Peter Kulakow

The paper validates SNP markers for marker-assisted selection in cassava to improve carotenoid and dry matter content, showing high correlation for carotenoids but lower for dry matter.

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 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 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.

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.

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.

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.

The paper analyzes crop breeding networks for cassava, potato, rice, and wheat to evaluate their structure and resilience in deploying resistance genes against diseases.

The paper quantifies non-additive genetic variation in cassava using marker-based matrices and shows total genetic value correlates better with phenotypes than additive value for yield traits.

This study identifies genetic loci associated with cassava mosaic disease resistance using genome-wide association and genomic prediction, revealing a major region and multiple smaller effect regions.