Many genetic variants are associated with disease risk and understanding the relationship between genetic variation and diseases is crucial to identifying preventative measures and developing treatments.
Guanghao Qi, an assistant professor of biostatistics with the University of Washington School of Public Health, recently received a $945, 000 K01 award from the NHGRI to study a rapidly growing type of data that can be used to better understand these mechanisms and answer questions such as “What are the target genes and cell types for these variants?”
The study will focus on single-cell expression quantitative trait loci (eQTL) data, which characterizes effects of genetic variants on gene expression at the cell level.
“Our study aims to develop methods for integrative analysis of single-cell eQTL data and other types of genetic data. These datasets can reveal various aspects of the disease pathway and collectively paint a comprehensive picture. We are especially interested in applying the methods to autoimmune diseases due to the wide availability of single-cell eQTL data in blood samples,” said Qi.
Qi noted that the wildly diverse nature of the data sets and the fact that the project involves multiple sources of large-scale data present significant obstacles to developing statistical methods to estimate the effect of genes in heterogeneous cell populations.
“This project is motivated by a fundamental problem in human genetics, and presents many statistical analysis challenges due to its complex data structure. For example, modern genetic studies often involve millions of genetic variants and hundreds of thousands of individuals. Cohort-level single-cell data often involve millions of cells and tens of thousands of genes. The sheer scale of data presents challenges for statistical and computational analysis,” said Qi.
Previous genetic studies have provided critical information for the development of drugs, improving their success rate in clinical trials. Qi’s study will characterize the genetic mechanisms of diseases at cell resolution, and provide high-resolution drug target information. The finding can be used to further refine the drug development pipeline, save costs and reduce side effects.
“Our statistical methods will be powerful tools for researchers to gain biological insights from the rapidly growing single-cell eQTL studies, and facilitate discoveries in domains beyond autoimmune diseases. The results will have a direct impact in human genetics and biology and I am excited to see statistics making a real-world impact,” said Qi.
