Yihe Yang, Ph.D.

Research Associate
Department of Population and Quantitative Health Sciences
Case Western Reserve University School of Medicine

I am a statistician working on causal inference, statistical genetics, and high-dimensional methods, with applications to Mendelian randomization, gene prioritization, and personalized treatment policies. My current methodological work includes robust multivariable and multi-ancestry Mendelian randomization, transcriptome-wide association analysis, and sparse policy learning for continuous treatments.

I received my Ph.D. in Statistics from Sichuan University in 2021 and my B.S. in Mathematics from Wuhan University in 2015. I joined Case Western Reserve University in 2022 as a postdoctoral scholar and have been a research associate since March 2026.

Email Google Scholar GitHub CV (PDF)

Research

Fine-mapping and gene prioritization. A multivariate TWAS that controls for infinitesimal effects to uncover causal gene-tissue pairs and variants (TGVIS); fine-mapping gene-gene and gene-environment interactions at phenotype-associated loci; tools for finding genes with causal evidence from eQTLs.

Mendelian randomization. Bias-corrected multivariable MR (MRBEE), cis-MR robust to weak instrument bias and horizontal pleiotropy (cis-MRBEE), transfer learning across ancestries (MRBEE-TL), and robust multi-exposure and multi-outcome frameworks.

Gene-environment interaction. GAM-based tests of parametric structure in genetic age-effect curves; partitioned blood pressure polygenic risk by tissue-specific enhancers and their interactions with environment.

Mathematical statistics. Regularized estimation of high-dimensional covariance and precision matrices, genetic Gaussian networks from GWAS summary data, factor-augmented group effect selection, and sparse policy learning for continuous treatments.

Publications

† co-first author; ∗ corresponding author. Full list in the CV; code on GitHub.

Fine-mapping

Uncovering causal gene-tissue pairs and variants through a multivariate TWAS controlling for infinitesimal effects

Yang, Y., Lorincz-Comi, N., & Zhu, X.∗ (2025). Nature Communications, 16(1), 6098 · paper

HORNET: Tools to find genes with causal evidence and their regulatory networks using eQTLs

Lorincz-Comi, N., Yang, Y., Zhu, X.∗, et al. (2025). Bioinformatics Advances, 5(1), vbaf068 · paper

Mendelian randomization

MRBEE-TL: Improving causal effect estimation in multi-ancestry multivariable Mendelian randomization with transfer learning

Yang, Y. & Zhu, X.∗ (2026). Genome Biology · paper

Cis-MRBEE: A multivariable cis-Mendelian randomization method robust to weak instrument bias and horizontal pleiotropy bias

Yang, Y., Lorincz-Comi, N., Li, M., & Zhu, X.∗ (2025). Briefings in Bioinformatics, 26(3), bbaf250 · paper

MRBEE: A bias-corrected multivariable Mendelian randomization method Best of Cell Press 2024 & 2025

Lorincz-Comi, N.†, Yang, Y.†, Li, G., & Zhu, X.∗ (2024). Human Genetics and Genomics Advances, 5(3) · paper

simmrd: An open-source tool to perform simulations in Mendelian randomization

Lorincz-Comi, N., Yang, Y., & Zhu, X.∗ (2024). Genetic Epidemiology, 48(2), 59–73 · paper

Gene-environment interaction

Partitioned blood pressure polygenic risk reveals differential genetic effects of tissue-specific enhancers and their interactions on cardiovascular disease

Yang, Y., Lee, D., Chakravarti, A.∗, & Zhu, X.∗ (2026). Research Square · preprint

Testing parametric structure in genetic age-effect curves: A GAM-based framework with application to the UK Biobank

Yang, Y.∗ & Zhu, X. (2025). medRxiv · preprint

An approach to identify gene-environment interactions and reveal new biological insight in complex traits

Zhu, X.∗, Yang, Y., et al. (2024). Nature Communications, 15(1), 3385 · paper

Mathematical statistics

Factor-augmented group effect selection with application to gene set analysis

Yang, Y.∗ (2026). Sole author. Statistics Innovation, 3(1) · paper

Estimation of a genetic Gaussian network using GWAS summary data

Yang, Y., Lorincz-Comi, N., & Zhu, X.∗ (2024). Biometrics, 80(4), ujae148 · paper

Block-diagonal precision matrix regularization for ultra-high dimensional data

Yang, Y., Dai, H., & Pan, J.∗ (2023). Computational Statistics & Data Analysis, 179, 107630 · paper

Latent Gaussian copula models for longitudinal binary data

Peng, C., Yang, Y., Zhou, J., & Pan, J.∗ (2022). Journal of Multivariate Analysis, 189, 104940 · paper

Estimation and optimal structure selection of high-dimensional Toeplitz covariance matrix

Yang, Y., Zhou, J., & Pan, J.∗ (2021). Journal of Multivariate Analysis, 184, 104739 · paper

Honors and Awards

Featured Plenary Abstract Presentation, ASHG Annual Meeting 2026, Montréal. One of 12 abstract-driven plenary talks, the meeting's most selective abstract presentation category. Fine-mapping gene-gene and gene-environment interactions at phenotype-associated loci.
Best of AJHG and HGG Advances, Cell Press, 2024 and 2025. Editorial selection of the most impactful papers across the two ASHG journals, in two consecutive years; highlighted in the ASHG 2024–2025 Annual Report. MRBEE: A bias-corrected multivariable Mendelian randomization method.
Neel Award Finalist, IGES Annual Meeting 2023.
Reviewers' Choice Poster, ASHG Annual Meeting 2023 and 2024.

Selected Talks

ASHG 2026, Montréal. Fine-mapping gene-gene and gene-environment interactions at phenotype-associated loci. Featured Plenary Oral Presentation.
IGES 2025, Cologne. Improving causal effect estimation in multi-ancestry Mendelian randomization with transfer learning. Selected Oral Presentation.
ASHG 2025, Boston. Partitioning polygenic risk scores using tissue-CREs and their impacts on cardiovascular disease. Poster.
ASHG 2024, Denver. Uncovering causal gene-tissue pairs and variants: A multivariable TWAS method that controls for infinitesimal effects. Reviewers' Choice Poster.
IGES 2023, Nashville. Joint selection of exposures and horizontal pleiotropy in multivariable Mendelian randomization with application to causal gene identification. Selected Oral Presentation; Neel Award Finalist.
ASHG 2023, Washington, D.C. Multivariable Mendelian randomization reveals potential causal genes that contribute to blood pressure traits in diverse populations. Reviewers' Choice Poster.

Professional Memberships

Member, American Society of Human Genetics (ASHG), 2022–present.
Member, International Genetic Epidemiology Society (IGES), 2023–present.