Alain Charcosset

This study identifies heterotic group-specific haplotypes and shows that residual inbreeding from commercial hybrids negatively impacts maize grain yield.

This paper introduces CPS-MT, a breeding strategy that improves a target trait while maintaining essential traits within ranges by evaluating cross-pairs based on predicted progeny distributions.

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 integrates multi-omics data to identify genes influencing yield in maize, showing improved resolution and predictive power compared to GWAS.

The paper uses multi-omics data from controlled conditions to predict platform and field traits in maize hybrids, showing higher predictive abilities than GBLUP for platform traits but only slight improvements for field traits.

The paper introduces qch_copula, a composite hypothesis testing method for omics data that controls Type I error and detects joint associations, validated on human and plant genetics data.

The paper introduces a method to detect directional and non-directional epistasis in bi-parental populations using genomic data and applies it to maize and soybean populations, finding significant epistatic effects.

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 analyzes diversity patterns and selective sweeps in a Southeast European maize panel combined with two West European panels to identify genomic regions under selection for adaptation to SEE conditions.

This study identifies environment-dependent SNP-protein co-expression and trait associations in maize under drought conditions, revealing pleiotropic loci and candidate genes for drought tolerance.

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

The paper proposes optimal cross selection strategies to broaden genetic diversity in breeding programs while maintaining short-term performance.

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

This study improves genetic gain in breeding programs by incorporating within family variance into optimal cross selection, enhancing both short and long term gains.

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.

The paper introduces UCPC to evaluate genetic gain and parental contributions in multi-parental crosses for plant breeding, validated in maize introgression scenarios.