(0.0000)
Note: TS indicates the trace statistic, * signifies rejection of the hypothesis at the 0.05 level.
Table 4 reports the estimated results of long-term effect of climate variables and other control variables on wheat yield in Hebei, Henan, and Shandong Provinces, respectively.
Results of FMOLS estimator for top three provinces in northern China.
Variables | Hebei Province | Henan Province | Shandong Province | |||
---|---|---|---|---|---|---|
Coefficient | Prob. | Coefficient | Prob. | Coefficient | Prob. | |
LTEMP | 1.1600 *** | 0.0000 | −0.5129 * | 0.0929 | −0.0701 | 0.7446 |
LRF | 0.0136 | 0.8277 | −0.0576 | 0.1387 | 0.0823 * | 0.0522 |
LFER | 0.1726 | 0.5623 | −0.6117 ** | 0.0325 | 0.2917 * | 0.0695 |
LPC | −0.3626 *** | 0.0009 | 0.4885 *** | 0.0037 | −0.1421 * | 0.0980 |
LWA | 0.5805 *** | 0.0019 | 2.9805 *** | 0.0000 | 1.1690 *** | 0.0000 |
LLF | 1.3669 *** | 0.0000 | 0.2161 | 0.1962 | 0.2351 | 0.7447 |
C | −3.9019 *** | 0.0001 | −7.8904 *** | 0.0000 | −2.1196 | 0.3335 |
R | 0.8061 | 0.9722 | 0.9332 | |||
Adj-R | 0.7415 | 0.9630 | 0.9058 |
In the case of Hebei Province, the climate variables (i.e., temperature and rainfall) have a positive, significant impact on wheat production. This means the climate conditions are more favorable for wheat cultivation in Hebei Province. Specific to the North China Plain, where this study area is located, some research evidence shows that in the north of this plain, the impact of rainfall on wheat production is positive, while in the south of this plain, the impact of rainfall turns negative [ 49 ]. Similarly, for temperature, the increase in temperature increases the winter wheat yield in the northern part of the North China Plain but decreases the wheat yield produced in winter in the south of the North China Plain [ 50 ]. The top three wheat-producing provinces are selected for this investigation. Hebei, Shandong, and Henan Provinces are distributed in the North China Plain. The three provinces’ yearly mean temperature and yearly mean precipitation are ranked from low to high in Hebei, Shandong, and Henan (see Figure 2 ). The temperature and rainfall in Hebei Province are low, and the impacts of temperature and precipitation on wheat yield are positive.
Further results reveal that fertilizer use, cultivated area, and labor force also have a positive, significant influence on wheat production. The long-run coefficients of fertilizer use, cultivated area, and labor force indicate that a 1% increase in fertilizer treatment use, cultivated area, and labor force wheat production improved by 0.17%, 0.58%, and 1.36%, respectively.
In the case of Henan Province, the climatic factors (i.e., temperature and rainfall) and wheat production relationship was significant and negative. This means that climatic factors severely impact wheat production in Henan Province. The long-run coefficient of both climate variables, temperature and rainfall, indicates that with a 1% increase in both climate variables (i.e., temperature and rainfall), wheat production decreases by 0.51%, and 0.05%. Geng et al. [ 51 ] reported that high temperatures will be detrimental to wheat production by shortening the growth cycle of the wheat crop. Further, Song et al. [ 33 ] stated that excessive rainfall causes excessive water accumulation, which will aggravate the wet damage of wheat and negatively affect wheat production.
Moreover, the results show that fertilizer usage also significantly negatively impacts wheat production. The long-term coefficient of fertilizer usage reveals that if a farmer overuses the fertilizer by 1%, wheat production declines by 0.61%. Fertilization can not only supplement the nutrients needed by wheat but also improve the utilization rate of water, thus increasing the yield of wheat [ 52 , 53 ]. However, unreasonable and excessive use of chemical fertilizers will cause soil degradation and adversely affect wheat yield. This shows that the rational use of chemical fertilizers is very important for wheat production, and Henan Province should pay more attention to improving chemical fertilizer use efficiency.
In contrast, these variables (power usage, wheat farming area, and labor force) and the wheat production relationship were significant and positive. The long-run coefficient of power usage, wheat farming area, and labor force reveals that a 1% increase in power usage, wheat farming area, and labor force increases wheat production by 0.48%, 2.98%, and 0.21%, respectively.
In the case of Shandong Province, the climate variables, temperature, and wheat production displayed a diverse relationship. At the same time, rainfall had a significant and positive influence, suggesting that with a 1% increase in temperature and rainfall, wheat production decreased by 0.07% and improved by 0.08%. The heterogeneous effect of the climate variables on regional wheat yield is verified by some existing studies. For example, the evidence from Mexico and China verified that the sensitivity of wheat yield to climate variables is uneven in space [ 54 , 55 ]. Tao et al. [ 56 ] studied climate change’s influence on wheat productivity and found the prospective consequences of climate change on winter wheat output in northern China under 10 climatic scenarios and concluded that environmental variability might enhance wheat yield by 37.7% (18.6%), 67.8% (23.1%), and 87.2% (34.4%), with (without) CO 2 fertilization effects in the 2020s, 2050s, and 2080s, respectively, in the future. The temperature and rainfall in Shandong Province are in the middle of the three provinces, and the impact of temperature on wheat yield is negative, but the impact of rainfall is positive. In Henan Province, it is observed that the temperature is higher, and the rainfall is higher; the influence of temperature and rainfall on wheat is negative. Moreover, the results show that these variables (fertilizer use, cultivated area, and labor force) and wheat production association was significant and positive, suggesting that a 1% increase in fertilizer usage, cultivated area, and labor force enhanced wheat production by 0.29%, 1.16%, and 0.23%, respectively.
This study applied the DOLS and CCR long-run estimators as a robust check approach for the FMOLS findings. Table 5 shows that climate variables positively affect wheat production in the context of Hebei Province. The estimated coefficients of DOLS and CCR are consistent with the findings of the FMOLS model. Likewise, in Henan Province’s case, climatic factors negatively influence wheat production. These outcomes are also consistent with the outcomes of the FMOLS model. In addition, climatic factors, such as temperature, only have a negative impact on wheat production. Meanwhile, rainfall has a significant and positive linkage with wheat production. Hence, the results of both techniques, such as DOLS and CCR, are similar to the results of the FMOLS method.
Robustness check.
Hebei Province | Henan Province | Shandong Province | ||||
---|---|---|---|---|---|---|
DOLS | CCR | DOLS | CCR | DOLS | CCR | |
Variables | Coefficient | Coefficient | Coefficient | Coefficient | Coefficient | Coefficient |
LTEMP | 1.3956 *** (0.0003) | 1.3629 *** (0.0000) | −0.2405 (0.4173) | −0.6182 (0.1652) | −0.1411 (0.7269) | −0.0961 (0.4601) |
LRF | 0.0837 (0.4661) | 0.0293 (0.6728) | −0.0107 (0.8369) | −0.0187 (0.7913) | 0.4315 * (0.0791) | 0.1276 *** (0.0054) |
LFER | 0.2372 (0.5224) | 0.1416 (0.5718) | −0.5957 * (0.0900) | −0.7901 ** (0.0292) | 0.1267 (0.7971) | 0.3789 *** (0.0033) |
LPC | −0.2898 ** (0.0213) | −0.3205 *** (0.0002) | 0.1385 (0.4038) | 0.5703 *** (0.0023) | 0.0943 (0.7438) | −0.1125 * (0.0580) |
LWA | 0.5525 *** (0.0038) | 0.5663 *** (0.0000) | 2.2683 ** (0.0167) | 3.1978 *** (0.0000) | 1.6290 ** (0.0153) | 1.4074 *** (0.0000) |
LLF | 1.0557 *** (0.0081) | 1.2416 *** (0.0000) | −0.2059 (0.4506) | 0.2741 (0.1504) | −0.4386 (0.7924) | −0.8140 * (0.0798) |
C | −3.6129 *** (0.0016) | −3.7710 *** (0.0000) | −2.9953 (0.3278) | −8.7276 *** (0.0000) | −2.6588 (0.5218) | 0.3107 (0.8046) |
R | 0.9402 | 0.7980 | 0.9922 | 0.9662 | 0.9881 | 0.9251 |
Adj-R | 0.8805 | 0.7307 | 0.9814 | 0.9549 | 0.9454 | 0.8943 |
Although the long-run impact of the variables concerned was explored through the FMOLS, DOLS, and CCR estimators, the causal connection between the underlying variables is still in question. Therefore, we further apply the Granger causality method. The findings for Hebei, Henan, and Shandong Provinces are presented in Table 6 . A bidirectional causality between precipitation and fertilizer usage with wheat production in the context of Hebei Province can be observed. This means that rainfall and fertilizer usage significantly contributed to Hebei Province’s wheat production.
Granger causality test outcomes for Hebei, Henan, and Shandong Provinces.
Null Hypothesis: | Hebei Province | Henan Province | Shandong Province | |||
---|---|---|---|---|---|---|
F-Statistic | Prob. | F-Statistic | Prob. | F-Statistic | Prob. | |
LTEMP LWP | 0.32014 | 0.7299 | 0.48642 | 0.4928 | 6.9 × 10 | 0.9934 |
LOGWP LTEMP | 3.85467 ** | 0.0393 | 7.70193 ** | 0.0110 | 6.21589 ** | 0.0207 |
LRF LOGWP | 14.5460 *** | 0.0001 | 11.2664 *** | 0.0029 | 12.7836 *** | 0.0017 |
LWP LRF | 5.86390 ** | 0.0104 | 2.30976 | 0.1428 | 1.31051 | 0.2646 |
LFER LWP | 14.0077 *** | 0.0002 | 5.55914 ** | 0.0277 | 1.38081 | 0.2525 |
LWP LFER | 11.1690 *** | 0.0006 | 1.33921 | 0.2596 | 14.7157 *** | 0.0009 |
LPC LWP | 0.34197 | 0.7147 | 0.68055 | 0.4183 | 2.40316 | 0.1354 |
LWP LPC | 1.18261 | 0.3280 | 0.09899 | 0.7560 | 0.30692 | 0.5852 |
LWA LWP | 2.59154 | 0.1011 | 3.89070 * | 0.0613 | 7.44369 ** | 0.0123 |
LOGWP LWA | 1.66343 | 0.2159 | 4.21314 * | 0.0522 | 11.3607 *** | 0.0028 |
LLF LWP | 1.83122 | 0.1874 | 0.00149 | 0.9695 | 4.15717 * | 0.0537 |
LWP LLF | 0.25914 | 0.7744 | 2.30602 | 0.1431 | 0.15467 | 0.6979 |
Note: ⇏ indicates “does not cause Granger”, *** p value < 0.01, ** p value < 0.05, and * p value < 0.1.
Further, the results only discover a unidirectional causality association between wheat production and temperature. In the context of Henan Province, it is revealed that a unidirectional causality association runs from precipitation and fertilizer usage to wheat production. In contrast, a bidirectional causality exists between power consumption and wheat production. This depicts that climate change factors, such as rainfall, and other inputs also positively influence wheat production. In addition, a bidirectional causality is established between the farming area and wheat production, while a unidirectional causality is detected from precipitation and labor to wheat production. These results imply that the cultivated area, rainfall, and labor significantly improve wheat production in the context of Shandong Province.
The current study assesses the climate variables’ long-run impact on wheat production in China’s top three wheat-producing provinces. The other important factors considered in this paper include fertilizer usage, cultivated area, power consumption, and labor. The data set consists of observations from 1992 to 2020 on which several time-series techniques, namely, the DOLS, FMOLS, CCR, and Granger causality, were applied. Based on the estimations, the findings revealed that wheat production is negatively affected by climate change in Henan Province. In contrast, climate change is more favorable for wheat production in Hebei Province.
On the other hand, temperature negatively influenced wheat production but was not significant, while rainfall significantly contributed positively to wheat production in Shandong Province. Further findings showed that fertilizer usage, cultivated area, and labor positively and significantly improved wheat production in Hebei and Shandong Provinces. In contrast, power usage, wheat farming area, and labor force significantly and positively enhanced wheat production in Henan Province. In addition, the findings of the Granger causality test reported a bidirectional causality between rainfall and fertilizer use with wheat production in Hebei Province, while a unidirectional causality connection was revealed between wheat production and temperature. In the context of Henan Province, it was discovered that a unidirectional causality link was observed from rainfall and fertilizer use to wheat production. In contrast, a bidirectional causality existed between power consumption and wheat production. Moreover, a bidirectional causality was established between the cultivated area and wheat production, while a unidirectional causality was detected from the rainfall and labor to wheat production in Shandong Province.
Based on the estimated outcomes, the current paper offers several policy implications:
With both advantages and disadvantages, China’s wheat production is affected by global warming. To mitigate the effects of a changing climate on China’s wheat yield, it is vital to increase the adaptability of wheat production. First, modify wheat’s sowing date and area in a reasonable manner. Adjust the sowing date of crops, rationally plan the planting areas, fully utilize the additional heat resources brought about by climate change, decrease the impact of meteorological disasters, and increase the stability of wheat production based on the climatic conditions of various regions.
Second, agricultural technology advancement will continue to be important in ensuring wheat yield stability. On the one hand, the Chinese government must prioritize research and develop seed resources resistant to extreme weather conditions. It is crucial to develop and store wheat germplasm resources that can respond to adverse weather conditions, given the prevalence of extreme weather events (high-temperature resistance, waterlogging resistance, low-temperature resistance, etc.). On the other hand, it is essential to continue using advanced agricultural technologies to produce wheat. For instance, more fertilizer use techniques should be implemented to increase the input effectiveness of chemical fertilizers and ensure the sustainability of agricultural production.
Furthermore, there are regional differences in wheat planting varieties and methods in China, making it difficult to continuously improve wheat production levels by relying solely on a single technology. As a result, it is necessary to promote improved varieties in conjunction with good methods, agricultural machinery, and agronomy, as well as to further tap the potential of science and technology to increase production.
This research was funded by the National Social Science Fund of China (Grant number: 19CSH029).
Conceptualization, A.A.C. and H.Z.; methodology, A.A.C.; software, A.A.C. and Y.T.; validation, G.R.S.; formal analysis, A.A.C. and Y.T.; investigation, A.A.C. and Y.T.; resources, H.Z.; data curation, Y.T.; writing—original draft preparation, A.A.C. and Y.T.; writing—review and editing, M.A.T. and G.R.S.; visualization, H.Z.; supervision, A.A.C.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Data availability statement, conflicts of interest.
The authors declare no conflict of interest.
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Nature volume 588 , pages 277–283 ( 2020 ) Cite this article
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Advances in genomics have expedited the improvement of several agriculturally important crops but similar efforts in wheat ( Triticum spp.) have been more challenging. This is largely owing to the size and complexity of the wheat genome 1 , and the lack of genome-assembly data for multiple wheat lines 2 , 3 . Here we generated ten chromosome pseudomolecule and five scaffold assemblies of hexaploid wheat to explore the genomic diversity among wheat lines from global breeding programs. Comparative analysis revealed extensive structural rearrangements, introgressions from wild relatives and differences in gene content resulting from complex breeding histories aimed at improving adaptation to diverse environments, grain yield and quality, and resistance to stresses 4 , 5 . We provide examples outlining the utility of these genomes, including a detailed multi-genome-derived nucleotide-binding leucine-rich repeat protein repertoire involved in disease resistance and the characterization of Sm1 6 , a gene associated with insect resistance. These genome assemblies will provide a basis for functional gene discovery and breeding to deliver the next generation of modern wheat cultivars.
Wheat is a staple food across all parts of the world and is one of the most widely grown and consumed crops 7 . As the human population continues to grow, wheat production must increase by more than 50% over current levels by 2050 to meet demand 7 . Efforts to increase wheat production may be aided by comprehensive genomic resources from global breeding programs to identify within-species allelic diversity and determine the best allele combinations to produce superior cultivars 2 , 8 .
Two species dominate current global wheat production: allotetraploid (AABB) durum wheat ( Triticum turgidum ssp. durum ), which is used to make couscous and pasta 9 , and allohexaploid (AABBDD) bread wheat ( Triticum aestivum ), used for making bread and noodles. A, B and D in these designations correspond to separate subgenomes derived from three ancestral diploid species with similar but distinct genome structure and gene content that diverged between 2.5 and 6 million years ago 10 . The large genome size (16 Gb for bread wheat), high sequence similarity between subgenomes and abundance of repetitive elements (about 85% of the genome) hampered early wheat genome-assembly efforts 3 . However, chromosome-level assemblies have recently become available for both tetraploid 11 , 12 and hexaploid wheat 1 , 13 . Although these genome assemblies are valuable resources, they do not fully capture within-species genomic variation that can be used for crop improvement, and comparative genome data from multiple individuals is still needed to expedite bread wheat research and breeding. Until now, comparative genomics of multiple bread wheat lines have been limited to exome-capture sequencing 4 , 5 , 14 , low-coverage sequencing 2 and whole-genome scaffolded assemblies 13 , 15 , 16 , 17 . Here we report multiple reference-quality genome assemblies and explore genome variation that, owing to past breeder selection, differs greatly between bread wheat lines. These genome assemblies usher a new era for bread wheat and equip researchers and breeders with the tools needed to improve bread wheat and meet future food demands.
To expand on the genome assembly of wheat for Chinese Spring 1 , we generated ten reference-quality pseudomolecule assemblies (RQAs) and five scaffold-level assemblies of hexaploid wheat (Supplementary Note 1 , Supplementary Tables 1 – 3 ). For each RQA, we performed de novo assembly of contigs (contig N50 > 48 kb) that were combined into scaffolds (N50 > 10 Mb) spanning more than 14.2 Gb (Supplementary Note 1 ). The completeness of the genomes was supported by a universal single-copy orthologue (BUSCO) analysis that identified more than 97% of the expected gene content in each genome (Supplementary Note 1 ). More than 94% of the scaffolds were ordered, oriented and curated using 10X Genomics linked reads and three-dimensional chromosome conformation capture sequencing (Hi-C) to generate 21 pseudomolecules, as done previously for wheat 1 , 12 and barley ( Hordeum vulgare ) 18 . The size and structure of the genomes were similar to that of Chinese Spring, and we observed high collinearity between the pseudomolecules (Extended Data Fig. 1 ). We also independently validated the scaffold placement and orientation in the pseudomolecule assembly of CDC Landmark by Oxford Nanopore long-read sequencing (Extended Data Fig. 2a , Supplementary Note 2 ). To complement the RQAs, we generated scaffold-level assemblies of five additional bread wheat lines (Supplementary Note 1 ). To determine the global context of the 15 assemblies, we combined our data with existing datasets 4 , 5 , 19 (Fig. 1a , Supplementary Table 4 ). The genetic relationships were in agreement with those reported in previous studies 4 , 5 and reflected pedigree, geographical location and growth habit (that is, spring versus winter type). There was also a clear separation between the newly assembled genomes and Chinese Spring, supporting that they capture geographical and historical variation not represented in the Chinese Spring assembly.
a , Principal component analysis of polymorphisms from exome-capture sequencing of about 1,200 lines (grey markers), 16 lines from whole-genome shotgun resequencing (orange markers) and our new assemblies (black markers). Text colours reflect different geographical locations and winter or spring growth. b , Dendrogram of pairwise Jaccard similarities for gene PAV between all RQA assemblies. c , Number of unique NLRs at different per cent identity cut-offs as the number of genomes increases. Dashed vertical lines represent 90% of the NLR complement. Markers indicate the mean values of all permutations of the order of adding genomes. Whiskers show maximum and minimum values based on one million random permutations. d , Chromosomal location versus insertion age distribution of unique to (reading downward) increasingly shared syntenic full-length LTR retrotransposons.
Single-nucleotide polymorphisms (SNPs), insertions or deletions (indels), presence/absence variation (PAV) and gene copy number variation (CNV) influence agronomically important traits. This is particularly true for polyploid species such as wheat, in which gene redundancy can buffer the effect of genome variation 17 . To assess gene content, we projected around 107,000 high-confidence gene models from Chinese Spring 1 onto the RQAs (Supplementary Note 3 ). The total number of projected genes exhibited a narrow range, between 118,734 and 120,967 (Supplementary Table 5 ). We identified orthologous groups among projected genes and used the alignment of the orthologous groups to examine SNPs in coding sequences (Supplementary Note 3 ). The peak positions of nucleotide diversity across the three subgenomes were highly similar to those reported in previous studies 20 , supporting a strong representation of breeding diversity within the RQAs (Extended Data Fig. 3a, b ). The correlation of synonymous nucleotide diversity π ( r = 0.11–0.29) and Tajima’s D ( r = 0.02–0.06) between homeologues was low (Supplementary Tables 6 – 8 ). This suggested that polyploidization increased the number of targets of selection and contributed to broad adaptation of bread wheat, as in wild polyploid plant species 20 , 21 , 22 . Further investigation of orthologous groups indicated that 88.1% were unambiguous (clusters containing at most one member in each cultivar) (Extended Data Fig. 3c , Supplementary Table 5 ). Orthologous groups comprising exactly one gene in each line (‘complete’) were the most frequent (approximately 73.5% of genes per cultivar), suggesting strong retention of orthologous genes within the ten RQAs. The residual genes represented either singleton genes with no reciprocal best BLAST hits or genes located in complex clusters in at least one cultivar. Roughly 12% of genes showed PAVs, and their clustering resulted in relationships (Fig. 1b ) that were consistent with SNP-based phylogenetic similarities (Fig. 1a ). In addition, approximately 26% of the projected genes were found in tandem duplications, indicating that CNV is a strong contributor of genetic variation in wheat.
To provide an example of gene expansion on emerging breeding targets, we performed a more detailed analysis of the restorer of fertility ( Rf ) gene families (Supplementary Note 4 ). Rf genes are involved in restoring pollen fertility in hybrid breeding programs 23 , and we identified a previously undescribed clade within the mitochondrial transcription termination factor (mTERF) family (Supplementary Table 9 ), which has recently been implicated in fertility restoration in barley 24 . Of note, this clade shows evolutionary patterns similar to those of Rf- like pentatricopeptide repeat (PPR) proteins, representatives of which are associated with Rf3 , a major locus used in hybrid wheat breeding programs (Extended Data Fig. 4 ). Although wheat is currently not a hybrid crop, there is substantial interest in Rf genes and their potential application in hybrid wheat production systems 25 . To our knowledge, no Rf genes have been cloned in wheat and our analysis of Rf genes in multiple RQAs and identification of an Rf clade in wheat is an important step forward in tackling the challenges of hybrid wheat breeding.
To further exemplify the use of multi-genome comparisons for characterizing agronomically relevant gene families, we examined gene expansion in nucleotide-binding leucine-rich repeat (NLR) proteins, which are major components of the innate immune system and are often causal genes for disease resistance in plants 26 , 27 . We performed de novo annotation of loci that contain conserved NLR motifs (NB-ARC–leucine-rich repeat) and identified around 2,500 loci with NLR signatures in each RQA (Supplementary Tables 10 , 11 ). A redundancy analysis showed that only 31–34% of the NLR signatures are shared across all genomes, and the number of unique signatures ranged from 22 to 192 per wheat cultivar. We estimated the number of unique NLR signatures that can be detected by incrementally adding more wheat genomes to the dataset; this revealed that 90% of the NLR complement is reached at between 8 (considering 95% sequence identity) and 11 wheat lines (considering 100% protein sequence identity) (Fig. 1c ). The total NLR complement of all wheat lines consisted of 5,905 (98% identity) to 7,780 (100% identity) unique NLR signatures, highlighting the size and complexity of the repertoire of receptors involved in disease resistance.
Transposable elements make up a large majority of the wheat genome and have a critical role in genome structure and gene regulation. We characterized the overall transposable element content (81.6%) and its composition (69% long terminal-repeat retrotransposons (LTR) and 12.5% DNA transposons) in the RQAs (Supplementary Table 5 ). Across all RQAs, we annotated 1.22 × 10 6 full length (fl)-LTRs, which clustered lines into the same groups we observed from our analysis of PAV and SNPs (Fig. 1a, b , Extended Data Fig. 3d ). Generally, unique fl-LTRs (147,450) were young (median of 0.9 million years) and were enriched in the highly recombining, more distal chromosomal regions (Fig. 1d ). By contrast, shared fl-LTRs were older (median of 1.3 million years) and were more evenly distributed across the pericentric regions (Fig. 1d ). The RLC- Angela fl-LTRs were the most abundant (21,000–27,000 full-length copies per genome) and analysis of variant patterns identified several chromosomal segments that contained numerous unique or rare retrotransposon insertions (Extended Data Fig. 5 ), which, on the basis of breeding history, we hypothesize to represent introgressions. For example, the LongReach Lancer RQA revealed two unique regions, a pericentric region on chromosome 2B and a segment on the end of chromosome 3D (Fig. 2a, b ), both of which affect chromosome length (Extended Data Fig. 5 ). We used pedigree analysis to postulate the source of the introgressions and performed whole-genome sequencing of multiple accessions of putative donors. LongReach Lancer carries the stem rust resistance gene Sr36 , derived from an introgression from Triticum timopheevii , and the resistance genes Lr24 (leaf rust) and Sr24 (stem rust), derived from tall wheatgrass 28 , 29 ( Thinopyrum ponticum ). We generated whole-genome sequence reads from multiple T. ponticum and T. timopheevii accessions (Supplementary Table 12 ) and alignment to the LongReach Lancer RQA confirmed a T. ponticum introgression spanning a region of approximately 60 Mb of chromosome 3D (Fig. 2a ), whereas T. timopheevii aligned to the majority (427 Mb) of chromosome 2B (Fig. 2b ). Overall, we identified 341 chromosomal segments larger than 20 Mb with unique or rare fl-LTR insertion patterns that were present in only 1 to 4 of the RQA genomes, of which 273 insertion patterns were uniquely associated with a single genome (Supplementary Tables 13 – 16 ). The majority of unique regions were in PI190962 (spelt wheat; Triticum aestivum ssp. spelta ), which was expected, given that it diverged from modern bread wheat several thousand years ago.
a – c , T. ponticum introgression on chromosome 3D in LongReach Lancer ( a ), T. timopheevi introgression on chromosome 2B in LongReach Lancer ( b ) and A. ventricosa introgression on chromosome 3D in Jagger ( c ). Track i, map of polymorphic RLC- Angela retrotransposon insertions (legend at bottom); track ii, density of projected gene annotations from Chinese Spring (blue bars, scaled to maximum value); track iii, per cent identity to Chinese Spring based on chromosome alignment (yellow; scale is 0–100%); track iv, read depth of wheat wild relatives (blue–yellow heat map; legend at bottom). d , Dot plot alignment showing chromosome-level collinearity (black) with relative density of CENH3 ChIP–seq mapped to 100-kb bins for Chinese Spring (blue) and Julius (red); the arrow indicates a centromere shift. e , Robertsonian translocation between chromosomes 5B and 7B in Arina LrFor . f , g , Cytology ( f ) and Hi-C ( g ) confirm the 5B/7B translocation in SY Mattis (left) compared with the non-carrier Norin 61 (right). In f , five independent cells were observed; the translocation was confirmed independently ten times. Scale bar, 10 μm.
A similar strategy was used to confirm RLC- Angela variation at the telomeric region of chromosome 2A in Jagger, Mace, SY Mattis and CDC Stanley (Fig. 2c ), which corresponds to the 2NvS introgression from Aegilops ventricosa (Supplementary Note 5 ). This introgression is a well-known source of resistance to wheat blast 30 , and contains the Lr37–Yr17–Sr38 gene cluster, which provides resistance to several rust diseases 31 . Sequencing of A. ventricosa accessions (Supplementary Table 12 ) followed by comparison of chromosomes with the RQAs confirmed that Jagger, Mace, SY Mattis and CDC Stanley carry the 2NvS introgression, which spans about 33 Mb on chromosome 2A (Fig. 2c , Extended Data Fig. 6a ). We annotated the coding genes within this region and identified 535 high-confidence genes; more than 10% were predicted to be associated with disease resistance, including genes that encode putative NB-ARC and NLRs (Extended Data Fig. 6b , Supplementary Tables 17 , 18 ). Furthermore, we used genotyping by sequencing to detect the 2NvS segment in three wheat panels and discovered that its frequency has been increasing in breeding germplasm and its presence is consistently associated with higher grain yield (Extended Data Fig. 6c, d , Supplementary Tables 19 , 20 ). Of note, we identified about 60 genes belonging to the cytochrome P450 superfamily, which have been implicated in abiotic and biotic stress tolerance 32 and have been functionally validated to influence grain yield in wheat 33 . Together, these data indicate that the modern wheat gene pool contains many chromosomal segments of diverse ancestral origins, which can be identified by their transposable-element signatures. We also confirmed the wild-relative origins of three introgressions within the RQA assemblies—a first step towards characterizing causal genes for breeding targets, such as resistance to wheat blast and rust fungi.
Centromeres are vital for cell division and chromosome pairing during meiosis. In plants, functional centromeres are defined by the epigenetic placement of the modified histone CENH3 34 . We therefore used CENH3 chromatin immunoprecipitation and sequencing (ChIP–seq) 35 to determine the positions and sizes (about 7.5–9.6 Mb) of the centromeres for each RQA (Supplementary Tables 21 , 22 ), which were consistent with previous estimates for wheat 1 . Furthermore, all chromosomes showed a single active site, implying that previous reports of multiple active centromeres in Chinese Spring 1 were artefacts of misoriented scaffolds. However, we found examples in which the relative position of the centromere was shifted owing to several pericentric inversions, including inversions on chromosomes 4B and 5B (Extended Data Fig. 7a, b ). We also observed one instance in which the centromeric position changed, but was not associated with a structural event. Specifically, on chromosome 4D in Chinese Spring, the centromere is shifted by around 25 Mb relative to the consensus position (Fig. 2d ). This shift was previously recognized by cytology but was hypothesized to result from a pericentric inversion 36 . However, the high degree of collinearity between genomes supports the hypothesis that Cen4D in Chinese Spring has shifted to a non-homologous position; this shifting of centromeres to non-homologous sites has also been reported in maize 37 . By characterizing the centromere positions for these diverse wheat lines, we provide strong evidence for changes in centromere position caused by structural rearrangements and centromere shifts.
Structural variants are common in wheat 38 , and impact genome structure and gene content. We characterized large structural variants using pairwise genome alignments (Extended Data Fig. 1 ), changes in three-dimensional topology of chromosomes revealed by Hi-C conformation capture directionality biases along the genome 39 , 40 (Extended Data Fig. 8 , Supplementary Table 23 ), which were confirmed by Oxford Nanopore long-read sequencing (Extended Data Fig. 2 ) and cytological karyotyping (Extended Data Fig. 7c , Supplementary Table 24 , Supplementary Note 6 ). The most prominent event was a translocation between chromosomes 5B and 7B, observed in Arina LrFor , SY Mattis (Fig. 2e–g ) and Claire. Normally, chromosomes 5B and 7B are approximately 737 and 762 Mb long, respectively, and we estimated that the recombined chromosomes are 488 Mb (5BS/7BS) and 993 Mb (7BL/5BL) long, making 7BL/5BL the largest wheat chromosome (Extended Data Fig. 9a ). In Arina LrFor and SY Mattis, the 7BL/5BL breakpoint resides within an approximately 5-kb GAA microsatellite, which we were able to span using polymerase chain reaction (PCR) (Extended Data Fig. 9b, c ). By contrast, the breakpoint on 5BS/7BS was less syntenic, and we detected polymorphic fluorescence in situ hybridization signals between Arina LrFor and SY Mattis on the 5BS portion of the translocated chromosome segment, suggesting that the regions adjacent to the translocation events differ on 5BS/7BS (Supplementary Note 6 ). To determine the stability of the translocation in breeding, we genotyped for the translocation event in a panel of 538 wheat lines that represent most of the UK wheat gene pool grown since the 1920s 41 . The translocation occurred in 66% of the lines and was selectively neutral (Supplementary Note 7 ). Notably, the Ph1 locus on chromosome 5B, which controls the pairing of homeologous chromosomes during meiosis 42 , is near the translocation breakpoint, but remained highly syntenic between translocation carriers and non-carriers. Genetic mapping and analysis of short-read sequencing data indicated that the 5B/7B translocated chromosomes recombine freely with 5B and 7B chromosomes (Extended Data Fig. 9d ), suggesting that chromosome pairing is not affected by the translocation.
To develop improved wheat cultivars, breeders shuffle allelic variants by making targeted crosses and exploiting the recombination that occurs during meiosis. These alleles, however, are not inherited independently, but rather as haplotype blocks that often extend across multiple genes that are in genetic linkage 43 , 44 . We quantified haplotype variation along chromosomes across the assemblies, and developed visualization software to support its utility (Supplementary Note 8 ). We used these haplotypes to characterize a locus that provides resistance to the orange wheat blossom midge (OWBM, Sitodiplosis mosellana Géhin), one of the most damaging insect pests of wheat, which is endemic in Europe, North America, west Asia and the Far East. Upon hatching, the first-instar larvae feed on the developing grains and damage the kernels (Fig. 3a ). Sm1 is the only gene in wheat known to provide resistance to OWBM 6 . CDC Landmark, Robigus and Paragon are all resistant to the OWBM, and all three carry the same 7.3-Mb haplotype within the Sm1 locus on chromosome 2B (Fig. 3b ). To identify Sm1 gene candidates, we used high-resolution genetic mapping and refined the locus to a 587-kb interval in the CDC Landmark RQA (Fig. 3c , Extended Data Fig. 10a , Supplementary Table 25 ). Through extensive genotyping of diverse breeding lines, we found an OWBM-susceptible line, Waskada, that displayed a resistant haplotype except near one gene, which we annotated in CDC Landmark to encode a canonical NLR with kinase and major sperm protein (MSP) integrated domains (Fig. 3c ). Oxford Nanopore long-read sequencing further confirmed the structure of the gene in CDC Landmark (Extended Data Fig. 10b ). By contrast, the remaining assemblies (susceptible to OWBM) lacked the NB-ARC domain, but the kinase and MSP domains remained intact (Fig. 3c ). We sequenced the Waskada allele and found it contains the NB-ARC domain, but an alternative haplotype within the kinase domain (Fig. 3c , Extended Data Fig. 10c ). This gene is expressed in wheat kernels and seedlings of Sm1 carrier lines, and the lack of cDNA amplification of the NB-ARC domain for non-carrier lines further supported an alternative gene structure (Extended Data Fig. 10c ). We generated two knockout-mutant lines of this candidate gene in the Sm1 carrier line Unity 45 , and both were consistently rated as susceptible to OWBM (Supplementary Table 26 ). Sequencing of the candidate gene in these two mutants revealed a single point mutation in each line: a G>A mutation resulting in a Gly>Arg (G182R) amino acid substitution in the NB-ARC domain, and a G>A mutation, resulting in a stop codon (W98*) before the NB-ARC domain (Fig. 3c ). The kinase domain encoded by Sm1 belongs to the serine/threonine class 46 , similar to those of Rpg5 , which provides stem rust resistance 47 , and Tsn1 , which encodes sensitivity to the necrotrophic effector ToxA produced by Parastagonospora nodorum and Pyrenophora tritici-repentis 48 ; however, both Rpg5 and Tsn1 lack the MSP domain. To our knowledge, this is the first report of an NB-ARC-LRR-kinase-MSP coding gene associated with insect resistance. Additional research is needed to functionally validate these domains and their putative role in OWBM resistance using tools such as gene editing. Nevertheless, we developed a high-throughput and low-cost competitive allele-specific PCR marker (KASP) that discriminates between OWBM-susceptible and OWBM-resistant lines with perfect accuracy (Extended Data Fig. 10d , Supplementary Table 27 ). Our analyses, along with the haplotype and synteny viewers ( https://kiranbandi.github.io/10wheatgenomes/ , http://10wheatgenomes.plantinformatics.io/ and http://www.crop-haplotypes.com/ ), laid the foundation for identifying haplotypes for Sm1 . Haplotypes can now be genotyped in breeding programs using single-marker or high-throughput-sequencing-based approaches, which can integrate desirable genes into improved cultivars more efficiently.
a , The orange wheat blossom midge oviposits eggs on wheat spikes and the larvae feed on developing wheat grains, resulting in moderate to severe damage to mature kernels. b , Top, sections of chromosome 2B of the same colour in the same position share haplotypes (based on 5-Mb bins), with the exception of those in grey, which indicates a line-specific haplotype. The position of Sm1 is indicated with respect to the CDC Landmark assembly. Bottom, zoomed-in view of haplotype blocks (based on 250-kb bins) from 5 to 25 Mb positions on chromosome 2B, surrounding Sm1 . CDC Landmark, Robigus and Paragon all carry the same haplotype surrounding Sm1 (teal). c , Top, anchoring of the Sm1 fine map to the physical maps of Chinese Spring and CDC Landmark and graphical genotypes of three haplotypes critical to localizing the Sm1 candidate gene. Bottom, annotation of the Sm1 candidate gene, which encodes NB-ARC and LRR motifs in addition to the integrated serine/threonine (S/T) kinase and MSP domains. Two independent ethyl-methanesulfonate-induced mutations (W98* and G182R) result in loss of function and susceptibility to the orange wheat blossom midge (light blue lines). An alternative haplotype was observed in the kinase region of Waskada (black).
We have built on the genome-sequence resources available for wheat and related species to produce ten RQAs and five scaffolded assemblies that represent hexaploid wheat lines from different regions, growth habits and breeding programs 1 , 11 , 12 , 18 , 20 , 49 . We have identified and characterized SNPs, PAV, CNV, centromere shifts, large-scale structural variants and introgressions from wild relatives of wheat that can be used to identify and characterize important breeding targets. This was complemented by a transposable-element-analysis approach to identify candidate introgressions from wild relatives of wheat, for which we provided high-quality assemblies of segments already used in global breeding programs. Together, these RQAs present an opportunity for breeders and researchers to perform high-resolution manipulation of genomic segments and pave the way to identifying genes responsible for in-demand traits, as we demonstrated for resistance to the insect pest OWBM. Functional gene studies will also be facilitated by comparative gene analyses, as exemplified by our analyses of orthologous groups, Rf genes and NLR immune receptors 26 . Finally, we highlight haplotype blocks, which will facilitate marker development for applied breeding 43 , 50 . Equipped with multiple layers of data describing variation in wheat, we now have powerful tools to increase the rate of wheat improvement to meet future food demands.
No statistical methods were used to predetermine sample size. The field experiments were randomized, but the wheat lines sequenced and assembled were not selected at random. The investigators were not blinded to allocation during experiments and outcome assessment.
Genome assemblies.
We assembled the genomes of 15 diverse wheat lines using two approaches (Supplementary Table 1 ). The RQA approach used the DeNovoMAGIC v.3.0 assembly pipeline, previously used for the wild emmer wheat 11 , durum wheat 12 and Chinese Spring RefSeqv1.0 assemblies. In brief, high-molecular-weight DNA was extracted from wheat seedlings as described previously 51 . Illumina 450-bp paired-end (PE), 800-bp PE and mate-pair (MP) libraries of three different sizes (3 kb, 6 kb and 9 kb) were generated. Sequencing was performed at the University of Illinois Roy J. Carver Biotechnology Center. 10X Genomics Chromium libraries were prepared and sequenced at the Genome Canada Genome Innovation Centre using the manufacturers’ recommendations to achieve a minimum of 30 × coverage. Hi-C libraries were prepared using previously described methods 40 . Using the Illumina PE, MP, 10X Genomics Chromium, and Hi-C, chromosome scale assemblies were prepared as described previously 18 . For cultivars assembled to a scaffold level, we used the W2RAP-contigger using k = 200 (Supplementary Note 1 ). Two MP libraries (10 kb and 13 kb) were produced for each line except Weebill 1, for which two additional MP libraries were used. Mate pairs were processed, filtered and used to scaffold contigs as described in the W2RAP pipeline ( https://github.com/bioinfologics/w2rap ). Scaffolds of less than 500 bp were removed from the final assemblies. Additionally, we performed Oxford Nanopore sequencing of CDC Landmark using R9 flow cells and the GridION sequencing technology (Supplementary Note 2 ).
The variant call format data files from two wheat exome-capture studies 4 , 5 were retrieved, combined, and filtered to retain hexaploid accessions and polymorphisms detected in both studies. The 10X Genomics Chromium sequencing data for each of the RQA lines were aligned to Chinese Spring RefSeqv1.0 using the LongRanger v.2.1.6 software. Alignment files from the accessions assembled here and 16 Bioplatforms Australia lines 19 with alignments obtained from the DAWN project 52 were then used for variant calling by GATK v.3.8 at the same genomic positions identified by exome-capture sequencing. The variant files from the exome-capture studies, DAWN project and 10+ Wheat Genomes lines were then merged and subjected to principal component analysis (PCA) using the prcomp function in R v.3.6.1.
We used the previously published high-confidence gene models for Chinese Spring to assess the gene content in each assembly. Representative coding sequences of each informant locus were aligned to pseudomolecules of each line separately using BLAT 53 v.3.5 with the ‘fine’ parameter and a maximal intron size of 70 kb. BLAT matches seeded an additional alignment by exonerate 54 in the genomic neighbourhood encompassing 20 kb upstream and downstream of the match position. Exonerate alignments required a minimal and maximal intron sizes of 30 bp and 20 kb, respectively. A linear regression of colocalized matches with complete alignments of the informant were computed for 10,000 such pairs to derive a normalization function and to render comparable scoring schemes for both methods. Subsequently, we selected the top-scoring match for each mapping pair as the locus for the gene projection. Projections were then filtered by alignment coverage (Supplementary Note 3 ), the open reading frame (ORF) contiguity, the observed mapping frequency of the informant, coverage of start and stop codons, and the orthology or potential dislocation of the match scaffold relative to its informant chromosome. Identification of orthologous groups was analogous to the approach used previously 55 . Reciprocal best BLAST hit (RBH) graphs were derived from pairwise all-against-all BLASTn v2.8 transcript searches (minimal e -value ≤ 1 × 10 −30 ). Hits were assigned to homeologous groups on the basis of gene models of Chinese Spring following a previously described homeologue classification 9 . Multiple sequence alignments for the population genetics analysis were performed using MUSCLE v.3.8 with default parameters (Supplementary Note 3 ). Using the gene projections, we quantified average pairwise genetic diversity ( π ), polymorphism (Watterson’s θ W ), and Tajima’s D using compute and polydNdS in the libsequence v.1.0.3-1 package 56 . We retained diversity estimates for genes that were in all of the genomes and had ≤100 segregating sites. PAV was determined from the orthologous groups limited to one-to-one relations where there was no match in at least one genome.
For Rf genes, the genome sequences were scanned for ORFs in six frame translations with the getorf program of the EMBOSS v.6.6.0 package. ORFs longer than 89 codons were searched for the presence of PPR motifs using hmmsearch from the HMMER v.3.2.1 package ( http://hmmer.org ) and the hidden Markov models defined previously. The PF02536 profile from the Pfam v32.0 database ( http://pfam.xfam.org ) was used to screen for ORFs carrying mTERF motifs. Downstream processing of the hmmsearch results followed the pipeline described previously 57 . ORFs with low hmmsearch scores were removed from the analysis as they are unlikely to represent functional PPR proteins. Only genes encoding mTERF proteins longer than 100 amino acids were included in the analysis. RFL -PPR sequences were identified as described 23 . The phylogenetic analyses were performed as described previously 23 . Conserved, non-PPR genes delimiting the borders of analysed RFL clusters were identified in the Chinese Spring RefSeqv1.0 reference genome and used to search for syntenic regions in the remaining wheat accessions with BLAST v.2.8. See Supplementary Note 4 for more details.
NLR signatures were annotated using NLR-Annotator 58 , 59 ( https://github.com/steuernb/NLR-Annotator ) with the option -a. We estimated redundancy of NLR signatures between genomes at different thresholds of identity: 95%, 98% and 100%. For the 165 amino acids in the consensus of all NB-ARC motifs, this translates to 8, 3 and 0 mismatches of a concatenated motif sequence. To calculate the overall redundancy in all genomes, we counted the number of LR signatures added to a non-redundant set by adding genomes iteratively. This was done for 1 million random permutations.
Transposons were detected and classified by a homology search against the REdat_9.7_Poaceae section of the PGSB transposon library 60 using vmatch ( http://www.vmatch.de ) with the following parameters: identity ≥70%, minimal hit length 75 bp, seedlength 12 bp (exact command line: -d -p -l 75 -identity 70 -seedlength 12 -exdrop 5). To remove overlapping annotations, the output was filtered for redundant hits via a priority-based approach in which higher-scoring matches where assigned first and lower-scoring hits at overlapping positions were either shortened or removed if there was ≥90% overlap with a priority hit or if <50 bp remained. Tandem repeats where identified with TandemRepeatFinder v.4.09 under default parameters 61 and subjected to overlap removal as described above. Full-length LTR retrotransposons were identified with LTRharvest ( http://genometools.org/documents/ltrharvest.pdf ). All candidates were subsequently annotated for PfamA domains using HMMER v.3.0 and filtered to remove false positives, non-canonical hybrids and gene-containing elements. The inner domain order served as a criterion for the LTR retrotransposon superfamily classification, either Gypsy (RLG: RT-RH-INT), Copia (RLC: INT-RT-RH) or undetermined (RLX). The insertion age of fl-LTRs was calculated from the divergence between the 5′ and 3′ long terminal repeats, which are identical upon insertion. The genetic distance was calculated with EMBOSS v.6.6.0 distmat (Kimura2-parameter correction) using a random mutation rate of 1.3 × 10 −8 .
For each line with a RQA, ChIP was performed according to previous methods 62 with slight modification using a wheat-specific CENH3 antibody 36 . An antigen with the peptide sequence RTKHPAVRKTKALPKK, corresponding to the N terminus of wheat CENH3, was used to produce an antibody using the custom-antibody production facility provided by Thermo Fisher Scientific. The customized antibody was purified and obtained as pellets. The antibody pellet (0.396 mg) was dissolved in 2 ml PBS buffer, pH 7.4, resulting in a working concentration of 198 ng μl −1 . Nuclei were isolated from 2-week-old seedlings, digested with micrococcal nuclease and incubated overnight at 4 °C with 3 μg of antibody or rabbit serum (control). Antibodies were captured using Dynabeads Protein G and the chromatin eluted using 100 μl of 1% sodium dodecyl sulfate, 0.1 M NaHCO 3 preheated to 65 °C. DNA isolation was then performed using ChIP DNA Clean & Concentrator Kit, and ChIP–seq libraries were constructed using TruSeq ChIP Library Preparation Kit and sequenced with a NovoSeq S4, which generated 150-bp paired-end reads.
For Chinese Spring, we used two datasets, SRR1686799 63 (dataset 1) and the dataset generated in this study (dataset 2). Sequence reads were de-multiplexed, trimmed and aligned to each of the respective RQAs using HISAT2 v.2.1.0 64 . Alignments were sorted, filtered for minimum alignment quality of 30, counted in 100-kb bins using samtools v.1.10 and BEDtools v.2.29, and visualized in R v.3.6.1. To define the midpoint of each centromere, we identified the highest density of CENH3 ChIP–seq reads using a smoothing spline in R v.3.6.1 with smooth.spline function (number of knots = 1,000) and identified the peak of the smooth spline as the centre of the respective centromere for a given chromosome. To compare centromeric positions of different genomes, the CENH3 ChIP–seq density was plotted along with MUMmer v.4.0 chromosome alignments. To determine the overall size of wheat centromeres, we considered each 100-kb bin with CENH3 ChIP–seq read density that was greater than three times the background (genome average) level of read density to be an active centromeric bin. The number of enriched bins for each genome were counted and averaged to a total of 21 chromosomes. This calculation included counting of unanchored bins.
Identification of full-length rlc- angela retrotransposons.
Retrotransposon profiles were created for each genome using the RLC- Angela family 65 and consensus sequences obtained from the TREP database ( www.botinst.uzh.ch/en/research/genetics/thomasWicker/trep-db.html ). First, BLASTn was used to compare the ~1,700-bp LTR of RLC- Angela to each genome. Matching elements and 500 bp of flanking sequences were aligned to identify precise LTR borders as well as different sub-families and/or sequences variants. We then used BLASTn to compare the 18 consensus LTR sequences against each genome and then screened for pairs of full-length LTRs that are found in the same orientation within a window of 7.5–9.5 kb (RLC- Angela elements are ~8.7 kb long). These initial candidate full-length elements were screened for the presence of RLC- Angela polyprotein sequences by BLASTx, as well as for the typical 5-bp target-site duplications. We allowed a maximum of two mismatches between the two target-site duplications. All identified full-length RLC- Angela copies were then aligned to a RLC- Angela consensus sequence with the program Water from the EMBOSS v.6.6.0 package ( www.ebi.ac.uk/Tools/emboss/ ). These alignments were used to compile all nucleotide polymorphisms into a single file. The variant call file was then used for PCA using the snpgdsPCA function in the R package SNPrelate v.3.11.
Genomic DNA (gDNA) was extracted and purified from young leaf tissue collected from multiple accessions of T. timopheevii , A. ventricosa and T. ponticum (Supplementary Table 12 ) following a standard CTAB–chloroform extraction method. Yield and integrity were evaluated by fluorometry (Qubit 2.0) and agarose gel electrophoresis. Paired-end libraries were prepared following the Nextera DNA Flex protocol. In brief, 500 ng gDNA from each accession was fragmented and amplified with a limited-cycle PCR. Each library was uniquely dual-indexed with a distinct 10-bp index code (IDT for Illumina Nextera DNA UD) for multiplexing, and quantified by qPCR (Kapa Biosystems). Final average library size was estimated on a Tapestation 2200. Libraries were normalized and pooled for sequencing on an Illumina NovaSeq 6000 S4 to generate ~5× coverage per genotype. Sequencing data were de-multiplexed and aligned to appropriate RQAs (Supplementary Table 12 ) in semi-perfect mode using the BBMap v.38 short-read alignment software ( https://sourceforge.net/projects/bbmap/ ).
We karyotyped the lines using mitotic metaphase chromosomes prepared by the conventional acetocarmine-squash method. Non-denaturing fluorescence in situ hybridization (ND-FISH) of three repetitive sequence probes, Oligo-pSc119.2-1, Oligo-pTa535 and Oligo-pTa713, was performed as described 66 , 67 (Supplementary Note 6 ). Chromosomes were counterstained with DAPI. Chromosome images were captured with an Olympus BX61 epifluorescence microscope and a CCD camera DP80. Images were processed and pseudocoloured with ImageJ v.1.51n in the Fiji package. For karyotyping, at least four chromosomes per accession were examined and compared to the karyotype of Chinese Spring as described previously 68 . Hierarchal clustering of karyotype polymorphisms was performed using the Ward method in R v.3.0.2, which was used to estimate distance. Next, we applied Hi-C analysis for inversion calling as described previously 40 . In brief, adapters were removed and reads were mapped to Chinese Spring using minimap2 v.2.10 69 as we have done previously 21 . The raw Hi-C link counts were calculated in 1 Mb non-overlapping sliding windows and then normalized as described in our previous work 40 . Finally, the normalized Hi-C link matrix was subjected to inversion calling using R.
We performed flow cytometry of wheat cultivars Arina and Forno as previously described 70 , except that we used a FACSAria SORP flow cytometer and cell sorter (Becton Dickinson). The 5B/7B translocation breakpoints were identified by comparison of chromosomes 5B and 7B from Arina LrFor and Julius. Sequence collinearity between Arina LrFor and Julius was detected by BLASTn searches of 1,000-bp sequence windows every 100 kb along the chromosomes. Once an interruption of synteny was detected, sequence segments at the positions of synteny loss were extracted and used for local alignments to determine the precise breakpoint positions. PCR amplification of the 5BS/7BS and 7BL/5BL translocation sites was performed using standard PCR cycling conditions.
Development of a wheat genome haplotype database.
To identify haplotypes, pairwise chromosome alignments were performed between the RQA using MUMmer v.4.0, which were combined with pairwise nucleotide BLASTn analyses of the genes ± 2,000 bp using custom scripts in R v.3.6.1 ( https://github.com/Uauy-Lab/pangenome-haplotypes ) 71 (Supplementary Note 8). The resultant haplotypes were uploaded to an interactive viewer ( http://www.crop-haplotypes.com/ ). Pairwise BLASTn comparisons of the genes were also used to identify structural variants, and were uploaded into AccuSyn ( https://accusyn.usask.ca/ ) and SynVisio ( https://synvisio.github.io/#/ ) to create a wheat-specific database ( https://kiranbandi.github.io/10wheatgenomes/ ). Pretzel ( https://github.com/plantinformatics/pretzel ) was also used to visualize and compare the RQA and the projected gene annotations ( http://10wheatgenomes.plantinformatics.io/ ).
Sm1 -linked markers 6 were located in RQAs using BLAST v.2.8.0. Two high-resolution mapping populations were developed, 99B60-EJ2D/Thatcher and 99B60-EJ2G/Infinity. Progeny heterozygous for crossover events near Sm1 were identified in the F 2 generation, and the crossovers were fixed in the F 3 generation. The resulting F 2 -derived F 3 families were analysed with KASP markers within the Sm1 region and tested for resistance to OWBM in field nurseries to identify markers associated with Sm1 . Ethyl methanesulfonate was used to develop knockout mutants in the Sm1 gene. Approximately 3,200 seeds of the Canadian spring wheat variety Unity (an Sm1 carrier) were soaked in a 0.2% (v/v) aqueous ethyl methanesulfonate solution for 22 h at 22 °C. The seed was then rinsed in distilled water and sown in a field nursery. The M 1 seed was grown to maturity and bulk harvested. Approximately 6,000 M 2 seeds were space planted in two field nurseries located in Brandon and Glenlea, Manitoba, Canada. Spikes were collected on a per-plant basis at maturity and were classified as resistant, susceptible or undamaged as done previously 6 , 72 . Putative Sm1 -knockout mutants were re-tested for OWBM resistance in indoor cage tests 73 in the M 3 and M 4 generations. M 4 -derived families were tested for resistance to OWBM in field nurseries (randomized complete block design, six environments, and eight replicates per environment).
Candidate genes were identified between Sm1 flanking markers on the CDC Landmark assembly using the projected gene annotations and FGENESH v.2.6 ( http://www.softberry.com/ ), which were compared to the projected genes of non-carriers. Both 5′ and 3′ rapid amplification of cDNA ends (5′ and 3′ RACE) were used to verify the transcription initiation and termination sites of the gene candidate, whose structure was predicted by FGENESH v.2.6. In brief, RNA was extracted from the leaves of Unity ( Sm1 carrier) seedlings (using the Qiagen RNeasy kit), RACE PCR performed (Invitrogen GeneRacer kit), and the PCR product cloned (Invitrogen TOPO TA Cloning kit for sequencing) and sequenced by Sanger sequencing. Prediction of the conserved domains was done using the NCBI Conserved Domain Search tool ( https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi ) and PROSITE (release 2020_01; https://prosite.expasy.org/ ). The LRR domain was defined on the basis of the presence of 2–42 LRR motif repeats of 20–30 amino acids each. LRR motifs were manually annotated 74 . Prediction of transmembrane regions and orientation was performed using the program TMpred NCBI Conserved Domain Search tool ( https://embnet.vital-it.ch/software/TMPRED_form.html ).
To study the expression of Sm1 , total RNA was extracted from four biological replicates from four wheat genotypes (Unity, CDC Landmark, Waskada and Thatcher) from two different tissues; seedling leaves and developing kernels (five days post anthesis) using NucleoSpin RNA Plant kit (Macherey-Nagel) according to the manufacturer’s instructions. RNA was treated with RNase-free DNase (rDNase) (Macherey-Nagel) and reversed transcribed into cDNA using SuperScript IV Reverse Transcriptase kit (Invitrogen) according to the manufacturer’s instructions and the NB-ARC domain amplified by PCR.
Further information on research design is available in the Nature Research Reporting Summary linked to this paper.
All sequence reads assemblies have been deposited into the National Center for Biotechnology Information sequence read archive (SRA) (see Supplementary Table 1 for accession numbers). Sequence reads for the RQAs, T. ponticum , A. ventricosa and T. timopheevii have been deposited into the SRA (accession no. PRJNA544491 ) and ChIP–seq short read-data used for centromere characterization is deposited under accession no. PRJNA625537 . All Hi-C data have been deposited in the European Nucleotide Archive (Supplementary Table 1). The RQAs are available for direct user download at https://wheat.ipk-gatersleben.de/ . All assemblies and projected annotations are available for comparative analysis at Ensembl Plants ( https://plants.ensembl.org/index.html ). Comparative analysis viewers are also online for synteny ( https://kiranbandi.github.io/10wheatgenomes/ , http://10wheatgenomes.plantinformatics.io/ ) and haplotypes ( http://www.crop-haplotypes.com/ ). Seed stocks of the assembled lines are available at the UK Germplasm Resources Unit ( https://www.seedstor.ac.uk/ ).
Code for custom genome visualizers have been deposited in the public domain for haplotype viewer ( https://github.com/Uauy-Lab/pangenome-haplotypes ), Pretzel ( https://github.com/plantinformatics/pretzel ), AccuSyn ( https://github.com/jorgenunezsiri/accusyn ) and SynVisio ( https://github.com/kiranbandi/synvisio ). Additional scripts used for ChIP–seq analysis of the centromeres are provided at https://github.com/wheatgenetics/centromere .
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We are grateful for funding from the Canadian Triticum Applied Genomics research project (CTAG2) funded by Genome Canada, Genome Prairie, the Western Grains Research Foundation, Government of Saskatchewan, Saskatchewan Wheat Development Commission, Alberta Wheat Commission, Viterra and Manitoba Wheat and Barley Growers Association. Funding was also provided by the Biotechnology and Biological Sciences Research Council (BBSRC) via the projects Designing Future Wheat (BB/P016855/1), sLOLA (BB/J003557/1) and MAGIC Pangenome (BB/P010741/1, BB/P010733/1 and BB/P010768/1), by AMED NBRP (JP17km0210142), the German Federal Ministry of Education and Research (FKZ 031B0190, WHEATSeq, 2819103915 and 2819104015), German Network for Bioinformatics and Infrastructure de.NBI (FKZ 031A536A, 031A536B), German Federal Ministry of Food and Agriculture (BMEL FKZ 2819103915 WHEATSEQ), Israel Science Foundation (Grant 1137/17), JST CREST (JPMJCR16O3), US National Science Foundation (1339389), Kansas Wheat Commission and Kansas State University, MEXT KAKENHI, The Birth of New Plant Species (JP16H06469, JP16H06464, JP16H06466 and JP16K21727), National Agriculture and Food Research Organization (NARO) Vice President Fund, Swiss Federal Office of Agriculture (NAP-PGREL), Agroscope, Delley Seeds and Plants, ETH Zurich Institute of Agricultural Sciences, Fenaco Co-operative, IP-SUISSE, swisssem, JOWA, SGPV-FSPC, Swiss National Science Foundation (31003A_182318 and CRSII5_183578), University of Zurich Research Priority Program Evolution in Action, King Abdullah University of Science and Technology, Grains Research and Development Corporation (GRDC), Australian Research Council (CE140100008) and Groupe Limagrain. We are grateful for the computational support of the Functional Genomics Center Zurich, the Molecular Plant Breeding Group—ETH Zurich, and the Global Institute of Food Security (GIFS), Saskatoon. We acknowledge the contribution of the Australian Wheat Pathogens Consortium ( https://data.bioplatforms.com/organization/edit/bpa-wheat-cultivars ) in the generation of data used in this publication. The Initiative is supported by funding from Bioplatforms Australia through the Australian Government National Collaborative Research Infrastructure Strategy (NCRIS). We thank S. Wu for DNA preparations for assembly and ChIP–seq library preparations; O. Francisco-Pabalan and J. Santos, T. Wisk and S. Wolfe for their provision of OWBM images; M. Knauft, I. Walde, S. König, T. Münch, J. Bauernfeind and D. Schüler for their contribution to Hi-C data generation and sequencing, DNA sequencing and IT administration and sequence data management; J. Vrána for karyotyping the wheat cultivars Arina and Forno; and R. Regier for project management, administration and support.
These authors contributed equally: Sean Walkowiak, Liangliang Gao, Cecile Monat
Crop Development Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Sean Walkowiak, Valentyna Klymiuk, Brook Byrns, Kirby Nilsen, Jennifer Ens, Krystalee Wiebe, Amidou N’Diaye, Pierre J. Hucl & Curtis J. Pozniak
Grain Research Laboratory, Canadian Grain Commission, Winnipeg, Manitoba, Canada
Sean Walkowiak & Bin Xiao Fu
Department of Plant Pathology, Kansas State University, Manhattan, KS, USA
Liangliang Gao, Emily Delorean, Dal-Hoe Koo, Allen K. Fritz & Jesse Poland
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany
Cecile Monat, Axel Himmelbach, Anne Fiebig, Sudharsan Padmarasu, Uwe Scholz, Martin Mascher & Nils Stein
Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
Georg Haberer, Heidrun Gundlach, Klaus F. X. Mayer & Manuel Spannagl
Aquatic and Crop Resource Development, National Research Council Canada, Saskatoon, Saskatchewan, Canada
Mulualem T. Kassa, Pierre Fobert & Sateesh Kagale
John Innes Centre, Norwich Research Park, Norwich, UK
Jemima Brinton, Ricardo H. Ramirez-Gonzalez, Michael Bevan, Neil McKenzie, Burkhard Steuernagel & Cristobal Uauy
Department of Plant and Microbial Biology, University of Zurich, Zurich, Switzerland
Markus C. Kolodziej, Simon G. Krattinger, Beat Keller & Thomas Wicker
Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, Manitoba, Canada
Dinushika Thambugala & Curt A. McCartney
Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Venkat Bandi, Jorge Nunez Siri & Carl Gutwin
Brandon Research and Development Centre, Agriculture and Agri-Food Canada, Brandon, Manitoba, Canada
Kirby Nilsen
Genomics/Transcriptomics group, Functional Genomics Center Zurich, Zurich, Switzerland
Catharine Aquino & Masaomi Hatakeyama
Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
Dario Copetti, Gwyneth Halstead-Nussloch, Masaomi Hatakeyama, Timothy Paape, Rie Shimizu-Inatsugi & Kentaro K. Shimizu
Institute of Agricultural Sciences, ETHZ, Zurich, Switzerland
Dario Copetti
Kihara Institute for Biological Research, Yokohama City University, Yokohama, Japan
Tomohiro Ban, Kanako Kawaura, Toshiaki Tameshige, Hiroyuki Tsuji & Kentaro K. Shimizu
Life Sciences Department, Natural History Museum, London, UK
Luca Venturini & Matthew Clark
Earlham Institute, Norwich Research Park, Norwich, UK
Bernardo Clavijo, Christine Fosker, Gonzalo Garcia Accinelli, Darren Heavens, Ksenia Krasileva, David Swarbreck, Jonathan Wright & Anthony Hall
The John Bingham Laboratory, NIAB, Cambridge, UK
Keith A. Gardner, Nick Fradgley, Lawrence Percival-Alwyn & James Cockram
Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, USA
Juan Gutierrez-Gonzalez & Gary Muehlbauer
Global Institute for Food Security, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Chu Shin Koh & Andrew G. Sharpe
School of Plant Sciences and Food Security, Tel Aviv University, Ramat Aviv, Israel
Jasline Deek
Department of Entomology, University of Manitoba, Winnipeg, Manitoba, Canada
Alejandro C. Costamagna
Institute of Crop Science, NARO, Tsukuba, Japan
Hiroyuki Kanamori, Fuminori Kobayashi, Tsuyoshi Tanaka, Jianzhong Wu & Hirokazu Handa
Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, Canada
National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
Tony Kuo & Jun Sese
Laboratory of Plant Genetics, Graduate School of Agriculture, Kyoto University, Kyoto, Japan
Kazuki Murata, Yusuke Nabeka & Shuhei Nasuda
Humanome Lab, Tokyo, Japan
Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
Philomin Juliana & Ravi Singh
Montana BioAg, Missoula, MT, USA
Hikmet Budak
Australian Research Council Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, University of Western Australia, Perth, Western Australia, Australia
Ian Small & Joanna Melonek
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada
Sylvie Cloutier
Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia
Gabriel Keeble-Gagnère & Josquin Tibbets
Syngenta, Durham, NC, USA
Erik Legg & Arvind Bharti
School of Agriculture, Food and Wine, University of Adelaide, Adelaide, South Australia, Australia
Peter Langridge & Ken Chalmers
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
Martin Mascher
Biological and Environmental Science & Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Simon G. Krattinger
Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, Kyoto, Japan
Hirokazu Handa
Institute of Evolution and Department of Evolutionary and Environmental Biology, University of Haifa, Haifa, Israel
Assaf Distelfeld
School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
Klaus F. X. Mayer
Center for Integrated Breeding Research (CiBreed), Georg-August-University Göttingen, Göttingen, Germany
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Project establishment: K.C., A.D., A. Hall, B.K., S.G.K., E.L., P.L., K.F.X.M., J.P., C.J.P., K.K.S., M.S. and N.S. Project coordination: A. Hall, C.J.P. and N.S. Genome assemblies were contributed as follows: CDC Stanley and CDC Landmark: P.J.H., C.J.P., A.G.S., B.B., C.S.K., A.N., K.N. and S.W.; Julius: K.F.X.M., N.S., M.M., C.M. and U.S.; Jagger: G.M., J.P. and L.G.; Arina LrFor : B.K., S.G.K. and M.C.K.; Mace and LongReach Lancer: K.C., P.L., G.K.-G. and J.T.; Norin 61: K.K.S., H.H., S.N., J.S., K. Kawaura, H.T., T. Tameshige, T.B., D.C., M.H., R.S.-I., C.A., F.K., J.G.-G. and N.S.; SY Mattis: E.L. and A.B.; spelt (PI190962): A.D., C.J.P. and J.D.; Robigus, Claire, Paragon and Cadenza: M.B., M.C., B.C., C.F., N.F. and D.H.; Weebill 1: M.C., B.C., J.C., K.A.G., L.P.-A. and L.V. Sequencing, assembly and analysis were contributed by WRA2P computational assembly: A. Hall, B.C., G.G.A., K. Krasileva, N.M., D.S. and J. Wright; 10X Genomics: H.B., C.J.P., J.E., S.K. and K.W.; Hi-C and structural analysis: M.M., N.S., A. Himmelbach, C.M., S.P. and L.G.; pseudomolecule assemblies: M.M., C.M. and N.S.; gene projections and TE analysis: K.F.X.M., M.S., H.G. and G.H.; diversity and polymorphism analysis: K.K.S., E.D., T.P., G.H.-N., D.C., M.H., G.H., H.H., H.K., M.S., K.M., T. Tameshige, T. Tanaka, J.S. and J. Wu; centromere diversity: J.P. and D.H.K.; 5B/7B translocation: S.G.K., T.W., J.C. and M.C.K; 2N v S introgression: J.P., A.K.F., L.G., P.J., C.J.P., R.S. and S.W.; TE-based introgressions: T.W., B.B., J.E., M.C.K., J.P., C.J.P., J.T. and S.W; cytological karyotyping: S.N., K.M., Y.N., J.S. and T.K.; diversification of Rf genes: J.M. and I.S.; NLR repertoire: S.G.K. and B.S.; Sm1 gene cloning: C.A.M., C.J.P., C.U., J.B., A.C.C., S.C., P.F., M.T.K., V.K., D.T. and K.W.; haplotype database: C.U., J.B. and R.H.R.-G.; visualization software: C.G., V.B., G.K.-G., J.N.S., J.T. and J.M.; BLAST server: M.M., A.F. and U.S.; C.J.P and S.W. drafted the manuscript with input from all authors. All co-authors contributed to and edited the final version.
Correspondence to Curt A. McCartney , Manuel Spannagl , Thomas Wicker or Curtis J. Pozniak .
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The authors declare no competing interests.
Peer review information Nature thanks Victor Albert, Rudi Appels and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data fig. 1 chromosome-scale collinearity between the rqa..
Genomes were aligned chromosome by chromosome using MUMmer and are represented as dot plots. The introgression on chromosome 2B of LongReach Lancer (red rectangles) and 5B/7B translocation in SY Mattis and Arina LrFor (purple rectangles) are indicated.
a , Scaffold-scaffold long read contact map showing shared read IDs between scaffold ends along the ordered scaffolds in the CDC Landmark pseudomolecules. The diagonal pattern indicates that adjacent scaffolds share the same long reads and are therefore properly ordered and oriented by Hi-C in the RQA. b , Characterization of inversion events on chromosomes 2A, 3A, and 3D. The directionality biases estimated from alignments of Hi-C data against Chinese Spring (left, top), and chromosome alignment of the inversion events between CDC Landmark and Chinese Spring RQAs (left, bottom) are shown. Long reads spanning the inversion events and magnified views of the reads aligning to the left and right boundaries of the inversions (right) are provided.
a , Average pairwise genetic diversity of the homeologues (coding sequences only) of the A, B and D subgenomes. The mode of the A, B and D subgenome is 0.00057, 0.00082, and 0.0002, respectively. b , Tajima’s D estimates of coding sequences for each wheat subgenome. The lower and upper range of the boxplot hinges correspond to the first and third quartiles (the 25th and 75th percentiles). Boxplots show centre line, median; box limits, upper and lower quartiles; whiskers, 1.5 × interquartile range. c , Total gene counts and orthologues for the RQA. Genes in orthologous groups with exactly one gene for each line (Complete; dark brown), genes contained in unambiguous orthologous groups missing an orthologue for at least one line, that is, PAV (2-10 Lines; light brown), and genes with ambiguous orthologues or CNV (Other; pink) are indicated. d , Per cent of pairwise shared syntenic fl-LTRs between wheat lines.
a , The RFL clade is in blue and all remaining P-class PPRs are in green. b , Clustered mTERF sequences are in blue and the remaining mTERFs are shown in green. The scale bar represents number of substitutions per site. c , Sequence inversions and copy number variation at the Rf3 locus on chromosome 1B. RFL genes are shown as light pink triangles above the chromosome scale. Conserved non-PPR genes used as syntenic anchors are shown on the chromosome scale as coloured triangles. The total number (T) and the number of putatively functional RFL genes with 10 or more PPR motifs (F) are indicated on the right side of each panel.
A feature of foreign chromosomal introgressions is that they contain unique patterns of TE insertions. Shown are stretches of >20 Mb containing multiple polymorphic RLC- Angela retrotransposons that are found only in one or a few (≤4) of the sequenced lines. One representative chromosome for each wheat subgenome is shown. Individual polymorphic retrotransposons are indicated as coloured vertical lines. Colours correspond to the number of cultivars a foreign segment is found in. Regions of particular interest are indicated by black rectangles. These include the 2N v S alien introgression from A. ventricosa at the end of chromosome 2A in Jagger, Mace, SY Mattis and CDC Stanley, as well as introgression in the central region of chromosome 2B from T. timopheevi in LongReach Lancer, and introgression at the end of chromosome 3D from T. ponticum in LongReach Lancer.
a , Pairwise alignments of the first 50 Mb of chromosome 2A. The black arrow indicates a possible unique haplotype within spelt. b , Orthologous genes between the 2N v S introgression from A. ventricosa in Jagger and the genes on chromosomes 2A, 2B, and 2D in Chinese Spring. c , Frequency of 2N v S introgression carriers in North American datasets from CIMMYT, Kansas State, and the USDA Winter Wheat Regional Performance Nursery (RPN) over time. d , Per cent yield difference in lines that carry the 2N v S introgression. Two sided t -tests were performed to test for the significance of the impact of the 2N v S introgression. ** P < 0.01; *** P < 0.001.
Functional centromere positions in the RQA have undergone structural and positional rearrangement. Chromosome alignments showing collinearity (black scaffolds in same orientation, grey scaffolds in opposite orientation) with relative density of CENH3 ChIP–seq mapped to 100 kb genomic bins for Chinese Spring (blue) and a representative genome of comparison (red) for chromosome 4B of CDC Stanley ( a ), and chromosome 5B of Julius ( b ). c , Detailed list and clustering of cytological features carried by each wheat line (Supplementary Note 6 ). Features that are identical (dark grey) or have a gain (black) or loss (light grey) relative to Chinese Spring are indicated.
Pairwise alignments of chromosome 6B from the RQA and Chinese Spring are shown. Above each alignment dot plot, the directionality biases estimated from alignments of Hi-C data against Chinese Spring are shown. Boundaries of diagonal segments are indicative of inversions and coincide with inversion boundaries identified from the chromosome alignments.
a , Cytogenetic karyotypes of Forno (left) and Arina (right), the parents of Arina LrFor . Note that the large recombinant chromosome 7B is represented by a distinct peak. b , Sequence of the translocation breakpoint on chromosome 7B of Arina LrFor . Note that the exact breakpoint lies in a sequence gap (stretch of Ns). The bp positions are indicated at the left. Forward PCR primers are shown in red and reverse primers in blue. The overlap of the two reverse primers is shown in purple. The outer primer pair was used for PCR, while the inner pair was used for a nested PCR. c , PCR amplification of the fragment spanning the translocation breakpoint. The nested PCR yielded a ~5 kb fragment that spanned the translocation breakpoint and its identity was confirmed by sequencing. Both PCR and nested PCR were performed in duplicate; both replicates of the nested PCR were sequenced using the Sanger method. For gel source data, see Supplementary Fig. 1 . d , Mapping of Illumina reads from the cultivars Arina and Forno on to the pseudomolecules of Arina LrFor . Sequence derived from Forno is shown in blue, while sequenced derived from Arina is in red. Note that chromosomes 5B and 7B are derived from both parents, indicating that these parental chromosomes can recombine freely, despite the presence of a large 5B/7B translocation in Arina.
a , Critical recombinants from the 99B60-EJ2G/Infinity and 99B60-EJ2D/Thatcher populations used to fine map Sm1 . The 99B60-EJ2G/Infinity cross had 5,170 F 2 plants, while 99B60-EJ2D/Thatcher cross had 5,264 F 2 plants; only recombinant haplotypes between orange wheat blossom midge resistant (R) and susceptible (S) genotypes are shown. b , Oxford Nanopore long read confirmation of the Sm1 gene candidate in the CDC Landmark RQA (left), and alternative haplotype in Chinese Spring (right). Vertical coloured lines indicate sequence variants. c , Amplification of cDNA for the NB-ARC domain of the Sm1 gene candidate (top) and actin control (bottom) derived from RNA isolated from developing kernels (left) and wheat seedlings (right). Unity and CDC Landmark are carriers of Sm1 . Waskada carries an alternative haplotype and does not carry Sm1 (see main text). Thatcher was used as a susceptible parent for fine mapping of Sm1 and does not contain the associated NB-ARC domain. The experiment was replicated on four independent biological samples for each condition. d , Distribution of an Sm1 allele-specific PCR marker in a diverse panel of >300 wheat lines.
Supplementary data.
Supplementary Figure 1. Original gel source data used for spanning the breakpoint for the 7B/5B translocation.
Supplementary information.
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This file contains Supplementary Tables 1-27.
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Crop simulation models are essential tools to facilitate the evaluation and application of crop production practices under different climate scenarios. The present study analyzed the impact of climate change on wheat production in the semi-arid regions of western India by using the decision support system for the agrotechnology transfer (DSSAT-CERES) simulation model. We used ensemble and bias correction data of the coordinated regional downscaling experiment for South Asia (CORDEX-SA) driving global climate model (GCM) experiments for the future climate. The study considered the historical (1981–2010), experimental period (2014–2017), and future (2021–2050 and 2051–2080) climatic data to simulate grain yield. We used a randomized complete block design for different crop treatments, followed by a comparison of the simulated crop yield with the historical yield to evaluate the selected adaptation measures to reduce the impact of future climate scenarios. We observed that early sowing dates and medium planting density were the significant factors for achieving high wheat production. The simulation results revealed that the wheat yields would decrease in the near and far future under RCP 4.5 and RCP 8.5 scenarios. Our findings emphasize the requirement to adapt best measurements to improve yield, which involves early sowing by two weeks and maintaining a planting density of 150 plants per square meter. Experimentally, our results suggest that the DSSAT model, if calibrated carefully, can serve as a valuable tool for decision-making on adaptation practices of winter wheat under changing climates.
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Gunawat, A., Sharma, D., Sharma, A. et al. Assessment of climate change impact and potential adaptation measures on wheat yield using the DSSAT model in the semi-arid environment. Nat Hazards 111 , 2077–2096 (2022). https://doi.org/10.1007/s11069-021-05130-9
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Wheat ( Triticum aestivum ) is known as one of the most important cereal crops and is extensively grown worldwide [ 1 ]. Wheat contributes to 50% and 30% of the global grain trade and production respectively [ 2 ]. Wheat is also known as a staple food in more than 40 countries of the world. Wheat provides 82% of basic calories and 85% of proteins to the world population [ 3 , 4 ]. Wheat-based food is rich in fiber contents than meat-based food. Dough produced from bread wheat flour has different viscoelastic properties than other cereals. It is considered a higher fiber food. Therefore, its positive effects on controlling cholesterol, glucose, and intestinal functions in the body were observed [ 5 ]. Primarily, wheat is being used to make Chapatti (Bread) but it also contributes to other bakery products. Wheat utility and high nutritional value made it the staple food for more than 1/3rd population of the world. Wheat grain is separated from the chaff and stalks after the harvesting of wheat. Stalks of wheat are further used in animal bedding and construction material. Globally, the need for wheat production is enhancing even in countries having unfavorable climates for its production. Global climate changes are badly affecting the production of wheat and it raised the concern for food security.
It is estimated that annual cereal production should be increased by 1 billion tons to feed the expected population of 9.1 billion by 2050 [ 1 ]. The current scenario demands an increase in crop productivity to meet the increased requirements of food supply [ 6 ]. Wheat is grown in tropical and subtropical regions which experiences a lot of stress. These stresses result in a reduction of yield [ 7 ]. Major environmental stresses include cold, salinity, heat, and drought which are drastically affecting its yield. However, water and heat are considered as the key environmental stresses which caused in reduction of the wheat yield globally [ 8 , 9 ]. So, genetic improvements related to yield and stress tolerance are mandatory to enhance the production of wheat [ 10 , 11 ].
Genetically modified wheat plants have been produced by the use of bacteria. Wheat plants were inoculated with the plant-growth-promoting bacteria (PGPB) which resulted in the higher expression of abiotic stress (mainly drought and salinity) tolerant genes [ 12 ]. PGPB inoculated wheat cultivars also showed the higher expression of genes encoding antioxidant-enzymes, such as catalase (CAT), peroxidase , ascorbate peroxidase (APX), and glutathione peroxidase (GPX). So, it was concluded that PGPB used in wheat plants resulted in increased tolerance to abiotic stresses [ 12 ]. Cold shock proteins increase the survival of bacteria in severe environmental conditions. CspA and CspB genes from bacteria were transformed into wheat. Transgenic wheat plants expressing SeCspA and SeCspB were observed to have decreased water loss rate, increased proline and chlorophyll contents under salinity, and less water-stress conditions [ 13 ]. It was further investigated that SeCsp transgenic wheat plants resulted in enhanced weight and yield of grain than the control plants. SeCspA transgenic wheat plants were observed to have an improved water-stress tolerance than the control plants ( Table 1 , [ 13 ]).
S. No. | Gene Name | Trait/Phenotype | Reference |
---|---|---|---|
1. | Increased yield | [ ] | |
2. | Increased Nitrogen and Phosphorus uptake | [ ] | |
3. | More grain yield | [ ] | |
4. | More root growth | [ ] | |
5. | Increased heat tolerance | [ ] | |
6. | More yield | [ ] | |
7. | More yield | [ ] | |
8. | More yield, More seed protein contents | [ ] | |
9. | Iron biofortification | [ ] | |
10. | Drought and frost tolerance | [ ] | |
11. | Drought tolerant | [ ] | |
12. | Drought-stress tolerance | [ ] | |
13. | Abiotic-stress tolerance | [ ] | |
14. | Drought-stress tolerance | [ ] |
Development of transgenic wheat having various traits/phenotypes.
Gluten is a protein comprised of gliadins found in wheat. Gluten is the main cause of coeliac disease in individuals. Bread-making quality of wheat is determined by the gluten proteins. Wheat varieties with less gliadin contents were produced using gene-editing technologies and RNAi (RNA interference). Wheat lines lacking immunogenic gluten were produced. Low immunogenic gluten and more nutritional values were added in one wheat line named E82. A better microbiota profile (protection microorganisms available in the gut) was observed in the NCWS patients using the bread made with E82 [ 28 ]. Plant cuticle has a positive role in the protection of plant against biotic and abiotic stresses. Wheat plants transformed with TaSHN1 resulted in increased water-stress tolerance by reducing the leaf stomatal density and changing the composition of the cuticle [ 29 ].
Wheat is considered an excessive contributor toward the human calorie intake [ 30 ]. Pests and pathogens cause yield losses in wheat up to 21.5% of the total losses and could be reached to 28.1% [ 31 ]. Wheat is affected by the fungal disease, powdery mildew caused by Blumeria graminis f. sp. tritici (Bgt). Powdery mildew is a damaging disease that resulted in greater loss of wheat [ 32 ]. Broad-spectrum resistant genes (BSR) are considered to have the most significant role to control powdery mildew. CMPG1-V gene was cloned from the Hynaldia villosa and it was observed that higher expression of CMPG1-V gene resulted in the Broad-spectrum resistance against powdery mildew [ 33 , 34 ]. Barley chi26 gene could also be used to enhance the resistance against powdery mildew and rust through genetic modification [ 35 ]. Some epigenetic regulators were determined to have a role in wheat powdery mildew resistance. TaHDT701 is a histone deacetylase that was found as a negative regulator of wheat defense against powdery mildew. TaHDT701 was observed to be associated with the one repeat protein (TaHOS15) and RPD3 type histone deacetylase TaHDA6. Knockdown of this histone deacetylase complex ( TaHDT701 , TaHDA6 , TaHOS15 ) in wheat resulted in increased powdery mildew tolerance [ 36 ].
Fusarium graminearum is a plant fungal pathogen that causes a devastating disease called Fusarium head blight in wheat. It results in the reduction of wheat production. Genetic techniques were used to increase the FHB (Fusarium head blight) resistance in wheat. Transgenic wheat plants expressing barley class II chitinase gene 2 were observed to have a higher resistance against Fusarium graminearum [ 37 ]. Lr10 and Lr21 were cloned and transformed into wheat. The transgenic plants were reported to be resistant to leaf rust disease. Evolution and diversification of HIPPs (heavy metal-associated isoprenylated plant proteins) genes were studied in Triticeae [ 38 ]. HIPPs genes of Hynaldia villosa were cloned through homology-based cloning. Transgenic wheat having HIPP1-V was developed and the role of HIPP1-V in cadmium stress was characterized. It was observed that higher expression of this gene resulted in increased tolerance to cadmium stress. Therefore, HIPP1-V could be used to increase the tolerance in wheat against cadmium [ 39 ].
Grain number, weight, and size are greatly reduced under the negative effects of environmental stresses. However, the timing, duration, and intensity of stress determine the severity of the negative effects [ 40 , 41 ]. Wheat is a major source of protein and calories for the human diet. High temperature is badly affecting the yield of wheat which is a main concern worldwide. Drought and heat stresses are the two main abiotic stresses which are playing a greater role in the reduction of wheat yield. Reduction in starch contents, photosynthetic activity, grain number, and chlorophyll contents in the endosperm is caused due to rise in temperature. Heat stress results in the accumulation of reactive oxygen species (ROS) which is the main reason for higher oxidative damage to the plant. Heat stress also results in the variation of wheat biochemistry, morphology, and physiology. Tolerance, avoidance, and escape are known as the three major mechanisms that support the plant to grow in a heat-stress environment. Major heat tolerance mechanisms in wheat are known as stay green, heat shock proteins, and antioxidant defense [ 42 ]. Protein synthesis and folding were observed to be interrupted during heat stress. Heat stress also resulted in the production of several stress agents badly affecting transcription, translation, and DNA replication in plants [ 43 ]. Plants speed up the production of heat shock proteins as a defense mechanism [ 44 ]. Higher activity of antioxidants, such as peroxidases, catalase, and superoxide dismutase, was observed under heat stress. Wheat cultivar showing greater tolerance to heat stress was observed to have higher activity of catalase, ascorbate peroxidase, and S-transferase [ 45 ].
Salt stress greatly affects the growth of wheat plants. Salinity stress has a higher impact on the morphology and physiology of wheat plants. Plants having less tolerance to salinity are not suitable for cropping. Potassium transporter ( HKT ) genes have a greater role in achieving salinity tolerance in wheat. Sodium (Na + ) exclusion through HKT genes is a major mechanism in wheat to have a salinity tolerance. OsMYBSs and AtAB14 are the transcription factors having a role in regulating HKT genes, which are considered as the candidate targets for increasing salinity tolerance in wheat [ 46 ]. Wheat transformed with a mutated transcription factor, HaHB4 showed higher water-use efficiency and was more yielding under drought stress [ 26 ]. Transgenic wheat expressing GmDREB1 gene from soybean was also observed to have higher drought tolerance under water-stress conditions [ 47 ]. DREB1A gene from Arabidopsis thaliana was introduced to bread wheat and increased tolerance against water stress in the transgenic wheat was observed. Bread wheat under drought stress was observed to have a higher level of WRKY proteins [ 48 ]. Higher expression of AtHDG11 gene in transgenic wheat resulted in increased water-stress tolerance during drought-stress conditions. Enhanced TaNAC69 expression in root and leaf of wheat during drought stress was observed [ 49 ]. Researchers are working to develop transgenic wheat having various traits/phenotypes by using advanced approaches of biotechnology for the last several decades ( Table 1 ). Numbers of transgenic wheat cultivars are being grown in the fields and several more are under trial.
Gliadins and glutenins are known as the gluten proteins and ingestion of these proteins from barley, rye, and wheat could cause the disease called coeliac disease in humans. The only remedy is to develop gluten-free food. Transgenic wheat which retains baking quality and is safe for coeliac could not be produced using conventional methods because of the complexity of the wheat genome. Coeliac disease (CD) is activated by the immunogenic isotopes mainly gliadins. Gliadin families were downregulated by the use of RNA interference. CRISPR/Cas9 is a targeted gene manipulation tool considered to have a potential role in genetic modification ( Table 2 , [ 60 , 61 ]). CRISPR/Cas9 system was recently used for gene editing of gliadins. Offsprings with deleted, edited, or silenced gliadins were produced by CRISPR/Cas9. They helped to decrease the exposure of the patient to the CD epitopes [ 62 ]. This technology has been used to develop wheat cultivars having gluten genes with inactivated CD epitopes [ 62 , 63 ].
S. No. | Gene Name | Trait/Phenotype | Reference |
---|---|---|---|
1. | Powdery mildew resistance | [ ] | |
2. | Improved Phosphorus uptake | [ ] | |
3. | Improved yield | [ ] | |
4. | Powdery mildew resistance | [ ] | |
5. | Improved yield | [ ] | |
6. | Male sterility | [ ] | |
7. | High amylase contents | [ ] | |
8. | Improved quality | [ ] | |
9. | Herbicide tolerance | [ ] | |
10. | Herbicide tolerance | [ ] |
Genome edited wheat developed by CRISPR/Cas9 system.
CRISPR/Cas9 system and TALENS (transcription activator-like effector nuclease) were used in the bread wheat to generate the mutations in three homoeoalleles that encode MLO locus proteins against mildew. Mutations in all three TaMLO were generated by using TALENS which resulted in resistance against powdery mildew. The MLO homoeoalleles ( TaMLOA1 , TaMLOB1, and TaMLOD1 ) of bread wheat contributed to the mildew infection. Mutation of MLO alleles resulted in powdery mildew tolerance in wheat [ 50 ]. Genome editing was reported in which pds (phytoene desaturase) and inox (inositol oxygenase) genes in the cell suspension-culture of wheat were targeted. It was demonstrated that the genome-editing technique could also be applied in the cell suspension of wheat [ 64 ]. Very recently, various research groups are involved to develop transgenic wheat by using genome-editing technology. Some of the experiments are listed in Table 2 .
A comprehensive resource for wheat reference genome was developed by International Wheat Genome Sequencing Consortium. The URGI portal ( https://wheat-urgi.versailles.inra.fr/ ) was developed for the breeders and researchers to access the genome sequence data of bread-wheat. InterMine tools, genome browser, and BLAST were established for the exploration of genome sequences together with the additional linked datasets, including gene expression, physical maps, and sequence variation. Portal provided the higher browser and search features that facilitated the use of the latest genomic resources required for the upgradation of wheat [ 65 ].
DNA binding with one finger (Dof) transcription factors is known to have an important role in abiotic stress tolerance as well as the growth of plants. Ninety-six TaDof members of the gene family have been studied using computational approaches. By qPCR analysis, it was revealed that TaDof genes were upregulated under heavy metal and heat stress in wheat. Consequently, it could be concluded that detection of amino acid sites, genome-wide analysis, and identification of the Dof transcription factor family could provide us the new insight into the function, structure, and evolution of the Dof gene family [ 66 ].
This work was supported by funds from the Higher Education Commission of Pakistan.
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The paper summarized the state of wheat production, consumption, and international trade at the global and regional levels. ... Dixon J (2007) The economics of wheat: research challenges from field to fork. In: Buck H, Nisi J, Salomon N (eds) Wheat production in stressed environments. ... Published: 03 June 2022. Publisher Name: Springer, Cham ...
Wheat (Triticum aestivum L.) belonging to one of the most diverse and substantial families, Poaceae, is the principal cereal crop for the majority of the world's population.This cereal is polyploidy in nature and domestically grown worldwide. Wheat is the source of approximately half of the food calories consumed worldwide and is rich in proteins (gluten), minerals (Cu, Mg, Zn, P, and Fe ...
Wheat occupies a special role in global food security since, in addition to providing 20% of our carbohydrates and protein, almost 25% of the global production is traded internationally. The importance of wheat for food security was recognised by the Chief Agricultural Scientists of the G20 group of countries when they endorsed the establishment of the Wheat Initiative in 2011. The Wheat ...
A wheat simulation model was used at 53 study sites across the world under optimum local wheat cultivar management practices to estimate ... Research Highlight 21 Jul 2022. Bushmeat in Brazil ...
The global demand for food is continuously increasing and agricultural production must follow to ensure future global food security [1-5].Wheat (Triticum aestivum L.) is one of the most important crops contributing to global food security, providing approximately 20% of calories and protein in the human diet [].Although global wheat production has continued to increase over recent decades ...
To examine how wheat genotypes respond to future climate conditions, a quantitative genetic model integrating climatic covariables (Function 2) was created by fitting the 274,292 yield and ...
Grains of Kariega were germinated and grown in the dark on wet filter paper for 4 days at 4 °C and 3 days at 25 °C. ... al. Shifting the limits in wheat research and breeding through a fully ...
Abstract Background and Objectives Research on wheat grain proteins is reviewed, including achievements over the past century and priorities for future research. ... in Cereal Chemistry between 1945 and March 2022, of which 1678 papers included the words "wheat protein" or "gluten." ... over the period from 1886 to 1928. His studies of ...
U.S. winter wheat plantings for the 2022/23 marketing year were reported in the January 12 Winter Wheat and Canola Seedings report published by USDA's National Agricultural Statistics Service (NASS). Winter wheat is estimated to have been seeded on 34.4 million acres, up 2 percent from last year and the largest total since 2016/17 (figure 1).
Wheat Outlook: December 2022, WHS-22l, December 13, 2022 USDA, Economic Research Service. International Outlook. Global Production in 2022/23 is Lowered. Global wheat production in 2022/23 is lowered 2.1 million metric tons (MMT) to 780.6 MMT as. Argentine production is reduced further by the ongoing drought conditions.
Currently, wheat yield gains are estimated to be 0.9% per year, much less than the 1.5% per year, which is required to meet the projected 60% increase in global production needed by 2050 (Reserach Program on Wheat, 2016). At the current rate, the global production of wheat may only increase by 38%, which is far short of the projected demand.
Previous studies in various parts of the world have extensively documented the impact of changing climate on wheat crop yield. Most existing research on China discussed the relationship between the two at the national and regional levels [31,32,33] or only focused on a specific province, such as Henan Province, which has the highest wheat yield [30,34].
ISBN 978-1-83968-593-4, eISBN 978-1-83968-594-1, PDF ISBN 978-1-83968-595-8, Published 2022-05-11 Current Trends in Wheat Research is an interdisciplinary book dealing with diverse topics related to recent developments in wheat research. It discusses the latest research activities in biotic and abiotic stress tolerance in wheat.
USDA, Economic Research Service • 2022/23 all-wheat exports are projected at 775 million bushels based on tighter supplies and reduced competitiveness. This export total, if realized, would be the lowest since 1971/72. • The 2021/22 all-wheat export forecast is raised 20 million bushels to 805 million with March
Wheat (Triticum aestivum L.) is a global commodity, and its production is a key component underpinning worldwide food security. Yellow rust, also known as stripe rust, is a wheat disease caused by the fungus Puccinia striiformis Westend f. sp. tritici (Pst), and results in yield losses in most wheat growing areas. Recently, the rapid global spread of genetically diverse sexually derived Pst ...
Multiple wheat genomes reveal global variation in modern ...
Crop simulation models are essential tools to facilitate the evaluation and application of crop production practices under different climate scenarios. The present study analyzed the impact of climate change on wheat production in the semi-arid regions of western India by using the decision support system for the agrotechnology transfer (DSSAT-CERES) simulation model. We used ensemble and bias ...
the impacts of future climate change on wheat, the most widely grown cereal crop globally, in a temperate ... scope of this paper. 1 Introduction Globally, wheat is the most widely grown cereal crop by area, ... much research relating weather indices to potential crop vari-ability or projected damage (Harkness et al., 2020; Iizumi ...
1. Introduction. Wheat (Triticum aestivum) is known as one of the most important cereal crops and is extensively grown worldwide [1]. Wheat contributes to 50% and 30% of the global grain trade and production respectively [2]. Wheat is also known as a staple food in more than 40 countries of the world.
Received: 10-03-2022 Revised: 23-08-2022 Accepted: 05-09-2022 ABSTRACT The present study was conducted on trend analysis of wheat production in India and Afghanistan. The study was based on secondary data collected from various published and unpublished sources in India, Afghanistan and at the global level from 2000-19.
Meeting the projected 140 million tonnes of wheat demand by 2050 is imperative, requiring a substantial 46% increase in production and elevating the present productivity from 3.3 t ha −1 to 4.7 ...
Wheat (Triticum aestivum L) is the most extensively grown cereal crop. in the world, covering about 237 million hectares annually, accounting. for a total of 420 million tonnes (Isitor et al ...
The technical bulletin is a compilation of information pertaining to causes, impacts of recent heat waves of 2022 on crops, horticulture, livestock and fishery sector of Indian Agriculture.