Correction to: Genetic diversity and genetic variation in morpho-physiological traits to improve heat tolerance in Spring barley.

The correct spelling of the third author's surname is Elakhdar and his current address is Agri-Bio Research Laboratory, Kyushu University, Motooka 744, Japan. The correct address for the fourth author is Agri-Bio Research Laboratory, Kyushu University, Motooka 744, Japan.

Genetic architecture of common bunt resistance in winter wheat using genome-wide association study.

BACKGROUND: Common bunt (caused by Tilletia caries and T. foetida) has been considered as a major disease in wheat (Triticum aestivum) following rust (Puccinia spp.) in the Near East and is economically important in the Great Plains, USA. Despite the fact that it can be easily controlled using seed treatment with fungicides, fungicides often cannot or may not be used in organic and low-input fields. Planting common bunt resistant genotypes is an alternative. RESULTS: To identify resistance genes for Nebraska common bunt race, the global set of differential lines were inoculated. Nine differential lines carrying nine different genes had 0% infected heads and seemed to be resistant to Nebraska race. To understand the genetic basis of the resistance in Nebraska winter wheat, a set of 330 genotypes were inoculated and evaluated under field conditions in two locations. Out of the 330 genotypes, 62 genotypes had different degrees of resistance. Moreover, plant height, chlorophyll content and days to heading were scored in both locations. Using genome-wide association study, 123 SNPs located on fourteen chromosomes were identified to be associated with the resistance. Different degrees of linkage disequilibrium was found among the significant SNPs and they explained 1.00 to 9.00% of the phenotypic variance, indicating the presence of many minor QTLs controlling the resistance. CONCLUSION: Based on the chromosomal location of some of the known genes, some SNPs may be associated with Bt1, Bt6, Bt11 and Bt12 resistance loci. The remaining significant SNPs may be novel alleles that were not reported previously. Common bunt resistance seems to be an independent trait as no correlation was found between a number of infected heads and chlorophyll content, days to heading or plant height.

Genetic diversity and genetic variation in morpho-physiological traits to improve heat tolerance in Spring barley.

Heat stress is one of the abiotic stresses that limit the production and productivity of barley. Understanding the genetic variation, changes in physiological processes and level of genetic diversity existing among genotypes are needed to produce new cultivars not only having a high tolerance to heat stress, but also displaying high yield. To address this challenge, a set of 60 highly homozygous, diverse barley genotypes were evaluated under normal and heat stress conditions in two seasons of 2014/2015 and 2015/2016. Seedling vigor (SV) as a morphological trait was visually scored under normal conditions. Plant height (Ph), days to flowering (DOF), 1000-kernel weight (TKW), grain yield per spike (GYPS), yield per plot (YPP) and biological yield (BY) were measured. Moreover, proline content (ProC), soluble carbohydrate content (SCC), starch content, soluble protein (SP), and amino acid (AA) content as physiological parameters were analyzed from the grains. High genetic variation was observed among genotypes for all traits scored in this study. All traits had high broad-sense heritability estimates ranging from 0.59 (SV) to 0.97 (TKW) for yield traits. Seedling vigor was significantly correlated with all yield traits under both conditions. Among all physiological traits, the increase in ProC and reduction in starch content due to heat stress had significant correlations with the reduction due to heat stress in YPP, GYPS, TKW, and BY. Furthermore, the genetic diversity based on genetic distance (GD) among genotypes was investigated using 206 highly polymorphic SSR marker alleles. The GD ranged from 0.70 to 0.98 indicating that these genotypes are highly and genetically dissimilar. The combination of analyses using molecular markers, genetic variation in yield traits, and changes in physiological traits provided useful information in identifying the tolerant genotypes which can be used to improve heat tolerance in barley through breeding.

Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations.

Though epistasis has long been postulated to have a critical role in genetic regulation of important pathways as well as provide a major source of variation in the process of speciation, the importance of epistasis for genomic selection in the context of plant breeding is still being debated. In this paper, we report the results on the prediction of genetic values with epistatic effects for 280 accessions in the Nebraska Wheat Breeding Program using adaptive mixed least absolute shrinkage and selection operator (LASSO). The development of adaptive mixed LASSO, originally designed for association mapping, for the context of genomic selection is reported. The results show that adaptive mixed LASSO can be successfully applied to the prediction of genetic values while incorporating both marker main effects and epistatic effects. Especially, the prediction accuracy is substantially improved by the inclusion of two-locus epistatic effects (more than onefold in some cases as measured by cross-validation correlation coefficient), which is observed for multiple traits and planting locations. This points to significant potential in using non-additive genetic effects for genomic selection in crop breeding practices.

GWAS: Fast-forwarding gene identification and characterization in temperate Cereals: lessons from Barley - A review.

Understanding the genetic complexity of traits is an important objective of small grain temperate cereals yield and adaptation improvements. Bi-parental quantitative trait loci (QTL) linkage mapping is a powerful method to identify genetic regions that co-segregate in the trait of interest within the research population. However, recently, association or linkage disequilibrium (LD) mapping using a genome-wide association study (GWAS) became an approach for unraveling the molecular genetic basis underlying the natural phenotypic variation. Many causative allele(s)/loci have been identified using the power of this approach which had not been detected in QTL mapping populations. In barley (Hordeum vulgare L.), GWAS has been successfully applied to define the causative allele(s)/loci which can be used in the breeding crop for adaptation and yield improvement. This promising approach represents a tremendous step forward in genetic analysis and undoubtedly proved it is a valuable tool in the identification of candidate genes. In this review, we describe the recently used approach for genetic analyses (linkage mapping or association mapping), and then provide the basic genetic and statistical concepts of GWAS, and subsequently highlight the genetic discoveries using GWAS. The review explained how the candidate gene(s) can be detected using state-of-art bioinformatic tools.

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery.

BACKGROUND: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. RESULTS: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58-0.81) was higher than the other lines (r = 0.21-0.59) included in this study with different genetic backgrounds. CONCLUSIONS: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing.