Focus Areas in the Genomic Medicine Cycle
Center for Genomic Medicine
Our lab focuses on developing and applying statistical methods to uncover genetic risk factors for human diseases using large-scale biobanks as well as leveraging high-dimensional omics data to interpret the genetic association discoveries.
- Developing computational and statistical methods for genetic studies in large biobanks and data sets. We have developed widely used statistical programs, including SAIGE, SAIGE-GENE, and GATE, that enable the studies to uncover common and rare genetic variants associated with human disease susceptibility and progression in large-scale biobanks and cohorts. Currently, we are working on extension of the GATE method for studying the effects of rare variations on disease progression.
- Leading phenome-wide genetic studies in large-scale biobanks with EHRs linked to genetic data. Dr. Zhou co-leads the Global Biobank Meta-analysis Initiative (GBMI). She has led the flagship project that meta-analyzes 24 biobanks worldwide with > 2.2 million samples with genotypes across 14 carefully selected endpoints to demonstrate the opportunities and challenges for the collaborative effects of biobanks. She has made major contributions to genome-wide association studies for thousands of human diseases and traits in multiple large biobanks, including the Nord-Trøndelag Health (HUNT) Study, the UK Biobank, and the FinnGen study.
- Identifying novel genetic risk factors and biological insights for human diseases and traits. Dr. Zhou has led multiple large-scale genome-wide association studies (GWAS) for understanding the genetic contributions of complex human diseases and traits, including genetic studies for bicuspid aortic valve and thyroid stimulating hormone. She also makes contributions to several large consortia for complex human diseases, conditions, and traits such as blood lipid levels, atrial fibrillation, bone marrow density, kidney diseases, BMI, height, and myocardial infarction.
- Interpreting the genetic association discoveries for functional follow-up by leveraging multi-omics data. Hundreds of thousands of disease-associated genomic variations have been uncovered within the past two decades, but for most of these discoveries, the molecular and functional consequences remain unknown, representing a fundamental challenge for the translation of these discoveries into biological insight and medical value. Dr. Zhou has earned an K99/R00 award supporting her research aiming to bridge the gap from complex trait association to relevant biological processes through systematic genetic analysis of high-dimensional molecular and cellular datasets such as transcriptomics, epigenomics, proteomics, and metabolomics, and combining multi-omics data to understand the molecular mechanisms underlying human diseases. Currently, our lab is working on developing scalable and efficient methods for eQTL mapping at the single-cell level.