Peter J. Bradbury

This study examines competitive dynamics in maize varieties and finds limited competition effects, no yield penalty in mixtures, and increased yield stability.

This study evaluates genomic prediction strategies for carotenoid traits in maize, finding that genome-wide markers outperform gene-specific markers and that GP methods are effective across environments.

rTASSEL provides an R interface to TASSEL for association mapping, offering faster performance than other R packages for genomic analyses.

The paper introduces the Practical Haplotype Graph (PHG) platform for storing and using pangenomes, demonstrating its effectiveness in imputing genomes from low-coverage data in crops like maize.

This study identifies lignin-related genes associated with disease resistance in maize using mutant analysis and genome-wide association studies.

The paper demonstrates the effectiveness of the Practical Haplotype Graph for genotype imputation in the highly heterozygous crop cassava, achieving higher accuracy than Beagle.

The paper develops a wheat haplotype graph database for efficient genotype storage and imputation, showing improved accuracy over existing methods.

The paper identifies eleven biosynthetic genes explaining most of the natural variation in carotenoid levels in maize grain through QTL analysis and expression correlation.

The paper constructs a maize pangenome haplotype graph (PHG) to improve genotype imputation accuracy across diverse germplasm using WGS and GBS data.

This paper introduces a sorghum Practical Haplotype Graph (PHG) that enables accurate SNP calling and imputation from low-coverage sequencing data, improving genomic prediction accuracy and reducing genotyping costs.

This study shows dominance and gene annotations improve genomic prediction of agronomic traits in maize hybrids.

This study compares one-stage and two-stage GWAS methods, finding that a weighted two-stage approach reduces bias and improves power, especially in unbalanced data.

This study identifies independent temperate adaptations in maize flowering time across different populations using GWAS and machine learning.