Statistical methods to link genetic variants to regulatory landscapes

The number of disease-associated variants identified by GWAS continues to increase with larger sample sizes, but our ability to interpret these variants appears to have reached a plateau: the mechanisms underlying most GWAS loci still remain unclear. Importantly, a comprehensive list of causal genes and biological pathways is missing for nearly all complex diseases. Without such a list, progress in understanding human traits and in our ability to design effective treatments for complex diseases is greatly stunted. To link GWAS loci to causal genes, there have been large efforts to map the effects of non-coding genetic variants on the expression levels of nearby genes (i.e. cis-eQTLs) in many tissue- and cell-types. However, recent work pointed out that GWAS loci are less likely to have large cis-eQTL effects, especially those that harbor functionally important genes. Therefore, to comprehensively interpret GWAS loci and identify disease genes, we propose to move beyond cis gene regulation, and focus on 1) trans regulation of genes and proteins and 2) genetic regulation of the epigenome (such as histone modification). We will continue developing novel statistical approaches for large-scale data generated from cutting-edge technologies. We will (a) comprehensively map trans-regulatory signals the proteome and characterize trans regulatory mechanism of the proteome; (b) develop statistical approaches to identify trans-perturb-QTLs in perturb-seq data; (c) develop statistical methods to identify cis-by-trans genetic interaction effects and advance understanding of the genetic architecture of gene expression; (d) identify core disease genes of immune-related disorders and neurodegenerative disorders, by leveraging trans regulatory signals; (e) experimentally validate core disease genes through collaborations and reveal the trans regulatory networks of core genes; (f) develop statistical approaches to map chromatin-QTLs in bulk and single cell datasets. Applying these methods to large-scale proteome datasets, scRNA-seq, scATAC-seq, CUT&TAG and perturb-seq data of disease relevant tissues and cell types will improve the interpretation of disease-associated variants, reveal disease mechanisms, and unlock the full potential of genomics for translational research. Project Number: 1R35GM161534-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: Xuanyao Liu | Institution: UNIVERSITY OF CHICAGO, CHICAGO, IL | Award Amount: $451,000 | Activity Code: R35 | Study Section: Special Emphasis Panel[ZRG1 MGG-D (55)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11260532