Haixiao Hu

The paper reports on a competition to predict maize yield using genetic and environmental data, showing that diverse modeling approaches can achieve good results.

MegaLMM improves genomic predictions in new environments by incorporating environmental covariates into a statistical model, showing higher accuracy than traditional methods in maize breeding.

The paper develops genomic prediction methods for plant metabolites using metabolite GWAS and kernel models, improving accuracy for LC-MS metabolites but not GC-MS.

This study evaluates multi-omics prediction models for oat traits across environments and populations, showing improved accuracy over genomic prediction with SNP data.

This study uses metabolomics and genomic approaches to improve prediction of healthful compounds in oat seeds by identifying latent factors associated with lipid metabolism.

This study identifies heritable temporal gene expression patterns in oat seeds and links them to metabolomic seed content, providing insights for breeding improved oat varieties.