Amidou N’Diaye

QTLs associated with agronomic traits in the Attila × CDC Go spring wheat population evaluated under conventional management

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.

High density mapping and haplotype analysis of the major stem-solidness locus SSt1 in durum and common wheat

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.

Quantitative trait loci for resistance to stripe rust of wheat revealed using global field nurseries and opportunities for stacking resistance genes

Key messageQuantitative trait loci controlling stripe rust resistance were identified in adapted Canadian spring wheat cultivars providing opportunity for breeders to stack loci using marker-assisted breeding.AbstractStripe rust or yellow rust, caused by Puccinia striiformis Westend. f. sp. tritici Erikss., is a devastating disease of common wheat (Triticum aestivum L.) in many regions of the world. The objectives of this research were to identify and map quantitative trait loci (QTL) associated with stripe rust resistance in adapted Canadian spring wheat cultivars that are effective globally, and investigate opportunities for stacking resistance. Doubled haploid (DH) populations from the crosses Vesper/Lillian, Vesper/Stettler, Carberry/Vesper, Stettler/Red Fife and Carberry/AC Cadillac were phenotyped for stripe rust severity and infection response in field nurseries in Canada (Lethbridge and Swift Current), New Zealand (Lincoln), Mexico (Toluca) and Kenya (Njoro), and genotyped with SNP markers. Six QTL for stripe rust resistance in the population of Vesper/Lillian, five in Vesper/Stettler, seven in Stettler/Red Fife, four in Carberry/Vesper and nine in Carberry/AC Cadillac were identified. Lillian contributed stripe rust resistance QTL on chromosomes 4B, 5A, 6B and 7D, AC Cadillac on 2A, 2B, 3B and 5B, Carberry on 1A, 1B, 4A, 4B, 7A and 7D, Stettler on 1A, 2A, 3D, 4A, 5B and 6A, Red Fife on 2D, 3B and 4B, and Vesper on 1B, 2B and 7A. QTL on 1A, 1B, 2A, 2B, 3B, 4A, 4B, 5B, 7A and 7D were observed in multiple parents. The populations are compelling sources of recombination of many stripe rust resistance QTL for stacking disease resistance. Gene pyramiding should be possible with little chance of linkage drag of detrimental genes as the source parents were mostly adapted cultivars widely grown in Canada.

Mapping of QTLs associated with resistance to common bunt, tan spot, leaf rust, and stripe rust in a spring wheat population

Spring wheat (Triticum aestivum L.) breeding goals in western Canada include good agronomic characteristics and good end-use quality, and also moderate to elevated resistance to diseases of economic importance. In this study, we aimed to identify quantitative trait loci (QTL) associated with resistance to common bunt (Tilletia tritici and Tilletia laevis), tan spot (Pyrenophora tritici-repentis), leaf rust (Puccinia triticina), and stripe rust (Puccinia striiformis f. sp. tritici). A total of 167 recombinant inbred lines (RILs) derived from a cross between two spring wheat cultivars, ‘Attila’ and ‘CDC Go’, were evaluated for reactions to the four diseases in nurseries from three to eight environments, and genotyped with the Wheat 90K SNP array and three gene-specific markers (Ppd-D1, Vrn-A1, and Rht-B1). The RILs exhibited transgressive segregation for all four diseases, and we observed several lines either superior or inferior to the parents. Broad-sense heritability varied from 0.25 for leaf rust to 0.48 for common bunt. Using a subset of 1203 informative markers (1200 SNPs and 3 gene-specific markers) and average disease scores across all environments, we identified two QTLs (QCbt.dms-1B.2 and QCbt.dms-3A) for common bunt, and three QTLs each for tan spot (QTs.dms-2B, QTs.dms-2D, and QTs.dms-6B), leaf rust (QLr.dms-2D.1, QLr.dms-2D.2, and QLr.dms-3A), and stripe rust (QYr.dms-3A, QYr.dms-4A, and QYr.dms-5B). Each QTL individually explained between 5.9 and 18.7% of the phenotypic variation, and altogether explained from 21.5 to 26.5% of phenotypic and from 52.2 to 86.0% of the genetic variation. The resistance alleles for all QTLs except one for stripe rust (QYr.dms-5B) were from CDC Go. Some of the QTLs are novel, while others mapped close to QTLs and/or genes reported in other studies.

Characterization and mapping of leaf rust resistance in four durum wheat cultivars

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.

Genetic analysis of resistance to stripe rust in durum wheat (Triticum turgidum L. Var. Durum)

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.

High density genetic mapping of Fusarium head blight resistance QTL in tetraploid wheat

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.

Genomic selection for grain yield and quality traits in durum wheat

The prediction accuracies of genomic selection depend on several factors, including the genetic architecture of target traits, the number of traits considered at a given time, and the statistical models. Here, we assessed the potential of single-trait (ST) and multi-trait (MT) genomic prediction models for durum wheat on yield and quality traits using a breeding panel (BP) of 170 varieties and advanced breeding lines, and a doubled-haploid (DH) population of 154 lines. The two populations were genotyped with the Infinium iSelect 90K SNP assay and phenotyped for various traits. Six ST-GS models (RR-BLUP, G-BLUP, BayesA, BayesB, Bayesian LASSO, and RKHS) and three MT prediction approaches (MT-BayesA, MT-Matrix, and MT-SI approaches which use economic selection index as a trait value) were applied for predicting yield, protein content, gluten index, and alveograph measures. The ST prediction accuracies ranged from 0.5 to 0.8 for the various traits and models and revealed comparable prediction accuracies for most of the traits in both populations, except BayesA and BayesB, which better predicted gluten index, tenacity, and strength in the DH population. The MT-GS models were more accurate than the ST-GS models only for grain yield in the BP. Using BP as a training set to predict the DH population resulted in poor predictions. Overall, all the six ST-GS models appear to be applicable for GS of yield and gluten strength traits in durum wheat, but we recommend the simple computational models RR-BLUP or G-BLUP for predicating single trait and MT-SI for predicting yield and protein simultaneously.

Effect of Co-segregating Markers on High-Density Genetic Maps and Prediction of Map Expansion Using Machine Learning Algorithms

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.