Jianming Yu

This study uses CRISPR/Cas9 to edit three flowering repressor genes in tropical maize, reducing photoperiod sensitivity and enabling earlier flowering in both short-day and long-day conditions.

The paper develops BRIDGEcereal, a web tool using unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes, demonstrating its use in wheat candidate genes.

This study evaluates prediction models for tocochromanol content in maize grain using genomic and transcriptomic data, finding that incorporating biological knowledge improves predictive ability.

The paper combines GWAS and TWAS to identify 13 candidate causal genes for tocochromanol levels in maize grain, including five previously unassociated genes.

A multi-genome analysis of maize reveals previously unknown variation in gene content, genome structure, and methylation.

The paper develops methods that integrate day length data with genomic prediction tools to improve prediction of time-related traits like days to heading in unobserved environments.