Teacher: Prof. Zhou Xiang, University of Michigan, USA

This course provides an in-depth exploration ofstate-of-artstatistical and computational methods and tools applied to various large scale genomic datasets, focusing on genome-wide association studies (GWAS), RNA sequencing studies, spatial transcriptomics, and integrative analysis techniques. The course will provide an overview of GWAS study design, data structure, common analysis approaches, adjustment of population structure and individual relatedness, pleiotropic mapping and modeling of multiple correlated traits, fine-mapping of causal associations, heritability and genetic correlation analysis, polygenic risk score analysis; differential gene expression analysis, expression quantitative trait loci (eQTL) mapping; introduction to spatial transcriptomics, detection of spatially variable genes, cell type deconvolution, tissue segmentation and functional domain detection; transcriptome-wide association studies, Mendelian randomization for integrative analysis, and fine-mapping in transcriptome-wide association studies. Through lectures, students will gain exposure to real-world applications and learn skills in analyzing and interpreting genomic data, with a focus on addressing complex biological questions and uncovering insights into the genetic basis of human traits and diseases.