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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.

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.

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 0.01x sequence coverage, which was slightly lower than the accuracy obtained using the 0.5x sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (p-value < 2 x 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 2x 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 disequlibrium 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.

Nested association mapping (NAM) populations emerged as a multi-parental strategy that combines the high statistical power of biparental linkage mapping with greater allelic richness of association mapping. Several statistical models have been developed for marker-trait associations (MTAs) in genome-wide association studies (GWAS), which ranges from simple to increasingly complex models. These statistical models vary in their performance for detecting real association with the avoidance of false positives and false negatives. Furthermore, significant threshold methods play an equally important role for controlling spurious associations. In this study, we compared the performance of seven different statistical models ranging from single to multi-locus models on eight different simulated traits with varied genetic architecture for a NAM population of spring wheat (Triticum aestivum L.). The best identified model was further used to identify MTAs for 11 different agronomic and spectral reflectance traits, which were collected on the NAM population between 2014 and 2016. The "Bayesian information and linkage disequilibrium iteratively nested keyway (BLINK)" model performed better than all other models observed based on QQ plots and detection of real association in a simulated data set. The results from model comparison suggest that BLINK controls both false positives and false negatives under the different genetic architecture of simulated traits. Comparison of multiple significant threshold methods suggests that Bonferroni correction performed superior for controlling false positives and false negatives and complements the performance of GWAS models. BLINK identified 45 MTAs using Bonferroni correction of 0.05 for 11 different phenotypic traits in the NAM population. This study helps identify the best statistical model and significant threshold method for performing association analysis in subsequent NAM population studies. [ABSTRACT FROM AUTHOR]

Inbred cultivars and advanced breeding lines have been subjected to numerous recombination cycles, have strong allelic selection for desired traits, and share important attributes for adaptation and agronomic performance. Genetic variation in elite gene pools captured using molecular markers is immediately useful for cultivar development. The primary goal of this study was to implement a genome‐wide association study for 17 agronomic traits using elite inbred lines. A panel consisting of 237 elite hard red spring wheat (Triticum aestivum L.) lines from different wheat breeding institutions in North America were evaluated in 11 locations over 2 yr. A total of 19,192 polymorphic single‐nucleotide polymorphism (SNP) markers from the Illumina 90K SNP array and markers linked to major genes controlling plant height, photoperiod sensitivity, and vernalization were used to assay the population. Linkage disequilibrium was observed to decay within a map distance of ∼3 cM in the A and B genomes and 7 cM in the D genome. A total of 226 marker‐trait associations were identified. Potentially novel associations were detected for grain yield on chromosome 2B and kernels per spike on 1B and 7D, whereas others colocalized with well‐known adaptation loci for photoperiod response, vernalization, and plant height. The frequency of positive alleles for specific marker‐trait associations differed among the programs, suggesting targets for introgression by the respective breeding programs.

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 (

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.

High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS‐collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset. Core Ideas: UAS imagery can be useful in plant breeding programs for routine trait and previously infeasible trait measurement.High‐throughput phenotyping approaches to date differ among crops and each could learn from others.Example applications of UAS imagery are provided for breeding programs of four major crops.The rise of phenomics tools is likely to prove similarly useful to current genomic tools. [ABSTRACT FROM AUTHOR]

In recent years, stripe rust has become an increasingly problematic disease affecting wheat (Triticum aestivum L.). Although stripe rust resistance genes Yr28 and YrAS2388R have been successfully transferred from Aegilops tauschii Coss. to hexaploid wheat, these genes have not been widely used for stripe rust resistance breeding largely due to a lack of publicly available and well‐adapted germplasm. Here we describe U6719‐004 (Entry 538 of D‐genome nested association mapping population, Reg. No. MP‐14, NSL 536301 MAP), a hexaploid wheat germplasm carrying YrAS2388R stripe rust resistance derived from Ae. tauschii accession TA1718 that is well adapted to U.S. wheat production environments. U6719‐004 (KS05HW14‐3/3/KS05HW14‐3/TA1718//KS05HW14‐3) was developed using a combination of direct hybridization of TA1718 with the hard white winter wheat line KS05HW14‐3 followed by two rounds of backcrossing and three generations of single seed descent. U6719‐004 stripe rust resistance was characterized under controlled and natural conditions. Using gene‐based markers, TA1718 and U6719‐004 were confirmed to be YrAS2388R carriers. Grain yield for U6719‐004 was evaluated in 11 year‐by‐location environments across the United States. Grain quality parameters were measured in four year‐by‐location environments. Compared with the recurrent wheat parent KS05HW14‐3 and locally adapted check cultivars, the Ae. tauschii introgression had little to no impact on U6719‐004 grain yield or quality. The availability of U6719‐004 will enable the use of YrAS2388R for stripe rust resistance breeding efforts. Core Ideas: The hexaploid wheat germplasm U6719‐004 carries stripe rust resistance gene YrAS2388R.U6719‐004 is broadly adapted to wheat production environments of the United States.U6719‐004 has acceptable grain yield and grain quality. [ABSTRACT FROM AUTHOR]

A doubled haploid mapping population was developed from a cross between the hard red winter wheat (Triticum aestivum L. subsp. aestivum) landrace PI 173438 and WA 8137, a soft white winter wheat breeding line developed by the Washington State University winter wheat breeding program in Pullman, WA. The PI 173438/WA 8137 (Reg. no. MP‐16, NSL 543858 MAP) population consists of 437 individuals, with 358 of these individuals used to identify quantitative trait loci associated with snow mold (Typhula spp.) tolerance in PI 173438. Genotypic information was gathered on these individuals using genotype‐by‐sequencing and processed using a pipeline developed by the USDA Eastern Regional Small Grains Genotyping Laboratory. Twenty‐three linkage groups covering the whole genome with groups for the long and short arms of chromosomes 3D and 7D were constructed using 4,029 single nucleotide polymorphisms from the sequenced individuals. Quantitative trait loci associated with snow mold tolerance and snow mold recovery attributed to PI 173438 were successfully identified in this population. PI 173438 has also been used to identify other traits associated with snow mold tolerance, such as freezing tolerance and carbohydrate reserves, and has been important in research investigating dwarf and common bunt. This population may continue to expand current knowledge in these areas, and, given the landrace status of PI 173438, the population may be useful in identifying other novel traits of interest. Core Ideas: Landraces remain vital sources of genetic diversity for trait improvement.The PI 173438/WA 8137 population can aid in research of novel genetics.Snow mold tolerance QTL have been identified in the PI 173438/WA 8137 population. [ABSTRACT FROM AUTHOR]