Skip to main content

Professor, Genetics Professor, Biostatistics Adjunct Associate Professor, Applied Physical Sciences

Research Interests

Key words: statistical genetics, multi-omics dissection of complex human diseases and traits

The focus of the Li Lab research is on the development of statistical methods and their application to the genetic dissection of complex diseases and traits. In particular, the Li Lab has developed genotype imputation methods (software MaCH and MaCH-Admix) that have become standard practice. The Lab have also developed methods for meta-analysis, local ancestry inference, and region-based association of rare variants in both genetically homogeneous and in admixed populations, and proposed different approaches to handle imputation uncertainty in association analysis. We have worked on genome-wide scans for genetic variants underlying several metabolic, auto-immune, cardiovascular, neuropsychiatric diseases and related quantitative traits. In addition, we have developed methods to accommodate low-coverage sequencing data for genotype calling and for association testing and have been actively involved in a number of sequencing (NGS) based studies including the 1000 Genomes Project (Project Leader on calling SNP genotypes from low-coverage pilot), identification of RNA-DNA differences, Exome Sequencing Project (ESP), and the Trans-Omics for Precision Medicine (TOPMed) project. In addition, we have developed methods for DNA methylation data and actively participated in multiple epigenome-wide association studies. Moreover, we have also developed methods for single-cell RNA-seq and spatial transcriptomics data, particularly on ensemble clustering, batch effect correction, and association in medical genetics context. Furthermore, the Li Lab has worked on method development and data analysis for Hi-C and derived data, particularly detection of long-range chromatin interactions and integration with GWAS and eQTL data. PI Dr. Yun Li has been playing leadership roles in multiple multi-site consortia efforts including leading the Systems Biology and Bioinformatics Working Group in the Back Pain Consortium (BACPAC) Research Program, and co-chairing the 4DN Predictive Modeling Working Group.

Mentor Training:

  • Bias 101
  • DEI Program Part 1 – Unconscious Bias
  • DEI Program Part 2 – Respecting All
  • REI Racial Equity Workshop Phase 1

Training Program Affiliations:

  • Bioinformatics and Computational Biology


Yun Li in UNC Genetics News

Yun Li, PhD