Jean‐Luc Jannink

The paper investigates the genetic and metabolic basis of the trade-off between carotenoids and dry matter in cassava roots, finding no direct transcriptional co-regulation but suggesting regulatory and metabolic links.

Breedbase 2.0 introduces new features for managing plant breeding data, including improved data handling, user permissions, trial quality control, and tools for transgenics and field layout.

The paper evaluates long-term genomic selection outcomes in Brazilian cassava breeding programs, showing genetic gains and changes in diversity across four selection cycles.

This study compares single-trait and multi-trait genomic prediction models for cassava yield traits, showing that multi-trait models improve predictive accuracy and reduce phenotyping burden.

This study identifies genomic regions associated with carotenoid and dry matter content in cassava using GWAS models, finding six SNPs explaining most of the phenotypic variance.

The paper identifies four genetic variants associated with post-harvest physiological deterioration in cassava using GWAS and links them to genes involved in PPD pathways.

The paper analyzes a cassava introgression from M. glaziovii, finding no direct effect on dry matter but segregation distortion and minor effects on vigor.

This study identifies genetic loci associated with branching traits in cassava, showing their genetic tractability and potential for genomic selection in breeding programs.

DeltaBreed is a breeding data management system that integrates with BrAPI-compliant applications and provides interoperability test cases and future development plans.

The paper presents a pangenome and pantranscriptome of hexaploid oat, describing gene expression diversity and structural genome variation across 33 accessions and six tissues.

The paper evaluates a cassava crop growth model by calibrating it with multi-environment trial data, finding improved predictions but persistent biases in dry and wet environments.

This paper uses bivariate genome-wide association study to investigate the genetic architecture of the negative covariance between total carotenoid and dry matter contents in cassava, finding polygenic mediated pleiotropy rather than genetic linkage.

The paper applies genomic selection to sugar kelp with a biphasic life cycle, predicting breeding values for gametophytes and achieving moderate prediction accuracy for yield and morphology traits.

The paper develops genomic prediction methods for plant metabolites using metabolite GWAS and kernel models, improving accuracy for LC-MS metabolites but not GC-MS.

The paper examines recombination patterns in cassava, finding higher female recombination rates and suppressed recombination in wild introgression segments.

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

This study evaluates multi-omics prediction models for oat traits across environments and populations, showing improved accuracy over genomic prediction with SNP data.

The paper presents a population-based transcriptomics atlas of cassava, identifying differentially expressed genes and gene modules related to disease resistance and physiological traits.

The paper develops genomic mate selection criteria for outbred species, demonstrating accurate prediction of genetic variances and covariances in cassava to improve breeding outcomes.

This study simulates sugar kelp breeding to identify obstacles to genetic gain and suggests strategies to improve breeding efficiency.

This study performs a genome-wide association analysis on cassava to identify genetic loci associated with defensive, agro-morphological, and quality traits, uncovering 41 significant associations and candidate genes.

This study uses metabolomics and genomic approaches to improve prediction of healthful compounds in oat seeds by identifying latent factors associated with lipid metabolism.

This study uses image analysis and genome-wide association to identify QTLs for cassava root shape and size traits, finding lower predictive accuracy compared to other traits but promising for high-throughput methods.

This study analyzes the population genetics of sugar kelp in the Northwest Atlantic using genome-wide markers to assess genetic diversity and detect selection signatures.

The paper simulates the effects of selfing in cassava genomic selection to purge deleterious alleles and finds that selfing reduces genomic prediction accuracy and limits long-term gains.

This study identifies heritable temporal gene expression patterns in oat seeds and links them to metabolomic seed content, providing insights for breeding improved oat varieties.

The paper analyzes recombination patterns in cassava using pedigree and GBS data, finding sexual dimorphism in crossover frequency and introgression effects on recombination.

The paper identifies genetic loci associated with seed fatty acid composition in oat using multivariate GWAS approaches, finding more significant SNPs than univariate analyses.

The paper identifies beneficial introgressed alleles from M. glaziovii in modern cassava germplasm, showing they improve traits like yield and disease tolerance and are under balancing selection.

The paper develops a subfunctionalization epistasis model to assess homeologous gene interactions in allopolyploid wheat and evaluates their contribution to genetic variation.