Genomic selection (GS) has proven to be an efficient tool for predicting crop-rank performance of untested genotypes; however, when the traits have intermediate optima (phenology stages), this implementation might not be the most convenient. GS might deliver high-rank correlations but incurring in serious bias. Days to heading (DtH) is a crucial development stage in rice for regional adaptability with a significant impact on yield potential. The objective of this research consisted in develop a novel method that accurately predicts time-related traits such as DtH in unobserved environments. for this, we propose an implementation that incorporates day length information (DL) in the prediction process for two relevant scenarios: CV0, predicting tested genotypes in unobserved environments (C method); and CV00, predicting untested genotypes in unobserved environments (cB method). the use of DL has advantages over weather data since it can be determined in advance just by knowing the location and planting date. the proposed methods showed that DL information significantly helps to improve the predictive ability of DTH in unobserved environments. Under CV0, the C method returned a root-mean-square error (RMSE) of 3.9 days, a Pearson correlation (PC) of 0.98 and the differences between the predicted and observed environmental means (EMD) ranged between-4.95 and 4.67 days. For CV00, the CB method returned an RMSE of 7.3 days, a PC of 0.93 and the EMD ranged between-6.4 and 4.1 days while the conventional GS implementation produced an RMSE of 18.1 days, a PC of 0.41 and the EMD ranged between-31.5 and 28.7 days.
The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture information and its interaction with molecular markers via covariance structures for enhancing predictive ability across breeding scenarios. A total of 797 soybean lines derived from 367 unique bi-parental populations were genotyped using the Illumina BARCSoySNP6K and tested for yield during 5 years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including the GBLUP model (M1), the reaction norm model (M2) including the interaction between molecular markers and the environment (G×E), an extended version of M2 that also includes soil type (S), and the interaction between soil type and molecular markers (G×S) (M3), and a parsimonious version of M3 which discards the G×E term (M4). Four cross-validation scenarios simulating progeny testing and line selection of tested–untested genotypes (TG, UG) in observed–unobserved environments [OE, UE] were implemented (CV2 [TG, OE], CV1 [UG, OE], CV0 [TG, UE], and CV00 [UG, UE]). Across environments, the addition of G×S interaction in M3 decreased the amount of variability captured by the environment (−30.4%) and residual (−39.2%) terms as compared to M1. Within environments, the G×S term in M3 reduced the variability captured by the residual term by 60 and 30% when compared to M1 and M2, respectively. M3 outperformed all the other models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). In addition to the Pearson correlation, other measures were considered to assess predictive ability and these showed that the addition of soil texture seems to structure/dissect the environmental term revealing its components that could enhance or hinder the predictability of a model, especially in the most complex prediction scenario (CV00). Hence, the availability of soil texture information before the growing season could be used to optimize the efficiency of a breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.
The availability of molecular markers has revolutionized conventional ways to improve genotypes in plant and animal breeding through genome-based predictions. Several models and methods have been developed to leverage the genomic information in the prediction context to allow more efficient ways to screen and select superior genotypes. In plant breeding, usually, grain yield (yield) is the main trait to drive the selection of superior genotypes; however, in many cases, the information of associated traits is also routinely collected and it can potentially be used to enhance the selection. In this research, we considered different prediction strategies to leverage the information of the associated traits ([AT]; full: all traits observed for the same genotype; and partial: some traits observed for the same genotype) under an alternative single-trait model and the multi-trait approach. The alternative single-trait model included the information of the AT for yield prediction via the phenotypic covariances while the multi-trait model jointly analyzed all the traits. The performance of these strategies was assessed using the marker and phenotypic information from the Soybean Nested Association Mapping (SoyNAM) project observed in Nebraska in 2012. The results showed that the alternative single-trait strategy, which combines the marker and the information of the AT, outperforms the multi-trait model by around 12% and the conventional single-trait strategy (baseline) by 25%. When no information on the AT was available for those genotypes in the testing sets, the multi-trait model reduced the baseline results by around 6%. For the cases where genotypes were partially observed (i.e., some traits observed but not others for the same genotype), the multi-trait strategy showed improvements of around 6% for yield and between 2% to 9% for the other traits. Hence, when yield drives the selection of superior genotypes, the single-trait and multi-trait genomic prediction will achieve significant improvements when some genotypes have been fully or partially tested, with the alternative single-trait model delivering the best results. These results provide empirical evidence of the usefulness of the AT for improving the predictive ability of prediction models for breeding applications.
28Genomic selection (GS) has proven to be an efficient tool for predicting crop-rank performance 29 of untested genotypes; however, when the traits have intermediate optima (phenology stages) 30 this implementation might not be the most convenient. GS might deliver high rank correlations 31 but incurring in serious bias. Days to heading (DTH) is a crucial development stage in rice for 32 regional adaptability with significant impact in yield potential. We developed two methods that 33 incorporate day length information (DL) in the prediction process for two relevant scenarios: 34 CV0, predicting tested genotypes in unobserved environments (C method); and CV00, predicting 35 untested genotypes in unobserved environments (CB method). The use of DL has advantages 36 over weather data since it can be determined in advance knowing the location and planting date. 37Under CV00, CB method returned a square root of the mean square error (SMSE) of 7.3 days, a 38 Pearson correlation of 0.93 and the environmental mean differences (EMD) ranged between -6.4 39 and 4.1 days while GS method respectively produced 18.1 days, 0.41 and (-31.5 to 28.7 days). 40For CV0, C method respectively returned 3.9 days, 0.98 and (-4.95, 4.67 days). The proposed 41 methods showed that DL information aids to improve significantly the predictive ability of DTH. 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 65phenotypic and/or pedigree information to perform selection of experimental lines for 66 developing superior breeding lines. However, phenotypic selection may not be permissible in 67 many situations due to the high phenotyping costs; also, pedigree selection may not be accurate 68 since recombination effects are not accounted for under this method. 69Genomic selection 3 is an emerging tool that allows screening genotypes from a very large 70 population without having to observe them in fields 4,5 . This method only requires phenotypic and 71 genomic information for calibrating models, then other genotyped candidates are selected based 72 on the predicted values obtained via their marker profiles 6 . In this context, the implementation of 73 genomic tools and resources translates our understanding of the relationship between genotype 74 and phenotype into predicted genetic merits for direct selection. This is highly desirable dealing 75 with those traits that are controlled by a large number of genes with small individual effects, also 76 known as complex traits 5,7 . GS has shown to be an effective tool for plant and animal breeding 77 applications, especially dealing with complex traits 8 . The successfulness of GS predicting 78 phenotypes from genotypic information has been reported in various crop species 9 . 79In GS, the estimated breeding values (EBVs) of complex traits of untested genotypes are 80 computed based on genome-wide marker information. The EBVs can be considered as 81 deviations around a fixed mean (e.g., zero). Once the EBVs are obtained, the breeders may 82 conduct selections based on the ranked values by sele...
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