Jinliang Yang

The paper identifies genetic variants in the shikimate dehydrogenase gene Sad1 that affect enzyme activity and homoserine accumulation in maize kernels through integrated metabolite profiling and genome-scale modeling.

This study examines how domestication and improvement in sorghum have affected genetic load and nitrogen-response variation by integrating genomic and phenotypic data.

This study identifies balancing and positive selection in sunflower genomic regions related to abiotic stress tolerance, finding that modern cultivars have higher deleterious mutation loads and that some ancestral alleles enhance salt tolerance.

The paper investigates how trans-eQTL hotspots contribute to phenotypic heterosis in maize by regulating non-additive gene expression through transcription factors.

This paper estimates heritability and identifies QTL for aerial root traits and yield components in maize, finding genetic overlap with introgression from wild mexicana.

This study uses CRISPR-Cas9 to generate sorghum mutants and analyzes their nitrogen response and growth trajectory using high-throughput phenotyping.

This study uses k-mer GWAS to identify genetic associations with maize traits and shows comparable prediction accuracy to SNP-based methods.

This study applies transcriptome-wide association study (TWAS) to soybean to identify trait-associated genes, finding novel genes and causal variations related to flowering time.

The paper predicts grain yield using non-yield traits and shows that a trait-based model performs comparably to genomic prediction models in cross-environment settings.

The paper investigates how host genotype and source environment influence the microbiome of maize, finding genetic variants linked to microbiome composition and environmental adaptation.

This study develops a microbiome-enabled genomic selection method that improves prediction accuracy for nitrogen-related traits in maize by integrating host SNPs and rhizobiome ASVs.

This study estimates genetic parameters of yield-related traits in maize under different nitrogen conditions, finding that nitrogen-responsive traits are highly polygenic and show negative selection patterns.

This study uses GWAS to identify genetic loci associated with sunflower head diameter, finding 106 and 8 significant SNPs using MLM and farmCPU methods, respectively.

MedDiC estimates high dimensional indirect effects using a difference-in-coefficients approach, showing improved power and speed over existing methods in simulations and biological applications.

The paper identifies a historically balanced locus under recent directional selection in maize linked to nitrogen response traits, revealing genetic changes associated with modern breeding practices.

The paper applies genome-wide mediation analysis to maize data, identifying 736 mediating genes that explain 12.7% of phenotypic variance, with pleiotropic effects observed in 11% of genes.

The paper applies genomic selection to optimize doubled haploid breeding in maize by improving prediction accuracy for DH production and agronomic traits.

This study evaluates statistical methods for GWAS in crops with varying genetic architectures, finding that different approaches are optimal for different trait complexities.

The paper investigates the role of deleterious alleles in trait variation and heterosis in maize by integrating sequence conservation and genomic prediction models.