XUANYAO LIU LAB

Trans- gene regulation and its role in complex diseases

Trans gene regulation, which is the regulation of gene expression by variants that are far away, is a major component of gene regulation and complex trait genetics. Trans genetic effects manifested in gene regulatory networks explain the highly polygenic (omnigenic) architecture of complex traits. It was also estimated that more than 70% of trait variance comes from trans regulation of core genes. However, detecting trans-regulatory effects is very challenging, and very little is known about trans-regulatory mechanism to date.

We take on the challenge to identify and understand trans gene regulation by using statistical/computational and experimental approaches. We developed statistical methods that drastically improved the detection powers of trans genetic effects in gene expression data. We are also working on  combing CRISPR perturbations, RNA-sequencing and new statistical tools to identify trans regulation signals.

Understanding the sharing of genetic effects across different ancestries

Genome-wide association studies (GWAS) are overwhelmingly biased toward European ancestries. However, how well is the genetic knowledge gained from European GWAS transferrable to individuals of other ancestries? In recent years, nearly all studies agree that predicting complex traits in non-European populations has very low accuracy. This is known as the low cross-ancestry portability of the polygenic prediction problem, which highlighted the need to understand the sharing and population-specific genetic effects of disease and complex traits.

We answer important questions on the sharing of genetic effects across ancestries and their impact on cross-ancestry genetic prediction. The goal is to develop new strategies to improve genetic prediction in Non-European populations.

Genetics of cancer progression

In collaboration with cancer biologists and clinicians from the University of Chicago, we are interested in understanding the genetics of cancer progression. The key question is: what are the driving mutations or expression changes that lead to tumors? With a comprehensive collection of whole-exome sequencing, RNA sequencing and mitochondrial DNA sequencing data and statistical tools, we will investigate the problem in depth. For example, with data from sinonasal squamous cell carcinoma (SNSCC), a rare tumor of the upper respiratory tract, and inverted sinonasal papilloma (IP), a locally aggressive, benign epithelial neoplasm arising in the paranasal sinuses and transforming to invasive SNSCC in 10% to 25% of cases, we aim to understand the molecular relationship between IP and SNSCC and what the critical genetic alterations that transforms to SNSCC are.