Jean‐Luc Jannink

The paper introduces a low-resolution epistasis mapping method to detect chromosome arm interactions in allohexaploid wheat, finding significant epistatic interactions for 2% of tested pairs.

The paper presents a statistical framework to predict subgenome additive and interaction effects in allohexaploid wheat, showing reliable variance estimation and comparable predictive ability to existing methods.

The paper identifies enhancer regions in cassava using RNA polymerase mapping and shows they are enriched with causal variants affecting fitness and root composition.

This study identifies genomic regions associated with CBSD resistance in cassava using genome-wide association mapping and genomic prediction models.

This study compares univariate and multivariate genomic prediction models for cassava traits, finding that multivariate models improve prediction accuracy in both single and multi-environment evaluations.

The paper evaluates genomic prediction models for Cassava Brown Streak Disease using transcriptomics and GWAS data, finding only marginal improvements in prediction accuracy.

This study evaluates genomic selection accuracy in cassava breeding across different prediction models and scenarios, finding that training population size significantly impacts accuracy.

This paper identifies genetic markers for dry matter and carotenoid content in cassava, finding physical linkage rather than pleiotropy explains their negative correlation.