Live via Zoom

Understanding the genetics of complex traits through statistical integration of genetic and genomic data

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. We developed two statistical methods for integrative analysis of genetic and genomic data. First, I will introduce a new method for integrating GWAS data across many traits. A joint analysis of 116 traits characterized the variation of pleiotropy across the genome and linked it to several functional genomic signatures. Our analysis identified 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 cis-regulatory effects and can reveal the molecular mechanisms underlying variant-trait associations. Application of this method identified 657 genes that are dynamically regulated during endoderm differentiation. These genes can play an important role in early-life diseases. Finally, I will discuss future directions.

Host: Dr. Greg Gibson