Delphine Madur

The paper introduces a genomic prediction model that forecasts yield performance across environments and future climates and identifies adapted maize landraces by integrating genomic offset and gene diversity.

This study analyzes the genetic diversity of 194 maize landraces from South-West France using a 50K SNP array, identifying two genetic groups and a longitudinal gradient shaped by genetic drift and restricted gene flow.

The paper develops a DNA bulk SNP genotyping method for landraces and applies it to maize, showing accurate allelic frequency estimation and low ascertainment bias.

The paper uses SNP genotyping of DNA pools to identify landraces and genomic regions with untapped genetic diversity for maize breeding.

The paper introduces a GWAS method to identify QTLs with group-specific allele effects in admixed populations, applied to maize flowering time data.

The paper develops a high-throughput method to genotype InDels in maize using an Affymetrix Axiom array, achieving high reliability and reproducibility.

The paper compares genotyping-by-sequencing and microarrays for detecting QTLs, showing they complement each other by tagging different haplotypes and improving detection power and localization.