Abstract
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.
Highlights
Genomic selection (GS) has proven to be an efficient tool for predicting crop-rank performance of untested genotypes; when the traits have intermediate optima, this implementation might not be the most convenient
Phenotypic selection may not be permissible in many situations due to the high phenotyping costs and limited availability of resources; pedigree selection may not be accurate since it assumes a constant rate of recombination (50% of the genetic material coming from mother and the remaining 50% coming from father) and this might not be a feasible premise
The box-plot (Supplementary Fig. S1) showed a high heterogeneity between the environments of this study with those with a “late” planting date exhibiting smaller phenotypic variability and shorter occurrence time for Days to heading (DTH) compared with the “early” planting environments
Summary
Genomic selection (GS) has proven to be an efficient tool for predicting crop-rank performance of untested genotypes; when the traits have intermediate optima (phenology stages), this implementation might not be the most convenient. Genomic selection[3] (GS) is an emerging tool that allows screening genotypes from a very large population without having to observe them in fields[4,5] This method only requires phenotypic and genomic information for calibrating models, other genotyped candidates are selected based on the predicted genetic values obtained. Via their marker p rofiles[6] In this context, the implementation of genomic tools and resources translates our understanding of the relationship between genotype and phenotype into predicted genetic merits for direct selection. Genotypes in the lower tail are selected for reducing traits such as lodging, shattering, the content of anti-nutrients, etc In these cases, the GEBVs provide sufficient information when the GEBVs and the direct genetic values (or phenotypic values) exhibit a high correlation. These results highlight the importance of improving the accuracy of predicting untested genotypes in unobserved environments despite the fact of obtaining high correlations
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