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UID:20261027T120458EDT-2947J3fdZ6@132.216.98.100
DTSTAMP:20261027T160458Z
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 McGill College 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:https://www.mcgill.ca/epi-biostat-occh/channels/event/understanding-gen
 etics-complex-traits-through-statistical-integration-genetic-and-genomic-d
 ata-360255
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