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Villiers, K.

Publications and source records attributed to Villiers, K..

3 recordsLinked to original sources

Comparison of localGEBV and Optimal Haplotype Stacking Fitness Functions using a Novel R Package: HapSelect

Haplotype-based breeding strategies have emerged as promising approaches to maximize long-term genetic gain by identifying complementary parental combinations while maintaining genetic diversity. However, these methods typically require phased genotypes and more intensive workflow pipelines and skillsets. We developed a novel local genomic estimated breeding value (localGEBV) fitness function with similar intent to the optimal haplotype stacking (OHS) framework fitness function and implemented both in the novel R package, HapSelect. Our aim was to evaluate whether phased haplotypes provide additional benefit over the more easily available dosage-based unphased genotypes in highly inbred crops. A subset of bread wheat nested association mapping (NAM) population comprising 444 lines genotyped with 6,054 DArT-Seq markers was analysed. Marker effects were estimated using rrBLUP, localGEBV and haplotype effects were calculated across linkage disequilibrium-defined haploblocks, and genetic algorithms (GA) were used to identify optimal sets of 30 founders using either a localGEBV derived fitness function with unphased, dosage inputs or the OHS fitness function with phased inputs. Selected parental sets were compared with conventional truncation selection (TS) through 150 generations of forward simulation. The OHS fitness function achieved a marginally greater optimized ultimate GEBV than the localGEBV fitness function during GA optimization, with only 18 of the 30 selected founders overlapped between the two methods. Despite these differences, forward simulations demonstrated nearly identical long-term genetic gain for localGEBV and OHS-selected founders, with both approaches outperforming conventional truncation selection by maintaining greater genetic diversity and delaying the genetic plateau. The minimal difference between localGEBV and OHS is likely attributable to the high homozygosity of the population, where localGEBV and haplotype effects are nearly confounded. These results demonstrate that dosage-based localGEBV provides a practical alternative to phased haplotype approaches for parent selection in inbred crops, substantially simplifying genomic workflows while maintaining long-term breeding performance. Future work should evaluate these methods in more diverse inbred populations and outbred species, where great haplotypic diversity may increase the advantage of true haplotype-based optimizations.

genetics↗

Maintaining long-term genetic gain using a segment-stacking algorithm

Loss of genetic diversity in elite crop breeding pools can severely limit long-term genetic gains, and limit ability to make gains in new traits, like heat tolerance, that are becoming important as the climate changes. Here we investigate and propose potential breeding program applications of optimal haplotype selection (OHS), a selection method which retains useful diversity in the population. OHS selects sets of candidates containing, between them, haplotype segments with very high segment breeding values for the target trait. We compared the performance of OHS, the similar method optimal population value (OPV), truncation selection on genomic estimated breeding values (GEBVs), and optimal cross selection (OCS) in stochastic simulations of recurrent selection on founder wheat genotypes. After 100 generations of inter-crossing and selection, OCS and truncation selection had exhausted the genetic diversity, while considerable diversity remained in the OHS population. Gain under OHS in these simulations ultimately exceeded that from truncation selection or OCS. OHS achieved faster gains when the population size was small, with many progeny per cross. A promising hybrid strategy, involving a single cycle of OHS selection in the first generation followed by recurrent truncation selection, substantially improved long term gain compared with truncation selection, and performed similarly to OCS. The results of this study provide initial insights into where OHS could be incorporated into breeding programs. Core IdeasO_LIWe investigate potential uses for a haplotype-stacking strategy, optimal haplotype selection (OHS) C_LIO_LISeveral selection strategies were compared in stochastic simulations of recurrent selection in wheat C_LIO_LIOHS maintained more useful diversity than optimal cross selection or truncation-based genomic selection C_LIO_LIRates of gain from OHS are competitive in small populations C_LIO_LIOne generation of OHS in a truncation selection program can increase short and long term genetic gain C_LI Plain language summaryBreeders use selection strategies based on genetic and phenotypic information to choose parents that will improve agriculturally-relevant traits (eg. grain yield) in their progeny. Generally, this involves estimating breeding values (scores) for each candidate parent. This study investigated an alternative haplotype stacking approach called Optimal Haplotype Selection (OHS), which instead estimates breeding values for each unique genome segment in the population, then selects a group of parents who, between them, carry the haplotypes with the highest estimated breeding value at each chromosomal segment. In simulations, OHS gives improvements close to existing methods when populations are small, and outperforms them in the long term (100+ generations). Using just one generation of OHS boosts the performance of other methods in the short and long term. Breeders might consider adopting haplotype stacking in their programs, once techniques to do so are established.

genetics↗

genomicSimulation: fast R functions for stochastic simulation of breeding programs

Simulation tools are key to designing and optimising breeding programs that are multi-year, high-effort endeavours. Tools that operate on real genotypes and integrate easily with other analysis software are needed to guide users to crossing decisions that best balance genetic gains and diversity to maintain gains in the future. This paper presents genomicSimulation, a fast and flexible tool for the stochastic simulation of crossing and selection on real genotypes. It is fully written in C for high execution speeds, has minimal dependencies, and is available as an R package for integration with Rs broad range of analysis and visualisation tools. Comparisons of a simulated recreation of a breeding program to the real data shows that the tools simulated offspring correctly show key population features, such as linkage disequilibrium patterns and genomic relationships. Both versions of genomicSimulation are freely available on GitHub: The R package version at https://github.com/vllrs/genomicSimulation/ and the C library version at https://github.com/vllrs/genomicSimulationC/

genetics↗