Domesticated crops experience strong human-mediated selection aimed at developing high-yielding varieties adapted to local conditions. To detect regions of the wheat genome subject to selection during improvement, we developed a high-throughput array to interrogate 9,000 gene-associated single-nucleotide polymorphisms (SNP) in a worldwide sample of 2,994 accessions of hexaploid wheat including landraces and modern cultivars. Using a SNP-based diversity map we characterized the impact of crop improvement on genomic and geographic patterns of genetic diversity. We found evidence of a small population bottleneck and extensive use of ancestral variation often traceable to founders of cultivars from diverse geographic regions. Analyzing genetic differentiation among populations and the extent of haplotype sharing, we identified allelic variants subjected to selection during improvement. Selective sweeps were found around genes involved in the regulation of flowering time and phenology. An introgression of a wild relative-derived gene conferring resistance to a fungal pathogen was detected by haplotype-based analysis. Comparing selective sweeps identified in different populations, we show that selection likely acts on distinct targets or multiple functionally equivalent alleles in different portions of the geographic range of wheat. The majority of the selected alleles were present at low frequency in local populations, suggesting either weak selection pressure or temporal variation in the targets of directional selection during breeding probably associated with changing agricultural practices or environmental conditions. The developed SNP chip and map of genetic variation provide a resource for advancing wheat breeding and supporting future population genomic and genomewide association studies in wheat.SNP genotyping | polyploid wheat | selection scans | wheat improvement | breeding history

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Potential novel and known QTL for race-specific all-stage and adult plant resistance to stripe rust were identified by genome-wide association mapping in the US PNW winter wheat accessions. Stripe rust (Puccinia striiformis F. sp. tritici; also known as yellow rust) is a globally devastating disease of wheat (Triticum aestivum L.) and a major threat to wheat production in the US Pacific Northwest (PNW), therefore both adult plant and all-stage resistance have been introduced into the winter wheat breeding programs in the PNW. The goal of this study was to identify quantitative trait loci (QTL) and molecular markers for these resistances through genome-wide association (GWAS) mapping in winter wheat accessions adapted to the PNW. Stripe rust response for adult plants was evaluated in naturally occurring epidemics in a total of nine environments in Washington State, USA. Seedling response was evaluated with three races under artificial inoculation in the greenhouse. The panel was genotyped with the 9K Illumina Wheat single nucleotide polymorphism (SNP) array and additional markers linked to previously reported genes and QTL for stripe rust resistance. The population was grouped into three sub-populations. Markers linked to Yr17 and previously reported QTL for stripe rust resistance were identified on chromosomes 1B, 2A, and 2B. Potentially novel QTL associated with race-specific seedling response were identified on chromosomes 1B and 1D. Potentially novel QTL associated with adult plant response were located on chromosomes 2A, 2B, 3B, 4A, and 4B. Stripe rust was reduced when multiple alleles for resistance were present. The resistant allele frequencies were different among sub-populations in the panel. This information provides breeders with germplasm and closely linked markers for stripe rust resistance to facilitate the transfer of multiple loci for durable stripe rust resistance into wheat breeding lines and cultivars.

Key message Chromosome regions affecting grain yield, grain yield components and plant water status were identified and validated in fall-sown spring wheats grown under full and limited irrigation. AbstractIncreases in wheat production are required to feed a growing human population. To understand the genetic basis of grain yield in fall-sown spring wheats, we performed a genome-wide association study (GWAS) including 262 photoperiod-insensitive spring wheat accessions grown under full and limited irrigation treatments. Analysis of molecular variance showed that 4.1% of the total variation in the panel was partitioned among accessions originally developed under fall-sowing or spring-sowing conditions, 11.7% among breeding programs within sowing times and 84.2% among accessions within breeding programs. We first identified QTL for grain yield, yield components and plant water status that were significant in at least three environments in the GWAS, and then selected those that were also significant in at least two environments in a panel of eight biparental mapping populations. We identified and validated 14 QTL for grain yield, 15 for number of spikelets per spike, one for kernel number per spike, 11 for kernel weight and 9 for water status, which were not associated with differences in plant height or heading date. We detected significant correlations among traits and colocated QTL that were consistent with those correlations. Among those, grain yield and plant water status were negatively correlated in all environments, and six QTL for these traits were colocated or tightly linked (< 1 cM). QTL identified and validated in this study provide useful information for the improvement of fall-sown spring wheats under full and limited irrigation.Electronic supplementary materialThe online version of this article (10.1007/s00122-018-3111-9) contains supplementary material, which is available to authorized users.

Over time, many single, all-stage resistance genes to stripe rust (Puccinia striiformis f. sp. tritici) in wheat (Triticum aestivum L.) are circumvented by race changes in the pathogen. In contrast, high-temperature, adult-plant resistance (HTAP), which only is expressed during the adult-plant stage and when air temperatures are warm, provides durable protection against stripe rust. Our objective was to identify major quantitative trait loci (QTL) for HTAP resistance to stripe rust in the spring wheat cultivar 'Louise'. The mapping population consisted of 188 recombinant inbred lines (RIL) from a Louise (resistant) by 'Penawawa' (susceptible) cross. F(5:6) lines were evaluated for stripe rust reaction under natural infection in replicated field trials at five locations in the US Pacific Northwest in 2007 and 2008. Infection type (IT) and disease severity were recorded for each RIL 2-4 times per location. In all environments, Penawawa, the susceptible parent, was rated with an IT ranging from 6 to 8 at all growth stages evaluated. In contrast, Louise, the resistant parent, was rated with an IT of 2 or 3 across growth stages. Distribution of IT values was bimodal, indicating a single major gene was affecting the trait. The parents and RIL population were evaluated with 295 polymorphic simple sequence repeat and one single nucleotide polymorphism markers. One major QTL, designated QYrlo.wpg-2BS, associated with HTAP resistance in Louise, was detected on chromosome 2BS (LOD scores ranging from 5.5 to 62.3 across locations and years) within a 16.9 cM region flanked by Xwmc474 and Xgwm148. SSR markers associated with QYrlo.wpg-2BS are currently being used in marker-based forward breeding strategies to transfer the target region into adapted germplasm to improve the durability of resistance in resulting cultivars.

Hansen, Jennifer L.; Zemetra, Robert S.; Santra, Dipak K.; Campbell, Kimberly G.; and Riera-Lizarazu, Oscar, "Identification of a candidate gene for the wheat endopeptidase Ep-D1 locus and two other STS markers linked to the eyespot resistance gene Pch1" (2008 Abstract Wheat is prone to strawbreaker foot rot (eyespot), a fungal disease caused by Oculimacula yallundae and O. acuformis. The most eVective source of genetic resistance is Pch1, a gene derived from Aegilops ventricosa. The endopeptidase isozyme marker allele Ep-D1b, linked to Pch1, has been shown to be more eVective for tracking resistance than DNA-based markers developed to date. Therefore, we sought to identify a candidate gene for Ep-D1 as a basis for a DNA-based marker. Comparative mapping suggested that the endopeptidase loci Ep-D1 (wheat), enp1 (maize), and Enp (rice) were orthologous. Since the product of the maize endopeptidase locus enp1 has been shown to exhibit biochemical properties similar to oligopeptidase B puriWed from E. coli, we reasoned that Ep-D1 may also encode an oligopeptidase B. Consistent with this hypothesis, a sequence-tagged-site (STS) marker, Xorw1, derived from an oligopeptidase B-encoding wheat expressed-sequence-tag (EST) showed complete linkage with Ep-D1 and Pch1 in a population of 254 recombinant inbred lines (RILs) derived from a cross between wheat cultivars Coda and Brundage. Two other STS markers, Xorw5 and Xorw6, and three microsatellite markers (Xwmc14, Xbarc97, and Xcfd175) were also completely linked to Pch1. On the other hand, Xwmc14, Xbarc97, and Xcfd175 showed recombination in the W7984 £ Opata85 RIL population suggesting that recombination near Pch1 is reduced in the Coda/Brundage population. In a panel of 44 wheat varieties with known eyespot reactions, Xorw1, Xorw5, and Xorw6 were 100% accurate in predicting the presence or absence of Pch1 whereas Xwmc14, Xbarc97, and Xcfd175 were less eVective. Thus, linkage mapping and a germplasm survey suggest that the STS markers identiWed here should be useful for indirect selection of Pch1.

Wheat is marketed based on end-use quality characteristics and better knowledge of the underlying genetics of specific quality parameters is essential to enhance the breeding process. A set of 188 recombinant inbred lines from a 'Louise' by 'Penawawa' mapping population was grown in two crop years at two locations in the Pacific Northwest region of the United States and data were collected on 17 end-use quality traits using established quality analysis protocols. Using an established genetic linkage map, composite interval mapping was used to identify QTL associated with 16 of the 17 quality traits. QTL were found on 13 of the 21 wheat chromosomes. A large number of QTL were located on chromosomes 3B and 4D and coincided with traits for milling quality and starch functionality. Chromosome 3B contained 10 QTL, which were localized to a 26.2 cM region. Chromosome 4D contained 7 QTL, all of which were located on an 18.8 cM region of this chromosome. The majority of the alleles for superior end-use quality were associated with the cultivar Louise. The identified QTL detected remained highly significant independent of grain yield and protein quantity. The identification of these QTL for end-use quality gives key insight into the relationship and complexity of end-use quality traits. It also improves our understanding of these relationships, thereby allowing plant breeders to make valuable gains from selection for these important traits.

Genomic selection (GS) is transforming the field of plant breeding and implementing models that improve prediction accuracy for complex traits is needed. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy in GS. Deep learning (DL) is a branch of machine learning (ML) which focuses on densely connected networks using artificial neural networks for training the models. The objective of this research was to evaluate the potential of DL models in the Washington State University spring wheat breeding program. We compared the performance of two DL algorithms, namely multilayer perceptron (MLP) and convolutional neural network (CNN), with ridge regression best linear unbiased predictor (rrBLUP), a commonly used GS model. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat nested association mapping (NAM) population planted from 2014–2016 growing seasons. We predicted five different quantitative traits with varying genetic architecture using cross-validations (CVs), independent validations, and different sets of SNP markers. Hyperparameters were optimized for DL models by lowering the root mean square in the training set, avoiding model overfitting using dropout and regularization. DL models gave 0 to 5% higher prediction accuracy than rrBLUP model under both cross and independent validations for all five traits used in this study. Furthermore, MLP produces 5% higher prediction accuracy than CNN for grain yield and grain protein content. Altogether, DL approaches obtained better prediction accuracy for each trait, and should be incorporated into a plant breeder’s toolkit for use in large scale breeding programs.