What and why we science?

Each one of us has a unique nucleotide sequence of DNA. The genomes of two humans differ by 0.4%, and these genetic variants make us unique and influence our disease risks. Genetic studies systematically identified hundreds of thousands associations of genotypes with phenotypes.

Despite this potential, a significant gap still exists between the discovery of genetic risk loci and understanding of disease biology and improving healthcare. How do these variants collectively alter gene functions to cause complex disease phenotypes?

Our lab’s approach to tackle these challenges is to develop and apply statistical methods that integrate novel multimodal functional genomics data with genetic disease associations. Excitingly, population-scale genomic data types, such as whole-genome sequences with deep clinical data and multimodal molecular features (e.g., gene expression, epigenomics and spatial organization of genome) at single-cell resolution are being generated in recent years. These new modalities will provide us with unprecedented opportunities to connect risk alleles, intermediate molecules, and causal context to disease phenotypes.

About the PI

I am a physician-scientist with an interdisciplinary background in statistical genetics, functional genomics and medicine (rheumatology). I study how genetic variations among us affect our disease susceptibility. I develop statistical genetics tools that combine genetics and novel high-dimensional experimental genomic data. I will use these tools to bridge the current gap between disease risk alleles and causal disease mechanisms, diagnosis, and data-driven treatment strategy, especially in diseases with dysfunctions in immune system. Throughout my clinical training, I strongly thought that there should be better methods to diagnose patients based on precise biological mechanisms with use of molecular data. I hope that our research can someday contribute to deeper understanding of disease mechanisms and improved patient care.