A central goal of genetics is to understand the links between genetic variation and disease. Recent advances in population-level genetic profiling (such as Genome-wide association studies) have discovered tens of thousands of susceptible genetic loci associated with thousands of complex traits and disease risks. However, these associations are just a starting point to really understanding the links. What are the causal variants and causal genes? What molecular mechanism links the black box from genetic variation to phenotypic variation? How can we translate our existing genetic knowledge into clinically beneficial improvements in patient care? Our lab combines statistical methods and large-scale genomic data to answer these questions. Below are some examples of topics that we are interested in.

Functional Genomics

Regulatory variants and complex trait genetics

Only ~1/3 of GWAS variants colocalize with steady-state cis- expression QTLs, and the majority GWAS variants have unexplained regulatory mechanisms. To interpret and understand trait-associated variants, we work with different types of molecular phenotypes (RNA-seq, ChIP-seq, ATAC-seq, CUT&TAG etc) and develop methods to identify genetic effects of these molecular phenotypes. 

Trans gene regulation

Previously, we proposed that​ trans-genetic effects manifested in gene regulatory networks might explain the omnigenic nature of complex trait. Disruption of regulatory networks is also a key mechanism underlying human diseases. However, very little is known about trans-regulatory mechanism to date, because detecting trans-regulatory effects is very challenging.

I am very interested and passionate about trans gene regulation! Our team take on the challenge of identifying and understanding 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/protein expression data. With high quality trans-eQTLs, we will be able to study the trans regulatory mechanism in more depth. We are also working on combing CRISPR perturbations, RNA-sequencing, and new statistical tools to identify trans regulation signals. 

Natural selection and polygenic score (PRS) portability across 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 prediction accuracy of complex traits in non-European populations is very low. 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 understand the factors (such as sharing of genetic effects, allele frequency differences and selection) leading to low portability of PRS and then develop new strategies to improve genetic prediction and prediction portability.