RESUMO
While sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1-M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them. To ensure a valid implementation, the sparse testing methods presented here require BLUEs (or BLUPs) of the lines to be computed at the first stage using an appropriate experimental design and statistical analyses in each location (or environment). The evaluation of the four cultivar allocation methods to environments of the second stage was done with four data sets (two large and two small) under a multi-trait and uni-trait framework. We found that the multi-trait model produced better genomic prediction (GP) accuracy than the uni-trait model and that methods M3 and M4 were slightly better than methods M1 and M2 for the allocation of lines to environments. Some of the most important findings, however, were that even under a scenario where we used a training-testing relation of 15-85%, the prediction accuracy of the four methods barely decreased. This indicates that genomic sparse testing methods for data sets under these scenarios can save considerable operational and financial resources with only a small loss in precision, which can be shown in our cost-benefit analysis.
Assuntos
Modelos Genéticos , Melhoramento Vegetal , Melhoramento Vegetal/métodos , Genoma de Planta/genética , Fenótipo , Genômica , Produtos Agrícolas/genéticaRESUMO
In plant breeding, the need to improve the prediction of future seasons or new locations and/or environments, also denoted as "leave one environment out," is of paramount importance to increase the genetic gain in breeding programs and contribute to food and nutrition security worldwide. Genomic selection (GS) has the potential to increase the accuracy of future seasons or new locations because it is a predictive methodology. However, most statistical machine learning methods used for the task of predicting a new environment or season struggle to produce moderate or high prediction accuracies. For this reason, in this study we explore the use of the partial least squares (PLS) regression methodology for this specific task, and we benchmark its performance with the Bayesian Genomic Best Linear Unbiased Predictor (GBLUP) method. The benchmarking process was done with 14 real datasets. We found that in all datasets the PLS method outperformed the popular GBLUP method by margins between 0% (in the Indica data) and 228.28% (in the Disease data) across traits, environments, and types of predictors. Our results show great empirical evidence of the power of the PLS methodology for the prediction of future seasons or new environments.
RESUMO
Eastern North America is one of at least six regions of the world where agriculture is thought to have arisen wholly independently. The primary evidence for this hypothesis derives from morphological changes in the archaeobotanical record of three important crops--squash, goosefoot and sunflower--as well as an extinct minor cultigen, sumpweed. However, the geographical origins of two of the three primary domesticates--squash and goosefoot--are now debated, and until recently sunflower (Helianthus annuus L.) has been considered the only undisputed eastern North American domesticate. The discovery of 4,000-year-old domesticated sunflower remains from San Andrés, Tabasco, implies an earlier and possibly independent origin of domestication in Mexico and has stimulated a re-examination of the geographical origin of domesticated sunflower. Here we describe the genetic relationships and pattern of genetic drift between extant domesticated strains and wild populations collected from throughout the USA and Mexico. We show that extant domesticates arose in eastern North America, with a substantial genetic bottleneck occurring during domestication.