Response to early generation genomic selection for yield in wheat

We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248, P < 0.001) and GBLUP (0.195, P < 0.01) but no correlation with rrGBLUP_imp. Results demonstrate the potential for the application of GS in early generations of wheat breeding and the importance of using the appropriate statistical model for GEBV calculation, which may not be the same as the best model for inbreds

Genetic Mapping Reveals Large-Effect QTL for Anther Extrusion in CIMMYT Spring Wheat

Hybrid breeding facilitates the exploitation of heterosis and it can result in significant genetic gains and increased crop yields. Inefficient cross-pollination is a major limiting factor that hampers hybrid wheat seed production. In this study, we examined the genetic basis of anther extrusion (AE), which is an important trait in increasing cross-pollination, and thus improving seed set on the female lines and hybrid wheat seed production. We studied 300 segregating F2 plants and F2:3 families that result from a cross of two elite spring wheat lines. We observed that F2 and F2:3 populations hold significant and continuous genetic variation for AE, which suggests its reliable phenotypic selection. Composite interval mapping detected three quantitative trait loci (QTL) on chromosomes 3A, 5A, and 5D. The QTL on chromosome 5A (i.e., QAe.cimmyt-5A) was of large-effect, being consistently identified across generations, and spanned over 25 cM. Our study shows that (1) AE possesses strong genetic control (heritability), and (2) the QTL QAe.cimmyt-5A that imparted on an average of 20% of phenotypic variation can be used for marker-assisted selection (MAS) in breeding programs. In addition, pyramiding the large-effect QTL for MAS could efficiently complement the phenotypic selection since it is relatively easy and cheap to visually phenotype AE. This study reports the first large-effect QTL for AE in spring wheat, endorsing the use of this analysis in current hybrid wheat breeding and future Mendelization for the detection of underlying gene(s)

QTL Analysis and Nested Association Mapping for Adult Plant Resistance to Powdery Mildew in Two Bread Wheat Populations

CIMMYT wheat (Triticum aestivum L.) lines Francolin#1 and Quaiu#3 displayed effective and stable adult plant resistance (APR) to Chinese Blumeria graminis f. sp. tritici isolates in the field. To elucidate their genetic basis of resistance, two recombinant inbred line (RIL) populations of their crosses with Avocet, the susceptible parent, were phenotyped in Zhengzhou and Shangqiu in the 2014–2015 and 2015–2016 cropping seasons. These populations were also genotyped with SSR (simple sequence repeat markers) and DArT (diversity arrays technology) markers. Two common significant quantitative trait loci (QTL) on wheat chromosomes 1BL and 4BL were detected in both populations by joint and individual inclusive composite interval mapping, explaining 20.3–28.7% and 9.6–15.9% of the phenotypic variance in Avocet × Francolin#1 and 4.8–11.5% and 10.8–18.9% in Avocet × Quaiu#3, respectively. Additional QTL were mapped on chromosomes 1DL and 5BL in Avocet × Francolin#1 and on 2DL and 6BS in Avocet × Quaiu#3. Among these, QPm.heau-1DL is probably a novel APR gene contributing 6.1–8.5% of total phenotypic variance. The QTL on 1BL corresponds to the pleiotropic multi-pathogen resistance gene Yr29/Lr46/Pm39, whereas the QTL on 2DL maps to a similar region where stripe rust resistance gene Yr54 is located. The QTL identified can potentially be used for the improvement of powdery mildew and rust resistance in wheat breeding

Hybrid Wheat Prediction Using Genomic, Pedigree, and Environmental Covariables Interaction Models

In this study, we used genotype × environment interactions (G×E) models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by environmental covariables. We use five genomic and pedigree models (M1–M5) under four cross-validation (CV) schemes: prediction of hybrids when the training set (i) includes hybrids of all males and females evaluated only in some environments (T2FM), (ii) excludes all progenies from a randomly selected male (T1M), (iii) includes all progenies from 20% randomly selected females in combination with all males (T1F), and (iv) includes one randomly selected male plus 40% randomly selected females that were crossed with it (T0FM). Models were tested on a total of 1888 wheat ( L.) hybrids including 18 males and 667 females in three consecutive years. For grain yield, the most complex model (M5) under T2FM had slightly higher prediction accuracy than the less complex model. For T1F, the prediction accuracy of hybrids for grain yield and other traits of the most complete model was 0.50 to 0.55. For T1M, Model M3 exhibited high prediction accuracies for flowering traits (0.71), whereas the more complex model (M5) demonstrated high accuracy for grain yield (0.5). For T0FM, the prediction accuracy for grain yield of Model M5 was 0.61. Including genomic and pedigree gave relatively high prediction accuracy even when both parents were untested. Results show that it is possible to predict unobserved hybrids when modeling genomic general combining ability (GCA) and specific combining ability (SCA) and their interactions with environments

Characterization and Mapping of Leaf Rust and Stripe Rust Resistance Loci in Hexaploid Wheat Lines UC1110 and PI610750 under Mexican Environments

Growing resistant wheat varieties is a key method of minimizing the extent of yield losses caused by the globally important wheat leaf rust (LR) and stripe rust (YR) diseases. In this study, a population of 186 F8 recombinant inbred lines (RILs) derived from a cross between a synthetic wheat derivative (PI610750) and an adapted common wheat line (cv. “UC1110”) were phenotyped for LR and YR response at both seedling and adult plant stages over multiple seasons. Using a genetic linkage map consisting of single sequence repeats and diversity arrays technology markers, in combination with inclusive composite interval mapping analysis, we detected a new LR adult plant resistance (APR) locus, QLr.cim-2DS, contributed by UC1110. One co-located resistance locus to both rusts, QLr.cim-3DC/QYr.cim-3DC, and the known seedling resistance gene Lr26 were also mapped. QLr.cim-2DS and QLr.cim-3DC showed a marginally significant interaction for LR resistance in the adult plant stage. In addition, two previously reported YR APR loci, QYr.ucw-3BS and Yr48, were found to exhibit stable performances in rust environments in both Mexico and the United States and showed a highly significant interaction in the field. Yr48 was also observed to confer intermediate seedling resistance against Mexican YR races, thus suggesting it should be re-classified as an all-stage resistance gene. We also identified 5 and 2 RILs that possessed all detected YR and LR resistance loci, respectively. With the closely linked molecular markers reported here, these RILs could be used as donors for multiple resistance loci to both rusts in wheat breeding programs

Correction to: Adult plant stem rust resistance in durum wheat Glossy Huguenot: mapping, marker development and validation

In the original manuscript we characterize the KASP marker IWB32429, linked to the adult plant stem rust resistance locus Sr63. We describe allele specific primers which were tagged with FAM and HEX. Unfortunately, we have since found that the Tags should be reversed. We present here the correction in the methods section and also the revised Table 4 and Table S3

Rice–wheat comparative genomics: Gains and gaps

Rice and wheat provide nearly 40% of human calorie and protein requirements. They share a common ancestor and belong to the Poaceae (grass) family. Characterizing their genetic homology is crucial for developing new cultivars with enhanced traits. Several wheat genes and gene families have been characterized based on their rice orthologs. Rice–wheat orthology can identify genetic regions that regulate similar traits in both crops. Rice–wheat comparative genomics can identify candidate wheat genes in a genomic region identified by association or QTL mapping, deduce their putative functions and biochemical pathways, and develop molecular markers for marker-assisted breeding. A knowledge of gene homology facilitates the transfer between crops of genes or genomic regions associated with desirable traits by genetic engineering, gene editing, or wide crossing

Adult plant stem rust resistance in durum wheat Glossy Huguenot: Mapping, marker development and validation

An F3 population from a Glossy Huguenot (GH)/Bansi cross used in a previous Australian study was advanced to F6 for molecular mapping of adult plant stem rust resistance. Maturity differences among F6 lines confounded assessments of stem rust response. GH was crossed with a stem rust susceptible F6 recombinant inbred line (RIL), GHB14 (M14), with similar maturity and an F6:7 population was developed through single seed descent method. F7 and F8 RILs were tested along with the parents at different locations. The F6 individual plants and both parents were genotyped using the 90 K single nucleotide polymorphism (SNP) wheat array. Stem rust resistance QTL on the long arms of chromosomes 1B (QSrGH.cs-1BL) and 2A (QSrGH.cs-2AL) were detected. QSrGH.cs-1BL and QSrGH.cs-2AL were both contributed by GH and explained 22% and 18% adult plant stem rust response variation, respectively, among GH/M14 RIL population. RILs carrying combinations of these QTL reduced more than 14% stem rust severity compared to those that possessed QSrGH.cs-1BL and QSrGH.cs-2AL individually. QSrGH.cs1BL was demonstrated to be the same as Sr58/Lr46/Yr29/Pm39 through marker genotyping. Lines lacking QSrGH.cs-1BL were used to Mendelise QSrGH.cs-2AL. Based on genomic locations of previously catalogued stem rust resistance genes and the QSrGH.cs-2AL map, it appeared to represent a new APR locus and was permanently named Sr63. SNP markers associated with Sr63 were converted to kompetetive allele-specific PCR (KASP) assays and were validated on a set of durum cultivars