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Durum wheat is more susceptible to Fusarium head blight (FHB) than other types or classes of wheat. The disease is one of the most devastating in wheat; it reduces yield and end-use quality and contaminates the grain with fungal mycotoxins such as deoxynivalenol (DON). A panel of 265 Canadian and European durum wheat cultivars, as well as breeding and experimental lines, were tested in artificially inoculated field environments (2019–2022, inclusive) and two greenhouse trials (2019 and 2020). The trials were assessed for FHB severity and incidence, visual rating index, Fusarium-damaged kernels, DON accumulation, anthesis or heading date, maturity date, and plant height. In addition, yellow pigment and protein content were analyzed for the 2020 field season. To capture loci underlying FHB resistance and related traits, GWAS was performed using single-locus and several multi-locus models, employing 13,504 SNPs. Thirty-one QTL significantly associated with one or more FHB-related traits were identified, of which nine were consistent across environments and associated with multiple FHB-related traits. Although many of the QTL were identified in regions previously reported to affect FHB, the QTL QFhb-3B.2, associated with FHB severity, incidence, and DON accumulation, appears to be novel. We developed KASP markers for six FHB-associated QTL that were consistently detected across multiple environments and validated them on the Global Durum Panel (GDP). Analysis of allelic diversity and the frequencies of these revealed that the lines in the GDP harbor between zero and six resistance alleles. This study provides a comprehensive assessment of the genetic basis of FHB resistance and DON accumulation in durum wheat. Accessions with multiple favorable alleles were identified and will be useful genetic resources to improve FHB resistance in durum breeding programs through marker-assisted recurrent selection and gene stacking.

Fusarium head blight (FHB) is one the most globally destructive fungal diseases in wheat and other small grains, causing a reduction in grain yield by 10–70%. The present study was conducted in a panel of historical and modern Canadian spring wheat (Triticum aestivum L.) varieties and lines to identify new sources of FHB resistance and map associated quantitative trait loci (QTLs). We evaluated 249 varieties and lines for reaction to disease incidence, severity, and visual rating index (VRI) in seven environments by artificially spraying a mixture of four Fusarium graminearum isolates. A subset of 198 them were genotyped with the Wheat 90K iSelect single nucleotide polymorphisms (SNPs) array. Genome-wide association mapping performed on the overall best linear unbiased estimators (BLUE) computed from all seven environments and the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map of 26,449 polymorphic SNPs out of the 90K identified sixteen FHB resistance QTLs that individually accounted for 5.7–10.2% of the phenotypic variance. The positions of two of the FHB resistance QTLs overlapped with plant height and flowering time QTLs. Four of the QTLs (QFhb.dms-3B.1, QFhb.dms-5A.5, QFhb.dms-5A.7, and QFhb.dms-6A.4) were simultaneously associated with disease incidence, severity, and VRI, which accounted for 27.0–33.2% of the total phenotypic variance in the combined environments. Three of the QTLs (QFhb.dms-2A.2, QFhb.dms-2D.2, and QFhb.dms-5B.8) were associated with both incidence and VRI and accounted for 20.5–22.1% of the total phenotypic variance. In comparison with the VRI of the checks, we identified four highly resistant and thirty-three moderately resistant lines and varieties. The new FHB sources of resistance and the physical map of the associated QTLs would provide wheat breeders valuable information towards their efforts in developing improved varieties in western Canada.

Fusarium head blight (FHB) has rapidly become a major challenge to successful wheat production and competitive end-use quality in western Canada. Continuous effort is required to develop germplasm with improved FHB resistance and understand how to incorporate the material into crossing schemes for marker-assisted selection and genomic selection. The aim of this study was to map quantitative trait loci (QTL) responsible for the expression of FHB resistance in two adapted cultivars and to evaluate their co-localization with plant height, days to maturity, days to heading, and awnedness. A large doubled haploid population of 775 lines developed from cultivars Carberry and AC Cadillac was assessed for FHB incidence and severity in nurseries near Portage la Prairie, Brandon, and Morden in different years, and for plant height, awnedness, days to heading, and days to maturity near Swift Current. An initial linkage map using a subset of 261 lines was constructed using 634 polymorphic DArT and SSR markers. QTL analysis revealed five resistance QTL on chromosomes 2A, 3B (two loci), 4B, and 5A. A second genetic map with increased marker density was constructed using the Infinium iSelect 90k SNP wheat array in addition to the previous DArT and SSR markers, which revealed two additional QTL on 6A and 6D. The complete population was genotyped, and a total of 6,806 Infinium iSelect 90k SNP polymorphic markers were used to identify 17 putative resistance QTL on 14 different chromosomes. As with the smaller population size and fewer markers, large-effect QTL were detected on 3B, 4B, and 5A that were consistently expressed across environments. FHB resistance QTL were co-localized with plant height QTL on chromosomes 4B, 6D, and 7D; days to heading on 2B, 3A, 4A, 4B, and 5A; and maturity on 3A, 4B, and 7D. A major QTL for awnedness was identified as being associated with FHB resistance on chromosome 5A. Nine small-effect QTL were not associated with any of the agronomic traits, whereas 13 QTL that were associated with agronomic traits did not co-localize with any of the FHB traits. There is an opportunity to select for improved FHB resistance within adapted cultivars by using markers associated with complementary QTL.

IntroductionWheat rust diseases are widespread and affect all wheat growing areas around the globe. Breeding strategies focus on incorporating genetic disease resistance. However, pathogens can quickly evolve and overcome the resistance genes deployed in commercial cultivars, creating a constant need for identifying new sources of resistance.MethodsWe have assembled a diverse tetraploid wheat panel comprised of 447 accessions of three Triticum turgidum subspecies and performed a genome-wide association study (GWAS) for resistance to wheat stem, stripe, and leaf rusts. The panel was genotyped with the 90K Wheat iSelect single nucleotide polymorphism (SNP) array and subsequent filtering resulted in a set of 6,410 non-redundant SNP markers with known physical positions.ResultsPopulation structure and phylogenetic analyses revealed that the diversity panel could be divided into three subpopulations based on phylogenetic/geographic relatedness. Marker-trait associations (MTAs) were detected for two stem rust, two stripe rust and one leaf rust resistance loci. Of them, three MTAs coincide with the known rust resistance genes Sr13, Yr15 and Yr67, while the other two may harbor undescribed resistance genes.DiscussionThe tetraploid wheat diversity panel, developed and characterized herein, captures wide geographic origins, genetic diversity, and evolutionary history since domestication making it a useful community resource for mapping of other agronomically important traits and for conducting evolutionary studies.

Abstract Previous molecular characterization studies conducted in Canadian wheat cultivars shed some light on the impact of plant breeding on genetic diversity, but the number of varieties and markers used was small. Here, we used 28,798 markers of the wheat 90K single nucleotide polymorphisms to (a) assess the extent of genetic diversity, relationship, population structure, and divergence among 174 historical and modern Canadian spring wheat varieties registered from 1905 to 2018 and 22 unregistered lines (hereinafter referred to as cultivars), and (b) identify genomic regions that had undergone selection. About 91% of the pairs of cultivars differed by 20–40% of the scored alleles, but only 7% of the pairs had kinship coefficients of

Abstract Background Discovering single nucleotide polymorphisms (SNPs) from agriculture crop genome sequences has been a widely used strategy for developing genetic markers for several applications including marker-assisted breeding, population diversity studies for eco-geographical adaption, genotyping crop germplasm collections, and others. Accurately detecting SNPs from large polyploid crop genomes such as wheat is crucial and challenging. A few variant calling methods have been previously developed but they show a low concordance between their variant calls. A gold standard of variant sets generated from one human individual sample was established for variant calling tool evaluations, however hitherto no gold standard of crop variant set is available for wheat use. The intent of this study was to evaluate seven SNP variant calling tools (FreeBayes, GATK, Platypus, Samtools/mpileup, SNVer, VarScan, VarDict) with the two most popular mapping tools (BWA-mem and Bowtie2) on wheat whole exome capture (WEC) re-sequencing data from allohexaploid wheat. Results We found the BWA-mem mapping tool had both a higher mapping rate and a higher accuracy rate than Bowtie2. With the same mapping quality (MQ) cutoff, BWA-mem detected more variant bases in mapping reads than Bowtie2. The reads preprocessed with quality trimming or duplicate removal did not significantly affect the final mapping performance in terms of mapped reads. Based on the concordance and receiver operating characteristic (ROC), the Samtools/mpileup variant calling tool with BWA-mem mapping of raw sequence reads outperformed other tests followed by FreeBayes and GATK in terms of specificity and sensitivity. VarDict and VarScan were the poorest performing variant calling tools with the wheat WEC sequence data. Conclusion The BWA-mem and Samtools/mpileup pipeline, with no need to preprocess the raw read data before mapping onto the reference genome, was ascertained the optimum for SNP calling for the complex wheat genome re-sequencing. These results also provide useful guidelines for reliable variant identification from deep sequencing of other large polyploid crop genomes.

The hexaploid spring wheat cultivar, Carberry, was registered in Canada in 2009, and has since been grown over an extensive area on the Canadian Prairies. Carberry has maintained a very high level of leaf rust (Puccinia triticina Eriks.) resistance since its release. To understand the genetic basis of Carberry’s leaf rust resistance, Carberry was crossed with the susceptible cultivar, Thatcher, and a doubled haploid (DH) population of 297 lines was generated. The DH population was evaluated for leaf rust in seven field environments at the adult plant stage. Seedling and adult plant resistance (APR) to multiple virulence phenotypes of P. triticina was evaluated on the parents and the progeny population in controlled greenhouse studies. The population was genotyped with the wheat 90 K iSelect single nucleotide polymorphism (SNP) array, and quantitative trait loci (QTL) analysis was performed. The analysis using field leaf rust response indicated that Carberry contributed nine QTL located on chromosomes 1B, 2B (2 loci), 2D, 4A, 4B, 5A, 5B, and 7D. The QTL located on 1B, 2B, 5B, and 7D chromosomes were observed in two or more environments, whereas the remainder were detected in single environments. The resistance on 1B, detected in five environments, was attributed to Lr46 and on 7D, detected in seven environments to Lr34. The first 2B QTL corresponded with the adult plant gene, Lr13, while the second QTL corresponded with Lr16. The seedling analysis showed that Carberry carries Lr2a, Lr16, and Lr23. Five epistatic effects were identified in the population, with synergistic interactions being observed for Lr34 with Lr46, Lr16, and Lr2a. The durable rust resistance of Carberry is attributed to Lr34 and Lr46 in combination with these other resistance genes, because the resistance has remained effective even though the P. triticina population has evolved virulent to Lr2a, Lr13, Lr16, and Lr23.

The likelihood of success in developing modern cultivars depend on multiple factors, including the identification of suitable parents to initiate new crosses, and characterizations of genomic regions associated with target traits. The objectives of the present study were to (a) determine the best economic weights of four major wheat diseases (leaf spot, common bunt, leaf rust, and stripe rust) and grain yield for multi-trait restrictive linear phenotypic selection index (RLPSI), (b) select the top 10% cultivars and lines (hereafter referred as genotypes) with better resistance to combinations of the four diseases and acceptable grain yield as potential parents, and (c) map genomic regions associated with resistance to each disease using genome-wide association study (GWAS). A diversity panel of 196 spring wheat genotypes was evaluated for their reaction to stripe rust at eight environments, leaf rust at four environments, leaf spot at three environments, common bunt at two environments, and grain yield at five environments. The panel was genotyped with the Wheat 90K SNP array and a few KASP SNPs of which we used 23,342 markers for statistical analyses. The RLPSI analysis performed by restricting the expected genetic gain for yield displayed significant (p < 0.05) differences among the 3125 economic weights. Using the best four economic weights, a subset of 22 of the 196 genotypes were selected as potential parents with resistance to the four diseases and acceptable grain yield. GWAS identified 37 genomic regions, which included 12 for common bunt, 13 for leaf rust, 5 for stripe rust, and 7 for leaf spot. Each genomic region explained from 6.6 to 16.9% and together accounted for 39.4% of the stripe rust, 49.1% of the leaf spot, 94.0% of the leaf rust, and 97.9% of the common bunt phenotypic variance combined across all environments. Results from this study provide valuable information for wheat breeders selecting parental combinations for new crosses to develop improved germplasm with enhanced resistance to the four diseases as well as the physical positions of genomic regions that confer resistance, which facilitates direct comparisons for independent mapping studies in the future.

Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with either the DArTseq technology or the wheat 90K single nucleotide polymorphism arrays, of which a subset of 1,058- 23,795 polymorphic markers were used for the analyses. In the CV1 scenario, the mean prediction accuracies of the M1, M2, and M3 models across the six populations varied from −0.11 to −0.07, from 0.22 to 0.49, and from 0.19 to 0.48, respectively. Mean accuracies obtained using the M3 model in the CV1 scenario were significantly greater than the M2 model in two populations, the same in three populations, and smaller in one population. In both the CV2 and CV0 scenarios, the mean prediction accuracies of the three models varied from 0.53 to 0.84 and were not significantly different in all populations, except the Attila/CDC Go in the CV2, where the M3 model gave greater accuracy than both the M1 and M2 models. Overall, the M3 model increased prediction accuracies in some populations by up to 12.4% and decreased accuracy in others by up to 17.4%, demonstrating inconsistent results among genetic backgrounds that require considering each population separately. This is the first comprehensive genome-wide prediction study that investigated details of the effect of GEI on stripe rust resistance across diverse spring wheat populations.

Abstract DNA methylation is a mechanism of epigenetic modification in eukaryotic organisms. Generally, methylation within genes promoter inhibits regulatory protein binding and represses transcription, whereas gene body methylation is associated with actively transcribed genes. However, it remains unclear whether there is interaction between methylation levels across genic regions and which site has the biggest impact on gene regulation. We investigated and used the methylation patterns of the bread wheat cultivar Chinese Spring to uncover differentially expressed genes (DEGs) between roots and leaves, using six machine learning algorithms and a deep neural network. As anticipated, genes with higher expression in leaves were mainly involved in photosynthesis and pigment biosynthesis processes whereas genes that were not differentially expressed between roots and leaves were involved in protein processes and membrane structures. Methylation occurred preponderantly (60%) in the CG context, whereas 35 and 5% of methylation occurred in CHG and CHH contexts, respectively. Methylation levels were highly correlated (r = 0.7 to 0.9) between all genic regions, except within the promoter (r = 0.4 to 0.5). Machine learning models gave a high (0.81) prediction accuracy of DEGs. There was a strong correlation (p‐value = 9.20×10−10) between all features and gene expression, suggesting that methylation across all genic regions contribute to gene regulation. However, the methylation of the promoter, the CDS and the exon in CG context was the most impactful. Our study provides more insights into the interplay between DNA methylation and gene expression and paves the way for identifying tissue‐specific genes using methylation profiles.

Growing resistant wheat (Triticum aestivum L) varieties is an important strategy for the control of leaf rust, caused by Puccinia triticina Eriks. This study sought to identify the chromosomal location and effects of leaf rust resistance loci in five Canadian spring wheat cultivars. The parents and doubled haploid lines of crosses Carberry/AC Cadillac, Carberry/Vesper, Vesper/Lillian, Vesper/Stettler and Stettler/Red Fife were assessed for leaf rust severity and infection response in field nurseries in Canada near Swift Current, SK from 2013 to 2015, Morden, MB from 2015 to 2017 and Brandon, MB in 2016, and in New Zealand near Lincoln in 2014. The populations were genotyped with the 90K Infinium iSelect assay and quantitative trait loci (QTL) analysis was performed. A high density consensus map generated based on 14 doubled haploid populations and integrating SNP and SSR markers was used to compare QTL identified in different populations. AC Cadillac contributed QTL on chromosomes 2A, 3B and 7B (2 loci), Carberry on 1A, 2B (2 loci), 2D, 4B (2 loci), 5A, 6A, 7A and 7D, Lillian on 4A and 7D, Stettler on 2D and 6B, Vesper on 1B, 1D, 2A, 6B and 7B (2 loci), and Red Fife on 7A and 7B. Lillian contributed to a novel locus QLr.spa-4A, and similarly Carberry at QLr.spa-5A. The discovery of novel leaf rust resistance QTL QLr.spa-4A and QLr.spa-5A, and several others in contemporary Canada Western Red Spring wheat varieties is a tremendous addition to our present knowledge of resistance gene deployment in breeding. Carberry demonstrated substantial stacking of genes which could be supplemented with the genes identified in other cultivars with the expectation of increasing efficacy of resistance to leaf rust and longevity with little risk of linkage drag.

Genetic resistance in the host plant is the most economical and environmentally friendly strategy for controlling wheat leaf rust, caused by Puccinia triticina Eriks. The durum wheat lines Gaza (Middle East), Arnacoris (France) and Saragolla (Italy) express high levels of resistance to the Mexican races of P. triticina. Three recombinant inbred line (RIL) populations, derived from crosses of each of these resistance sources to the susceptible line ATRED #2, were evaluated for leaf rust reactions at CIMMYT’s leaf rust nurseries in Mexico. Genetic analyses of host reactions suggested oligogenic control of resistance in all populations. The F8 RILs from each cross were genotyped using the Illumina iSelect 90K array, and high-density genetic maps were constructed for each population. Using composite interval mapping, a total of seven quantitative trait loci (QTL) that provide resistance to leaf rust were identified. Two QTL designated as QLr.usw-6BS and QLr.usw-6BL were identified on chromosome 6B in Gaza, which explained up to 78.5% and 21.3% of the observed leaf rust severity variance, respectively. A major QTL designated as QLr.usw-7BL was detected on the long arm of chromosome 7B in Arnacoris, which accounted for up to 65.9% of the disease severity variance. Arnacoris also carried a minor QTL on chromosome 1BL, designated as QLr.usw-1BL.1 that explained up to 17.7% of the phenotypic variance. Three QTL conferred leaf rust resistance in Saragolla, namely QLr.usw-2BS, QLr.usw-3B, and QLr.usw-1BL.2, which accounted for up to 42.3, 9.4, and 7.1% of the phenotypic variance, respectively. Markers flanking each QTL were physically mapped against the durum wheat reference sequence and candidate genes involved in disease resistance were identified within the QTL intervals. The QTL identified in this study and their closely linked markers are useful resources for gene pyramiding and breeding for durable leaf rust resistance in durum wheat.

In previous studies, we reported quantitative trait loci (QTL) associated with the heading, flowering, and maturity time in four hard red spring wheat recombinant inbred line (RIL) populations but the results are scattered in population-specific genetic maps, which is challenging to exploit efficiently in breeding. Here, we mapped and characterized QTL associated with these three earliness traits using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map. Our data consisted of (i) 6526 single nucleotide polymorphisms (SNPs) and two traits evaluated at five conventionally managed environments in the ‘Cutler’ × ‘AC Barrie’ population; (ii) 3158 SNPs and two traits evaluated across three organic and seven conventional managements in the ‘Attila’ × ‘CDC Go’ population; (iii) 5731 SilicoDArT and SNP markers and the three traits evaluated at four conventional and organic management systems in the ‘Peace’ × ‘Carberry’ population; and (iv) 1058 SNPs and two traits evaluated across two conventionally and organically managed environments in the ‘Peace’ × ‘CDC Stanley’ population. Using composite interval mapping, the phenotypic data across all environments, and the IWGSC RefSeq v2.0 physical maps, we identified a total of 44 QTL associated with days to heading (11), flowering (10), and maturity (23). Fifteen of the 44 QTL were common to both conventional and organic management systems, and the remaining QTL were specific to either the conventional (21) or organic (8) management systems. Some QTL harbor known genes, including the Vrn-A1, Vrn-B1, Rht-A1, and Rht-B1 that regulate photoperiodism, flowering time, and plant height in wheat, which lays a solid basis for cloning and further characterization.

Durum wheat was introduced in the southern prairies of western Canada in the late nineteenth century. Breeding efforts have mainly focused on improving quality traits to meet the pasta industry demands. For this study, 192 durum wheat lines were genotyped using the Illumina 90K Infinium iSelect assay, and resulted in a total of 14,324 polymorphic SNPs. Genetic diversity changed over time, declining during the first 20 years of breeding in Canada, then increased in the late 1980s and early 1990s. We scanned the genome for signatures of selection, using the total variance Fst-based outlier detection method (Lositan), the hierarchical island model (Arlequin) and the Bayesian genome scan method (BayeScan). A total of 407 outliers were identified and clustered into 84 LD-based haplotype loci, spanning all 14 chromosomes of the durum wheat genome. The association analysis detected 54 haplotype loci, of which 39% contained markers with a complete reversal of allelic state. This tendency to fixation of favorable alleles corroborates the success of the Canadian durum wheat breeding programs over time. Twenty-one haplotype loci were associated with multiple traits. In particular, hap_4B_1 explained 20.6, 17.9 and 16.6% of the phenotypic variance of pigment loss, pasta b∗ and dough extensibility, respectively. The locus hap_2B_9 explained 15.9 and 17.8% of the variation of protein content and protein loss, respectively. All these pleiotropic haplotype loci offer breeders the unique opportunity for further improving multiple traits, facilitating marker-assisted selection in durum wheat, and could help in identifying genes as functional annotations of the wheat genome become available.

Stripe rust, caused by the fungal pathogen Puccinia striiformis Westend. f. sp. tritici Eriks, is an important disease of bread wheat (Triticum aestivum L.) worldwide and there is an indication that it may also become a serious disease of durum wheat (T. turgidum L. var. durum). Therefore, we investigated the genetic architecture underlying resistance to stripe rust in adapted durum wheat germplasm. Wheat infection assays were conducted under controlled conditions in Canada and under field conditions in Mexico. Disease assessments were performed on a population of 155 doubled haploid (DH) lines derived from the cross of Kofa (susceptible) and W9262-260D3 (moderately resistant) and on a breeding panel that consisted of 92 diverse cultivars and breeding lines. Both populations were genotyped using the 90K single-nucleotide polymorphism (SNP) iSelect assay. In the DH population, QTL for stripe rust resistance were identified on chromosome 7B (LOD 6.87-11.47) and chromosome 5B (LOD 3.88-9.17). The QTL for stripe rust resistance on chromosome 7B was supported in the breeding panel. Both QTL were anchored to the genome sequence of wild emmer wheat, which identified gene candidates involved in disease resistance. Exome capture sequencing identified variation in the candidate genes between Kofa and W9262-260D3. These genetic insights will be useful in durum breeding to enhance resistance to stripe rust.

Breeding for Fusarium head blight (FHB) resistance in durum wheat is complicated by the quantitative trait expression and narrow genetic diversity of available resources. High-density mapping of the FHB resistance quantitative trait loci (QTL), evaluation of their co-localization with plant height and maturity QTL and the interaction among the identified QTL are the objectives of this study. Two doubled haploid (DH) populations, one developed from crosses between Triticum turgidum ssp. durum lines DT707 and DT696 and the other between T. turgidum ssp. durum cv. Strongfield and T. turgidum ssp. carthlicum cv. Blackbird were genotyped using the 90K Infinium iSelect chip and evaluated phenotypically at multiple field FHB nurseries over years. A moderate broad-sense heritability indicated a genotype-by-environment interaction for the expression of FHB resistance in both populations. Resistance QTL were identified for the DT707 × DT696 population on chromosomes 1B, 2B, 5A (two loci) and 7A and for the Strongfield × Blackbird population on chromosomes 1A, 2A, 2B, 3A, 6A, 6B and 7B with the QTL on chromosome 1A and those on chromosome 5A being more consistently expressed over environments. FHB resistance co-located with plant height and maturity QTL on chromosome 5A and with a maturity QTL on chromosome 7A for the DT707 × DT696 population. Resistance also co-located with plant height QTL on chromosomes 2A and 3A and with maturity QTL on chromosomes 1A and 7B for the Strongfield × Blackbird population. Additive × additive interactions were identified, for example between the two FHB resistance QTL on chromosome 5A for the DT707 × DT696 population and the FHB resistance QTL on chromosomes 1A and 7B for the Strongfield × Blackbird population. Application of the Single Nucleotide Polymorphic (SNP) markers associated with FHB resistance QTL identified in this study will accelerate combining genes from the two populations.

Widening the genetic basis of leaf rust resistance is a primary objective of the global durum wheat breeding effort at the International Wheat and Maize Improvement Center (CIMMYT). Breeding programs in North America are following suit, especially after the emergence of new races of Puccinia triticina such as BBG/BP and BBBQD in Mexico and the United States, respectively. This study was conducted to characterize and map previously undescribed genes for leaf rust resistance in durum wheat and to develop reliable molecular markers for marker-assisted breeding. Four recombinant inbred line (RIL) mapping populations derived from the resistance sources Amria, Byblos, Geromtel_3 and Tunsyr_2, which were crossed to the susceptible line ATRED #2, were evaluated for their reaction to the Mexican race BBG/BP of P. triticina. Genetic analyses of host reactions indicated that leaf rust resistance in these genotypes was based on major seedling resistance genes. Allelism tests among resistant parents supported that Amria and Byblos carried allelic or closely linked genes. The resistance in Geromtel_3 and Tunsyr_2 also appeared to be allelic. Bulked segregant analysis using the Infinium iSelect 90K single nucleotide polymorphism (SNP) array identified two genomic regions for leaf rust resistance; one on chromosome 6BS for Geromtel_3 and Tunsyr_2 and the other on chromosome 7BL for Amria and Byblos. Polymorphic SNPs identified within these regions were converted to kompetitive allele-specific PCR (KASP) assays and used to genotype the RIL populations. KASP markers usw215 and usw218 were the closest to the resistance genes in Geromtel_3 and Tunsyr_2, while usw260 was closely linked to the resistance genes in Amria and Byblos. DNA sequences associated with these SNP markers were anchored to the wild emmer wheat (WEW) reference sequence, which identified several candidate resistance genes. The molecular markers reported herein will be useful to effectively pyramid these resistance genes with other previously marked genes into adapted, elite durum wheat genotypes.

Advances in sequencing and genotyping methods have enable cost-effective production of high throughput single nucleotide polymorphism (SNP) markers, making them the choice for linkage mapping. As a result, many laboratories have developed high-throughput SNP assays and built high-density genetic maps. However, the number of markers may, by orders of magnitude, exceed the resolution of recombination for a given population size so that only a minority of markers can accurately be ordered. Another issue attached to the so-called ‘large p, small n’ problem is that high-density genetic maps inevitably result in many markers clustering at the same position (co-segregating markers). While there are a number of related papers, none have addressed the impact of co-segregating markers on genetic maps. In the present study, we investigated the effects of co-segregating markers on high-density genetic map length and marker order using empirical data from two populations of wheat, Mohawk × Cocorit (durum wheat) and Norstar × Cappelle Desprez (bread wheat). The maps of both populations consisted of 85% co-segregating markers. Our study clearly showed that excess of co-segregating markers can lead to map expansion, but has little effect on markers order. To estimate the inflation factor (IF), we generated a total of 24,473 linkage maps (8,203 maps for Mohawk × Cocorit and 16,270 maps for Norstar × Cappelle Desprez). Using seven machine learning algorithms, we were able to predict with an accuracy of 0.7 the map expansion due to the proportion of co-segregating markers. For example in Mohawk × Cocorit, with 10 and 80% co-segregating markers the length of the map inflated by 4.5 and 16.6%, respectively. Similarly, the map of Norstar × Cappelle Desprez expanded by 3.8 and 11.7% with 10 and 80% co-segregating markers. With the increasing number of markers on SNP-chips, the proportion of co-segregating markers in high-density maps will continue to increase making map expansion unavoidable. Therefore, we suggest developers improve linkage mapping algorithms for efficient analysis of high-throughput data. This study outlines a practical strategy to estimate the IF due to the proportion of co-segregating markers and outlines a method to scale the length of the map accordingly.

Breeding for solid-stemmed durum (Triticum turgidum L. var durum) and common wheat (Triticum aestivum L.) cultivars is one strategy to minimize yield losses caused by the wheat stem sawfly (Cephus cinctus Norton). Major stem-solidness QTL have been localized to the long arm of chromosome 3B in both wheat species, but it is unclear if these QTL span a common genetic interval. In this study, we have improved the resolution of the QTL on chromosome 3B in a durum (Kofa/W9262-260D3) and common wheat (Lillian/Vesper) mapping population. Coincident QTL (LOD = 94-127, R2 = 78-92%) were localized near the telomere of chromosome 3BL in both mapping populations, which we designate SSt1. We further examined the SSt1 interval by using available consensus maps for durum and common wheat and compared genetic to physical intervals by anchoring markers to the current version of the wild emmer wheat (WEW) reference sequence. These results suggest that the SSt1 interval spans a physical distance of 1.6 Mb in WEW (positions 833.4-835.0 Mb). In addition, minor QTL were identified on chromosomes 2A, 2D, 4A, and 5A that were found to synergistically enhance expression of SSt1 to increase stem-solidness. These results suggest that developing new wheat cultivars with improved stem-solidness is possible by combining SSt1 with favorable alleles at minor loci within both wheat species.

Association mapping is usually performed by testing the correlation between a single marker and phenotypes. However, because patterns of variation within genomes are inherited as blocks, clustering markers into haplotypes for genome-wide scans could be a worthwhile approach to improve statistical power to detect associations. The availability of high-density molecular data allows the possibility to assess the potential of both approaches to identify marker-trait associations in durum wheat. In the present study, we used single marker- and haplotype-based approaches to identify loci associated with semolina and pasta colour in durum wheat, the main objective being to evaluate the potential benefits of haplotype-based analysis for identifying quantitative trait loci. One hundred sixty-nine durum lines were genotyped using the Illumina 90K Infinium iSelect assay, and 12,234 polymorphic single nucleotide polymorphism (SNP) markers were generated and used to assess the population structure and the linkage disequilibrium (LD) patterns. A total of 8,581 SNPs previously localized to a high-density consensus map were clustered into 406 haplotype blocks based on the average LD distance of 5.3 cM. Combining multiple SNPs into haplotype blocks increased the average polymorphism information content (PIC) from 0.27 per SNP to 0.50 per haplotype. The haplotype-based analysis identified 12 loci associated with grain pigment colour traits, including the five loci identified by the single marker-based analysis. Furthermore, the haplotype-based analysis resulted in an increase of the phenotypic variance explained (50.4% on average) and the allelic effect (33.7% on average) when compared to single marker analysis. The presence of multiple allelic combinations within each haplotype locus offers potential for screening the most favorable haplotype series and may facilitate marker-assisted selection of grain pigment colour in durum wheat. These results suggest a benefit of haplotype-based analysis over single marker analysis to detect loci associated with colour traits in durum wheat.

Recently, we investigated the effect of the wheat 90K single nucleotide polymorphic (SNP) array and three gene-specific (Ppd-D1, Vrn-A1 and Rht-B1) markers on quantitative trait loci (QTL) detection in a recombinant inbred lines (RILs) population derived from a cross between two spring wheat (Triticum aestivum L.) cultivars, 'Attila' and 'CDC Go', and evaluated for eight agronomic traits at three environments under organic management. The objectives of the present study were to investigate the effect of conventional management on QTL detection in the same mapping population using the same set of markers as the organic management and compare the results with organic management. Here, we evaluated 167 RILs for number of tillers (tillering), flowering time, maturity, plant height, test weight (grain volume weight), 1000 kernel weight, grain yield, and grain protein content at seven conventionally managed environments from 2008 to 2014. Using inclusive composite interval mapping (ICIM) on phenotypic data averaged across seven environments and a subset of 1203 informative markers (1200 SNPs and 3 gene specific markers), we identified a total of 14 QTLs associated with flowering time (1), maturity (2), plant height (1), grain yield (1), test weight (2), kernel weight (4), tillering (1) and grain protein content (2). Each QTL individually explained from 6.1 to 18.4% of the phenotypic variance. Overall, the QTLs associated with each trait explained from 9.7 to 35.4% of the phenotypic and from 22.1 to 90.8% of the genetic variance. Three chromosomal regions on chromosomes 2D (61-66 cM), 4B (80-82 cM) and 5A (296-297 cM) harbored clusters of QTLs associated with two to three traits. The coincidental region on chromosome 5A harbored QTL clusters for both flowering and maturity time, and mapped about 2 cM proximal to the Vrn-A1 gene, which was in high linkage disequilibrium (0.70 ≤ r2 ≤ 0.75) with SNP markers that mapped within the QTL confidence interval. Six of the 14 QTLs (one for flowering time and plant height each, and two for maturity and kernel weight each) were common between the conventional and organic management systems, which suggests issues in directly utilizing gene discovery results based on conventional management to make in detail selection (decision) for organic management.

[This corrects the article DOI: 10.1371/journal.pone.0170941.].

We recently reported three earliness per se quantitative trait loci (QTL) associated with flowering and maturity in a recombinant inbred lines (RILs) population derived from a cross between the spring wheat (Triticum aestivum L.) cultivars 'Cutler' and 'AC Barrie' using 488 microsatellite and diversity arrays technology (DArT) markers. Here, we present QTLs associated with flowering time, maturity, plant height, and grain yield using high density single nucleotide polymorphic (SNP) markers in the same population. A mapping population of 158 RILs and the two parents were evaluated at five environments for flowering, maturity, plant height and grain yield under field conditions, at two greenhouse environments for flowering, and genotyped with a subset of 1809 SNPs out of the 90K SNP array and 2 functional markers (Ppd-D1 and Rht-D1). Using composite interval mapping on the combined phenotype data across all environments, we identified a total of 19 QTLs associated with flowering time in greenhouse (5), and field (6) conditions, maturity (5), grain yield (2) and plant height (1). We mapped these QTLs on 8 chromosomes and they individually explained between 6.3 and 37.8% of the phenotypic variation. Four of the 19 QTLs were associated with multiple traits, including a QTL on 2D associated with flowering, maturity and grain yield; two QTLs on 4A and 7A associated with flowering and maturity, and another QTL on 4D associated with maturity and plant height. However, only the QTLs on both 2D and 4D had major effects, and they mapped adjacent to well-known photoperiod response Ppd-D1 and height reducing Rht-D1 genes, respectively. The QTL on 2D reduced flowering and maturity time up to 5 days with a yield penalty of 436 kg ha-1, while the QTL on 4D reduced plant height by 13 cm, but increased maturity by 2 days. The high density SNPs allowed us to map eight moderate effect, two major effect, and nine minor effect QTLs that were not identified in our previous study using microsatellite and DArT markers. Results from this study provide additional information to wheat researchers developing early maturing and short stature spring wheat cultivars.

Adult height is a classic polygenic trait of high heritability (h(2) approximately 0.8). More than 180 single nucleotide polymorphisms (SNPs), identified mostly in populations of European descent, are associated with height. These variants convey modest effects and explain approximately10% of the variance in height. Discovery efforts in other populations, while limited, have revealed loci for height not previously implicated in individuals of European ancestry. Here, we performed a meta-analysis of genome-wide association (GWA) results for adult height in 20,427 individuals of African ancestry with replication in up to 16,436 African Americans. We found two novel height loci (Xp22-rs12393627, P = 3.4×10(-12) and 2p14-rs4315565, P = 1.2×10(-8)). As a group, height associations discovered in European-ancestry samples replicate in individuals of African ancestry (P = 1.7×10(-4) for overall replication). Fine-mapping of the European height loci in African-ancestry individuals showed an enrichment of SNPs that are associated with expression of nearby genes when compared to the index European height SNPs (P