Abstract
Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.
Highlights
Genomic prediction and selection have become a powerful tool for plant breeders to develop new improved varieties more quickly and efficiently (Crossa et al, 2017)
For the total of 8,555 phenotypic records corresponding to 1,481 hybrids tested in 30 environments that were part of the 2014 and 2015 G2F Maize G×E project, grain yield had a mean of 9065.5 kg ha−1 and a standard deviation (SD) of 2979.2 kg ha−1 (Supplementary Figure 1)
This study showed the practical advantages of considering G×E interactions for improving predictive ability of tested and untested genotypes in observed and unobserved environments via the general combining ability (GCA) and specific combining ability (SCA) models
Summary
Genomic prediction and selection have become a powerful tool for plant breeders to develop new improved varieties more quickly and efficiently (Crossa et al, 2017). An initial set of genotyped and phenotyped lines is needed for model calibration. The predictions of untested genotypes can be performed using their marker profiles and the calibrated/developed model. This methodology offers the opportunity to generate maize hybrids by constructing synthetic genotypes from inbred lines whose marker profiles are available (Kadam et al, 2016; AcostaPech et al, 2017). Model calibration can be performed using phenotypic information from hybrids and inbreds previously tested. Numerous factors affect prediction accuracy, including, among others, the quality of phenotypic and genomic information, the genetic relatedness between training and testing sets, and the trait’s genetic architecture (Jarquin et al, 2014)
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