Tristan Mary‐Huard

The paper investigates how genetic variation in common bean affects plant-plant interactions with maize, showing that effects depend on neighbor identity and environmental context.

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 proposes a nonlinear two-step model integrating proxy intermediate omics traits to improve phenotypic prediction in soybean, showing better accuracy than linear models.

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

This study identifies 44 candidate genes for temperature-stable resistance to bacterial wilt in tomato using a new panel and two-reference genome approach.

This paper evaluates deep learning's ability to predict genetic values in crossbred populations by capturing heterosis, finding it improves performance when heterosis is present.

WISER improves breeding value estimation by addressing population structure and genotype-environment confounding through spectral whitening, outperforming LS-means and BLUP across multiple species and traits.

This paper introduces an efficient framework for ridge RDA to analyze high-dimensional omics data by addressing computational challenges through SVD and reduced memory storage.

The paper reports on a competition to predict maize yield using genetic and environmental data, showing that diverse modeling approaches can achieve good results.

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.

This paper introduces a hybrid DLGBLUP model that integrates deep learning with GBLUP to improve prediction accuracy by capturing nonlinear genetic relationships between 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.

This paper develops two new GWAS models, SNPxGB and HBxGB, to detect QTLs interacting with discrete and continuous population structures without requiring prior population structure information.

The paper introduces a genome-wide segmentation method to detect selection footprints using FST, demonstrating its efficiency and accuracy in animal and human population genetics.

The paper introduces a fast algorithm for jointly inferring FST parameters and genetic structure trees from allele count data, applied to human populations and individuals.

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.

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.