Genomic selection models can be trained using historical data and filtering genotypes based on phenotyping intensity and reliability criterion are able to increase the prediction ability. We implemented genomic selection based on a large commercial population incorporating 2325 European winter wheat lines. Our objectives were (1) to study whether modeling epistasis besides additive genetic effects results in enhancement on prediction ability of genomic selection, (2) to assess prediction ability when training population comprised historical or less-intensively phenotyped lines, and (3) to explore the prediction ability in subpopulations selected based on the reliability criterion. We found a 5 % increase in prediction ability when shifting from additive to additive plus epistatic effects models. In addition, only a marginal loss from 0.65 to 0.50 in accuracy was observed using the data collected from 1 year to predict genotypes of the following year, revealing that stable genomic selection models can be accurately calibrated to predict subsequent breeding stages. Moreover, prediction ability was maximized when the genotypes evaluated in a single location were excluded from the training set but subsequently decreased again when the phenotyping intensity was increased above two locations, suggesting that the update of the training population should be performed considering all the selected genotypes but excluding those evaluated in a single location. The genomic prediction ability was substantially higher in subpopulations selected based on the reliability criterion, indicating that phenotypic selection for highly reliable individuals could be directly replaced by applying genomic selection to them. We empirically conclude that there is a high potential to assist commercial wheat breeding programs employing genomic selection approaches.
Improvement of end-use quality in bread wheat (Triticum aestivum L.) depends on a thorough understanding of the genetic basis of important quality traits. The main goal of our study was to investigate the genetic basis of 1,000-kernel weight, protein content, sedimentation volume, test weight, and starch concentration using an association mapping approach. We fingerprinted 207 diverse European elite soft winter wheat lines with 115 SSR markers and evaluated the genotypes in multi-environment trials. The principal coordinate analysis revealed absence of a clear population but presence of a family structure. Therefore, we used linear mixed models and marker-based kinship matrices to correct for family structure. In genome-wide scans, we detected main effect QTL for all five traits. In contrast, epistatic QTL were only observed for sedimentation volume and test weight explaining a small proportion of the genotypic variation. Consequently, our findings suggested that integrating epistasis in marker-assisted breeding will not lead to substantially increased selection gain for quality traits in soft winter wheat.
Plant height variation in European winter wheat cultivars is mainly controlled by the Rht - D1 and Rht - B1 semi-dwarfing genes, but also by other medium- or small-effect QTL and potentially epistatic QTL enabling fine adjustments of plant height. Plant height is an important goal in wheat (Triticum aestivum L.) breeding as it affects crop performance and thus yield and quality. The aim of this study was to investigate the genetic control of plant height in European winter wheat cultivars. To this end, a panel of 410 winter wheat varieties from across Europe was evaluated for plant height in multi-location field trials and genotyped for the candidate loci Rht-B1, Rht-D1, Rht8, Ppd-B1 copy number variation and Ppd-D1 as well as by a genotyping-by-sequencing approach yielding 23,371 markers with known map position. We found that Rht-D1 and Rht-B1 had the largest effects on plant height in this cultivar collection explaining 40.9 and 15.5 % of the genotypic variance, respectively, while Ppd-D1 and Rht8 accounted for 3.0 and 2.0 % of the variance, respectively. A genome-wide scan for marker–trait associations yielded two additional medium-effect QTL located on chromosomes 6A and 5B explaining 11.0 and 5.7 % of the genotypic variance after the effects of the candidate loci were accounted for. In addition, we identified several small-effect QTL as well as epistatic QTL contributing to the genetic architecture of plant height. Taken together, our results show that the two Rht-1 semi-dwarfing genes are the major sources of variation in European winter wheat cultivars and that other small- or medium-effect QTL and potentially epistatic QTL enable fine adjustments in plant height.
A powdery mildew resistance gene was introgressed from Aegilops speltoides into winter wheat and mapped to chromosome 5BL. Closely linked markers will permit marker-assisted selection for the resistance gene. Powdery mildew of wheat (Triticum aestivum L.) is a major fungal disease in many areas of the world, caused by Blumeria graminis f. sp. tritici (Bgt). Host plant resistance is the preferred form of disease prevention because it is both economical and environmentally sound. Identification of new resistance sources and closely linked markers enable breeders to utilize these new sources in marker-assisted selection as well as in gene pyramiding. Aegilops speltoides (2n = 2x = 14, genome SS), has been a valuable disease resistance donor. The powdery mildew resistant wheat germplasm line NC09BGTS16 (NC-S16) was developed by backcrossing an Ae. speltoides accession, TAU829, to the susceptible soft red winter wheat cultivar ‘Saluda’. NC-S16 was crossed to the susceptible cultivar ‘Coker 68-15’ to develop F2:3 families for gene mapping. Greenhouse and field evaluations of these F2:3 families indicated that a single gene, designated Pm53, conferred resistance to powdery mildew. Bulked segregant analysis showed that multiple simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers specific to chromosome 5BL segregated with the resistance gene. The gene was flanked by markers Xgwm499, Xwmc759, IWA6024 (0.7 cM proximal) and IWA2454 (1.8 cM distal). Pm36, derived from a different wild wheat relative (T. turgidum var. dicoccoides), had previously been mapped to chromosome 5BL in a durum wheat line. Detached leaf tests revealed that NC-S16 and a genotype carrying Pm36 differed in their responses to each of three Bgt isolates. Pm53 therefore appears to be a new source of powdery mildew resistance.
A new powdery mildew resistance gene Pm54 was identified on chromosome 6BL in soft red winter wheat.Powdery mildew is causing increasing damage to wheat production in the southeastern USA. To combat the disease, a continuing need exists to discover new genes for powdery mildew resistance and to incorporate those genes into breeding programs. Pioneer® variety 26R61 (shortened as 26R61) and AGS 2000 have been used as checks in the Uniform Southern Soft Red Winter Wheat Nursery for a decade, and both have provided good resistance across regions during that time. In the present study, a genetic analysis of mildew resistance was conducted on a RIL population developed from a cross of 26R61 and AGS 2000. Phenotypic evaluation was conducted in the field at Plains, GA, and Raleigh, NC, in 2012 and 2013, a total of four environments. Three quantitative trait loci (QTL) with major effect were consistently detected on wheat chromosomes 2BL, 4A and 6BL. The 2BL QTL contributed by 26R61 was different from Pm6, a widely used gene in the southeastern USA. The other two QTL were identified from AGS 2000. The 6BL QTL was subsequently characterized as a simple Mendelian factor when the population was inoculated with a single Blumeria graminis f. sp. tritici (Bgt) isolate in controlled environments. Since there is no known powdery mildew resistance gene (Pm) on this particular location of common wheat, the gene was designated Pm54. The closely linked marker Xbarc134 was highly polymorphic in a set of mildew differentials, indicating that the marker should be useful for pyramiding Pm54 with other Pm genes by marker-assisted selection.
Lr34 in wheat is a non-race-specific gene that confers resistance against multiple fungal pathogens. The resistant allele Lr34 and the susceptible allele Lr34s can be distinguished by three polymorphisms that cause alternation of deduced amino acid sequences of Lr34 at the protein level. In seedlings of a cultivar carrying the resistant Lr34r allele, only a portion (35%) of its transcripts was correctly spliced and the majority (65%) of its transcripts were incorrectly spliced due to multiple mis-splicing events. Lr34 mis-splicing events were also observed at adult plant age when this gene exerts its function. All of the mis-spliced Lr34r cDNA transcripts observed in this study resulted in a premature stop codon due to a shift of the open reading frame; hence, the mis-spliced Lr34r cDNAs were deduced to encode incomplete proteins. Even if a cultivar has a functional Lr34 gene, its transcripts might not completely splice in a correct pattern. These findings suggested that the partial resistance conferred by a quantitative gene might be due to mis-splicing events in its transcripts; hence, the resistance of the gene could be increased by eliminating or mutating regulators that cause mis-splicing events in wheat.
Wheat is one of the key cereal crops grown worldwide, providing the primary caloric and nutritional source for millions of people around the world. In order to ensure food security and sound, actionable mitigation strategies and policies for management of food shortages, timely and accurate estimates of global crop production are essential. This study combines a new BRDF-corrected, daily surface reflectance dataset developed from NASA's Moderate resolution Imaging Spectro-radiometer (MODIS) with detailed official crop statistics to develop an empirical, generalized approach to forecast wheat yields. The first step of this study was to develop and evaluate a regression-based model for forecasting winter wheat production in Kansas. This regression-based model was then directly applied to forecast winter wheat production in Ukraine. The forecasts of production in Kansas closely matched the USDA/NASS reported numbers with a 7% error. The same regression model forecast winter wheat production in Ukraine within 10% of the official reported production numbers six weeks prior to harvest. Using new data from MODIS, this method is simple, has limited data requirements, and can provide an indication of winter wheat production shortfalls and surplus prior to harvest in regions where minimal ground data is available.
▶ Variations in weeds were best explained at a 200 m radius. ▶ Weed richness increased as field size decreased and as the number of fields within 200 m increased. ▶ Weed richness and diversity seemed higher in heterogeneous landscapes. There is empirical evidence that landscape composition and structure can affect the distribution and long-term dynamics of the organisms that live in it. Weeds are no exception and in this paper, we investigated how weed richness and diversity in 123 winter wheat fields within a small agricultural region were affected by the landscape surrounding each field (radii ranging from 100 to 1000 m) and the field properties such as its size and the preceding crop. Landscape was described by its proportion (cover of spring crops, winter crops, woodland, grassland, set-aside) and its structure (number of fields, number of land use types). Akaïke criterion-based models indicated that variations in weeds were best explained at the 200 m radius. At that scale, hierarchical partitioning shows that the independent contributions of field level and landscape level variables were significant for two variables. Weed richness and weed diversity increased significantly as field size decreased and as the number of fields within 200 m increased. This suggests that weed richness and diversity are higher in landscapes that have a finer grain, probably because these landscapes offer more habitat heterogeneity within cultivated areas and contain more crop edges that can shelter many weed species.
Selecting contrasting environments allows decreasing phenotyping intensity but still maintaining high accuracy to assess yield stability. Improving yield stability of wheat varieties is important to cope with enhanced abiotic stresses caused by climate change. The objective of our study was to (1) develop and implement an improved heritability estimate to examine the required scale of phenotyping for assessing yield stability in wheat, (2) compare yield performance and yield stability of wheat hybrids and inbred lines, (3) investigate the association of agronomic traits with yield stability, and (4) explore the possibility of selecting subsets of environments allowing to portray large proportion of the variation of yield stability. Our study is based on phenotypic data from five series of official winter wheat registration trials in Germany each including 119–132 genotypes evaluated in up to 50 environments. Our findings suggested that phenotyping in at least 40 environments is required to reliably estimate yield stability to guarantee heritability estimates above 0.7. Contrasting the yield stability of hybrids versus lines revealed no significant differences. Absence of stable associations between yield stability and further agronomic traits suggested low potential of indirect selection to improve yield stability. Selecting posteriori contrasting environments based on the genotype-by-environment interaction effects allowed decreasing phenotyping intensity, but still maintaining high accuracy to assess yield stability. The huge potential of the developed strategy to select contrasting and informative environments has to be validated as a next step in an a priori scenario based on genotype-by-location interaction effects.
Modern genomics approaches rely on the availability of high-throughput and high-density genotyping platforms. A major breakthrough in wheat genotyping was the development of an SNP array. In this study, we used a diverse panel of 172 elite European winter wheat lines to evaluate the utility of the SNP array for genomic analyses in wheat germplasm derived from breeding programs. We investigated population structure and genetic relatedness and found that the results obtained with SNP and SSR markers differ. This suggests that additional research is required to determine the optimum approach for the investigation of population structure and kinship. Our analysis of linkage disequilibrium (LD) showed that LD decays within approximately 5–10 cM. Moreover, we found that LD is variable along chromosomes. Our results suggest that the number of SNPs needs to be increased further to obtain a higher coverage of the chromosomes. Taken together, SNPs can be a valuable tool for genomics approaches and for a knowledge-based improvement of wheat.