Key research projects

1. Statistical quantitative genetic analysis of high-throughput phenomics data

We develop and apply statistical methods to handle data generated from high-throughput phenotyping or precision agriculture.


2. Network analysis

Network-based quantitative genetics offers new perspectives and approaches.


3. Genomic connectedness

We showed that genetic connectedness can be used in the context of whole-genome prediction.


4. Medical Subject Headings (MeSH) analysis

We pioneered MeSH analysis in agricultural species.


5. Biological information driven whole-genome prediction

Annotation-based whole-genome regression methods can partition the source of genomic prediction accuracy.


6. Machine learning

Application of machine learning and data science to genetics.


7. Software development

We are interested in developing modern interactive visualization tools in agriculture science.