RESUMO

KEY MESSAGE: Reciprocal recurrent selection sometimes increases genetic gain per unit cost in clonal diploids with heterosis due to dominance, but it typically does not benefit autopolyploids. Breeding can change the dominance as well as additive genetic value of populations, thus utilizing 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. However, the relative performances of RRS and other breeding strategies have not been thoroughly compared. RRS can have relatively increased costs and longer cycle lengths, but these are sometimes outweighed by its ability to harness heterosis due to dominance. Here, we used stochastic simulation to compare genetic gain per unit cost of RRS, terminal crossing, recurrent selection on breeding value, and recurrent selection on cross performance considering different amounts of population 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. Diploid RRS required more population heterosis to outperform other strategies as its relative cycle length increased and as selection intensity and time horizon decreased. The optimal strategy depended on selection intensity, a proxy for inbreeding rate. Use of diploid fully inbred parents vs. outbred parents with RRS typically did not affect genetic gain. In autopolyploids, RRS typically did not outperform one-pool strategies regardless of the initial population heterosis.

Assuntos

RESUMO

Genomic prediction models are showing their power to increase the rate of genetic gain by boosting all the elements of the breeder's equation. Insight into the factors associated with the successful implementation of this prediction model is increasing with time but the technology has reached a stage of acceptance. Most genomic prediction models require specialized computer packages based mainly on linear models and related methods. The number of computer packages has exploded in recent years given the interest in this technology. In this chapter, we explore the main computer packages available to fit these models; we also review the special features, strengths, and weaknesses of the methods behind the most popular computer packages.

Assuntos

RESUMO

In all breeding programs, the decision about which individuals to select and intermate to form the next selection cycle is crucial. The improvement of genetic stocks requires considering multiple traits simultaneously, given that economic value and net genetic merits depend on many traits; therefore, with the advance of computational and statistical tools and genomic selection (GS), researchers are focusing on multi-trait selection. Selection of the best individuals is difficult, especially in traits that are antagonistically correlated, where improvement in one trait might imply a reduction in other(s). There are approaches that facilitate multi-trait selection, and recently a Bayesian decision theory (BDT) has been proposed. Parental selection using BDT has the potential to be effective in multi-trait selection given that it summarizes all relevant quantitative genetic concepts such as heritability, response to selection and the structure of dependence between traits (correlation). In this study, we applied BDT to provide a treatment for the complexity of multi-trait parental selection using three multivariate loss functions (LF), Kullback-Leibler (KL), Energy Score, and Multivariate Asymmetric Loss (MALF), to select the best-performing parents for the next breeding cycle in two extensive real wheat data sets. Results show that the high ranking lines in genomic estimated breeding value (GEBV) for certain traits did not always have low values for the posterior expected loss (PEL). For both data sets, the KL LF gave similar importance to all traits including grain yield. In contrast, the Energy Score and MALF gave a better performance in three of four traits that were different than grain yield. The BDT approach should help breeders to decide based not only on the GEBV per se of the parent to be selected, but also on the level of uncertainty according to the Bayesian paradigm.

Assuntos

RESUMO

Formalized breeding schemes are a key component of breeding program design and a gateway to conducting plant breeding as a quantitative process. Unfortunately, breeding schemes are rarely defined, expressed in a quantifiable format, or stored in a database. Furthermore, the continuous review and improvement of breeding schemes is not routinely conducted in many breeding programs. Given the rapid development of novel breeding methodologies, it is important to adopt a philosophy of continuous improvement regarding breeding scheme design. Here, we discuss terms and definitions that are relevant to formalizing breeding pipelines, market segments and breeding schemes, and we present a software tool, Breeding Pipeline Manager, that can be used to formalize and continuously improve breeding schemes. In addition, we detail the use of continuous improvement methods and tools such as genetic simulation through a case study in the International Institute of Tropical Agriculture (IITA) Cassava east-Africa pipeline. We successfully deploy these tools and methods to optimize the program size as well as allocation of resources to the number of parents used, number of crosses made, and number of progeny produced. We propose a structured approach to improve breeding schemes which will help to sustain the rates of response to selection and help to deliver better products to farmers and consumers.

RESUMO

The cranberry (Vaccinium macrocarpon Ait.) is a North American fruit crop domesticated less than 200 years ago. The USDA began the first cranberry breeding program in response to false-blossom disease in 1929, but after the first generation of cultivars were released in the 1950s, the program was discontinued. Decades later, renewed efforts for breeding cranberry cultivars at Rutgers University and the University of Wisconsin yielded the first modern cultivars in the 2000's. Phenotypic data suggests that current cultivars have changed significantly in terms of fruiting habits compared to original selections from endemic populations. However, due to the few breeding and selection cycles and short domestication period of the crop, it is unclear how much cultivated germplasm differs genetically from wild selections. Moreover, the extent to which selection for agricultural superior traits has shaped the genetic and phenotypic variation of cranberry remains mostly obscure. Here, a historical collection composed of 362 accessions, spanning wild germplasm, first-, second-, and third-generation selection cycles was studied to provide a window into the breeding and domestication history of cranberry. Genome-wide sequence variation of more than 20,000 loci showed directional selection across the stages of cranberry domestication and breeding. Diversity analysis and population structure revealed a partially defined progressive bottleneck when transitioning from early domestication stages to current cranberry forms. Additionally, breeding cycles correlated with phenotypic variation for yield-related traits and anthocyanin accumulation, but not for other fruit metabolites. Particularly, average fruit weight, yield, and anthocyanin content, which were common target traits during early selection attempts, increased dramatically in second- and third-generation cycle cultivars, whereas other fruit quality traits such as Brix and acids showed comparable variation among all breeding stages. Genome-wide association mapping in this diversity panel allowed us to identify marker-trait associations for average fruit weight and fruit rot, which are two traits of great agronomic relevance today and could be further exploited to accelerate cranberry genetic improvement. This study constitutes the first genome-wide analysis of cranberry genetic diversity, which explored how the recurrent use of wild germplasm and first-generation selections into cultivar development have shaped the evolutionary history of this crop species.

RESUMO

Image-based phenotyping methodologies are powerful tools to determine quality parameters for fruit breeders and processors. The fruit size and shape of American cranberry (Vaccinium macrocarpon L.) are particularly important characteristics that determine the harvests' processing value and potential end-use products (e.g., juice vs. sweetened dried cranberries). However, cranberry fruit size and shape attributes can be difficult and time consuming for breeders and processors to measure, especially when relying on manual measurements and visual ratings. Therefore, in this study, we implemented image-based phenotyping techniques for gathering data regarding basic cranberry fruit parameters such as length, width, length-to-width ratio, and eccentricity. Additionally, we applied a persistent homology algorithm to better characterize complex shape parameters. Using this high-throughput artificial vision approach, we characterized fruit from 351 progeny from a full-sib cranberry population over three field seasons. Using a covariate analysis to maximize the identification of well-supported quantitative trait loci (QTL), we found 252 single QTL in a 3-year period for cranberry fruit size and shape descriptors from which 20% were consistently found in all years. The present study highlights the potential for the identified QTL and the image-based methods to serve as a basis for future explorations of the genetic architecture of fruit size and shape in cranberry and other fruit crops.