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Guanghao Qi, PhD

Assistant Professor
Department of Biostatistics |
University of Washington

WHEN:聽Wednesday, October 30, 2024, from 3:30 to 4:30 p.m.
WHERE:聽Hybrid | 2001 看片视频 College Avenue, Room 1201;
NOTE: Guanghao Qi will be presenting from Washington

Abstract

Understanding the genetics of human traits requires data that capture different aspects of the mechanisms. Genome-wide association studies (GWAS) have identified variants associated with thousands of traits. Functional 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. First, I will introduce a new method based on meta-analysis and subset search for integrating GWAS data across many traits. A joint analysis of 116 traits characterizes the variation of pleiotropy across the genome and links it to several functional genomic signatures. Our analysis identifies variants with highly trait-specific effects for the first time. Second, I will describe a new method to identify genes that show differential allele-specific expression (ASE) using single-cell RNA-seq data. ASE is a powerful tool to study genetic regulation of gene expression and can reveal the molecular mechanisms underlying variant-trait associations. The model is based on beta-binomial regression and incorporates latent variables to conduct implicit haplotype phasing and account for repeated measurements. Application of this method identifies 657 genes dynamically regulated during endoderm differentiation. These genes can play an important role in early-life diseases.

Speaker bio

Guanghao Qi is an Assistant Professor of Biostatistics at the University of Washington. His research is centered around developing statistical and machine learning methods for large-scale genetic and genomic studies. Areas of interest include statistical genetics, Mendelian randomization, single-cell genomics and bioinformatics.