Shizhong Xu

EMmvGWAS improves multivariate GWAS efficiency by using an EM algorithm to reduce computation time while maintaining statistical power.

This paper introduces a linear mixed model to detect QTL-by-environment interactions by controlling polygenic background effects, showing improved power over existing methods in both simulations and rice and barley data.

This paper introduces an improved linear mixed model for multivariate genome-wide association studies that handles incomplete data and reduces computational complexity.

The paper introduces a rapid mixed model for genome-wide epistatic association analysis that handles multiple polygenic effects and demonstrates its efficiency on yeast and human datasets.

The paper develops efficient multivariate association algorithms for longitudinal GWAS, improving statistical power and computational speed for analyzing large-scale longitudinal data.

This study identifies 16 QTL associated with domestication-related traits in cowpea and locates candidate genes, providing genomic insights for breeding programs.

The paper develops a MAGIC population for cowpea to dissect genetic traits and improve breeding, identifying trait-associated SNPs and recombination patterns.