Dissection of the genetic basis of genotype-by-environment interactions for grain yield and main agronomic traits in Iranian bread wheat landraces and cultivars

Overcoming the "feast or famine" effect: improved interaction testing in genome-wide association studies

In genetic association analysis of complex traits, detection of interaction (either GxG or GxE) can help to elucidate the genetic architecture and biological mechanisms underlying the trait. Detection of interaction in a genome-wide association study (GWAS) can be methodologically challenging for various reasons, including a high burden of multiple comparisons when testing for epistasis between all possible pairs of a set of genomewide variants, as well as heteroscedasticity effects occurring in the presence of GxG or GxE interaction. In this paper, we address the problem of an even more striking phenomenon that we call the "feast or famine" effect that occurs when testing interaction in a genomewide context. As we verify, even in a simplified setting in which there is no interaction at all (and so no heteroscedasticity), in a GWAS to detect GxG or GxE interaction with a fixed genetic variant or environmental factor, the distribution of the genome-wide p-values under the null hypothesis is not the i.i.d. uniform one that is commonly assumed. Using standard methods, even if all SNPs are independent, some GWASs will have systematically underinflated p-values ("feast"), and others will have systematically overinflated p-values ("famine"), which can lead to false detection of interaction, reduced power, inconsistent results across studies, and failure to replicate true signal. This startling phenomenon is specific to detection of interaction in a GWAS, and it may partly explain why such detection has so far proved challenging and difficult to replicate. We show theoretically that the key cause of this phenomenon is which variables are conditioned on in the analysis, and this suggests an approach to correct the problem by changing the way the conditioning is done. Using this insight, we have developed the TINGA method to adjust the interaction test statistics to make their p-values closer to uniform under the null hypothesis. In simulations we show that TINGA both controls type 1 error and improves power. TINGA allows for covariates and population structure through use of a linear mixed model and accounts for heteroscedasticity. We apply TINGA to detection of epistasis in a study of flowering time in Arabidopsis thaliana.

Unravelling the dynamics of genotype and environment interactions on chilli (Capsicum annuum L.) yield-related attributes in soilless planting systems

Evaluation of genotypes to identify high-yielding and stable varieties is crucial for chilli production sustainability and food security. These analyses are essential, particularly when the breeding program aims to select lines with great adaptability and stability. Thirty chilli genotypes were evaluated for yield stability under four soilless planting systems viz; fertigation, HydroStock (commercial hydrogel), BioHydrogel (biodegradable hydrogel), and hydroponic to study the influence of genotype by environment interaction. The research used a split-plot randomized complete block design (RCBD) with two cropping cycles and five replications. The GGE biplot analysis was employed to assess the mean versus stability perspective in explaining the variation in genotypic and genotype-by-environment effects on the yield-related attributes for yield per plant, fruit number, fruit length, and width. Stability analysis denoted genotypes G26 and G30 as the most stable for yield per plant, while G16, G22, and G30 were stable for the number of fruits per plant. Among the four planting systems evaluated, HydroStock and BioHydrogel outperformed the others in yield per plant, demonstrating the highest level of informativeness or discrimination. These findings offer critical insights for future crop breeding programs and the optimization of agricultural practices.

Genetic analyses and prediction for lodging‑related traits in a diverse Iranian hexaploid wheat collection

Lodging is one of the most important limiting environmental factors for achieving the maximum yield and quality of grains in cereals, including wheat. However, little is known about the genetic foundation underlying lodging resistance (LR) in wheat. In this study, 208 landraces and 90 cultivars were phenotyped in two cropping seasons (2018-2019 and 2019-2020) for 19 LR-related traits. A genome-wide association study (GWAS) and genomics prediction were carried out to dissect the genomic regions of LR. The number of significant marker pairs (MPs) was highest for genome B in both landraces (427,017) and cultivars (37,359). The strongest linkage disequilibrium (LD) between marker pairs was found on chromosome 4A (0.318). For stem lodging-related traits, 465, 497, and 478 marker-trait associations (MTAs) and 45 candidate genes were identified in year 1, year 2, and pooled. Gene ontology exhibited genomic region on Chr. 2B, 6B, and 7B control lodging. Most of these genes have key roles in defense response, calcium ion transmembrane transport, carbohydrate metabolic process, nitrogen compound metabolic process, and some genes harbor unknown functions that, all together may respond to lodging as a complex network. The module associated with starch and sucrose biosynthesis was highlighted. Regarding genomic prediction, the GBLUP model performed better than BRR and RRBLUP. This suggests that GBLUP would be a good tool for wheat genome selection. As a result of these findings, it has been possible to identify pivotal QTLs and genes that could be used to improve stem lodging resistance in Triticum aestivum L.

Analysis of genetic diversity and genome-wide association study for drought tolerance related traits in Iranian bread wheat

BACKGROUND

Drought is most likely the most significant abiotic stress affecting wheat yield. The discovery of drought-tolerant genotypes is a promising strategy for dealing with the world's rapidly diminishing water resources and growing population. A genome-wide association study (GWAS) was conducted on 298 Iranian bread wheat landraces and cultivars to investigate the genetic basis of yield, yield components, and drought tolerance indices in two cropping seasons (2018-2019 and 2019-2020) under rainfed and well-watered environments.

RESULTS

A heatmap display of hierarchical clustering divided cultivars and landraces into four categories, with high-yielding and drought-tolerant genotypes clustering in the same group. The results of the principal component analysis (PCA) demonstrated that selecting genotypes based on the mean productivity (MP), geometric mean productivity (GMP), harmonic mean (HM), and stress tolerance index (STI) can help achieve high-yield genotypes in the environment. Genome B had the highest number of significant marker pairs in linkage disequilibrium (LD) for both landraces (427,017) and cultivars (370,359). Similar to cultivars, marker pairs on chromosome 4A represented the strongest LD (r2 = 0.32). However, the genomes D, A, and B have the highest LD, respectively. The single-locus mixed linear model (MLM) and multi-locus random-SNP-effect mixed linear model (mrMLM) identified 1711 and 1254 significant marker-trait association (MTAs) (-log10 P > 3) for all traits, respectively. A total of 874 common quantitative trait nucleotides (QTNs) were simultaneously discovered by both MLM and mrMLM methods. Gene ontology revealed that 11, 18, 6, and 11 MTAs were found in protein-coding regions (PCRs) for spike weight (SW), thousand kernel weight (TKW), grain number per spike (GN), and grain yield (GY), respectively.

CONCLUSION

The results identified rich regions of quantitative trait loci (QTL) on Ch. 4A and 5A suggest that these chromosomes are important for drought tolerance and could be used in wheat breeding programs. Furthermore, the findings indicated that landraces studied in Iranian bread wheat germplasm possess valuable alleles, that are responsive to water-limited conditions. This GWAS experiment is one of the few types of research conducted on drought tolerance that can be exploited in the genome-mediated development of novel varieties of wheat.

Identification of novel putative alleles related to important agronomic traits of wheat using robust strategies in GWAS

Principal component analysis (PCA) is widely used in various genetics studies. In this study, the role of classical PCA (cPCA) and robust PCA (rPCA) was evaluated explicitly in genome-wide association studies (GWAS). We evaluated 294 wheat genotypes under well-watered and rain-fed, focusing on spike traits. First, we showed that some phenotypic and genotypic observations could be outliers based on cPCA and different rPCA algorithms (Proj, Grid, Hubert, and Locantore). Hubert's method provided a better approach to identifying outliers, which helped to understand the nature of these samples. These outliers led to the deviation of the heritability of traits from the actual value. Then, we performed GWAS with 36,000 single nucleotide polymorphisms (SNPs) based on the traditional approach and two robust strategies. In the conventional approach and using the first three components of cPCA as population structure, 184 and 139 marker-trait associations (MTAs) were identified for five traits in well-watered and rain-fed environments, respectively. In the first robust strategy and when rPCA was used as population structure in GWAS, we observed that the Hubert and Grid methods identified new MTAs, especially for yield and spike weight on chromosomes 7A and 6B. In the second strategy, we followed the classical and robust principal component-based GWAS, where the first two PCs obtained from phenotypic variables were used instead of traits. In the recent strategy, despite the similarity between the methods, some new MTAs were identified that can be considered pleiotropic. Hubert's method provided a better linear combination of traits because it had the most MTAs in common with the traditional approach. Newly identified SNPs, including rs19833 (5B) and rs48316 (2B), were annotated with important genes with vital biological processes and molecular functions. The approaches presented in this study can reduce the misleading GWAS results caused by the adverse effect of outlier observations.

Marker-trait association analyses revealed major novel QTLs for grain yield and related traits in durum wheat

The growing global demand for wheat for food is rising due to the influence of population growth and climate change. The dissection of complex traits by employing a genome-wide association study (GWAS) allows the identification of DNA markers associated with complex traits to improve the productivity of crops. We used GWAS with 10,045 single nucleotide polymorphism (SNP) markers to search for genomic regions associated with grain yield and related traits based on diverse panels of Ethiopian durum wheat. In Ethiopia, multi-environment trials of the genotypes were carried out at five locations. The genotyping was conducted using the 25k Illumina Wheat SNP array to explore population structure, linkage disequilibrium (LD), and marker-trait associations (MTAs). For GWAS, the multi-locus Fixed and Random Model Circulating Probability Unification (FarmCPU) model was applied. Broad-sense heritability estimates were high, ranging from 0.63 (for grain yield) to 0.97 (for thousand-kernel weight). The population structure based on principal component analysis, and model-based cluster analysis revealed two genetically distinct clusters with limited admixtures. The LD among SNPs declined within the range of 2.02-10.04 Mbp with an average of 4.28 Mbp. The GWAS scan based on the mean performance of the genotypes across the environments identified 44 significant MTAs across the chromosomes. Twenty-six of these MTAs are novel, whereas the remaining 18 were previously reported and confirmed in this study. We also identified candidate genes for the novel loci potentially regulating the traits. Hence, this study highlights the significance of the Ethiopian durum wheat gene pool for improving durum wheat globally. Furthermore, a breeding strategy focusing on accumulating favorable alleles at these loci could improve durum wheat production in the East African highlands and elsewhere.

Assessing performance and stability of yellow rust resistance, heat tolerance, and agronomic performance in diverse bread wheat genotypes for enhancing resilience to climate change under Egyptian conditions

Yellow rust and heat stress adversatively impact the growth and production of bread wheat in particular under rising adverse environmental conditions. Stability of grain yield is a pivotal purpose of plant breeders to improve wheat production and ensure global food security especially under abrupt climate change. The objective of this study was to assess the performance and stability of diverse bread wheat genotypes for yellow rust resistance, heat stress, and yield traits. The studied genotypes were evaluated in two different locations under two sowing dates (timely and late sowing) during two growing seasons. The obtained results displayed significant differences among the tested locations, sowing dates, and genotypes for most measured traits. The yellow rust measurements evaluated under the field conditions including final rust severity (FRS), the average coefficient of infection (ACI), and area under disease progress curve (AUDPC) revealed that Giza-171, Misr-1, Gemmeiza-12, Shandweel-1, Sids-13, Line-1, Line-2, and Line-55 had better resistance. Based on heat sensitivity measurements, Line-1 and Line-2 followed by Line-35, Shandweel-1 and Line-55 were classified as more tolerant to heat stress compared with the remaining genotypes. The genotypes Line-55, Gemmeiza-12, Giza-171, Line-1, Line-2, and Misr-1 were able to maintain acceptable agronomic performance under timely and late sowing dates in all evaluated environments. Different statistical procedures were employed to explore the adaptability and stability of tested genotypes i.e., joint regression, stratified ranking, Wricke's Ecovalence values, cultivar superiority, additive main effects, and multiplicative interaction (AMMI), AMMI stability value, and genotype plus genotype-by-environment interaction (GGE). The applied stability parameters were quite similar for describing the stability of the evaluated wheat genotypes. The results indicated that Gemmeiza-12, Giza-171, Sids-12, Sids-13, Misr-1 Shandweel-1, Line-1, Line-2, and Line-55 were desirable and stable. The heatmap and hierarchical clustering were exploited for dividing the evaluated bread wheat genotypes into different clusters based on yellow rust resistance measurements, heat tolerance indices, and agronomic performance. Line-1 and Line-2 had the best performance for all rust resistance, heat tolerance, and agronomic performance followed by Giza-171, Line-55, Line-35, Gemmeiza-12, Shandweel-1, Misr-1, and Sids-13. In conclusion, our findings provide evidence of utilizing promising genotypes in rust resistance, heat tolerance, and agronomic performance in breeding programs for improving wheat grain yield stability mainly under climate change.

Genome-wide association mapping for wheat morphometric seed traits in Iranian landraces and cultivars under rain-fed and well-watered conditions

Seed traits in bread wheat are valuable to breeders and farmers, thus it is important exploring putative QTLs responsible for key traits to be used in breeding programs. GWAS was carried out using 298 bread wheat landraces and cultivars from Iran to uncover the genetic basis of seed characteristics in both rain-fed and well-watered environments. The analyses of linkage disequilibrium (LD) between marker pairs showed that the largest number of significant LDs in landraces (427,017) and cultivars (370,359) was recorded in genome B, and the strongest LD was identified on chromosome 4A (0.318). LD decay was higher in the B and A genomes, compared to the D genome. Mapping by using mrMLM (LOD > 3) and MLM (0.05/m, Bonferroni) led to 246 and 67 marker-trait associations (MTAs) under rain-fed, as well as 257 and 74 MTAs under well-watered conditions, respectively. The study found that 3VmrMLM correctly detected all types of loci and estimated their effects in an unbiased manner, with high power and accuracy and a low false positive rate, which led to the identification of 140 MTAs (LOD > 3) in all environments. Gene ontology revealed that 10 and 10 MTAs were found in protein-coding regions for rain-fed and well-watered conditions, respectively. The findings suggest that landraces studied in Iranian bread wheat germplasm possess valuable alleles, which are responsive to water-limited conditions. MTAs uncovered in this study can be exploited in the genome-mediated development of novel wheat cultivars.

Multivariate analyses of Ethiopian durum wheat revealed stable and high yielding genotypes

Improving crop adaptation and stability across diverse and changing environmental conditions is essential to increasing grain yield per unit area. In turn, this contributes to meeting the increasing global food demand. Nevertheless, a number of factors challenge the efficiency of crop improvement programs, of which genotype-by-environment interaction (GEI) is one of the major factors. This study aimed to evaluate the performance and phenotypic stability of 385 Ethiopian durum wheat landraces and 35 cultivars; assess the pattern of genotype by environment interaction (GEI) effect, and identify stable and high-yielding landraces or cultivars using the additive main effect and multiplicative interaction (AMMI) and genotype main effect plus genotype by environment interaction biplot (GGE-biplot). The experiment was laid out in an alpha lattice design with two replications at five test sites (Akaki, Chefe Donsa, Holeta, Kulumsa, and Sinana). The combined analysis of variance revealed highly significant effects (P ≤ 0.01) of environments (E), genotype (G), and GEI on a phenotypic variation of traits evaluated, including grain yield. For all traits, the amount of phenotypic variance and GEI explained by the GGE biplot was higher than in AMMI2, but both exhibited significant effects of E and GEI on the genotypes. The AMMI model identified G169, G420, G413, G139, G415, G416, G417, and G418 as stable genotypes across testing sites. Whereas, the GGE biplot identified G169, G420, G415, G139, G106, G412, G413, and G417 as both high-yielding and stable across test sites. Hence, genotypes identified as stable and high yielding in the present study could be used in a durum wheat breeding program aimed at identifying genes and molecular markers associated with the crop’s productivity traits as well as developing stable and high-yielding cultivars for use in East Africa and beyond.

Genome-wide association mapping and genomic prediction for pre‑harvest sprouting resistance, low α-amylase and seed color in Iranian bread wheat

BACKGROUND

Pre-harvest sprouting (PHS) refers to a phenomenon, in which the physiologically mature seeds are germinated on the spike before or during the harvesting practice owing to high humidity or prolonged period of rainfall. Pre-harvest sprouting (PHS) remarkably decreases seed quality and yield in wheat; hence it is imperative to uncover genomic regions responsible for PHS tolerance to be used in wheat breeding. A genome-wide association study (GWAS) was carried out using 298 bread wheat landraces and varieties from Iran to dissect the genomic regions of PHS tolerance in a well-irrigated environment. Three different approaches (RRBLUP, GBLUP and BRR) were followed to estimate prediction accuracies in wheat genomic selection.

RESULTS

Genomes B, A, and D harbored the largest number of significant marker pairs (MPs) in both landraces (427,017, 328,006, 92,702 MPs) and varieties (370,359, 266,708, 63,924 MPs), respectively. However, the LD levels were found the opposite, i.e., genomes D, A, and B have the highest LD, respectively. Association mapping by using GLM and MLM models resulted in 572 and 598 marker-trait associations (MTAs) for imputed SNPs (- log10 P > 3), respectively. Gene ontology exhibited that the pleitropic MPs located on 1A control seed color, α-Amy activity, and PHS. RRBLUP model indicated genetic effects better than GBLUP and BRR, offering a favorable tool for wheat genomic selection.

CONCLUSIONS

Gene ontology exhibited that the pleitropic MPs located on 1A can control seed color, α-Amy activity, and PHS. The verified markers in the current work can provide an opportunity to clone the underlying QTLs/genes, fine mapping, and genome-assisted selection.Our observations uncovered key MTAs related to seed color, α-Amy activity, and PHS that can be exploited in the genome-mediated development of novel varieties in wheat.