Genomic prediction of grain yield in a barley MAGIC population modelling genotype per environment interaction

Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following predefined 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 GE 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 environmentalspecific effects outperform simpler multi-environment models

Molecular Cloning and Characterization of Two Genes Encoding Dihydroflavonol-4-Reductase from Populus trichocarpa

Dihydroflavonol 4-reductase (DFR, EC 1.1.1.219) is a rate-limited enzyme in the biosynthesis of anthocyanins and condensed tannins (proanthocyanidins) that catalyzes the reduction of dihydroflavonols to leucoanthocyanins. In this study, two full-length transcripts encoding for PtrDFR1 and PtrDFR2 were isolated from Populus trichocarpa. Sequence alignment of the two PtrDFRs with other known DFRs reveals the homology of these genes. The expression profile of PtrDFRs was investigated in various tissues of P. trichocarpa. To determine their functions, two PtrDFRs were overexpressed in tobacco (Nicotiana tabacum) via Agrobacterium-mediated transformation. The associated color change in the flowers was observed in all 35S:PtrDFR1 lines, but not in 35S:PtrDFR2 lines. Compared to the wild-type control, a significantly higher accumulation of anthocyanins was detected in transgenic plants harboring the PtrDFR1. Furthermore, overexpressing PtrDFR1 in Chinese white poplar (P. tomentosa Carr.) resulted in a higher accumulation of both anthocyanins and condensed tannins, whereas constitutively expressing PtrDFR2 only improved condensed tannin accumulation, indicating the potential regulation of condensed tannins by PtrDFR2 in the biosynthetic pathway in poplars

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

In plants, the study of belowground traits is gaining momentum due to their importance on yield formation and the uptake of water and nutrients. In several cereal crops, seminal root number and seminal root angle are proxy traits of the root system architecture at the mature stages, which in turn contributes to modulating the uptake of water and nutrients. Along with seminal root number and seminal root angle, experimental evidence indicates that the transpiration rate response to evaporative demand or vapor pressure deficit is a key physiological trait that might be targeted to cope with drought tolerance as the reduction of the water flux to leaves for limiting transpiration rate at high levels of vapor pressure deficit allows to better manage soil moisture. In the present study, we examined the phenotypic diversity of seminal root number, seminal root angle, and transpiration rate at the seedling stage in a panel of 8-way Multiparent Advanced Generation Inter-Crosses lines of winter barley and correlated these traits with grain yield measured in different site-by-season combinations. Second, phenotypic and genotypic data of the Multiparent Advanced Generation Inter-Crosses population were combined to fit and cross-validate different genomic prediction models for these belowground and physiological traits. Genomic prediction models for seminal root number were fitted using threshold and log-normal models, considering these data as ordinal discrete variable and as count data, respectively, while for seminal root angle and transpiration rate, genomic prediction was implemented using models based on extended genomic best linear unbiased predictors. The results presented in this study show that genome-enabled prediction models of seminal root number, seminal root angle, and transpiration rate data have high predictive ability and that the best models investigated in the present study include first-order additive additive epistatic interaction effects. Our analyses indicate that beyond grain yield, genomic prediction models might be used to predict belowground and physiological traits and pave the way to practical applications for barley improvement