Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction

Abstract

Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models.

High accuracy of genome-enabled prediction of belowground and physiological traits in barley seedlings

“…The MAGIC population examined in this study and used for genome-enabled predictions has been extensively described elsewhere ( Puglisi et al 2021 ). Particularly, the set of 89 MAGIC lines used in the present study ( Supplementary Tables 1 and 2 ) corresponds to the TP previously assembled for fitting multienvironment GP models and genotyped using the barley 50K SNP chip ( Puglisi et al 2021 ). In the present study, 20,426 polymorphic SNPs were used for fitting GP models ( Supplementary File 1 ).…”

“…The set of MAGIC lines was phenotyped in the following site-by-season combinations to examine GY and heading date: Fiorenzuola d’Arda (Italy, 2016, 2017, 2018, 2019) at CREA-Centro di Genomica e Bioinformatica (44°55'39.0′′N 9°53'40.6′′E, 78 m above sea level), Marchouch (Morocco, 2016, 2019) at ICARDA Experimental station (33°36'43.5′′N 6°42'53.0′′W, 390 m above sea level), Adana (36°59′52.9′′N 35°20′28.0′′E, 24 m above sea level), and Konya (37°53′37.9′′N 32°37′26.0′′E, 1,005 m above sea level) (Turkey, 2019). These data, excluding phenotypic data collected in Marchouch during the growing season 2018–2019, are part of the data set previously analyzed ( Puglisi et al 2021 ). All these experiments were conducted following local management practices, except for field trials organized in Fiorenzuola d’Arda during 2017–2018 and 2018–2019 growing seasons as they were conducted using 2 different levels of nitrogen fertilization as previously described ( Puglisi et al 2021 ).…”

“…These data, excluding phenotypic data collected in Marchouch during the growing season 2018–2019, are part of the data set previously analyzed ( Puglisi et al 2021 ). All these experiments were conducted following local management practices, except for field trials organized in Fiorenzuola d’Arda during 2017–2018 and 2018–2019 growing seasons as they were conducted using 2 different levels of nitrogen fertilization as previously described ( Puglisi et al 2021 ). The detailed procedure for analyzing field trial data and deriving the adjusted means of GY is described in the Supplementary text 1 “Procedure for deriving the adjusted means of grain yield.”…”

“…In the present study, 2 different GP models were fitted to SRN combining phenotypic data with genotypic information obtained with the Barley 50 k SNP chip ( Puglisi et al 2021 ). For this trait, GP models were fitted following 2 different hypotheses.…”

“…In the present study, we examined the diversity and distribution of belowground (SRA and SRN) and physiological (TR response to increasing VPD) traits at the seedling stage in an 8-way Multiparent Advanced Generation Inter-crosses (MAGIC) population of winter barley and in its founder parents ( Puglisi et al 2021 ). We correlated phenotypic data of SRA, SRN, and TR scored in controlled conditions with GY obtained in different site-by-season combinations to re-assess the relevance of belowground and physiological traits of seedlings for the uptake of nutrients and water.…”