Enhancing winter wheat prediction with genomics, phenomics and environmental data.

In the realm of multi-environment prediction, when the goal is to predict a complete environment using the others as a training set, the efficiency of genomic selection (GS) falls short of expectations. Genotype by environment interaction poses a challenge in achieving high prediction accuracies. Consequently, current efforts are focused on enhancing efficiency by integrating various types of inputs, such as phenomics data, environmental information, and other omics data. In this study, we sought to evaluate the impact of incorporating environmental information into the modeling process, in addition to genomic and phenomics information. Our evaluation encompassed five data sets of soft white winter wheat, and the results revealed a significant improvement in prediction accuracy, as measured by the normalized root mean square error (NRMSE), through the integration of environmental information. Notably, there was an average gain in prediction accuracy of 49.19% in terms of NRMSE across the data sets. Moreover, the observed prediction accuracy ranged from 5.68% (data set 3) to 60.36% (data set 4), underscoring the substantial effect of integrating environmental information. By including genomic, phenomic, and environmental data in prediction models, plant breeding programs can improve selection efficiency across locations.

Genetic architecture of end-use quality traits in soft white winter wheat.

BACKGROUND: Genetic improvement of end-use quality is an important objective in wheat breeding programs to meet the requirements of grain markets, millers, and bakers. However, end-use quality phenotyping is expensive and laborious thus, testing is often delayed until advanced generations. To better understand the underlying genetic architecture of end-use quality traits, we investigated the phenotypic and genotypic structure of 14 end-use quality traits in 672 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the United States. RESULTS: This collection of germplasm had continuous distributions for the 14 end-use quality traits with industrially significant differences for all traits. The breeding lines and cultivars were genotyped using genotyping-by-sequencing and 40,518 SNP markers were used for association mapping (GWAS). The GWAS identified 178 marker-trait associations (MTAs) distributed across all wheat chromosomes. A total of 40 MTAs were positioned within genomic regions of previously discovered end-use quality genes/QTL. Among the identified MTAs, 12 markers had large effects and thus could be considered in the larger scheme of selecting and fixing favorable alleles in breeding for end-use quality in soft white wheat germplasm. We also identified 15 loci (two of them with large effects) that can be used for simultaneous breeding of more than a single end-use quality trait. The results highlight the complex nature of the genetic architecture of end-use quality, and the challenges of simultaneously selecting favorable genotypes for a large number of traits. This study also illustrates that some end-use quality traits were mainly controlled by a larger number of small-effect loci and may be more amenable to alternate selection strategies such as genomic selection. CONCLUSIONS: In conclusion, a breeder may be faced with the dilemma of balancing genotypic selection in early generation(s) versus costly phenotyping later on.

Genome-wide association mapping of the 'super-soft' kernel texture in white winter wheat.

KEY MESSAGE: The novel super-soft kernel phenotype has the potential to improve wheat processing and flour quality. We identified genomic regions associated with this kernel texture in white winter wheat. Grain hardness is a key determinant of wheat milling and baking quality. The recently discovered 'super-soft' kernel phenotype has the potential to improve wheat processing and flour quality. However, the genetic basis underlying the super-soft trait in wheat is not yet well understood. In this study, we investigated the phenotypic and genotypic structure of the super-soft trait in a collection of 172 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the USA. This collection had a continuous distribution for grain hardness index (single-kernel characterization system). Ten super-soft genotypes showed hardness index ≤ 12 including the cultivar Jasper. Over 98,000 SNP markers from genotyping-by-sequencing were used for association mapping (GWAS). The GWAS identified 20 significant markers associated with grain hardness. These significant SNPs corresponded to seven QTL on chromosomes 2B, 3A, 3B, 5A, 6B,7A, and one unaligned chromosome. Two of these QTL, QSKhard.wql-3A and QSKhard.wql-5A, had large effects and distinguished between the normal soft and the super-soft classes. QSKhard.wql-3A and QSKhard.wql-5A reduced the hardness index by 11.7 and 13.1 on average, respectively. The remaining QTL had small effects and reduced grain hardness within the normal soft range. QSKhard.wql-2B, QSKhard.wql-3A, QSKhard.wql-3B, and QSKhard.wql-6B were not previously reported to be in genomic regions of grain hardness-related genes/QTL. The identified super-soft genotypes as well as the SNPs associated with lower grain hardness will be useful to assist breeding for this grain texture trait.

Identification of a major dominant gene for race-nonspecific tan spot resistance in wild emmer wheat.

KEY MESSAGE: A single dominant gene found in tetraploid and hexaploid wheat controls broad-spectrum race-nonspecific resistance to the foliar disease tan spot caused by Pyrenophora tritici-repentis. Tan spot is an important foliar disease of durum and common wheat caused by the necrotrophic fungal pathogen Pyrenophora tritici-repentis. Genetic studies in common wheat have shown that pathogen-produced necrotrophic effectors interact with host genes in an inverse gene-for-gene manner to cause disease, but quantitative trait loci (QTLs) with broad race-nonspecific resistance also exist. Less work has been done to understand the genetics of tan spot interactions in durum wheat. Here, we evaluated a set of Langdon durum-wild emmer (Triticum turgidum ssp. dicoccoides) disomic chromosome substitution lines for reaction to four P. tritici-repentis isolates representing races 1, 2, 3, and 5 to identify wild emmer chromosomes potentially containing tan spot resistance genes. Chromosome 3B from the wild emmer accession IsraelA rendered the tan spot-susceptible durum cultivar Langdon resistant to all four fungal isolates. Genetic analysis indicated that a single dominant gene, designated Tsr7, governed resistance. Detailed mapping experiments showed that the Tsr7 locus is likely the same as the race-nonspecific QTL previously identified in the hexaploid wheat cultivars BR34 and Penawawa. Four user-friendly SNP-based semi-thermal asymmetric reverse PCR (STARP) markers cosegregated with Tsr7 and should be useful for marker-assisted selection of resistance. In addition to 3B, other wild emmer chromosomes contributed moderate levels of tan spot resistance, and, as has been shown previously for tetraploid wheat, the Tsn1-Ptr ToxA interaction was not associated with susceptibility. This is the first report of a major dominant gene governing resistance to tan spot in tetraploid wheat.

Genomic Prediction and Indirect Selection for Grain Yield in US Pacific Northwest Winter Wheat Using Spectral Reflectance Indices from High-Throughput Phenotyping.

Secondary traits from high-throughput phenotyping could be used to select for complex target traits to accelerate plant breeding and increase genetic gains. This study aimed to evaluate the potential of using spectral reflectance indices (SRI) for indirect selection of winter-wheat lines with high yield potential and to assess the effects of including secondary traits on the prediction accuracy for yield. A total of five SRIs were measured in a diversity panel, and F5 and doubled haploid wheat breeding populations planted between 2015 and 2018 in Lind and Pullman, WA. The winter-wheat panels were genotyped with 11,089 genotyping-by-sequencing derived markers. Spectral traits showed moderate to high phenotypic and genetic correlations, indicating their potential for indirect selection of lines with high yield potential. Inclusion of correlated spectral traits in genomic prediction models resulted in significant (p < 0.001) improvement in prediction accuracy for yield. Relatedness between training and test populations and heritability were among the principal factors affecting accuracy. Our results demonstrate the potential of using spectral indices as proxy measurements for selecting lines with increased yield potential and for improving prediction accuracy to increase genetic gains for complex traits in US Pacific Northwest winter wheat.

Identification and validation of QTL for grain yield and plant water status under contrasting water treatments in fall-sown spring wheats.

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. Increases 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.

Genetic relationships between race-nonspecific and race-specific interactions in the wheat-Pyrenophora tritici-repentis pathosystem.

KEY MESSAGE: We identified a major QTL conferring race-nonspecific resistance and revealed its relationships with race-specific interactions in the wheat- Pyrenophora tritici-repentis pathosystem. Tan spot, caused by the fungus Pyrenophora tritici-repentis (Ptr), is a destructive disease of wheat worldwide. The disease system is known to include inverse gene-for-gene, race-specific interactions involving the recognition of fungal-produced necrotrophic effectors (NEs) by corresponding host sensitivity genes. However, quantitative trait loci (QTLs) conferring race-nonspecific resistance have also been identified. In this work, we identified a major race-nonspecific resistance QTL and characterized its genetic relationships with the NE-host gene interactions Ptr ToxA-Tsn1 and Ptr ToxC-Tsc1 in a recombinant inbred wheat population derived from the cross between 'Louise' and 'Penawawa.' Both parental lines were sensitive to Ptr ToxA, but Penawawa and Louise were highly resistant and susceptible, respectively, to conidial inoculations of all races. Resistance was predominantly governed by a major race-nonspecific QTL on chromosome arm 3BL for resistance to all races. Another significant QTL was detected at the distal end of chromosome arm 1AS for resistance to the Ptr ToxC-producing isolates, which corresponded to the known location of the Tsc1 locus. The effects of the 3B and 1A QTLs were largely additive, and the 3B resistance QTL was epistatic to the Ptr ToxA-Tsn1 interaction. Resistance to race 2 in F1 plants was completely dominant; however, race 3-inoculated F1 plants were only moderately resistant because they developed chlorosis presumably due to the Ptr ToxC-Tsc1 interaction. This work provides further understanding of genetic resistance in the wheat-tan spot system as well as important guidance for tan spot resistance breeding.

Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars.

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 genome-wide association studies in wheat.

Tolerance of wheat (Poales: Poaceae) seedlings to wireworm (Coleoptera: Elateridae).

Wireworms (Coleoptera: Elateridae), the subterranean larval stage of the click beetle, are becoming more prevalent in many cropping systems and posing an increasing economic threat to wheat growers in the Pacific Northwest following the cancellation of the insecticide lindane in 2006. Current insecticide seed treatments alone are not adequate for wireworm control. The objective of this study was to evaluate a diverse set of 163 wheat genotypes for tolerance to wireworm feeding. Entries were planted in replicated field trials over 3 yr and evaluated for their performance when grown in the presence of wireworms. Entries were rated based on survival and given a tolerance score. Results indicated that differences exist among wheat genotypes in their level of tolerance to wireworm feeding. In particular, consistently high-ranking genotypes of interest may be 'BR 18', 'Sonalika', 'Safed Lerma', and 'Hollis'. These genotypes, used in conjunction with other cultural or chemical control methods, may help provide an economic means of controlling wireworms.

Remote sensing continuity: a comparison of HTP platforms and potential challenges with field applications.

In an era of climate change and increased environmental variability, breeders are looking for tools to maintain and increase genetic gain and overall efficiency. In recent years the field of high throughput phenotyping (HTP) has received increased attention as an option to meet this need. There are many platform options in HTP, but ground-based handheld and remote aerial systems are two popular options. While many HTP setups have similar specifications, it is not always clear if data from different systems can be treated interchangeably. In this research, we evaluated two handheld radiometer platforms, Cropscan MSR16R and Spectra Vista Corp (SVC) HR-1024i, as well as a UAS-based system with a Sentera Quad Multispectral Sensor. Each handheld radiometer was used for two years simultaneously with the unoccupied aircraft systems (UAS) in collecting winter wheat breeding trials between 2018-2021. Spectral reflectance indices (SRI) were calculated for each system. SRI heritability and correlation were analyzed in evaluating the platform and SRI usability for breeding applications. Correlations of SRIs were low against UAS SRI and grain yield while using the Cropscan system in 2018 and 2019. Dissimilarly, the SVC system in 2020 and 2021 produced moderate correlations across UAS SRI and grain yield. UAS SRI were consistently more heritable, with broad-sense heritability ranging from 0.58 to 0.80. Data standardization and collection windows are important to consider in ensuring reliable data. Furthermore, practical aspects and best practices for these HTP platforms, relative to applied breeding applications, are highlighted and discussed. The findings of this study can be a framework to build upon when considering the implementation of HTP technology in an applied breeding program.

Genomics combined with UAS data enhances prediction of grain yield in winter wheat.

With the human population continuing to increase worldwide, there is pressure to employ novel technologies to increase genetic gain in plant breeding programs that contribute to nutrition and food security. Genomic selection (GS) has the potential to increase genetic gain because it can accelerate the breeding cycle, increase the accuracy of estimated breeding values, and improve selection accuracy. However, with recent advances in high throughput phenotyping in plant breeding programs, the opportunity to integrate genomic and phenotypic data to increase prediction accuracy is present. In this paper, we applied GS to winter wheat data integrating two types of inputs: genomic and phenotypic. We observed the best accuracy of grain yield when combining both genomic and phenotypic inputs, while only using genomic information fared poorly. In general, the predictions with only phenotypic information were very competitive to using both sources of information, and in many cases using only phenotypic information provided the best accuracy. Our results are encouraging because it is clear we can enhance the prediction accuracy of GS by integrating high quality phenotypic inputs in the models.

Genetic mapping of new seed-expressed polyphenol oxidase genes in wheat (Triticum aestivum L.).

Polyphenol oxidase (PPO) enzymatic activity is a major cause in time-dependent discoloration in wheat dough products. The PPO-A1 and PPO-D1 genes have been shown to contribute to wheat kernel PPO activity. Recently a novel PPO gene family consisting of the PPO-A2, PPO-B2, and PPO-D2 genes has been identified and shown to be expressed in wheat kernels. In this study, the sequences of these five kernel PPO genes were determined for the spring wheat cultivars Louise and Penawawa. The two cultivars were found to be polymorphic at each of the PPO loci. Three novel alleles were isolated from Louise. The Louise X Penawawa mapping population was used to genetically map all five PPO genes. All map to the long arm of homeologous group 2 chromosomes. PPO-A2 was found to be located 8.9 cM proximal to PPO-A1 on the long arm of chromosome 2A. Similarly, PPO-D1 and PPO-D2 were separated by 10.7 cM on the long arm of chromosome 2D. PPO-B2 mapped to the long arm of chromosome 2B and was the site of a novel QTL for polyphenol oxidase activity. Five other PPO QTL were identified in this study. One QTL corresponds to the previously described PPO-D1 locus, one QTL corresponds to the PPO-D2 locus, whereas the remaining three are located on chromosome 2B.

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat.

Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait-based GS models. This study's main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait- and multi-trait-based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait-based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.

Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Triticum aestivum L.).

Most genomic prediction models are linear regression models that assume continuous and normally distributed phenotypes, but responses to diseases such as stripe rust (caused by Puccinia striiformis f. sp. tritici) are commonly recorded in ordinal scales and percentages. Disease severity (SEV) and infection type (IT) data in germplasm screening nurseries generally do not follow these assumptions. On this regard, researchers may ignore the lack of normality, transform the phenotypes, use generalized linear models, or use supervised learning algorithms and classification models with no restriction on the distribution of response variables, which are less sensitive when modeling ordinal scores. The goal of this research was to compare classification and regression genomic selection models for skewed phenotypes using stripe rust SEV and IT in winter wheat. We extensively compared both regression and classification prediction models using two training populations composed of breeding lines phenotyped in 4 years (2016-2018 and 2020) and a diversity panel phenotyped in 4 years (2013-2016). The prediction models used 19,861 genotyping-by-sequencing single-nucleotide polymorphism markers. Overall, square root transformed phenotypes using ridge regression best linear unbiased prediction and support vector machine regression models displayed the highest combination of accuracy and relative efficiency across the regression and classification models. Furthermore, a classification system based on support vector machine and ordinal Bayesian models with a 2-Class scale for SEV reached the highest class accuracy of 0.99. This study showed that breeders can use linear and non-parametric regression models within their own breeding lines over combined years to accurately predict skewed phenotypes.

Genomic Analysis and Delineation of the Tan Spot Susceptibility Locus Tsc1 in Wheat.

The necrotrophic fungal pathogen Pyrenophora tritici-repentis (Ptr) causes the foliar disease tan spot in both bread wheat and durum wheat. Wheat lines carrying the tan spot susceptibility gene Tsc1 are sensitive to the Ptr-produced necrotrophic effector (NE) Ptr ToxC. A compatible interaction results in leaf chlorosis, reducing yield by decreasing the photosynthetic area of leaves. Developing genetically resistant cultivars will effectively reduce disease incidence. Toward that goal, the production of chlorosis in response to inoculation with Ptr ToxC-producing isolates was mapped in two low-resolution biparental populations derived from LMPG-6 × PI 626573 (LP) and Louise × Penawawa (LouPen). In total, 58 genetic markers were developed and mapped, delineating the Tsc1 candidate gene region to a 1.4 centiMorgan (cM) genetic interval spanning 184 kb on the short arm of chromosome 1A. A total of nine candidate genes were identified in the Chinese Spring reference genome, seven with protein domains characteristic of resistance genes. Mapping of the chlorotic phenotype, development of genetic markers, both for genetic mapping and marker-assisted selection (MAS), and the identification of Tsc1 candidate genes provide a foundation for map-based cloning of Tsc1.

A Comparison between Three Tuning Strategies for Gaussian Kernels in the Context of Univariate Genomic Prediction.

Genomic prediction is revolutionizing plant breeding since candidate genotypes can be selected without the need to measure their trait in the field. When a reference population contains both phenotypic and genotypic information, it is trained by a statistical machine learning method that is subsequently used for making predictions of breeding or phenotypic values of candidate genotypes that were only genotyped. Nevertheless, the successful implementation of the genomic selection (GS) methodology depends on many factors. One key factor is the type of statistical machine learning method used since some are unable to capture nonlinear patterns available in the data. While kernel methods are powerful statistical machine learning algorithms that capture complex nonlinear patterns in the data, their successful implementation strongly depends on the careful tuning process of the involved hyperparameters. As such, in this paper we compare three methods of tuning (manual tuning, grid search, and Bayesian optimization) for the Gaussian kernel under a Bayesian best linear unbiased predictor model. We used six real datasets of wheat (Triticum aestivum L.) to compare the three strategies of tuning. We found that if we want to obtain the major benefits of using Gaussian kernels, it is very important to perform a careful tuning process. The best prediction performance was observed when the tuning process was performed with grid search and Bayesian optimization. However, we did not observe relevant differences between the grid search and Bayesian optimization approach. The observed gains in terms of prediction performance were between 2.1% and 27.8% across the six datasets under study.

Genomic variants affecting homoeologous gene expression dosage contribute to agronomic trait variation in allopolyploid wheat.

Allopolyploidy greatly expands the range of possible regulatory interactions among functionally redundant homoeologous genes. However, connection between the emerging regulatory complexity and expression and phenotypic diversity in polyploid crops remains elusive. Here, we use diverse wheat accessions to map expression quantitative trait loci (eQTL) and evaluate their effects on the population-scale variation in homoeolog expression dosage. The relative contribution of cis- and trans-eQTL to homoeolog expression variation is strongly affected by both selection and demographic events. Though trans-acting effects play major role in expression regulation, the expression dosage of homoeologs is largely influenced by cis-acting variants, which appear to be subjected to selection. The frequency and expression of homoeologous gene alleles showing strong expression dosage bias are predictive of variation in yield-related traits, and have likely been impacted by breeding for increased productivity. Our study highlights the importance of genomic variants affecting homoeolog expression dosage in shaping agronomic phenotypes and points at their potential utility for improving yield in polyploid crops.

Development of the Wheat Practical Haplotype Graph database as a resource for genotyping data storage and genotype imputation.

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10-14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs.

Traits with a complex unknown genetic architecture are common in breeding programs. However, they pose a challenge for selection due to a combination of complex environmental and pleiotropic effects that impede the ability to create mapping populations to characterize the trait's genetic basis. One such trait, seedling emergence of wheat (Triticum aestivum L.) from deep planting, presents a unique opportunity to explore the best method to use and implement genetic selection (GS) models to predict a complex trait. Seventeen GS models were compared using two training populations, consisting of 473 genotypes from a diverse association mapping panel phenotyped from 2015 to 2019 and the other training population consisting of 643 breeding lines phenotyped in 2015 and 2020 in Lind, WA, with 40,368 markers. There were only a few significant differences between GS models, with support vector machines reaching the highest accuracy of 0.56 in a single breeding line trial using cross-validations. However, the consistent moderate accuracy of the parametric models indicates little advantage of using nonparametric models within individual years, but the nonparametric models show a slight increase in accuracy when combing years for complex traits. There was an increase in accuracy using cross-validations from 0.40 to 0.41 using diversity panels lines to breeding lines. Overall, our study showed that breeders can accurately predict and implement GS for a complex trait by using nonparametric machine learning models within their own breeding programs with increased accuracy as they combine training populations over the years.

An ALA122 THR substitution in the AHAS/ALS gene confers imazamox-resistance in Aegilops cylindrica.

BACKGROUND: Wheat growers have limited herbicide options to manage Aegilops cylindrica Host (jointed goatgrass), with many relying on mesosulfuron or imazamox in Clearfield™ winter wheat. Both imazamox and mesosulfuron inhibit acetohydroxyacid synthase/acetolactate synthase (AHAS/ALS). In 2015, a suspected imazamox resistant biotype of Ae. cylindrica was found in eastern Washington. RESULTS: Imazamox and mesosulfuron were applied to the suspected resistant and susceptible Ae. cylindrica biotypes in increasing application rates to evaluate herbicide dose needed to cause 50% growth reduction (GR50 ). The imazamox resistant biotype had a GR50 of 308.5 g ai ha-1 and was more than 5000 times more resistant to imazamox than a known susceptible biotype with a GR50 of 0.06 g ai ha-1 . The Ae. cylindrica resistant biotype was also resistant to mesosulfuron, with an GR50 of 46.82 g ai ha-1 , which was five times more than the susceptible GR50 of 8.6 g ai ha-1 . Sequencing of the AHAS/ALS gene revealed an Ala122 Thr substitution in the herbicide binding region of the AHAS/ALS gene on the D genome of Ae. cylindrica. The resistance trait was inherited as a dominant trait, and the Ala122 Thr co-segregates with the resistance phenotype. CONCLUSIONS: An Ala122 Thr substitution in the AHAS/ALS gene on the D genome of Ae. cylindrica confers resistance to imazamox in Ae. cylindrica. © 2021 Society of Chemical Industry.