Thursday, September 19, 2024
9/19/2024
ABSTRACT Recently, as part of the enhancing Genotype Tissue-Expression (eGTEx) project, methylome data on subsets of GTEx samples (N=987) from nine tissue types of 424 subjects have been generated by co-I Pierce’s lab to complement existing expression quantitative trait locus (eQTL) data. As part of eGTEx, our group conducted the standard methylation QTL (mQTL) mapping for each of the nine tissues and multi-tissue mQTL analysis using existing methods. The challenges in our mQTL analyses motivates the development of the methods in our first aim. In Aim 1, we propose to develop methods for integrative QTL mapping, integrating multi-tissue mQTL with multi-tissue eQTL statistics to improve the detection of QTLs with co-occurring effects in related tissues and/or omics data types. In addition, we propose to extend the method to map multi-cell-type single-cell eQTLs by integrating bulk-tissue QTL statistics, and to map trans-ethnic QTLs with supervised learning using GWAS summary statistics as reference. In Aim 2, we propose to develop multivariable Mendelian randomization (MR) methods for mapping risk genes accounting for confounding from DNA methylation. We illustrate that existing MR methods proposed for complex trait exposures insufficiently address challenges in studying gene expression as exposure, due to violations of instrumental variable assumptions. We propose to develop MR methods for modeling multi-tissue expression levels as exposure adjusting for multi-tissue methylation, leveraging multi-tissue eQTL and mQTL statistics to improve effect consistency of instrumental variables. In Aim 3, we will analyze GTEx and sc-eQTLGen data for cis- and trans-QTL analyses, will apply the proposed MR methods to map risk genes for complex diseases and traits with a focus on cardiovascular diseases and Alzheimer’s disease, and will conduct replication and validation analysis. We will develop efficient and scalable software. Our application highlights the importance of jointly examining multi-omics traits from multiple cellular contexts in studying genetic regulatory mechanisms underlying susceptibility to complex traits and diseases.
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