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Welcome to the Li Group Homepage!

Yun Li is a Professor in the Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill.

The focus of my research is on the development of statistical methods and their application to the genetic dissection of complex diseases and traits. In particular, I have developed genotype imputation methods (software MaCH and MaCH-Admix) that have become standard practice. I 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. I have worked on genome-wide scans for genetic variants underlying several metabolic, auto-immune, cardiovascular, neuropsychiatric diseases and related quantitative traits. In addition, I 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, I have developed methods for DNA methylation data and actively participated in multiple epigenome-wide association studies. I 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, I have 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. I have been playing leadership roles in multiple multi-site consortia efforts including serving as the contact PI of a study site in the NIH PRIMED consortium, 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.

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