Speaker: Dr Chang Su (Emory University) Abstract: Mapping enhancers and target genes in disease-related cell types has provided critical insights into the functional mechanisms of genetic variants identified by Genome-wide associated studies (GWAS). However, most existing analyses rely on bulk data, which may overlook cell-type-specific enhancers and target genes. Recently, single-cell multimodal data measuring both gene expression and chromatin accessibility within the same cells have enabled the inference of enhancer-gene pairs in a cell-type-specific and context-specific manner. However, this task is challenged by the data’s high sparsity, sequencing depth variation, and the computational burden of analyzing a large number of enhancer-gene pairs. To address these challenges, we propose scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for confounding, permits fast moment-based estimation and provides analytically derived p-values. In systematic analyses of blood and brain data, scMultiMap shows appropriate type I error control, high statistical power with greater reproducibility across independent datasets and stronger consistency with orthogonal data modalities. Meanwhile, its computational cost is less than 1% of existing methods. When applied to study Alzheimer’s disease (AD), scMultiMap gave the highest heritability enrichment in microglia and revealed new insights into the regulatory mechanisms of AD GWAS variants in microglia. About the speaker: Chang Su is an Assistant Professor in Biostatistics and Bioinformatics at Emory University. Her research aims to develop statistical methodologies to address interesting biology questions with single-cell genomics and genetics data. Her current research topics include gene regulation in specific contexts and the genetics basis of single-cell genomics. This event will be online. Zoom: https://uni-sydney.zoom.us/j/84087321707