BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250505T093752EDT-5016817GE8@132.216.98.100 DTSTAMP:20250505T133752Z DESCRIPTION:Guanghao Qi\, PhD\n\nAssistant Professor\n Department of Biostat istics |\n University of Washington\n\nWHEN: Wednesday\, October 30\, 2024\ , from 3:30 to 4:30 p.m.\n WHERE: Hybrid | 2001 51³Ô¹ÏÍøCollege Avenue\, Roo m 1201\; Zoom\n NOTE: Guanghao Qi will be presenting from Washington\n\nAbs tract\n\nUnderstanding the genetics of human traits requires data that cap ture different aspects of the mechanisms. Genome-wide association studies (GWAS) have identified variants associated with thousands of traits. Funct ional genomic data such as transcriptomics can reveal underlying genes and cell types. Integrating different sources of data is crucial for gaining biological insights but poses great challenges for statistical analysis. F irst\, I will introduce a new method based on meta-analysis and subset sea rch for integrating GWAS data across many traits. A joint analysis of 116 traits characterizes the variation of pleiotropy across the genome and lin ks it to several functional genomic signatures. Our analysis identifies va riants with highly trait-specific effects for the first time. Second\, I w ill describe a new method to identify genes that show differential allele- specific expression (ASE) using single-cell RNA-seq data. ASE is a powerfu l tool to study genetic regulation of gene expression and can reveal the m olecular mechanisms underlying variant-trait associations. The model is ba sed on beta-binomial regression and incorporates latent variables to condu ct implicit haplotype phasing and account for repeated measurements. Appli cation of this method identifies 657 genes dynamically regulated during en doderm differentiation. These genes can play an important role in early-li fe diseases.\n\nSpeaker bio\n\nGuanghao Qi is an Assistant Professor of Bi ostatistics at the University of Washington. His research is centered arou nd developing statistical and machine learning methods for large-scale gen etic and genomic studies. Areas of interest include statistical genetics\, Mendelian randomization\, single-cell genomics and bioinformatics.\n DTSTART:20241030T193000Z DTEND:20241030T203000Z SUMMARY:Understanding the genetics of complex traits through statistical in tegration of genetic and genomic data URL:/epi-biostat-occh/channels/event/understanding-gen etics-complex-traits-through-statistical-integration-genetic-and-genomic-d ata-360255 END:VEVENT END:VCALENDAR