Cheng Lab at UC Davis
Statistical and Quantitative Genetics
Department of Animal Science, UC Davis
Lab Overview
Our research focuses on the development of statistical, machine learning, and computational methods that bridge the genome and phenome through more accurate, efficient, and biologically interpretable analyses. We leverage genomics, phenomics, and other sources of omics data in various species to better predict desired traits and infer the underlying biological mechanisms. Our work encompasses three main areas:
- Theoretical foundations of quantitative genetics, including statistical models and computational algorithms
- Software development for applying these methods to real-world, large-scale omic datasets
- Applied quantitative genetics, employing various methods to analyze extensive datasets covering a wide range of traits across multiple species
Research Interests
As a quantitative geneticist, I have focused on predicting phenotypes from genotypes for traits of interest—often inferring the underlying biological mechanisms—by utilizing SNPs, empirical phenotypes, and multi-omics data, integrating both public and private data. I have worked to enhance predictive models that can be applied in practical programs for genetic improvement and basic biological research. This approach allows for a comprehensive understanding of how genetic variations influence observable traits, ultimately contributing to advancements in biology.
My comprehensive approach integrates knowledge from different disciplines, including animal and human genetics, evolutionary biology, bioinformatics, and artificial intelligence. By leveraging various data sources and interdisciplinary methodologies, I aim to develop holistic Genome-to-Phenome (G2P) models that address fundamental questions in genetics and genomics while also providing practical solutions to challenges in biology. My work seeks to develop a more comprehensive understanding of how genomes influence phenomes across different contexts and scales, ultimately transforming our approach to genetics and genomics research and its applications in agriculture, medicine, and beyond.