024] AMSTATS News [1] [2] spotlight on Dr. Wang.
[2024] Penn State News features our EXPRESSO work.
[2022] Penn State News and Penn State Health News features our PUMICE work.

Contact

Address: 375 Longwood Ave, Boston, MA
Email: lida.wang.96 (at) gmail.com