Multi-environment QTL mixed models for drought stress adaptation in wheat

Modeling QTL-by-environment interactions for multi-parent populations

Multi-parent populations (MPPs) are attractive for genetic and breeding studies because they combine genetic diversity with an easy-to-control population structure. Most methods for mapping QTLs in MPPs focus on the detection of QTLs in single environments. Little attention has been given to mapping QTLs in multienvironment trials (METs) and to detecting and modeling QTL-by-environment interactions (QEIs). We present mixed model approaches for the detection and modeling of consistent versus environment-dependent QTLs, i.e., QTL-by-environment interaction (QEI). QTL effects are assumed to be normally distributed with variances expressing consistency or dependence on environments and families. The entries of the corresponding design matrices are functions of identity-by-descent (IBD) probabilities between parents and offspring and follow from the parental origin of offspring DNA. A polygenic effect is added to the models to account for background genetic variation. We illustrate the wide applicability of our method by analyzing several public MPP datasets with observations from METs. The examples include diallel, nested association mapping (NAM), and multi-parent advanced inter-cross (MAGIC) populations. The results of our approach compare favorably with those of previous studies that used tailored methods.

Recombinant inbred line panels inform the genetic architecture and interactions of adaptive traits in Drosophila melanogaster

The distribution of allelic effects on traits, along with their gene-by-gene and gene-by-environment interactions, contributes to the phenotypes available for selection and the trajectories of adaptive variants. Nonetheless, uncertainty persists regarding the effect sizes underlying adaptations and the importance of genetic interactions. Herein, we aimed to investigate the genetic architecture and the epistatic and environmental interactions involving loci that contribute to multiple adaptive traits using two new panels of Drosophila melanogaster recombinant inbred lines (RILs). To better fit our data, we re-implemented functions from R/qtl (Broman et al. 2003) using additive genetic models. We found 14 quantitative trait loci (QTL) underlying melanism, wing size, song pattern, and ethanol resistance. By combining our mapping results with population genetic statistics, we identified potential new genes related to these traits. None of the detected QTLs showed clear evidence of epistasis, and our power analysis indicated that we should have seen at least one significant interaction if sign epistasis or strong positive epistasis played a pervasive role in trait evolution. In contrast, we did find roles for gene-by-environment interactions involving pigmentation traits. Overall, our data suggest that the genetic architecture of adaptive traits often involves alleles of detectable effect, that strong epistasis does not always play a role in adaptation, and that environmental interactions can modulate the effect size of adaptive alleles.

A Large-Scale Candidate-Gene Association Mapping for Drought Tolerance and Agronomic Traits in Sugarcane

Dissection of the genetic loci controlling drought tolerance traits with a complex genetic inheritance is important for drought-tolerant sugarcane improvement. In this study, we conducted a large-scale candidate gene association study of 649 candidate genes in a sugarcane diversity panel to identify genetic variants underlying agronomic traits and drought tolerance indices evaluated in plant cane and ratoon cane under water-stressed (WS) and non-stressed (NS) environments. We identified 197 significant marker-trait associations (MTAs) in 141 candidate genes associated with 18 evaluated traits with the Bonferroni correction threshold (α = 0.05). Out of the total, 95 MTAs in 78 candidate genes and 62 MTAs in 58 candidate genes were detected under NS and WS conditions, respectively. Most MTAs were found only in specific water regimes and crop seasons. These MTAs explained 7.93-30.52% of phenotypic variation. Association mapping results revealed that 34, 59, and 104 MTAs involved physiological and molecular adaptation, phytohormone metabolism, and drought-inducible genes. They identified 19 pleiotropic genes associated with more than one trait and many genes related to drought tolerance indices. The genetic and genomic resources identified in this study will enable the combining of yield-related traits and sugar-related traits with agronomic value to optimize the yield of sugarcane cultivars grown under drought-stressed and non-stressed environments.

Graphical analysis of multi-environmental trials for wheat grain yield based on GGE-biplot analysis under diverse sowing dates

BACKGROUND

Information on the nature and extent of genetic and genotype × environment (GE) interaction is extremely rare in wheat varieties under different sowing dates. In the present study, the GGE biplot method was conducted to investigate genotype × environment interaction effects and evaluate the adaptability and yield stability of 13 wheat varieties across eight sowing dates, in order to facilitate comparison among varieties and sowing dates and identify suitable varieties for the future breeding studies.

RESULTS

Considerable genotypic variation was observed among genotypes for all of the evaluated traits, demonstrating that selection for these traits would be successful. Low broad sense heritability obtained for grain yield showed that, both genetic and non-genetic gene actions played a role in the control of this trait, and suggested that indirect selection based on its components which had high heritability and high correlation with yield, would be more effective to improve grain yield in this germplasm. Hence, selection based on an index may be more useful for improvement of this trait in recurrent selection programs. The results of the stability analysis showed that the environmental effect was a major source of variation, which captured 72.21% of total variation, whereas G and GE explained 6.94% and 18.33%, respectively. The partitioning of GGE through GGE biplot analysis showed that, the first two PCs accounted for 54.64% and 35.15% of the GGE sum of squares respectively, capturing a total of 89.79% variation. According to the GGE biplot, among the studied varieties, the performance of Gascogen was the least stable, whereas Sirvan, Roshan, and Pishtaz had superior performance under all sowing dates, suggesting that they have a broad adaptation to the diverse sowing dates. These varieties may be recommended for genetic improvement of wheat with a high degree of adaptation.

CONCLUSION

The results obtained in this study demonstrated the efficiency of the GGE biplot technique for selecting high yielding and stable varieties across sowing dates.

Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame

Introduction: Sesame is an ancient oilseed crop containing many valuable nutritional components. The demand for sesame seeds and their products has recently increased worldwide, making it necessary to enhance the development of high-yielding cultivars. One approach to enhance genetic gain in breeding programs is genomic selection. However, studies on genomic selection and genomic prediction in sesame have yet to be conducted.

Methods: In this study, we performed genomic prediction for agronomic traits using the phenotypes and genotypes of a sesame diversity panel grown under Mediterranean climatic conditions over two growing seasons. We aimed to assess prediction accuracy for nine important agronomic traits in sesame using single- and multi-environment analyses.

Results: In single-environment analysis, genomic best linear unbiased prediction, BayesB, BayesC, and reproducing kernel Hilbert spaces models showed no substantial differences. The average prediction accuracy of the nine traits across these models ranged from 0.39 to 0.79 for both growing seasons. In the multi-environment analysis, the marker-by-environment interaction model, which decomposed the marker effects into components shared across environments and environment-specific deviations, improved the prediction accuracies for all traits by 15%–58% compared to the single-environment model, particularly when borrowing information from other environments was made possible.

Discussion: Our results showed that single-environment analysis produced moderate-to-high genomic prediction accuracy for agronomic traits in sesame. The multi-environment analysis further enhanced this accuracy by exploiting marker-by-environment interaction. We concluded that genomic prediction using multi-environmental trial data could improve efforts for breeding cultivars adapted to the semi-arid Mediterranean climate.

Genomic selection for agronomic traits in a winter wheat breeding program

rAMP-seq based genomic selection for agronomic traits has been shown to be a useful tool for winter wheat breeding programs by increasing the rate of genetic gain. Genomic selection (GS) is an effective strategy to employ in a breeding program that focuses on optimizing quantitative traits, which results in the ability for breeders to select the best genotypes. GS was incorporated into a breeding program to determine the potential for implementation on an annual basis, with emphasis on selecting optimal parents and decreasing the time and costs associated with phenotyping large numbers of genotypes. The design options for applying repeat amplification sequencing (rAMP-seq) in bread wheat were explored, and a low-cost single primer pair strategy was implemented. A total of 1870 winter wheat genotypes were phenotyped and genotyped using rAMP-seq. The optimization of training to testing population size showed that the 70:30 ratio provided the most consistent prediction accuracy. Three GS models were tested, rrBLUP, RKHS and feed-forward neural networks using the University of Guelph Winter Wheat Breeding Program (UGWWBP) and Elite-UGWWBP populations. The models performed equally well for both populations and did not differ in prediction accuracy (r) for most agronomic traits, with the exception of yield, where RKHS performed the best with an r = 0.34 and 0.39 for each population, respectively. The ability to operate a breeding program where multiple selection strategies, including GS, are utilized will lead to higher efficiency in the program and ultimately lead to a higher rate of genetic gain.

Genome-Wide Association Study of Rice Grain Shape and Chalkiness in a Worldwide Collection of Xian Accessions

Rice (Oryza sativa L.) appearance quality, which is mainly defined by grain shape and chalkiness, is an important target in rice breeding. In this study, we first re-sequenced 137 indica accessions and then conducted a genome-wide association study (GWAS) for six agronomic traits with the 2,998,034 derived single nucleotide polymorphisms (SNPs) by using the best linear unbiased prediction (BLUP) values for each trait. The results revealed that 195 SNPs had significant associations with the six agronomic traits. Based on the genome-wide linkage disequilibrium (LD) blocks, candidate genes for the target traits were detected within 100 kb upstream and downstream of the relevant SNP loci. Results indicate that six quantitative trait loci (QTLs) significantly associated with six traits (qTGW4.1, qTGW4.2, qGL4.1, qGL12.1, qGL12.2, qGW2.1, qGW4.1, qGW6.1, qGW8.1, qGW8.2, qGW9.1, qGW11.1, qGLWR2.1, qGLWR2.2, qGLWR4.2, qPGWC5.1 and qDEC6.1) were identified for haplotype analysis. Among these QTLs, two (qTGW4.2 and qGW6.1), were overlapped with FLO19 and OsbZIP47, respectively, and the remaining four were novel QTLs. These candidate genes were further validated by haplotype block construction.

Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions

BACKGROUND

The markers detected by genome-wide association study (GWAS) make it possible to dissect genetic structure and diversity at many loci. This can enable a wheat breeder to reveal and used genomic loci controlling drought tolerance. This study was focused on determining the population structure of Iranian 208 wheat landraces and 90 cultivars via genotyping-by-sequencing (GBS) and also on detecting marker-trait associations (MTAs) by GWAS and genomic prediction (GS) of wheat agronomic traits for drought-tolerance breeding. GWASs were conducted using both the original phenotypes (pGWAS) and estimated breeding values (eGWAS). The bayesian ridge regression (BRR), genomic best linear unbiased prediction (gBLUP), and ridge regression-best linear unbiased prediction (rrBLUP) approaches were used to estimate breeding values and estimate prediction accuracies in genomic selection.

RESULTS

Population structure analysis using 2,174,975 SNPs revealed four genetically distinct sub-populations from wheat accessions. D-Genome harbored the lowest number of significant marker pairs and the highest linkage disequilibrium (LD), reflecting different evolutionary histories of wheat genomes. From pGWAS, BRR, gBLUP, and rrBLUP, 284, 363, 359 and 295 significant MTAs were found under normal and 195, 365, 362 and 302 under stress conditions, respectively. The gBLUP with the most similarity (80.98 and 71.28% in well-watered and rain-fed environments, correspondingly) with the pGWAS method in the terms of discovered significant SNPs, suggesting the potential of gBLUP in uncovering SNPs. Results from gene ontology revealed that 29 and 30 SNPs in the imputed dataset were located in protein-coding regions for well-watered and rain-fed conditions, respectively. gBLUP model revealed genetic effects better than other models, suggesting a suitable tool for genome selection in wheat.

CONCLUSION

We illustrate that Iranian landraces of bread wheat contain novel alleles that are adaptive to drought stress environments. gBLUP model can be helpful for fine mapping and cloning of the relevant QTLs and genes, and for carrying out trait introgression and marker-assisted selection in both normal and drought environments in wheat collections.

High-Density Linkage Mapping of Agronomic Trait QTLs in Wheat under Water Deficit Condition using Genotyping by Sequencing (GBS)

Improvement of grain yield is the ultimate goal for wheat breeding under water-limited environments. In the present study, a high-density linkage map was developed by using genotyping-by-sequencing (GBS) of a recombinant inbred line (RIL) population derived from the cross between Iranian landrace #49 and cultivar Yecora Rojo. The population was evaluated in three locations in Iran during two years under irrigated and water deficit conditions for the agronomic traits grain yield (GY), plant height (PH), spike number per square meter (SM), 1000 kernel weight (TKW), grain number per spike (GNS), spike length (SL), biomass (BIO) and harvest index (HI). A linkage map was constructed using 5831 SNPs assigned to 21 chromosomes, spanning 3642.14 cM of the hexaploid wheat genome with an average marker density of 0.62 (markers/cM). In total, 85 QTLs were identified on 19 chromosomes (all except 5D and 6D) explaining 6.06–19.25% of the traits phenotypic variance. We could identify 20 novel QTLs explaining 8.87–19.18% of phenotypic variance on chromosomes 1A, 1B, 1D, 2B, 3A, 3B, 6A, 6B and 7A. For 35 out of 85 mapped QTLs functionally annotated genes were identified which could be related to a potential role in drought stress.

Genome-wide association mapping of bread wheat genotypes for sustainable food security and yield potential under limited water conditions

Determining the genetic basis of yield and water deficient tolerance in wheat is vital for wheat breeding programs. Herein, a genome-wide association study (GWAS) was performed for water deficient and yield-related attributes on wheat genotypes with high-density Illumina 90K Infinium SNP array. Major yield and drought-related attributes were phenotyped on a panel of Pakistani and foreign accessions grown in non-stressed and water deficient stressed environments during two crop cycles. Among all accessions, highly significant variations were shown in studied environments for examined characters. Water deficient conditions, reduced the wheat yield and had strong and positive correlation among relative water content and grain yield per plant. Population structure analyses based on 90,000 SNP data, classify the accessions into 4 sub-populations. Marker-trait association analyses (MTA) revealed that 134 significant SNPs were linked with yield and drought tolerance attributes. Pleotropic loci RAC875_s117925_244 and RAC875_c16333_340 located on chromosome 5A and 2A respectively, were significantly linked with relative water contents (RWC), cell membrane thermo-stability (CMT), grain per spike (GPS), spikelet per spike (SPS) and grain yield per plant (GYP). The markers Ra_c58279_684, BobWhite_c23828_341 and IAAV3414 located on chromosomes 2A, 6B and 7B respectively, showed pleotropic effects for RWC, GPS and GYP under both environments. The current experiment not only validated several MTAs reported in other studies but also discovered novel MTAs which significant under drought-stressed conditions. A total of 171 candidate genes were recognized that could be cloned and functionally characterized for the respective associated traits. For RWC and CMT, total 11 and 3 associated SNPs were mapped on coding DNA sequence (CDS) of the identified candidate genes. Isolation and characterization of the candidate genes herein mapped SNPs will be useful in discovering novel genes underpinning drought tolerance in bread wheat to fulfill the wheat demand and sustainable food security under limited water conditions.

Transcriptome Analyses of Near Isogenic Lines Reveal Putative Drought Tolerance Controlling Genes in Wheat

Drought stress, especially at the grain-filling stage, is a major constraint for wheat production. Drought tolerance is a complex trait controlled by a large array of genes and pathways. This study conducted gene expression profiling on two pairs of near-isogenic lines (NILs) for an important qDSI.4B.1 QTL conferring drought tolerance on the short arm of chromosome 4B in wheat. Analysis showed 1,614 genome-wide differentially expressed genes (DEGs) between the tolerant and susceptible isolines in both NIL pairs. Six common DEGs were found between NIL1 and NIL2 at both 7 and 14 days after stress induction, with two of them having single nucleotide polymorphism (SNP) variants. These six genes that were confirmed by quantitative real-time PCR (qRT-PCR) expression analysis are considered candidate genes for drought tolerance mediated by qDSI.4B.1 QTL with their main contributions to gene regulation, cell elongation, protein quality control, secondary metabolism, and hormone signaling. These six candidate genes and the highest number of DEGs and variants (SNPs/indels) were located between 49 and 137 Mbp of 4BS, making this interval the most probable location for the qDSI.4B.1 locus. Additionally, 765 and 84 DEGs were detected as responsive genes to drought stress in tolerant and susceptible isolines, respectively. According to gene ontology (GO), protein phosphorylation, oxidation reduction, and regulation of transcription were top biological processes involved in the drought response and tolerance. These results provide insights into stress responses regulated by the 4BS locus and have identified candidate genes and genetic markers that can be used for fine mapping of the qDSI.4B.1 locus and, ultimately, in wheat breeding programs for drought tolerance.

Gene Action Governing the Inheritance of Stomatal Conductance in Four Wheat Crosses Under High Temperature Stress Condition

The knowledge pertaining to gene action and interactions involved in the inheritance of a character in different generations is crucial for determining the breeding strategies in crop improvement program. In the present study, the gene action of stomatal conductance was determined in four wheat populations under high elevated temperatures coupled with late sowing at experimental farm, ICAR-Indian Agricultural Research Institute, New Delhi, India. Steady-state SC-1 leaf porometer was used to record stomatal conductance on adaxial and abaxial leaf surface at late boot (Z 49-50), early milk (Z 73), and late milk (Z 77) growth stages. Evidence for nuclear genetic control of stomatal conductance was strong, with large and repeatable genetic difference observed for parents and progeny across all the four crosses. Mean stomatal conductance for genotypes, GW 322 and GW 366, was consistently low at late boot, early milk, and late milk under timely sown, late sown, and very late sown condition, whereas the converse was true for the high-conducting parents, KAUZ/AA//KAUZ and RAC 875. Additive and additive x additive epistatic effects were large and reasonably consistent at three stages and in all crosses. Detection of epistasis and evidence of transgressive segregation suggested that variation for stomatal conductance was under oligo or polygenic control. Thus, it is conceivable that independent alleles at two or more loci could be pyramided into a single family for increased or decreased stomatal conductance. Additive-based gene action also facilitates with simple selection at early generation to improve stomatal conductance in expected direction. This is the first report on estimates of gene action for stomatal conductance of flag leaf under heat stress condition during reproductive and grain filling stage.

Multi-Locus GWAS for Grain Weight-Related Traits Under Rain-Fed Conditions in Common Wheat (Triticum aestivum L.)

In wheat, a multi-locus genome-wide association study (ML-GWAS) was conducted for the four grain weight-related traits (days to anthesis, grain filling duration, grain number per ear, and grain weight per ear) using data recorded under irrigated (IR) and rain-fed (RF) conditions. Seven stress-related indices were estimated for these four traits: (i) drought resistance index (DI), (ii) geometric mean productivity (GMP), (iii) mean productivity index (MPI), (iv) relative drought index (RDI), (v) stress tolerance index (STI), (vi) yield index, and (vii) yield stability index (YSI). The association panel consisted of a core collection of 320 spring wheat accessions representing 28 countries. The panel was genotyped using 9,627 single nucleotide polymorphisms (SNPs). The genome-wide association (GWA) analysis provided 30 significant marker-trait associations (MTAs), distributed as follows: (i) IR (15 MTAs), (ii) RF (14 MTAs), and (iii) IR+RF (1 MTA). In addition, 153 MTAs were available for the seven stress-related indices. Five MTAs co-localized with previously reported QTLs/MTAs. Candidate genes (CGs) associated with different MTAs were also worked out. Gene ontology (GO) analysis and expression analysis together allowed the selection of the two CGs, which may be involved in response to drought stress. These two CGs included: TraesCS1A02G331000 encoding RNA helicase and TraesCS4B02G051200 encoding microtubule-associated protein 65. The results supplemented the current knowledge on genetics for drought tolerance in wheat. The results may also be used for future wheat breeding programs to develop drought-tolerant wheat cultivars.

Exploring Efficient Linear Mixed Models to Detect Quantitative Trait Locus-by-Environment Interactions

Genotype-by-environment (G × E) interactions are important for understanding genotype–phenotype relationships. To date, various statistical models have been proposed to account for G × E effects, especially in genomic selection (GS) studies. Generally, GS does not focus on the detection of each quantitative trait locus (QTL), while the genome-wide association study (GWAS) was designed for QTL detection. G × E modeling methods in GS can be included as covariates in GWAS using unified linear mixed models (LMMs). However, the efficacy of G × E modeling methods in GS studies has not been evaluated for GWAS. In this study, we performed a comprehensive comparison of LMMs that integrate the G × E modeling methods to detect both QTL and QTL-by-environment (Q × E) interaction effects. Model efficacy was evaluated using simulation experiments. For the fixed effect terms representing Q × E effects, simultaneous scoring of specific and nonspecific environmental effects was recommended because of the higher recall and improved genomic inflation factor value. For random effects, it was necessary to account for both G × E and genotype-by-trial (G × T) effects to control genomic inflation factor value. Thus, the recommended LMM includes fixed QTL effect terms that simultaneously score specific and nonspecific environmental effects and random effects accounting for both G × E and G × T. The LMM was applied to real tomato phenotype data obtained from two different cropping seasons. We detected not only QTLs with persistent effects across the cropping seasons but also QTLs with Q × E effects. The optimal LMM identified in this study successfully detected more QTLs with Q × E effects.

Meta-QTL analysis and identification of candidate genes for drought tolerance in bread wheat (Triticum aestivum L.)

Meta-QTL (MQTL) analysis for drought tolerance was undertaken in bread wheat to identify consensus and robust MQTLs using 340 known QTLs from 11 earlier studies; 13 MQTLs located on 6 chromosomes (1D, 3B, 5A, 6D, 7A and 7D) were identified, with maximum of 4 MQTLs on chromosome 5A. Mean confidence intervals for MQTLs were much narrower (mean, 6.01 cM; range 2.07-19.46 cM), relative to those in original QTLs (mean, 13.6 cM; range, 1.0-119.1 cM). Two MQTLs, namely MQTL4 and MQTL12, were major MQTLs with potential for use in marker-assisting breeding. As many as 228 candidate genes (CGs) were also identified using 6 of the 13 MQTLs. In-silico expression analysis of these 228 CGs allowed identification of 14 important CGs, with + 3 to - 8 fold change in expression under drought (relative to normal conditions) in a tolerant cv. named TAM107. These CGs encoded proteins belonging to the following families: NAD-dependent epimerase/dehydratase, protein kinase, NAD(P)-binding domain protein, heat shock protein 70 (Hsp70), glycosyltransferase 2-like, etc. Important MQTLs and CGs identified in the present study should prove useful for future molecular breeding and for the study of molecular basis of drought tolerance in cereals in general and wheat in particular.

Detection of QTLs for genotype × environment interactions in tomato seeds and seedlings

Seed quality and seedling establishment are the most important factors affecting successful crop development. They depend on the genetic background and are acquired during seed maturation and therefor, affected by the maternal environment under which the seeds develop. There is little knowledge about the genetic and environmental factors that affect seed quality and seedling establishment. The aim of this study is to identify the loci and possible molecular mechanisms involved in acquisition of seed quality and how these are controlled by adverse maternal conditions. For this, we used a tomato recombinant inbred line (RIL) population consisting of 100 lines which were grown under two different nutritional environmental conditions, high phosphate and low nitrate. Most of the seed germination traits such as maximum germination percentage (G_max ), germination rate (t₅₀ ) and uniformity (U₈₄₁₆ ) showed ample variation between genotypes and under different germination conditions. This phenotypic variation leads to identification of quantitative trait loci (QTLs) which were dependent on genetic factors, but also on the interaction with the maternal environment (QTL × E). Further studies of these QTLs may ultimately help to predict the effect of different maternal environmental conditions on seed quality and seedling establishment which will be very useful to improve the production of high-performance seeds.

Genetic control of some plant growth characteristics of bread wheat (Triticum aestivum L.) under aluminum stress

BACKGROUND

Biomass yield is an important trait for wheat breeding programs. Enhancing the yield of the aerial components of wheat cultivars will be an integral part of future wheat improvement. Aluminum (Al) toxicity is one of the main factors limiting wheat growth and production in acid soils, which occur on up to 50% of the arable lands of the world especially in tropical and subtropical regions.

OBJECTIVE

Our objective was to identify quantitative trait loci (QTL) of plant growth characteristics and yield in wheat.

METHODS

A recombinant inbred line (RIL) population consisting of 167 lines, derived from a cross between SeriM82 and Babax were evaluated under two Al treatments (+ Al, 800 µM of Al; -Al, 0 µM of Al) in the field based on an alpha lattice design with two replications for two consecutive crop seasons.

RESULTS

A total of 40 QTLs including nine putative and 31 suggestive QTLs were found for all traits using the composite interval mapping (CIM) method. By mixed model-based composite interval mapping (MCIM) method, 42 additive QTLs and nine pairs of epistatic effects were detected for studied traits, of which 20 additive and six pairs of epistatic QTLs showed significant QTL × environment interactions. Most of the detected QTLs across environments were stable, and the highest number of stable QTLs was related to A genome. Co-localization of QTL was found on linkage groups (LGs) 2B, 4B, 6A-a, and 7A (CIM method) and 2A-d, and 6A-a (MCIM method).

CONCLUSION

These results have implications for selection strategies in biomass yield and for increasing the yield of the aerial part of wheat following further evaluations in various genetic backgrounds and environments.

Using intervarietal substitution lines for the identification of wheat chromosomes involved in early responses to water-deficit stress

Water deficit induces reactive oxygen species (ROS) overproduction, which in turn inhibits plant growth and development. High concentrations of ROS disrupt the osmotic balance in plant cells and alter membrane integrity. Chromosomes carrying structural or regulatory genes must be detected to better understand plant response mechanisms to stress. The aim of our study was to identify Triticum aestivum L. chromosomes involved in early responses to short-term water-deficit stress (1, 3 and 6 h). In the present study, intervarietal substitution lines of drought-tolerant 'Saratovskaya 29' and sensitive 'Janetzkis Probat' wheat cultivars were examined. We studied the biochemical plant response system and conducted an analysis of catalase, ascorbate peroxidase and guaiacol peroxidase activities, levels of lipid peroxidation and changes in relative water content. Our results determined that the first reaction was a significant increase in guaiacol peroxidase (GPX) activity. However, the strongest impact on plant responses was found for catalase (CAT), which caused a significant decrease in lipid peroxidation (LPO) levels. Our findings indicate that chromosomes 5A, 4B, 6B and 7D are associated with early responses to short-term osmotic stress in wheat.

Heritability in Plant Breeding on a Genotype-Difference Basis

In plant breeding, heritability is often calculated (i) as a measure of precision of trials and/or (ii) to compute the response to selection. It is usually estimated on an entry-mean basis, since the phenotype is usually an aggregated value, as genotypes are replicated in trials, which stands in contrast with animal breeding and human genetics. When this was first proposed, assumptions such as balanced data and independent genotypic effects were made that are often violated in modern plant breeding trials/analyses. Due to this, multiple alternative methods have been proposed, aiming to generalize heritability on an entry-mean basis. In this study, we propose an extension of the concept for heritability on an entry-mean to an entry-difference basis, which allows for more detailed insight and is more meaningful in the context of selection in plant breeding, because the correlation among entry means can be accounted for. We show that under certain circumstances our method reduces to other popular generalized methods for heritability estimation on an entry-mean basis. The approach is exemplified via four examples that show different levels of complexity, where we compare six methods for heritability estimation on an entry-mean basis to our approach (example codes: https://github.com/PaulSchmidtGit/Heritability). Results suggest that heritability on an entry-difference basis is a well-suited alternative for obtaining an overall heritability estimate, and in addition provides one heritability per genotype as well as one per difference between genotypes.

Drought Stress Tolerance in Wheat and Barley: Advances in Physiology, Breeding and Genetics Research

Climate change is a major threat to most of the agricultural crops grown in tropical and sub-tropical areas globally. Drought stress is one of the consequences of climate change that has a negative impact on crop growth and yield. In the past, many simulation models were proposed to predict climate change and drought occurrences, and it is extremely important to improve essential crops to meet the challenges of drought stress which limits crop productivity and production. Wheat and barley are among the most common and widely used crops due to their economic and social values. Many parts of the world depend on these two crops for food and feed, and both crops are vulnerable to drought stress. Improving drought stress tolerance is a very challenging task for wheat and barley researchers and more research is needed to better understand this stress. The progress made in understanding drought tolerance is due to advances in three main research areas: physiology, breeding, and genetic research. The physiology research focused on the physiological and biochemical metabolic pathways that plants use when exposed to drought stress. New wheat and barley genotypes having a high degree of drought tolerance are produced through breeding by making crosses from promising drought-tolerant genotypes and selecting among their progeny. Also, identifying genes contributing to drought tolerance is very important. Previous studies showed that drought tolerance is a polygenic trait and genetic constitution will help to dissect the gene network(s) controlling drought tolerance. This review explores the recent advances in these three research areas to improve drought tolerance in wheat and barley.

Drought Stress Tolerance in Wheat and Barley: Advances in Physiology, Breeding and Genetics Research

Climate change is a major threat to most of the agricultural crops grown in tropical and sub-tropical areas globally. Drought stress is one of the consequences of climate change that has a negative impact on crop growth and yield. In the past, many simulation models were proposed to predict climate change and drought occurrences, and it is extremely important to improve essential crops to meet the challenges of drought stress which limits crop productivity and production. Wheat and barley are among the most common and widely used crops due to their economic and social values. Many parts of the world depend on these two crops for food and feed, and both crops are vulnerable to drought stress. Improving drought stress tolerance is a very challenging task for wheat and barley researchers and more research is needed to better understand this stress. The progress made in understanding drought tolerance is due to advances in three main research areas: physiology, breeding, and genetic research. The physiology research focused on the physiological and biochemical metabolic pathways that plants use when exposed to drought stress. New wheat and barley genotypes having a high degree of drought tolerance are produced through breeding by making crosses from promising drought-tolerant genotypes and selecting among their progeny. Also, identifying genes contributing to drought tolerance is very important. Previous studies showed that drought tolerance is a polygenic trait and genetic constitution will help to dissect the gene network(s) controlling drought tolerance. This review explores the recent advances in these three research areas to improve drought tolerance in wheat and barley.

Multiple Near-Isogenic Lines Targeting a QTL Hotspot of Drought Tolerance Showed Contrasting Performance Under Post-anthesis Water Stress

The complex quantitative nature of drought-related traits is a major constraint to breed tolerant wheat varieties. Pairs of near-isogenic lines (NILs) with a common genetic background but differing in a particular locus could turn quantitative traits into a Mendelian factor facilitating our understanding of genotype and phenotype interactions. In this study, we report our fast track development and evaluation of NILs from C306 × Dharwar Dry targeting a wheat 4BS QTL hotspot in C306, which confers drought tolerance following the heterogeneous inbreed family (HIF) analysis coupled with immature embryo culture-based fast generation technique. Molecular marker screening and phenotyping for grain yield and related traits under post-anthesis water stress (WS) confirmed four isoline pairs, viz., qDSI.4B.1-2, qDSI.4B.1-3, qDSI.4B.1-6, and qDSI.4B.1-8. There were significant contrasts of responses between the NILs with C306 QTL (+NILs) and the NILs without C306 QTL (-NILs). Among the four confirmed NIL pairs, mean grain yield per plant of the +NILs and -NILs showed significant differences ranging from 9.61 to 10.81 and 6.30 to 7.56 g, respectively, under WS condition, whereas a similar grain yield was recorded between the +NILs and -NILs under well-watered condition. Isolines of +NIL and -NIL pairs showed similar chlorophyll content (SPAD), assimilation rate (A), and transpiration rate (Tr) at the beginning of the stress. However, the +NILs showed significantly higher SPAD (12%), A (66%), stomatal conductance (75%), and Tr (97%) than the -NILs at the seventh day of stress. Quantitative RT-PCR analysis targeting the MYB transcription factor gene Triticum aestivum MYB 82 (TaMYB82), within this genomic region which was retrieved from the wheat reference genome TGACv1, also revealed differential expression in +NILs and -NILs under stress. These results confirmed that the NILs can be invaluable resources for fine mapping of this QTL, and also for cloning and functional characterization of the gene(s) responsible for drought tolerance in wheat.

Mapping QTLs for physiological and biochemical traits related to grain yield under control and terminal heat stress conditions in bread wheat (Triticum aestivum L.)

In order to detect genomic regions with different effects for some of the physiological and biochemical traits of wheat, four experiments were conducted at Research Farm of Agricultural and Natural Resources Research Center of Zabol in 2015-2016 and 2016-2017 growing seasons. The experiments were carried out using four alpha lattice designs with two replications under non-stress and terminal heat stress conditions. Plant materials used in this study included 167 recombinant inbred lines and their parents ('SeriM82' and 'Babax'). Six traits including grain yield (GY), proline content (PRO), water soluble carbohydrates (WSC), maximum efficiency of photosystem II (Fv/Fm), cytoplasmic membrane stability (CMS) and chlorophyll content (CHL) were evaluated. Genetic linkage map consisted of 211 AFLP marker, 120 SSR marker and 144 DArT markers with 1864 cm length and 4.4 cm mean distance. QTL analysis was carried out using a mixed-model-based composite interval mapping (MCIM) method. By the combined analysis of normal phenotypic values, 27 additive QTLs and five pairs of epistatic effects were identified for studied traits, among which two additive and one epistatic QTL showed significant QTL × environment interactions. By the combined analysis of stress phenotypic values, a total of 26 QTLs with additive effects and 5 epistatic QTLs were detected, among which one additive and one epistatic QTL showed QTL × environment interactions. Six QTLs with major effects (QGY-2B, QGY-2D, QPro-5B, QWSC-4A, QFv/Fm-6A and QCMS-4B), which were common between two conditions could be useful for marker-assisted selection (MAS) in order to develop heat tolerant and high-performance wheat varieties.

Genome-Wide Association Study Using Historical Breeding Populations Discovers Genomic Regions Involved in High-Quality Rice

Rice ( L.) is one of the most important staple food crops in the world; however, there has recently been a shift in consumer demand for higher grain quality. Therefore, understanding the genetic architecture of grain quality has become a key objective of rice breeding programs. Genome-wide association studies (GWAS) using large diversity panels have successfully identified genomic regions associated with complex traits in diverse crop species. Our main objective was to identify genomic regions associated with grain quality and to identify and characterize favorable haplotypes for selection. We used two locally adapted rice breeding populations and historical phenotypic data for three rice quality traits: yield after milling, percentage of head rice recovery, and percentage of chalky grain. We detected 22 putative quantitative trait loci (QTL) in the same genomic regions as starch synthesis, starch metabolism, and cell wall synthesis-related genes are found. Additionally, we found a genomic region on chromosome 6 in the population that was associated with all quality traits and we identified favorable haplotypes. Furthermore, this region is linked to the gene that codes for a starch branching enzyme I, which is implicated in starch granule formation. In , we also found two putative QTL linked to , , and . Our study provides an insight into the genetic basis of rice grain chalkiness, yield after milling, and head rice, identifying favorable haplotypes and molecular markers for selection in breeding programs.

Genome-Wide Association Mapping of Seedling Heat Tolerance in Winter Wheat

Heat stress during the seedling stage of early-planted winter wheat (Triticum aestivum L.) is one of the most abiotic stresses of the crop restricting forage and grain production in the Southern Plains of the United States. To map quantitative trait loci (QTLs) and identify single-nucleotide polymorphism (SNP) markers associated with seedling heat tolerance, a genome-wide association mapping study (GWAS) was conducted using 200 diverse representative lines of the hard red winter wheat association mapping panel, which was established by the Triticeae Coordinated Agricultural Project (TCAP) and genotyped with the wheat iSelect 90K SNP array. The plants were initially planted under optimal temperature conditions in two growth chambers. At the three-leaf stage, one chamber was set to 40/35°C day/night as heat stress treatment, while the other chamber was kept at optimal temperature (25/20°C day/night) as control for 14 days. Data were collected on leaf chlorophyll content, shoot length, number of leaves per seedling, and seedling recovery after removal of heat stress treatment. Phenotypic variability for seedling heat tolerance among wheat lines was observed in this study. Using the mixed linear model (MLM), we detected multiple significant QTLs for seedling heat tolerance on different chromosomes. Some of the QTLs were detected on chromosomes that were previously reported to harbor QTLs for heat tolerance during the flowering stage of wheat. These results suggest that some heat tolerance QTLs are effective from the seedling to reproductive stages in wheat. However, new QTLs that have never been reported at the reproductive stage were found responding to seedling heat stress in the present study. Candidate gene analysis revealed high sequence similarities of some significant loci with candidate genes involved in plant stress responses including heat, drought, and salt stress. This study provides valuable information about the genetic basis of seedling heat tolerance in wheat. To the best of our knowledge, this is the first GWAS to map QTLs associated with seedling heat tolerance targeting early planting of dual-purpose winter wheat. The SNP markers identified in this study will be used for marker-assisted selection (MAS) of seedling heat tolerance during dual-purpose wheat breeding.

QTL mapping for nine drought-responsive agronomic traits in bread wheat under irrigated and rain-fed environments

In bread wheat, QTL interval mapping was conducted for nine important drought responsive agronomic traits. For this purpose, a doubled haploid (DH) mapping population derived from Kukri/Excalibur was grown over three years at four separate locations in India, both under irrigated and rain-fed environments. Single locus analysis using composite interval mapping (CIM) allowed detection of 98 QTL, which included 66 QTL for nine individual agronomic traits and 32 QTL, which affected drought sensitivity index (DSI) for the same nine traits. Two-locus analysis allowed detection of 19 main effect QTL (M-QTL) for four traits (days to anthesis, days to maturity, grain filling duration and thousand grain weight) and 19 pairs of epistatic QTL (E-QTL) for two traits (days to anthesis and thousand grain weight). Eight QTL were common in single locus analysis and two locus analysis. These QTL (identified both in single- and two-locus analysis) were distributed on 20 different chromosomes (except 4D). Important genomic regions on chromosomes 5A and 7A were also identified (5A carried QTL for seven traits and 7A carried QTL for six traits). Marker-assisted recurrent selection (MARS) involving pyramiding of important QTL reported in the present study, together with important QTL reported earlier, may be used for improvement of drought tolerance in wheat. In future, more closely linked markers for the QTL reported here may be developed through fine mapping, and the candidate genes may be identified and used for developing a better understanding of the genetic basis of drought tolerance in wheat.

Quantitative trait loci for yield and grain plumpness relative to maturity in three populations of barley (Hordeum vulgare L.) grown in a low rain-fall environment

Identifying yield and grain plumpness QTL that are independent of developmental variation or phenology is of paramount importance for developing widely adapted and stable varieties through the application of marker assisted selection. The current study was designed to dissect the genetic basis of yield performance and grain plumpness in southern Australia using three doubled haploid (DH) populations developed from crosses between adapted parents that are similar in maturity and overall plant development. Three interconnected genetic populations, Commander x Fleet (CF), Commander x WI4304 (CW), and Fleet x WI4304 (FW) developed from crossing of Australian elite barley genotypes, were used to map QTL controlling yield and grain plumpness. QTL for grain plumpness and yield were analysed using genetic linkage maps made of genotyping-by-sequencing markers and major phenology genes, and field trials at three drought prone environments for two growing seasons. Seventeen QTL were detected for grain plumpness. Eighteen yield QTL explaining from 1.2% to 25.0% of the phenotypic variation were found across populations and environments. Significant QTL x environment interaction was observed for all grain plumpness and yield QTL, except QPlum.FW-4H.1 and QYld.FW-2H.1. Unlike previous yield QTL studies in barley, none of the major developmental genes, including Ppd-H1, Vrn-H1, Vrn-H2 and Vrn-H3, that drive barley adaption significantly affected grain plumpness and yield here. Twenty-two QTL controlled yield or grain plumpness independently of known maturity QTL or genes. Adjustment for maturity effects through co-variance analysis had no major effect on these yield QTL indicating that they control yield per se.

Development of a QTL-environment-based predictive model for node addition rate in common bean

This work reports the effects of the genetic makeup, the environment and the genotype by environment interactions for node addition rate in an RIL population of common bean. This information was used to build a predictive model for node addition rate. To select a plant genotype that will thrive in targeted environments it is critical to understand the genotype by environment interaction (GEI). In this study, multi-environment QTL analysis was used to characterize node addition rate (NAR, node day^- 1) on the main stem of the common bean (Phaseolus vulgaris L). This analysis was carried out with field data of 171 recombinant inbred lines that were grown at five sites (Florida, Puerto Rico, 2 sites in Colombia, and North Dakota). Four QTLs (Nar1, Nar2, Nar3 and Nar4) were identified, one of which had significant QTL by environment interactions (QEI), that is, Nar2 with temperature. Temperature was identified as the main environmental factor affecting NAR while day length and solar radiation played a minor role. Integration of sites as covariates into a QTL mixed site-effect model, and further replacing the site component with explanatory environmental covariates (i.e., temperature, day length and solar radiation) yielded a model that explained 73% of the phenotypic variation for NAR with root mean square error of 16.25% of the mean. The QTL consistency and stability was examined through a tenfold cross validation with different sets of genotypes and these four QTLs were always detected with 50-90% probability. The final model was evaluated using leave-one-site-out method to assess the influence of site on node addition rate. These analyses provided a quantitative measure of the effects on NAR of common beans exerted by the genetic makeup, the environment and their interactions.

Gene interactions and genetics for yield and its attributes in grass pea (Lathyrus sativus L.)

Grain yield is a complex character representing a multiplicative end product of many yield attributes. However, understanding the genetics and inheritance that underlies yield and its component characters pose a prerequisite to attain the actual yield potential of any crop species. The knowledge pertaining to gene actions and interactions is likely to direct and strengthen the crop breeding programmes. With this objective, the present investigation was undertaken by using six generations derived from three different crosses in grass pea. The study underscores the significance of additive-dominance model, gene action involved in inheritance of quantitative characters and heritability. Of note, nonallelic interactions influencing the traits were detected by both scaling test and joint scaling test, indicating the inadequacy of the additive-dominance model alone in explaining the manifestation of complex traits such as yield. Besides, additive (d) and dominance (h) gene effects, different types of interallelic interactions (i, j, l) contributed towards the inheritance of traits in the given crosses. Nevertheless, predominance of additive variance suggests a difference between homozygotes at a locus with positive and negative alleles being distributed between the parents. Duplicate epistasis was prevalent in most of the cases for traits like plant height, seeds/pod, 100-seed weight and pod width. In view of the diverse gene actions, i.e. additive, dominant and epistasis, playing important roles in the manifestation of complex traits like yield, we advocate implementation of population improvement techniques in particular reciprocal recurrent selection to improve productivity gains in grass pea.

Genome-wide association analysis of agronomic traits in wheat under drought-stressed and non-stressed conditions

This study determined the population structure and genome-wide marker-trait association of agronomic traits of wheat for drought-tolerance breeding. Ninety-three diverse bread wheat genotypes were genotyped using the Diversity Arrays Technology sequencing (DArTseq) protocol. The number of days-to-heading (DTH), number of days-to-maturity (DTM), plant height (PHT), spike length (SPL), number of kernels per spike (KPS), thousand kernel weight (TKW) and grain yield (GYLD), assessed under drought-stressed and non-stressed conditions, were considered for the study. Population structure analysis and genome-wide association mapping were undertaken based on 16,383 silico DArTs loci with < 10% missing data. The population evaluated was grouped into nine distinct genetic structures. Inter-chromosomal linkage disequilibrium showed the existence of linkage decay as physical distance increased. A total of 62 significant (P < 0.001) marker-trait associations (MTAs) were detected explaining more than 20% of the phenotypic variation observed under both drought-stressed and non-stressed conditions. Significant (P < 0.001) MTA event(s) were observed for DTH, PHT, SPL, SPS, and KPS; under both stressed and non-stressed conditions, while additional significant (P < 0.05) associations were observed for TKW, DTM and GYLD under non-stressed condition. The MTAs reported in this population could be useful to initiate marker-assisted selection (MAS) and targeted trait introgression of wheat under drought-stressed and non-stressed conditions, and for fine mapping and cloning of the underlying genes and QTL.

Combining Genome-Wide Information with a Functional Structural Plant Model to Simulate 1-Year-Old Apple Tree Architecture

In crops, optimizing target traits in breeding programs can be fostered by selecting appropriate combinations of architectural traits which determine light interception and carbon acquisition. In apple tree, architectural traits were observed to be under genetic control. However, architectural traits also result from many organogenetic and morphological processes interacting with the environment. The present study aimed at combining a FSPM built for apple tree, MAppleT, with genetic determinisms of architectural traits, previously described in a bi-parental population. We focused on parameters related to organogenesis (phyllochron and immediate branching) and morphogenesis processes (internode length and leaf area) during the first year of tree growth. Two independent datasets collected in 2004 and 2007 on 116 genotypes, issued from a 'Starkrimson' × 'Granny Smith' cross, were used. The phyllochron was estimated as a function of thermal time and sylleptic branching was modeled subsequently depending on phyllochron. From a genetic map built with SNPs, marker effects were estimated on four MAppleT parameters with rrBLUP, using 2007 data. These effects were then considered in MAppleT to simulate tree development in the two climatic conditions. The genome wide prediction model gave consistent estimations of parameter values with correlation coefficients between observed values and estimated values from SNP markers ranging from 0.79 to 0.96. However, the accuracy of the prediction model following cross validation schemas was lower. Three integrative traits (the number of leaves, trunk length, and number of sylleptic laterals) were considered for validating MAppleT simulations. In 2007 climatic conditions, simulated values were close to observations, highlighting the correct simulation of genetic variability. However, in 2004 conditions which were not used for model calibration, the simulations differed from observations. This study demonstrates the possibility of integrating genome-based information in a FSPM for a perennial fruit tree. It also showed that further improvements are required for improving the prediction ability. Especially temperature effect should be extended and other factors taken into account for modeling GxE interactions. Improvements could also be expected by considering larger populations and by testing other genome wide prediction models. Despite these limitations, this study opens new possibilities for supporting plant breeding by in silico evaluations of the impact of genotypic polymorphisms on plant integrative phenotypes.

Ascertainment bias from imputation methods evaluation in wheat

BACKGROUND

Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel.

RESULTS

In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available.

CONCLUSIONS

Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel.

Identification of manganese efficiency candidate genes in winter barley (Hordeum vulgare) using genome wide association mapping

BACKGROUND

Manganese (Mn) has several essential functions in plants, including a role as cofactor in the oxygen evolving complex (OEC) of photosystem II (PSII). Manganese deficiency is a major plant nutritional disorder in winter cereals resulting in significant yield reductions and winter kill in more severe cases. Among the winter cereals, genotypes of winter barley are known to differ considerably in tolerance to Mn deficiency, but the genes controlling the Mn deficiency trait remains elusive.

RESULTS

Experiments were conducted using 248 barley varieties, cultivated in six distinct environments prone to induce Mn deficiency. High-throughput phenotyping for Mn deficiency was performed by chlorophyll a (Chl a) fluorescence analysis to quantify the quantum yield efficiency of PSII. High-throughput phenotyping in combination with ICP-OES based multi-element analyses allowed detection of marker-trait associations by genome wide association (GWA) mapping. Several key candidate genes were identified, including PSII subunit proteins, germin like proteins and Mn superoxide dismutase. The putative roles of the encoded proteins in Mn dependent metabolic processes are discussed.

CONCLUSIONS

Fifty-four candidate genes were identified by Chl a fluorescence phenotyping and association genetics. Tolerance of plants to Mn deficiency, which is referred to as Mn efficiency, appeared to be a complex trait involving many genes. Moreover, the trait appeared to be highly dependent on the environmental conditions in field. This study provides the basis for an improved understanding of the parameters influencing Mn efficiency and is valuable in future plant breeding aiming at producing new varieties with improved tolerance to cultivation in soil prone to induce Mn deficiency.

Mapping QTLs associated with agronomic and physiological traits under terminal drought and heat stress conditions in wheat (Triticum aestivum L.)

Wheat crops frequently experience a combination of abiotic stresses in the field, but most quantitative trait loci (QTL) studies have focused on the identification of QTLs for traits under single stress field conditions. A recombinant inbred line (RIL) population derived from SeriM82 × Babax was used to map QTLs under well-irrigated, heat, drought, and a combination of heat and drought stress conditions in two years. A total of 477 DNA markers were used to construct linkage groups that covered 1619.6 cM of the genome, with an average distance of 3.39 cM between adjacent markers. Moderate to relatively high heritability estimates (0.60-0.70) were observed for plant height (PHE), grain yield (YLD), and grain per square meter (GM2). The most important QTLs for days to heading (DHE), thousand grain weight (TGW), and YLD were detected on chromosomes 1B, 1D-a, and 7D-b. The prominent QTLs related to canopy temperature were on 3B. Results showed that common QTLs for DHE, YLD, and TGW on 7D-b were validated in heat and drought trials. Three QTLs for chlorophyll content in SPAD unit (on 1A/6B), leaf rolling (ROL) (on 3B/4A), and GM2 (on 1B/7D-b) showed significant epistasis × environment interaction. Six heat- or drought-specific QTLs (linked to 7D-acc/cat-10, 1B-agc/cta-9, 1A-aag/cta-8, 4A-acg/cta-3, 1B-aca/caa-3, and 1B-agc/cta-9 for day to maturity (DMA), SPAD, spikelet compactness (SCOM), TGW, GM2, and GM2, respectively) were stable and validated over two years. The major DHE QTL linked to 7D-acc/cat-10, with no QTL × environment (QE) interaction increased TGW and YLD. This QTL (5.68 ≤ LOD ≤ 10.5) explained up to 19.6% variation in YLD in drought, heat, and combined stress trials. This marker as a candidate could be used for verification in other populations and identifying superior allelic variations in wheat cultivars or its wild progenitors to increase the efficiency of selection of high yielding lines adapted to end-season heat and drought stress conditions.

Genome-wide association mapping of quantitative traits in a breeding population of sugarcane

BACKGROUND

Molecular markers associated with relevant agronomic traits could significantly reduce the time and cost involved in developing new sugarcane varieties. Previous sugarcane genome-wide association analyses (GWAS) have found few molecular markers associated with relevant traits at plant-cane stage. The aim of this study was to establish an appropriate GWAS to find molecular markers associated with yield related traits consistent across harvesting seasons in a breeding population. Sugarcane clones were genotyped with DArT (Diversity Array Technology) and TRAP (Target Region Amplified Polymorphism) markers, and evaluated for cane yield (CY) and sugar content (SC) at two locations during three successive crop cycles. GWAS mapping was applied within a novel mixed-model framework accounting for population structure with Principal Component Analysis scores as random component.

RESULTS

A total of 43 markers significantly associated with CY in plant-cane, 42 in first ratoon, and 41 in second ratoon were detected. Out of these markers, 20 were associated with CY in 2 years. Additionally, 38 significant associations for SC were detected in plant-cane, 34 in first ratoon, and 47 in second ratoon. For SC, one marker-trait association was found significant for the 3 years of the study, while twelve markers presented association for 2 years. In the multi-QTL model several markers with large allelic substitution effect were found. Sequences of four DArT markers showed high similitude and e-value with coding sequences of Sorghum bicolor, confirming the high gene microlinearity between sorghum and sugarcane.

CONCLUSIONS

In contrast with other sugarcane GWAS studies reported earlier, the novel methodology to analyze multi-QTLs through successive crop cycles used in the present study allowed us to find several markers associated with relevant traits. Combining existing phenotypic trial data and genotypic DArT and TRAP marker characterizations within a GWAS approach including population structure as random covariates may prove to be highly successful. Moreover, sequences of DArT marker associated with the traits of interest were aligned in chromosomal regions where sorghum QTLs has previously been reported. This approach could be a valuable tool to assist the improvement of sugarcane and better supply sugarcane demand that has been projected for the upcoming decades.

Pot size matters revisited: does container size affect the response to elevated CO2 and our ability to detect genotypic variability in this response in wheat?

Many studies have investigated the effect of elevated CO2 (eCO2) in wheat, although few have evaluated the potential of genotypic variability in the response. Such studies are the next logical step in wheat climate change adaptation research, and they will require the evaluation of large numbers of genotypes. For practical reasons the preliminary studies are most likely to be conducted in controlled environments. There have been concerns that the root restriction related to container-grown plants can influence (1) the response to eCO2, (2) the detection of genotypic variability for various traits of interest, and (3) the ability to find the genotypes most responsive to eCO2. In the present study we evaluated two sizes of container - 1.4L pots and 7.5L columns - side-by side in a glasshouse environment and found that for 14 of 23 traits observed environment effects (ambient CO2, eCO2 or eCO2 and high temperature) were not consistent between plants grown in pots and in columns. More importantly, of the 21 traits showing genotypic variability, only 8 showed consistent genotype differences and rankings across both container types. Statistical analyses conducted separately for plants grown in pots or in columns showed different cultivars as being the most responsive to elevated CO2 and would thus, have led to different conclusions. This study is intended as a message of caution to controlled environment experimenters: using small containers can artificially create conditions that could either hide or overly express genotypic variability in some traits in response to eCO2 compared with what might be expected in larger containers.

QTL Analysis of Spike Morphological Traits and Plant Height in Winter Wheat (Triticum aestivum L.) Using a High-Density SNP and SSR-Based Linkage Map

Wheat yield can be enhanced by modifying the spike morphology and the plant height. In this study, a population of 191 F₉ recombinant inbred lines (RILs) was developed from a cross between two winter cultivars Yumai 8679 and Jing 411. A dense genetic linkage map with 10,816 markers was constructed by incorporating single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) marker information. Five spike morphological traits and plant height were evaluated under nine environments for the RILs and parental lines, and the number of detected environmentally stable QTLs were 18 and three, respectively. The 1RS/1BL (rye) translocation increased both spike length and spikelet number with constant spikelet compactness. The QPht.cau-2D.1 was identical to gene Rht8, which decreased spike length without modifying spikelet number. Notably, four novel QTLs locating on chromosomes 1AS (QSc.cau-1A.1), 2DS (QSc.cau-2D.1), and 7BS (QSl.cau-7B.1 and QSl.cau-7B.2) were firstly identified in this study, which provide further insights into the genetic factors that shaped the spike morphology in wheat. Moreover, SNP markers tightly linked to previously reported QTLs will eventually facilitate future studies including their positional cloning or marker-assisted selection.

Genome wide identification of C1-2i zinc finger proteins and their response to abiotic stress in hexaploid wheat

The C1-2i wheat Q-type C2H2 zinc finger protein (ZFP) transcription factor subclass has been reported to play important roles in plant stress responses. This subclass of ZFPs has not been studied in hexaploid wheat (Triticum aestivum) and we aimed to identify all members of this subclass and evaluate their responses to different abiotic stresses causing oxidative stress. Exploiting the recently published wheat draft genome sequence, we identified 53 members (including homoeologs from A, B and D genomes) of the C1-2i wheat Q-type C2H2 ZFPs (TaZFPs) representing 21 genes. Evolution analysis revealed that 9 TaZFPs members are directly inherited from the parents Triticum urartu and Aegilops tauschii, while 15 diverged through neoploidization events. This TaZFP subclass is responsive to the oxidative stress generator H2O2 and to high light, drought stress and flooding. Most TaZFPs are responsive to H2O2 (37/53), high light (44/53), flooding (31/53) or drought (37/53); 32 TaZFPs were up-regulated by at least 3 stresses and 16 were responsive to all stresses tested. A large number of these TaZFPs were physically mapped on different wheat draft genome sequences with known markers useful for QTL mapping. Our results show that the C1-2i subclass of TaZFPs is associated with responses to different abiotic stresses and that most TaZFPs (30/53 or 57 %) are located on group 5 chromosomes known to be involved in environment adaptation. Detailed characterization of these novel wheat TaZFPs and their association to QTL or eQTL may help to design wheat cultivars with improved tolerance to abiotic stress.

Drought susceptibility of modern rice varieties: an effect of linkage of drought tolerance with undesirable traits

Green Revolution (GR) rice varieties are high yielding but typically drought sensitive. This is partly due to the tight linkage between the loci governing plant height and drought tolerance. This linkage is illustrated here through characterization of qDTY1.1, a QTL for grain yield under drought that co-segregates with the GR gene sd1 for semi-dwarf plant height. We report that the loss of the qDTY1.1 allele during the GR was due to its tight linkage in repulsion with the sd1 allele. Other drought-yield QTLs (qDTY) also showed tight linkage with traits rejected in GR varieties. Genetic diversity analysis for 11 different qDTY regions grouped GR varieties separately from traditional drought-tolerant varieties, and showed lower frequency of drought tolerance alleles. The increased understanding and breaking of the linkage between drought tolerance and undesirable traits has led to the development of high-yielding drought-tolerant dwarf lines with positive qDTY alleles and provides new hope for extending the benefits of the GR to drought-prone rice-growing regions.

Common genetic basis for canopy temperature depression under heat and drought stress associated with optimized root distribution in bread wheat

QTL related to cooler canopy temperatures are associated with optimal root distribution whereby roots proliferate at depth under drought or near to surface under hot, irrigated conditions. Previous research using a bread wheat RIL population of the Seri/Babax cross showed that common QTL were associated with cooler canopies under both drought and heat-stressed conditions. A subset of RIL was grown under water-limited and hot-irrigated field environments to test how cooler canopies are related to root development. Eight sisters and the two parents were used in the study with genotypes grouped as COOL or HOT according to their respective QTL for canopy temperature and previous phenotypic data. Root mass production and residual available soil moisture were measured around anthesis at four depth profiles (from 0 to 120 cm depth). When considering different root profiles, there was a clear interaction of QTL with environment. Under water stress, the COOL genotypes showed a deeper root system allowing the extraction of 35% more water from the 30-90 cm soil profile. The strategy under heat was to concentrate more roots at the surface, in the 0-60 cm soil layer where water was more available from surface irrigation. Since COOL genotypes showed better agronomic performance, it can be concluded that their QTL are associated with more optimal root distribution in accordance with water availability under the respective stresses. The study demonstrates the importance of root development under both water-limited and hot-irrigated environments, and shows a common genetic basis for adaptation to both stresses that appears to be associated with sensitivity of roots to proliferate where water is available in the soil profile.

Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America

Multi-environment multi-QTL mixed models were used in a GWAS context to identify QTL for disease resistance. The use of mega-environments aided the interpretation of environment-specific and general QTL. Diseases represent a major constraint for barley (Hordeum vulgare L.) production in Latin America. Spot blotch (caused by Cochliobolus sativus), stripe rust (caused by Puccinia striiformis f.sp. hordei) and leaf rust (caused by Puccinia hordei) are three of the most important diseases that affect the crop in the region. Since fungicide application is not an economically or environmentally sound solution, the development of durably resistant varieties is a priority for breeding programs. Therefore, new resistance sources are needed. The objective of this work was to detect genomic regions associated with field level plant resistance to spot blotch, stripe rust, and leaf rust in Latin American germplasm. Disease severities measured in multi-environment trials across the Americas and 1,096 SNPs in a population of 360 genotypes were used to identify genomic regions associated with disease resistance. Optimized experimental design and spatial modeling were used in each trial to estimate genotypic means. Genome-Wide Association Mapping (GWAS) in each environment was used to detect Quantitative Trait Loci (QTL). All significant environment-specific QTL were subsequently included in a multi-environment-multi-QTL (MEMQ) model. Geographical origin and inflorescence type were the main determinants of population structure. Spot blotch severity was low to intermediate while leaf and stripe rust severity was high in all environments. Mega-environments were defined by locations for spot blotch and leaf rust. Significant marker-trait associations for spot blotch (9 QTL), leaf (6 QTL) and stripe rust (7 QTL) and both global and environment-specific QTL were detected that will be useful for future breeding efforts.

Genome wide association study for drought, aflatoxin resistance, and important agronomic traits of maize hybrids in the sub-tropics

The primary maize (Zea mays L.) production areas are in temperate regions throughout the world and this is where most maize breeding is focused. Important but lower yielding maize growing regions such as the sub-tropics experience unique challenges, the greatest of which are drought stress and aflatoxin contamination. Here we used a diversity panel consisting of 346 maize inbred lines originating in temperate, sub-tropical and tropical areas testcrossed to stiff-stalk line Tx714 to investigate these traits. Testcross hybrids were evaluated under irrigated and non-irrigated trials for yield, plant height, ear height, days to anthesis, days to silking and other agronomic traits. Irrigated trials were also inoculated with Aspergillus flavus and evaluated for aflatoxin content. Diverse maize testcrosses out-yielded commercial checks in most trials, which indicated the potential for genetic diversity to improve sub-tropical breeding programs. To identify genomic regions associated with yield, aflatoxin resistance and other important agronomic traits, a genome wide association analysis was performed. Using 60,000 SNPs, this study found 10 quantitative trait variants for grain yield, plant and ear height, and flowering time after stringent multiple test corrections, and after fitting different models. Three of these variants explained 5-10% of the variation in grain yield under both water conditions. Multiple identified SNPs co-localized with previously reported QTL, which narrows the possible location of causal polymorphisms. Novel significant SNPs were also identified. This study demonstrated the potential to use genome wide association studies to identify major variants of quantitative and complex traits such as yield under drought that are still segregating between elite inbred lines.

Multiple QTLs Linked to Agro-Morphological and Physiological Traits Related to Drought Tolerance in Potato

Dissection of the genetic architecture of adaptation and abiotic stress-related traits is highly desirable for developing drought-tolerant potatoes and enhancing the resilience of existing cultivars, particularly as agricultural production in rain-fed areas may be reduced by up to 50 % by 2020. The "DMDD" potato progeny was developed at International Potato Center (CIP) by crossing the sequenced double monoploid line DM and a diploid cultivar of the Solanum tuberosum diploid Andigenum Goniocalyx group. Recently, a high-density integrated genetic map based on single nucleotide polymorphism (SNP), diversity array technology (DArT), simple sequence repeats (SSRs), and amplified fragment length polymorphism (AFLP) markers was also made available for this population. Two trials were conducted, in greenhouse and field, for drought tolerance with two treatments each, well-watered and terminal drought, in which watering was suspended 60 days after planting. The DMDD population was evaluated for agro-morphological and physiological traits before and after initiation of stress, at multiple time points. Two dense parental genetic maps were constructed using published genotypic data, and quantitative trait locus (QTL) analysis identified 45 genomic regions associated with nine traits in well-watered and terminal drought treatments and 26 potentially associated with drought stress. In this study, the strong influence of environmental factors besides water shortage on the expression of traits and QTLs reflects the multigenic control of traits related to drought tolerance. This is the first study to our knowledge in potato identifying QTLs for drought-related traits in field and greenhouse trials, giving new insights into genetic architecture of drought-related traits. Many of the QTLs identified have the potential to be used in potato breeding programs for enhanced drought tolerance.

Efficiency of augmented p-rep designs in multi-environmental trials

The paper shows that unreplicated designs in multi-environmental trials are most efficient. If replication per environment is needed then augmented p-rep designs outperform augmented and replicated designs in triticale and maize. In plant breeding, augmented designs with unreplicated entries are frequently used for early generation testing. With limited amount of seed, this design allows to use a maximum number of environments in multi-environmental trials (METs). Check plots enable the estimation of block effects, error variances and a connection of otherwise unconnected trials in METs. Cullis et al. (J Agri Biol Environ Stat 11:381-393, 2006) propose to replace check plots from a grid-plot design by plots of replicated entries leading to partially replicated (p-rep) designs. Williams et al. (Biom J 53:19-27, 2011) apply this idea to augmented designs (augmented p-rep designs). While p-rep designs are increasingly used in METs, a comparison of the efficiency of augmented p-rep designs and augmented designs in the range between replicated and unreplicated designs in METs is lacking. We simulated genetic effects and allocated them according to these four designs to plot yields of a triticale and a maize uniformity trial. The designs varied in the number of environments, but have a fixed number of entries and total plots. The error model and the assumption of fixed or random entry effects were varied in simulations. We extended our simulation for the triticale data by including correlated entry effects which are common in genomic selection. Results show an advantage of unreplicated and augmented p-rep designs and a preference for using random entry effects, especially in case of correlated effects reflecting relationships among entries. Spatial error models had minor advantages compared to purely randomization-based models.

More fertile florets and grains per spike can be achieved at higher temperature in wheat lines with high spike biomass and sugar content at booting

An understanding of processes regulating wheat floret and grain number at higher temperatures is required to better exploit genetic variation. In this study we tested the hypothesis that at higher temperatures, a reduction in floret fertility is associated with a decrease in soluble sugars and this response is exacerbated in genotypes low in water soluble carbohydrates (WSC). Four recombinant inbred lines contrasting for stem WSC were grown at 20/10°C and 11h photoperiod until terminal spikelet, and then continued in a factorial combination of 20/10°C or 28/14°C with 11h or 16h photoperiod until anthesis. Across environments, High WSC lines had more grains per spike associated with more florets per spike. The number of fertile florets was associated with spike biomass at booting and, by extension, with glucose amount, both higher in High WSC lines. At booting, High WSC lines had higher fixed 13C and higher levels of expression of genes involved in photosynthesis and sucrose transport and lower in sucrose degradation compared with Low WSC lines. At higher temperature, the intrinsic rate of floret development rate before booting was slower in High WSC lines. Grain set declined with the intrinsic rate of floret development before booting, with an advantage for High WSC lines at 28/14°C and 16h. Genotypic and environmental action on floret fertility and grain set was summarised in a model.

Plot size matters: interference from intergenotypic competition in plant phenotyping studies

Genetic and physiological studies often comprise genotypes diverse in vigour, size and flowering time. This can make the phenotyping of complex traits challenging, particularly those associated with canopy development, biomass and yield, as the environment of one genotype can be influenced by a neighbouring genotype. Limited seed and space may encourage field assessment in single, spaced rows or in small, unbordered plots, whereas the convenience of a controlled environment or greenhouse makes pot studies tempting. However, the relevance of such growing conditions to commercial field-grown crops is unclear and often doubtful. Competition for water, light and nutrients necessary for canopy growth will be variable where immediate neighbours are genetically different, particularly under stress conditions, where competition for resources and influence on productivity is greatest. Small hills and rod-rows maximise the potential for intergenotypic competition that is not relevant to a crop's performance in monocultures. Response to resource availability will typically vary among diverse genotypes to alter genotype ranking and reduce heritability for all growth-related traits, with the possible exception of harvest index. Validation of pot experiments to performance in canopies in the field is essential, whereas the planting of multirow plots and the simple exclusion of plot borders at harvest will increase experimental precision and confidence in genotype performance in target environments.

Association mapping for root architectural traits in durum wheat seedlings as related to agronomic performance

Association mapping provides useful insights on the genetic architecture of quantitative traits across a large number of unrelated genotypes, which in turn allows an informed choice of the lines to be crossed for a more accurate characterization of major QTLs in a biparental genetic background. In this study, seedlings of 183 durum wheat elite accessions were evaluated in order to identify QTLs for root system architecture (RSA). The QTLs identified were compared with QTLs detected for grain yield and its component traits, plant height and peduncle length measured in a previous study where the same accessions were evaluated in 15 field trials with a broad range of soil moisture availability and productivity (Maccaferri et al. in J Exp Bot 62:409-438, 2011). The following RSA features were investigated in seedlings at the four-leaf stage: seminal root angle, primary root length, total root length, average root length, root number and shoot length. Highly significant differences among accessions were detected for all traits. The highest repeatability (h² = 0.72) was observed for seminal root angle. Out of the 48 QTLs detected for RSA, 15 overlapped with QTLs for agronomic traits and/or grain yield in two or more environments. The congruency of the effects of RSA traits and agronomic traits was evaluated. Seminal root angle and root number appear the most promising traits for further studies on the adaptive role of RSA plasticity on field performance in environments differing for water availability. Our results provide novel insights on the genetic control of RSA and its implications on field performance of durum wheat.