Frank Technow

An industry perspective on whole genome-informed hybrid maize disease resistance characterization to improve breeding decisions

This paper introduces a genome-informed method to improve disease resistance characterization in hybrid maize using whole genome ordered probit regression, validated with field trial data.

Back to the future 2: the implications of germplasm structure on thebalance between short and long-term genetic gain in a changingtarget population of environments

The paper investigates how environmental changes affect genetic gain in plant breeding by simulating germplasm structure and selection strategies.

Predicting inbred parent synchrony at flowering for maize hybrid seed production by integrating crop growth model with whole genome prediction

Back to the future: Implications of genetic complexity for hybrid breeding strategies

This study uses simulations to show that constraining genetic space and reducing population size in hybrid breeding improves heritable gains, explaining the success of traditional breeding strategies.

Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions

The paper explores using crop models as gene-to-phenotype link functions to optimize selection in plant breeding programs by integrating quantitative genetic theory and trait modeling.

Use of F2 bulks in training sets for genomic prediction of combining ability and hybrid performance

This study evaluates the use of F1:2 bulks for genomic prediction of hybrid performance and combining ability, finding they offer lower accuracy than F1 hybrids but are still viable.

Leveraging biological insight and environmental variation to improve phenotypic prediction: Integrating crop growth models (CGM) with whole genome prediction (WGP)

This paper integrates crop growth models with whole genome prediction to improve phenotypic prediction by accounting for genotype-by-environment-by-management interactions in maize breeding.

Parent-progeny imputation from pooled samples forcost-efficient genotyping in plant breeding

This paper introduces a parent-progeny imputation method using pooled DNA samples and a Hidden Markov Model to reduce genotyping costs in plant breeding.

Integrating crop growth models with whole genome prediction through approximate Bayesian computation

This paper integrates crop growth models with whole genome prediction using approximate Bayesian computation to improve prediction accuracy for non-additive gene effects and genotype by environment interactions.

Using Bayesian multilevel whole-genome regression models for partial pooling of estimation sets in genomic prediction

This paper develops a Bayesian multilevel whole-genome regression model for genomic prediction, showing that partial pooling improves accuracy for small populations but not for large ones.