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Welcome to the Li Group Homepage!

Yun Li is an Associate 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 (implemented in software MaCH and MaCH-Admix) that have become standard in the analysis of genome-wide association scans. I have developed methods for meta-analysis, imputation, local ancestry inference, and region-based association analysis of rare variants in both genetically homogeneous populations and in admixed populations, and assessed different approaches to handle imputation uncertainty in subsequent association analysis. I have worked on genomewide scans for genetic variants underlying several metabolic, auto-immune and cardiovascular diseases and related quantitative traits. In addition, I have developed methods to accommodate low-coverage sequencing data for genotype calling and for association testing (implemented in software thunder [component of GotCloud], BETASEQ, UNCcombo) and have been actively involved in a number of next-generation sequencing (NGS) based studies including the 1000 Genomes Project (Project Leader on calling SNP genotypes from low-coverage pilot), identification of RNA-DNA differences (RDDs), targeted sequencing of selected exons in >14,000 individuals, the WHI whole exome sequencing project (WHISP), and whole genome sequencing based studies for type 2 diabetes, for cannabis and stimulant dependence, and for blood lipid levels, Exome Sequencing Project (ESP), and TOPMed project. More recently, I have worked on method development for Hi-C data, particularly to aid in the annotation of GWAS associated regulatory variants in terms of their target gene(s) and potential causal mechanism. I have also developed methods for DNA methylation data and actively participated in multiple epigenomewide association studies.

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