Areas of Interest
Genetic and genomic analyses of complex phenotypes, including bipolar disorder, cancer, blood clotting disease, and traits involving animal models and human microbiomes. Our approach emphasizes statistical analysis of genome-scale datasets (e.g, gene expression and genotyping data, results from next-generation sequencing), evolutionary history, bioinformatics, and pattern recognition.
Main Funding Sources
NIH 1R01DK099034: Genotype-Phenotype relationships underlying aerobic capacity and metabolic health (contact PI: Li, MPIs: Burant, Britton)
NIH 1R01GM118928: High-resolution map of human germline mutation patterns and inference of mutagenic mechanisms (contact PI: Li, MPI: Zoellner)
NIH U01DA043098: Genetics of novelty seeking and propensity for drug abuse in outbred rats (contact PI: Akil, MPI: Li)
NIH 1U24DK112342: Michigan MoTrPAC Chemical Analysis Site (MiCAS) (contact PI: Burant, MPI: Li)
NIH 5P40OD021331: Resource for Rat Genetic Models of Aerobic Capacity (MPI: Britton, Koch, Co-I: Li)
Michigan Institute for Data Science (MIDAS): Michigan Center for Single-Cell Genomic Data Analytics (Co-PI: Li, Gilbert, Co-I: 8 others)
UMHS-PUHSC Joint institute Discovery Grant: Genomic evolution and mutational signature of esophageal cancer in Anyang, China (PI: Li)
Michigan Diabetes Research Ctr. Study Grant: Elucidating the landscape of liver cell heterogeneity in normal and insulin resistant states by single cell RNA sequencing (co-PI: Lin, Li, Kurabayashi)
Cancer Center Research Committee Innovation Grant: Identifying the cellular source of relapse in AML (Co-PI: Malek, Li)
Taubman Institute Grand Challenge Phase I Grants: Individualized Disease Prediction Using a Multi-Dimensional, Time-Resolved “Big Data” Approach (Co-PI: Choi, Tewari, Co-I, Li)
Pritzker Neuropsychiatric Disorders Research Consortium: Genetic and genomic analyses of psychiatric disorders (PI of subcontract: Li)
The Li lab is recruiting talented individuals with background in quantitative analysis or interest in statistical genetics, bioinformatics, or cancer genomics. Positions are open at multiple levels: graduate student, postdoc, research staff, or research-track faculty. Current projects (2016-2020) include: single-cell RNAseq data analysis, methodological and theoretical research in clustering, mutation patterns in the human genome, multi-omics data integration involving metabolomic, proteomic, and transcriptomic data, QTL mapping in rat models of metabolomic traits and emotional traits. Please contact email@example.com.