Biology subjects

Werner, C. R.

Publications and source records attributed to Werner, C. R..

6 recordsLinked to original sources

bioRxiv2025

Early-stage sparse testing strategies to increase genetic gain in plant breeding programmes

Early-stage sparse testing can significantly increase genetic gain in plant breeding programmes, facilitating the development of varieties with high and stable performance across farmers fields. This is achieved through (1) increased selection accuracy, enabled by broader sampling of trial sites across the Target Population of Environments (TPE), and (2) increased selection intensity by testing more selection candidates at a reduced replication rate. Early-stage agronomic testing typically involves evaluating a large number of selection candidates at one or a few experimental sites. Although these sites may poorly represent the TPE, strong selection pressure is generally applied. However, if the genetic correlation between performance at the experimental sites and in the TPE is low, most of the genetic gain achieved through selection will not be expressed under farmers conditions. Sparse testing addresses this challenge by using farm-as-incomplete-block designs, in which different subsets of selection candidates are evaluated across sites. By leveraging a genomic relationship matrix (GRM) to connect genotypes across environments, sparse testing enables early-stage multi-environment trials with broader coverage of the TPE. Here, we used stochastic simulation to compare various partially replicated sparse testing strategies to three fully replicated conventional early-stage testing strategies, with and without GRM. All sparse testing strategies achieved substantially higher and more stable genetic gain than the conventional strategies. Our results show that sparse testing provides breeders with a powerful and flexible framework to rethink early-stage trials and design cost-effective, multi-location experiments that lay the foundation for increased genetic gains in farmers fields. Key messageEarly-stage sparse testing can significantly increase genetic gain by (1) improving selection accuracy through broader sampling of the Target Population of Environments (TPE), and (2) increasing selection intensity.

genetics↗

bioRxiv2025

Sparse testcrossing for early-stage genomic prediction of general combining ability to increase genetic gain in maize hybrid breeding programs

1Sparse testcrossing is an effective strategy for increasing both short- and long-term genetic gain in hybrid breeding programs. Maize hybrid breeding programs aim to develop new hybrid varieties by crossing genetically distinct parents from different heterotic pools, exploiting heterosis for improved performance. The programs typically consist of two main components: population improvement and product development. The population improvement component aims to enhance the heterotic pools through reciprocal recurrent selection based on general combining ability (GCA). However, especially in the early stages of testing, evaluating large numbers of hybrid combinations to estimate GCA is impractical due to considerable logistical challenges and costs. Therefore, breeders often evaluate the initial population of selection candidates using only a single tester to narrow down the candidate pool before further evaluation. Using a single tester, however, may not adequately represent the heterotic pool, leading to inaccurate GCA estimates and suboptimal selection decisions. To address this, we propose sparse testcrossing for early-stage testing, where subsets of candidate genotypes are testcrossed with different testers, connected through a genomic relationship matrix. We conducted stochastic simulations to compare various sparse testcrossing designs with a conventional testcross strategy using a single tester over 15 cycles of reciprocal recurrent genomic selection. Our results show that using 3-5 testers, sparsely distributed among full-sibs, sparse testcrossing offers breeders a practical balance between simple testcross designs, resource efficiency, and increased prediction accuracy for GCA, ultimately resulting in increased rates of genetic gain. Key messageSparse testcrossing with 3-5 testers enhances genetic gain in hybrid breeding programs, offering a practical balance of simple testcross designs, resource efficiency, and increased prediction accuracy for general combining ability.

genomics↗

bioRxiv2024

Reviving the Desired Gains Index: An optimal solution for parent selection in public plant breeding programs

The Desired Gains Index is an optimal solution for parent selection in public plant breeding programs. It enables breeders to quantify their breeding objectives in terms of desired genetic gains and facilitates the efficient simultaneous improvement of multiple quantitative traits in a breeding population without the need for economic weights. We deliberately chose the term "optimal" here, which is typically associated with the profit-oriented selection indices commonly used in animal breeding, such as the Smith-Hazel Index. Our intention is to refute the perception that the Desired Gains Index is less efficient than the Smith-Hazel Index since both approaches maximise expected genetic gains in proportion to the breeding objective. To achieve this, we first review the relationship between the Desired Gains Index and the Smith-Hazel Index to show that desired gains are actually a form of economic weighting expressed as an improvement ratio for the traits under selection. We then present a general form of the Desired Gains Index to leverage best linear unbiased prediction (BLUP), which enables seamless integration of pedigree or genomic relationship information (e.g., genomic estimated breeding values) between the selection candidates and other related individuals. Finally, using stochastic simulation, we compare the performance of different parent selection strategies, including index selection, independent culling, and selection of extreme genotypes. The objective of these demonstrations is to convey the potential impact and benefits of the Desired Gains Index to a broader audience without the need for a deep understanding of selection theory and the equations presented here.

genetics↗

bioRxiv2022

Clonal breeding strategies to harness heterosis: insights from stochastic simulation

To produce genetic gain, hybrid crop breeding can change the additive as well as dominance genetic value of populations, which can lead to utilization of heterosis. A common hybrid breeding strategy is reciprocal recurrent selection (RRS), in which parents of hybrids are typically recycled within pools based on general combining ability (GCA). However, the relative performance of RRS and other possible breeding strategies have not been thoroughly compared. RRS can have relatively increased costs and longer cycle lengths which reduce genetic gain, but these are sometimes outweighed by its ability to harness heterosis due to dominance and increase genetic gain. Here, we used stochastic simulation to compare gain per unit cost of various clonal breeding strategies with different amounts of population inbreeding depression and heterosis due to dominance, relative cycle lengths, time horizons, estimation methods, selection intensities, and ploidy levels. In diploids with phenotypic selection at high intensity, whether RRS was the optimal breeding strategy depended on the initial population heterosis. However, in diploids with rapid cycling genomic selection at high intensity, RRS was the optimal breeding strategy after 50 years over almost all amounts of initial population heterosis under the study assumptions. RRS required more population heterosis to outperform other strategies as its relative cycle length increased and as selection intensity decreased. Use of diploid fully inbred parents vs. outbred parents with RRS typically did not affect genetic gain. In autopolyploids, RRS typically was not beneficial regardless of the amount of population inbreeding depression. Key MessageReciprocal recurrent selection sometimes increases genetic gain per unit cost in clonal diploids with heterosis due to dominance, but it typically does not benefit autopolyploids.

genetics↗

bioRxiv2020

Genomic selection strategies for clonally propagated crops

For genomic selection in clonal breeding programs to be effective, crossing parents should be selected based on genomic predicted cross performance unless dominance is negligible. Genomic prediction of cross performance enables a balanced exploitation of the additive and dominance value simultaneously. Here, we compared different strategies for the implementation of genomic selection in clonal plant breeding programs. We used stochastic simulations to evaluate six combinations of three breeding programs and two parent selection methods. The three breeding programs included i) a breeding program that introduced genomic selection in the first clonal testing stage, and ii) two variations of a two-part breeding program with one and three crossing cycles per year, respectively. The two parent selection methods were i) selection of parents based on genomic estimated breeding values, and ii) selection of parents based on genomic predicted cross performance. Selection of parents based on genomic predicted cross performance produced faster genetic gain than selection of parents based on genomic estimated breeding values because it substantially reduced inbreeding when the dominance degree increased. The two-part breeding programs with one and three crossing cycles per year using genomic prediction of cross performance always produced the most genetic gain unless dominance was negligible. We conclude that i) in clonal breeding programs with genomic selection, parents should be selected based on genomic predicted cross performance, and ii) a two-part breeding program with parent selection based on genomic predicted cross performance to rapidly drive population improvement has great potential to improve breeding clonally propagated crops.

genetics↗

bioRxiv2020

A Two-Part Strategy using Genomic Selection in Hybrid Crop Breeding Programs

Hybrid crop breeding programs using a two-part strategy produced the most genetic gain, but a maximum avoidance of inbreeding crossing scheme was required to increase long-term genetic gain. The two-part strategy uses outbred parents to complete multiple generations per year to reduce the generation interval of hybrid crop breeding programs. The maximum avoidance of inbreeding crossing scheme manages genetic variance by maintaining uniform contributions and inbreeding coefficients across all crosses. This study performed stochastic simulations to quantify the potential of a two-part strategy in combination with two crossing schemes to increase the rate of genetic gain in hybrid crop breeding programs. The two crossing schemes were: (i) a circular crossing scheme, and (ii) a maximum avoidance of inbreeding crossing scheme. The results from this study show that the implementation of genomic selection increased the rate of genetic gain, and that the two-part hybrid crop breeding program generated the highest genetic gain. This study also shows that the maximum avoidance of inbreeding crossing scheme increased long-term genetic gain in two-part hybrid crop breeding programs completing multiple selection cycles per year, as a result of maintaining higher levels of genetic variance over time. The flexibility of the two-part strategy offers further opportunities to integrate new technologies to further increase genetic gain in hybrid crop breeding programs, such as the use of outbred training populations. However, the practical implementation of the two-part strategy will require the development of bespoke transition strategies to fundamentally change the data, logistics, and infrastructure that underpin hybrid crop breeding programs. Key messageHybrid crop breeding programs using a two-part strategy produced the most genetic gain by using outbred parents to complete multiple generations per year. However, a maximum avoidance of inbreeding crossing scheme was required to manage genetic variance and increase long-term genetic gain.

genetics↗