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Summary of re-sequencing panel. (a) Evolution of the hexaploid wheat genome. The tetraploid wheat T. turgidum (AABB) originated by the hybridization of T. urartu with the close unidentified relative of Ae. speltoides occurred about 0.58 to 0.82 million years ago according to the genome-wide divergence time estimate 10 . The origin of hexaploid wheat occurred about 10,000 years ago 11 by the hybridization of T. turgidum with Ae. tauschii (DD) 12 . Marcussen et al. 10 suggested that Ae. tauschii might have originated by homoploid hybrid speciation (shown by dashed arrows). (b) Geographic distribution of 62 accessions of wheat accessions. Pie charts indicate the proportion of genetic ancestry for K = 4 inferred using Structure. (c) Efficiency of homoeologous gene capture. The depth of read coverage was extracted for each of the three copies of 47,739 homoeologous gene sets. The histogram of the log2 transformed ratio of read coverage between A and B (red), A and D (blue), and B and D (green) genomes was plotted. Each plot shows a normal distribution with the overall mean at 0. (d) Overlap between the SNP and indel datasets generated by WEC and GBS. (e) Minor allele frequency of different functional classes of SNPs as a proportion of total SNPs within each genome and class. PTC: premature termination codons; SSD: splice-site disruptions.
Source publication
Bread wheat is an allopolyploid species with a large, highly repetitive genome. To investigate the impact of selection on variants distributed among homoeologous wheat genomes and to build a foundation for understanding genotype-phenotype relationships, we performed population-scale re-sequencing of a diverse panel of wheat lines.
A sample of 62 di...
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We untangled key regions of the genetic architecture of grain yield (GY) in CIMMYT spring bread wheat by conducting a haplotype-based, genome-wide association study (GWAS), together with an investigation of epistatic interactions using seven large sets of elite yield trials (EYTs) consisting of a total of 6,461 advanced breeding lines. These lines...
Citations
... A combination of transcriptome data and highresolution marker maps for the TGW QTL initially thought to be on 5AL, in fact indicated that the QTL resulted from linkage to the presence/absence of the 5AS arm. On a larger scale, the resequencing of 145 land marker cultivars in China, it was found that there were more long-range haplotypes on A and B sub-genomes rather than on D sub-genome in common wheat (Jordan et al. 2015;Hao et al. 2020). The first reason was that the gene flow occurred from the wild tetraploid T. dicoccoides during early cocultivation of tetraploid and hexaploid wheat, where wild emmer was also present as a weed in wheat fields. ...
... The second reason is asymmetric distribution of agronomic traits among the three sub-genomes. There are more QTLs or genes controlling domestication and yield traits mapped on the A sub-genome than either on B or D sub-genomes, leading to stronger selection on the A sub-genomes (Peng et al. 2011;Jordan et al. 2015). ...
Common wheat is a hexaploid species crop that is widely recognized as an important staple food crop. The establishment of a gold standard reference genome sequences of the well-studied CHINESE SPRING, and its progenitors (including Triticum turgidum ssp. dicoccoides accession Zavitan , Triticum durum accession Svevo, Triticum urartu , Aegilops tauschii ), in the last 5 years has dramatically promoted our understanding of wheat genome diversity and evolution through the resequencing of collections of wheat and its progenitors. In this chapter, we review progress in the analysis and interpretation of genome‑based studies of wheat focusing on geographic genome differentiation, interspecies gene flow, haplotype blocks, and gene diversity in breeding. We also consider approaches for efficiently discovering and integrating the genes and genome variations, hidden in Genebank collections, into wheat breeding programs.
... Wheat (Triticum spp.) is a major source of carbohydrates and is used as a staple food for global inhabitants. Genetically, diverse wheat resources show variable ploidy level (diploid, tetraploid, and hexaploid) as a result of prolonged evolution and the wheat domestication process (Jordan et al. 2015). As an allopolyploid crop, wheat breeding and genetics investigations are generally considered challenging and has ...
This century is facing huge challenges such as climate change, water shortage, malnutrition, and food safety and security across the world. These challenges can only be addressed by (i) the deliberate application and utilization of cutting-edge technologies and (ii) combining/using interdisciplinary, multidisciplinary, and even transdisciplinary tools and methods. For scientists to respond to these challenges in a timely manner, it is required the adoption of new tools and technologies and then transforming the technological outcomes into “knowledge”. It is highly unlikely that we could maintain or meet the demands in year 2050 unless we use scientific and technological resources effectively and efficiently. Multidisciplinary and interdisciplinary approaches combined with all available tools are integral for academic and industry programs. This chapter summarizes wheat breeding and genetics coupled with genomics and speed breeding tools to assist with crop development and improvement.
... These populations were previously genotyped with the Illumina 90 K SNP chip (Jordan et al. 2015) and among these SNPs, we identified IWB56221 (RefSeq v1.1 5B 616,652,623) as the closest diagnostic marker for the H1 haplotype (H1 = T, H2, H3 and H4 = C). Using this marker, we performed t-tests between the two alleles for the different traits in the different environments (Table 4). ...
Key message
The wheat transcription factor bZIPC1 interacts with FT2 and affects spikelet and grain number per spike. We identified a natural allele with positive effects on these two economically important traits.
Abstract
Loss-of-function mutations and natural variation in the gene FLOWERING LOCUS T2 (FT2) in wheat have previously been shown to affect spikelet number per spike (SNS). However, while other FT-like wheat proteins interact with bZIP-containing transcription factors from the A-group, FT2 does not interact with any of them. In this study, we used a yeast-two-hybrid screen with FT2 as bait and identified a grass-specific bZIP-containing transcription factor from the C-group, designated here as bZIPC1. Within the C-group, we identified four clades including wheat proteins that show Y2H interactions with different sets of FT-like and CEN-like encoded proteins. bZIPC1 and FT2 expression partially overlap in the developing spike, including the inflorescence meristem. Combined loss-of-function mutations in bZIPC-A1 and bZIPC-B1 (bzipc1) in tetraploid wheat resulted in a drastic reduction in SNS with a limited effect on heading date. Analysis of natural variation in the bZIPC-B1 (TraesCS5B02G444100) region revealed three major haplotypes (H1–H3), with the H1 haplotype showing significantly higher SNS, grain number per spike and grain weight per spike than both the H2 and H3 haplotypes. The favorable effect of the H1 haplotype was also supported by its increased frequency from the ancestral cultivated tetraploids to the modern tetraploid and hexaploid wheat varieties. We developed markers for the two non-synonymous SNPs that differentiate the bZIPC-B1b allele in the H1 haplotype from the ancestral bZIPC-B1a allele present in all other haplotypes. These diagnostic markers are useful tools to accelerate the deployment of the favorable bZIPC-B1b allele in pasta and bread wheat breeding programs.
... Li et al., 2015). Yet, the SNP markers generated by GBS are mostly non-genic and have a sizable proportion of missing data (Elshire et al., 2011;Jordan et al., 2015). Additionally, GBS has been patented by KeyGene (KeyGene), making it unappealing to use in genomics-assisted breeding. ...
... For targeted sequencing, an attractive alternative is targeting the protein-coding genomic regions (i.e., exome). Exome enrichment can be achieved through probe hybridization to fragmented genomic DNA, isolating genic regions prior to sequencing (Bayer et al., 2019;Jordan et al., 2015). Its main advantage is that it captures gene-coding regions that can be used to identify causative polymorphisms related to important agronomic traits (Kaur & Gaikwad, 2017;Krasileva et al., 2017). ...
Next‐generation sequencing (NGS) technology advancements continue to reduce the cost of high‐throughput genome‐wide genotyping for breeding and genetics research. Skim sequencing, which surveys the entire genome at low coverage, has become feasible for quantitative trait locus (QTL) mapping and genomic selection in various crops. However, the genome complexity of allopolyploid crops such as wheat (Triticum aestivum L.) still poses a significant challenge for genome‐wide genotyping. Targeted sequencing of the protein‐coding regions (i.e., exome) reduces sequencing costs compared to whole genome re‐sequencing and can be used for marker discovery and genotyping. We developed a method called skim exome capture (SEC) that combines the strengths of these existing technologies and produces targeted genotyping data while decreasing the cost on a per‐sample basis compared to traditional exome capture. Specifically, we fragmented genomic DNA using a tagmentation approach, then enriched those fragments for the low‐copy genic portion of the genome using commercial wheat exome baits and multiplexed the sequencing at different levels to achieve desired coverage. We demonstrated that for a library of 48 samples, ∼7–8× target coverage was sufficient for high‐quality variant detection. For higher multiplexing levels of 528 and 1056 samples per library, we achieved an average coverage of 0.76× and 0.32×, respectively. Combining these lower coverage SEC sequencing data with genotype imputation using a customized wheat practical haplotype graph database that we developed, we identified hundreds of thousands of high‐quality genic variants across the genome. The SEC method can be used for high‐resolution QTL mapping, genome‐wide association studies, genomic selection, and other downstream applications.
... These populations were previously genotyped with the Illumina 90K SNP chip (Jordan et al. 2015) and among these SNPs, we identified IWB56221 (RefSeq v1.1 5B 616,652,623) as the closest diagnostic marker for the H1 haplotype (H1= T, H2, H3 and H4 = C). Using this marker, we performed t-tests between the two alleles for the different traits in the different environments (Table 4). ...
Loss-of-function mutations and natural variation in the gene FLOWERING LOCUS T2 ( FT2 ) in wheat have previously been shown to affect spikelet number per spike (SNS). However, while other FT-like wheat proteins interact with bZIP-containing transcription factors from the A-group, FT2 does not interact with any of them. In this study, we used a yeast-two-hybrid screen with FT2 as bait and identified a grass-specific bZIP-containing transcription factor from the C-group, designated here as bZIPC1. Within the C-group, we identified four clades including wheat proteins that show Y2H interactions with different sets of FT - like and CEN - like encoded proteins. bZIPC1 and FT2 expression partially overlap in the developing spike, including the inflorescence meristem. Combined loss-of-function mutations in bZIPC-A1 and bZIPC-B1 ( bzipc1 ) in tetraploid wheat resulted in a drastic reduction in SNS with a limited effect on heading date. Analysis of natural variation in the bZIPC-B1 (TraesCS5B02G444100) region revealed three major haplotypes (H1-H3), with the H1 haplotype showing significantly higher SNS, grain number per spike and grain weight per spike than both the H2 and H3 haplotypes. The favorable effect of the H1 haplotype was also supported by its increased frequency from the ancestral cultivated tetraploids to the modern durum and common wheat varieties. We developed markers for the two non-synonymous SNPs that differentiate the bZIPC-B1b allele in the H1 haplotype from the ancestral bZIPC-B1a allele present in all other haplotypes. These diagnostic markers are useful tools to accelerate the deployment of the favorable bZIPC-B1b allele in pasta and bread wheat breeding programs.
Key Message
The wheat transcription factor bZIPC1 interacts with FT2 and affects spikelet and grain number per spike. We identified a natural allele with positive effects on these two economically important traits.
... SNPs were detected using a reference-based pipeline in TASSEL 5.0 (Bradbury et al. 2007) using the IWGSC Ref-Seq v2.1 genome (IWGSC 2018) to assign the physical positions for each SNP. Exome-capture data were obtained for JagMut1095 and Jagger using an assay described previously (Jordan et al. 2015). The quality of raw sequencing reads was assessed using NGSQC toolkit v.2.3.3 (Patel and Jain 2012). ...
Key message
Two QTLs with major effects on rolled leaf trait were consistently detected on chromosomes 1A (QRl.hwwg-1AS) and 5A (QRl.hwwg-5AL) in the field experiments.
Abstract
Rolled leaf (RL) is a morphological strategy to protect plants from dehydration under stressed field conditions. Identification of quantitative trait loci (QTLs) underlining RL is essential to breed drought-tolerant wheat cultivars. A mapping population of 154 recombinant inbred lines was developed from the cross between JagMut1095, a mutant of Jagger, and Jagger to identify quantitative trait loci (QTLs) for the RL trait. A linkage map of 3106 cM was constructed with 1003 unique SNPs from 21 wheat chromosomes. Two consistent QTLs were identified for RL on chromosomes 1A (QRl.hwwg-1AS) and 5A (QRl.hwwg-5AL) in all field experiments. QRl.hwwg-1AS explained 24–56% of the phenotypic variation and QRl.hwwg-5AL explained up to 20% of the phenotypic variation. The combined percent phenotypic variation associated with the two QTLs was up to 61%. Analyses of phenotypic and genotypic data of recombinants generated from heterogeneous inbred families of JagMut1095 × Jagger delimited QRl.hwwg-1AS to a 6.04 Mb physical interval. This work lays solid foundation for further fine mapping and map-based cloning of QRl.hwwg-1AS.
... Popular options include restriction enzyme-based genotyping-by-sequencing and genotyping arrays. Sequencing-based methods that commence with a genomic library and capture all coding regions (whole exome capture, Jordan et al. 2015), capture a smaller distributed subset of informative regions (reduced exome capture, Fu et al. 2019) or conversely deplete the repetitive fraction of the genome are also viable options especially when combined with graphbased imputation to allow for skim level sequencing (Jordan et al. 2022). One untested option for wheat is rAMP-seq in which tailed PCR primers are designed and employed such that each primer pair simultaneously targets thousands of repetitive loci. ...
Key message
rAMP-seq based genomic selection for agronomic traits has been shown to be a useful tool for winter wheat breeding programs by increasing the rate of genetic gain.
Abstract
Genomic selection (GS) is an effective strategy to employ in a breeding program that focuses on optimizing quantitative traits, which results in the ability for breeders to select the best genotypes. GS was incorporated into a breeding program to determine the potential for implementation on an annual basis, with emphasis on selecting optimal parents and decreasing the time and costs associated with phenotyping large numbers of genotypes. The design options for applying repeat amplification sequencing (rAMP-seq) in bread wheat were explored, and a low-cost single primer pair strategy was implemented. A total of 1870 winter wheat genotypes were phenotyped and genotyped using rAMP-seq. The optimization of training to testing population size showed that the 70:30 ratio provided the most consistent prediction accuracy. Three GS models were tested, rrBLUP, RKHS and feed-forward neural networks using the University of Guelph Winter Wheat Breeding Program (UGWWBP) and Elite-UGWWBP populations. The models performed equally well for both populations and did not differ in prediction accuracy (r) for most agronomic traits, with the exception of yield, where RKHS performed the best with an r = 0.34 and 0.39 for each population, respectively. The ability to operate a breeding program where multiple selection strategies, including GS, are utilized will lead to higher efficiency in the program and ultimately lead to a higher rate of genetic gain.
... A large number of markers were found in the genomes B and A, while the younger D genome had a smaller number of markers as previously reported (25,26,23). The higher diversity observed in the A and B genomes can be related to their older evolutionary background and due to gene ow from T. turgidum and the lack of gene ow from Ae. tauschii to bread wheat (34,35). ...
Leaf or brown rust caused by Puccinia triticina Eriks. ( Pt ) is a limiting factor for wheat production. Thus, a constant search for new resistance genes or QTLs is essential to improve the resistance durability against the continued evolution of new races of Pt . This study was aimed at exploring potentially novel genes/QTLs resistance to leaf rust in Iranian wheat landraces and cultivars by using association mapping. Using a panel of 320 wheat accessions, genome-wide association study (GWAS) genotyping-by-sequencing (GBS) techniques were adopted to map loci associated with resistance to five races of Pt . A total of 17 major marker-trait associations (MTAs) were found on wheat chromosomes that were significantly linked with seedling resistance to Pt . Three markers including rs12954, rs15705, and rs42447 were detected as novel loci for resistance to PKTTS race. Our findings presented new and putative resources of leaf rust resistance in Iranian bread wheat accessions. The new identified SNPs will be valuable to expand the set of resistance genes available to control this serious disease.
... Immature pollen grains represent natural haploids and can be used for genetic studies (Aziz et al., 2017); however, only 60-70% of successfully germinated pollens captured are available for subsequent DNA analyses, as 30-40% of individually isolated pollen either did not germinate or did not provide genomic DNA (Aziz and Sauve, 2008). Haplotype maps have been developed for the hexaploid genome of wheat (Jordan et al., 2015). By targeting microspores, which are early-stage pollen, haplotype genetic information could also be revealed for sorghum. ...
Premise:
Sorghum is a multi-use crop, the efficient breeding of which requires the development of new genetic tools. One such tool could be the genetic assessment of free microspores, which are released just after the tetrad stage of pollen development. Microspores are ideal for DNA isolation as they have underdeveloped cell walls and can be readily lysed as natural protoplasts.
Methods:
Four cultivars of Sorghum bicolor ('Achi Turi', 'Dale', 'Local', and 'Topper 76-6') were grown in a greenhouse until flowering (7.7-11.5 cm flag leaf internode length), after which 30 immature microspores were isolated from each line. Plant height, time to flowering, boot radius, and spikelet maturation were recorded for each cultivar. The exine development of the microspores was observed under an inverted Nikon microscope, and those with underdeveloped exine were subjected to whole-genome amplification and sequencing.
Results:
Microspores in the early uninucleate to early binucleate stages had underdeveloped exine, and were therefore ideal for DNA extraction. High-quality DNA was obtained from these single-cell gametophytes. The average DNA concentration was 2902 ng/µL, with fragment sizes comparable to those obtained from leaf tissue extractions.
Discussion:
Harvesting panicles with immature microspores means the entire gametic population is accessible for DNA analyses. This is the first amplification of whole-genome DNA fragments from sorghum single-cell microspores isolated during gametogenesis.
... SNPs mapped to the genome B were about four times more common than those located on the genome D. The 3B and 2B chromosomes possess the most significant marker pairs, as reported previously 21 . The higher variation uncovered in the B and A genomes can be due to two reasons: (i) gene flow from T. turgidum as opposed to its absence from Ae. tauschii to T. aestivum; (ii) the output of older evolutionary history of the genomes B and A relative to genome D 39,40 . Furthermore, bottleneck impacts have likely happened owing to intense selection in native landraces during breeding schedules and this might lead to further impacts on genome D 15 . ...
