Biostatistics Dept Seminar: Integrative multi-omics multi-context association and causal inference for elucidating genetic mechanisms
Biostatistics Department Seminar
Title: Integrative multi-omics multi-context association and causal inference for elucidating genetic mechanisms
Abstract: Genome-wide association studies (GWAS) have identified tens of thousands of associations between single nucleotide polymorphisms (SNPs) and complex diseases or traits. Many of these SNPs likely influence diseases and traits through their effects on gene expression and other molecular (omics) traits. Extensive evaluations of genetic effects on omics traits have uncovered numerous quantitative trait loci (QTLs), with disease-associated QTLs often showing dynamic, context-specific regulatory patterns depending on tissues, cell types, and other conditions. To further understand the biological mechanisms underlying disease- and trait-associated SNPs, many efforts have been made to integrate GWAS summary statistics with expression QTL (eQTL) and multi-omics QTL statistics. Motivated by the joint analysis of multi-tissue methylation QTL (mQTL) and eQTL data, I will first present our recent work, X-ING (Cross-INtegrative Genomics), for cross-omics and cross-context integrative association analysis. X-ING takes as input multiple matrices of association statistics derived from different omics data types across various cellular contexts. It models the latent binary association status of each statistic, captures major association patterns, and outputs the posterior mean and probability for each input. X-ING enables the integration of effects from diverse omics data with varying effect distributions, and boosts the detection of multi-omics QTLs with co-occurring effects in other omics and tissue types.
I will also discuss a recent Mendelian randomization method for mapping expression and molecular traits as risk exposures for complex diseases. This method models molecular exposure effects across multiple tissues within gene regions while simultaneously estimating effects across genes. Applying our methods to GWAS and multi-tissue multi-omics QTL statistics, we reveal novel mechanisms underlying complex diseases and traits.
Speakers
Lin Chen is a professor of biostatistics in the Department of Public Health Sciences at the University of Chicago.
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2024-2025 Monday Seminar Series
All seminars are held at 12:05 PM via Zoom and onsite. View all seminar information here.