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Functional mutation allele mining of plant architecture and yield-related agronomic traits and characterization of their effects in wheat

BMC Genetics volume 20, Article number: 102 (2019) Cite this article

Abstract

Background

Wheat mutant resources with phenotypic variation have been developed in recent years. These mutants might carry favorable mutation alleles, which have the potential to be utilized in the breeding process. Plant architecture and yield-related features are important agronomic traits for wheat breeders and mining favorable alleles of these traits will improve wheat characteristics.

Results

Here we used 190 wheat phenotypic mutants as material and by analyzing their SNP variation and phenotypic data, mutation alleles for plant architecture and yield-related traits were identified, and the genetic effects of these alleles were evaluated. In total, 32 mutation alleles, including three pleiotropic alleles, significantly associated with agronomic traits were identified from the 190 wheat mutant lines. The SNPs were distributed on 12 chromosomes and were associated with plant height (PH), tiller number, flag leaf angle (FLA), thousand grain weight (TGW), and other yield-related traits. Further phenotypic analysis of multiple lines carrying the same mutant allele was performed to determine the effect of the allele on the traits of interest. PH-associated SNPs on chromosomes 2BL, 3BS, 3DL, and 5DL might show additive effects, reducing PH by 10.0 cm to 31.3 cm compared with wild type, which means that these alleles may be favorable for wheat improvement. Only unfavorable mutation alleles that reduced TGW and tiller number were identified. A region on chromosome 5DL with mutation alleles for PH and TGW contained several long ncRNAs, and their sequences shared more than 90% identity with cytokinin oxidase/dehydrogenase genes. Some of the mutation alleles we mined were colocalized with previously reported QTLs or genes while others were novel; these novel alleles could also result in phenotypic variation.

Conclusion

Our results demonstrate that favorable mutation alleles are present in mutant resources, and the region between 409.5 to 419.8 Mb on chromosome 5DL affects wheat plant height and thousand grain weight.

Background

In the past few decades, mutation induction has been used to improve crop varieties. Using this procedure, more than 3200 new mutant plant varieties have been bred from over 200 species worldwide (https://mvd.iaea.org/). The development of genomics and biological techniques has facilitated the use of mutation induction to elucidate the nature of mutations and for mining novel alleles and genes affecting target traits. Chemical mutagens can induce a high rate of single nucleotide polymorphism (SNP) variation, up to more than 5000 mutations on average in each M2 line of hexaploid wheat [1], while physical mutagens like fast neutrons and heavy ion beams induce more substitutions than small insertion-deletions or large deletions [2, 3]. Using an M2 population and reverse-genetic methodologies, such as TILLING (Targeting Induced Local Lesions in Genomes), novel alleles of known genes have been identified and functionally characterized in several plant species including wheat [4,5,6,7,8].

Wheat mutant resources have also been developed in the past decades from several different wild types [4, 9,10,11,12]. Various mutations affecting plant architecture, spike morphology, and yield-related traits have been identified, and there are many novel mutant genes/alleles available. In order to mine the mutated genes in these mutant lines, bi-parental genetic segregation populations, such as recombinant inbred lines (RILs) and near isogenic lines (NILs), need to be generated using a mutant as one of the parents. The process of developing genetic populations is very time-consuming, requiring at least two more years after the mutant is identified, and thus the speed with which mutants can be used is limited. A new methodology that could identify mutated genes/QTLs directly from the mutants should be developed which would be helpful in accelerating the gene mining process.

Plant architecture and yield-related traits are the main targets in wheat breeding and many genes/QTLs have been mapped that have dominant or/and additive effects. Over 20 wheat dwarfing genes (Rht) [13] are available, and more than half were produced by mutation induction. Among these Rht genes, Rht1, Rht2, Rht8, and Rht9 have been fully utilized in modern cultivars [14, 15], and different mutant alleles of Rht1 and Rht2 resulted in variance in plant height [16, 17], thus enriching genetic diversity. Moreover, there were many other potential loci distributed on multiple chromosomes that regulated plant height [18]. Yield and yield-related traits like thousand grain weight (TGW) and grain number per spike (GNS) are thought to be quantitative, and their genetic loci are distributed on chromosomes throughout the whole genome [19]. However, because of allelic selection during evolution and breeding processes, genetic diversity has remarkably declined [20, 21]. All of these genes/loci could be mutated to produce novel alleles, and the discovery and mining of favorable alleles to improve genetic diversity would greatly benefit wheat breeding.

Genome-wide association studies (GWAS) have played a crucial role in gene discovery in landraces, cultivars, and elite line resources of rice [22], maize [23], sorghum [24], and wheat [25]. Combined with high-throughput microarrays, such as 15 K [26], 90 K [27, 28], 660 K (http://wheat.pw.usda.gov/ggpages/topics/Wheat660_SNP_array_developed_by_CAAS.pdf) and 820 K [29] SNP and diversity array technology (DArT) arrays, novel alleles and QTLs for important agronomic traits, including plant height (PH), yield-related traits, and spike and flag leaf architecture, have been identified in wheat [25, 30,31,32]. In addition, some of the favorable alleles for TGW and GNS have been widely utilized in modern breeding [33].

In the current study, we detected mutated alleles affecting plant architecture and yield-related traits in a wheat mutant resource using the GWAS method combined with t-testing, and the genetic effects of mutated alleles were further evaluated. These results will improve mutant allele identification and the mining of novel mutation alleles affecting agronomic traits. The identification of favorable mutated alleles will in turn aid efforts to increase genetic diversity and improve wheat.

Results

Correlation between yield- and plant architecture-related traits

Correlation analysis (Table 1) showed that PH was significantly and positively correlated with the other four plant architecture-related traits, including pre-winter, maximum, and effective tiller numbers (PWT, MT, and ET, respectively), and flag leaf angle (FLA); ET was more highly correlated with MT than with PWT. TGW was significantly and positively correlated with all traits except FLA and spikelet density (SD), but the correlation coefficients were not very high.

Table 1 The correlation coefficients for plant architecture- and yield-related traits of wheat mutant lines averaged across environments
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SNPs potentially associated with plant architecture- and yield-related traits under different environments

The principal component analysis (PCA) indicated that the population was divided into five subpopulations (Additional file 1: Figure S1). High linkage disequilibrium (LD) was observed in the mutant population, with an average r2 of 0.91 at p ≤ 0.001.

A total of 150 potential significant SNPs were detected at the given P value threshold (0.001) in at least one environment by genome-wide association analysis (Additional file 2: Table S1; Additional file 3: Figure S2; Additional file 4: Figure S3). The SNPs were distributed on all chromosomes except chr. 1D, 2D, and 4D, with 35 located on chr. 3B. Among these significant SNPs, 62 were potentially associated with plant architecture-related traits, including eight SNPs associated with PH, five with PWT, 15 with MT, 27 with ET, and seven with FLA; 88 were potentially associated with yield-related traits, including 23 associated with TGW, 21 with spike length (SL), 13 with GNS, 15 with SD, and 16 with spikelet number per spike (NSL).

Candidate SNPs related to plant architecture- and yield-related traits

Based on t-tests, there was a significant difference in the phenotypes of plants with the wild type (WT) and mutation alleles in more than 50% of the environments for 32 out of the 150 SNPs (21.34%) (Table 2), and these SNPs were distributed in clusters on 12 chromosomes (Fig. 1). Among the plant architecture-related traits, all eight SNPs distributed on chr. 2B, 3B, 3D, and 5D were significantly associated with PH, and the percent variance explained (PVE) ranged from 7.68–9.40%. Six out of 15 SNPs (40.00%) distributed on chr. 3B, 3D, 4A, 6B, and 7D were significantly associated with MT, and PVE ranged from 6.19 to 10.23%. Six out of 27 SNPs (22.23%) distributed on chr. 3B, 5A, 7B, and 7D were significantly associated with ET, and PVE ranged from 8.21 to 11.39%. One out of seven SNPs (14.29%) on chr. 2B was significantly associated with FLA, and the PVE was 6.18%.

Table 2 Candidate SNPs that were significantly associated with a trait in more than 50% of environments based on t-tests
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Fig. 1
figure 1

Relative physical position of significantly associated SNPs on chromosomes. The long black rectangles represent the chromosomes. Significantly associated SNPs are shown on the right side of the chromosome. The traits associated with the SNP are shown on the left side of the chromosome. I: the beginning of the chromosome; E: the end of chromosome

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For the yield-related traits, two out of 23 SNPs (8.70%) distributed on chr. 3B and 5D were significantly associated with TGW based on t-tests, and PVE ranged from 8.23 to 11.59%. Two out of 13 SNPs (15.39%) distributed on chr. 2A and 3B were significantly associated with GNS, and PVE ranged from 10.45 to 14.87%. One out of 16 SNPs (6.25%) on chr. 2A was significantly associated with NSL and explained 7.40% of the phenotypic variation. Six out of 15 SNPs (40.00%) distributed on chr. 2A, 3B, 6A, and 7A were significantly associated with SD, and PVE ranged from 7.77 to 7.96%.

There were three candidate SNPs showing pleiotropic effects. SNP AX-109900989 was significantly associated with both PH and FLA, AX-89425861 was associated with both MT and ET, and AX-109655447 was associated with GNS and NSL.

Effects of candidate mutation alleles on plant architecture and yield-related traits

Plant height

Among the 190 mutant lines, 158 lines carried only the mutant allele of SNP AX-109900989; five lines carried the mutant alleles of both SNP AX-110409382 and SNP AX-111563435, which are located on chr. 3B and 3D, respectively; and 11 lines carried the mutant alleles of all eight significant SNPs located on chr. 2B, 3B, 3D, and 5D (Additional file 5: Table S2 and Table 3). The physical distance between the five SNPs on chr. 5D is about 4.97 Mb (Table 3).

Table 3 P values for t-tests between WT and mutants carrying candidate SNPs for plant height
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The presence of single mutation alleles and pyramided mutation alleles resulted in a significant reduction of PH compared with WT (Fig. 2a). The average PH of the mutants only carrying the mutation allele of AX-109900989 was 11.8 cm to 18.6 cm lower than that of WT in different environments. PH of lines carrying the mutant alleles of both AX-110409382 and AX-111563435 was reduced by 10.0 cm to 19.8 cm, and the PH of lines carrying all eight mutation alleles was reduced by 20.2 cm to 31.3 cm, with the pyramided alleles implying additive effects.

Fig. 2
figure 2

Average plant height and thousand grain weight of wild type (WT) and mutant lines carrying different mutation alleles. a, Plant height. All eight alleles indicate lines carrying the mutant alleles of AX-109900989, AX-111563435, AX-110409382, AX-109968486, AX-108930866, AX-109500865, AX-111118954, and AX-108907798. b, Thousand grain weight. Double alleles indicate lines carrying the mutant alleles of AX-109947280 and AX-109326075

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Thousand grain weight

There were three and seven mutant lines carrying single mutation alleles of AX-109326075 (chr. 3B) and AX-109947280 (chr. 5D), respectively. The TGW of these plants were lower than that of WT, but these differences were not significant (Additional file 6: Table S3 and Table 4). Six mutant lines carried both mutation alleles, and the TGW of these lines was significantly lower than that of WT (by 3.48 to 16.84 g) in different environments (Additional file 6: Table S3, Table 4, and Fig. 2b). The effects of these two alleles were additive. The physical positions of both SNPs are located far away from the other SNPs on chr. 3B and 5D that affect PH.

Table 4 P values for t-tests between WT and mutants carrying candidate SNPs for thousand grain weight
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Spikelet density

The average SD of the mutants carrying the mutation allele of AX-110371706 was higher than that of the WT (Table 5, Fig. 3d, and Additional file 10: Table S7), with the increase ranging from 0.07 to 0.52 in different environments.

Table 5 P values for t-tests between WT and mutants carrying candidate SNPs for maximum tiller number
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Fig. 3
figure 3

Average phenotypic values of wild type (WT) and mutant lines carrying different mutation alleles. a, Maximum tiller number; b, Effective tiller number; c, Flag leaf angle; d, Spikelet density

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Pleiotropic SNPs

There were 67 mutant lines that only carried the mutation allele of the candidate SNP AX-89425861, and the MT and ET of these lines, especially ET, were significantly lower than those of WT in some environments (Table 5, Fig. 3a, b, Additional file 7: Table S4 and Additional file 8: Table S5). The reduction in MT and ET ranged from 1.3 to 5.6 and 1.3 to 4.5, respectively.

AX-109900989 is another pleiotropic SNP. Compared with WT, lines carrying this mutation allele not only had lower PH, but also a lower FLA, which was reduced by 4.7 to 22.8° compared with WT (Table 5, Fig. 3c, Additional file 9: Table S6).

Genes flanking the mutation alleles

There were no candidate SNPs located in genic sequence, so the flanking genes of each SNP were further searched (Table 6). The functions of these genes are related to imidazoleglycerol-phosphate dehydratase, protein kinase domain, and Myb-like DNA-binding domain, for example. Because of the very short physical distance of the five SNPs on chr. 5DL, the genes between SNPs AX-109968486 and AX-108907798, 42 in total, were further blasted (Table 7). Among them, gene TraesCS5D02G323500 was known as an Auxin-Indole-3-Acetic Acid (Aux-IAA) family transcription factor.

Table 6 The nearest genes to the candidate SNPs and their annotations
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Table 7 The genes between SNPs AX-109968486 and AX-108907798 on chr. 5DL and their annotations
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There were six lncRNAs located between SNPs AX-108930866 and AX-108907798 within a physical distance of 2.73 Mb on chr. 5DL, including STRG_Root.49887.1, STRG_Stem.98330.1, STRG_Seed.78371.1, STRG_Leaf.1806.1, STRG_Stem.40979.1, and STRG_Stem.16565.1 (Fig. 4). The sequences of these ncRNAs shared more than 90% identity (data not shown) with cytokinin oxidase/dehydrogenase genes (CKX2.3 and CKX2.4).

Fig. 4
figure 4

Diagram of a portion of chromosome 5DL. The long black rectangle represents the chromosome. The SNPs significantly associated with phenotype (green) and ncRNAs (blue) are indicated to the right of the chromosome. The physical intervals (kb) are shown on the left side of the chromosome

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Discussion

The relationship between pleiotropy and phenotypic correlation

Three pleiotropic SNPs were identified in the study, which regulated two or three traits. Pleiotropy is a common phenomenon, and related traits are usually significantly correlated [22, 25]. For example, in barley, mutation alleles have pleiotropic effects on tillering and TGW traits [34]. In wheat, flag leaf length is correlated with yield-related traits [35]. It is well known that wheat yield is closely correlated with GNS, TGW, and number of effective spikes, and regions of chr. 5A and 6A were previously found to have pleiotropic effects on PH, grain yield, grain number, and TGW [28]. In our mutated population, GNS and NSL were correlated, which is consistent with previous results [36]. Previously, SNP markers BS00022896_51 (located at 612 Mb on chr. 2A) and wsnp_Ex_c40280_47375312 (located at 676 Mb on chr. 2A) were linked with GNS and NSL, respectively [36, 37], and in our study, another pleiotropic SNP, AX-109585477, was found to closly linked with these SNPs (695 Mb on chr. 2A) that also affects both GNS and NSL. Consistent with previous observations in rice [22], we found that PH was significantly correlated with FLA. Furthermore, SNP AX-109900989 was associated with both of these traits. MT and ET, which are two different indexes of tiller number, were also strongly correlated and shared the same candidate SNP, AX-89425861, on chr. 7D. These pleiotropic mutation alleles can be transferred into Kompetitive Allele Specific PCR (KASP) markers, which could be used to identify pyramided lines rapidly in earlier generations of segregating progenies, and will finally accelerate wheat breeding practice.

Known and potentially novel mutation alleles associated with the investigated traits

The identified mutation alleles were classified into two types, one was known alleles, such as mutation alleles located at the ends of chr. 2BL and 5DL and on chr. 3BS and 3DL which reduced PH, and some of them were located close to those known regions. The dwarf gene Rht4 was mapped to the terminus of 2BL, bin 2BL6–0.89-1.00 [38], which is from 444 to 739 Mb in the IWGSC Reference genome, and SNP AX-109900989 is located in this region at 679.5 Mb. SNP AX-111563435 is located close to a reported PH QTL on chr. 3B, QTL_height_3B_1 [39, 40]. QTLs for TGW were previously mapped to chr. 3BS, 4D, 5B, 5D, 6A, and 7B [20, 41,42,43], and two more mutation alleles affecting TGW were identified and they were mapped close to the known QTLs on chr. 3BS and 5DL [20, 42]. QTLs for tiller number were mapped to the terminus of 5AL [44], and a QTL on 5A affects tiller number [45]. The identified mutation allele on 5AL was also located at the 5AL terminus and might be the same as the tiller number QTL, but further phenotypic analysis of mutation lines and fine mapping data are needed to confirm this.

The other type was mutation alleles which did not colocalize with reported QTLs and might be novel alleles affecting PH-related traits. The mutation alleles on chr. 3DL have not yet been reported and might correspond to a novel gene. Genes and QTLs for tiller number have been mapped to chr. 1A, 1B, 1D, 2A, 2B, 2D, 3A, 4D, 5D, 6A, 6D, and 7A [43, 45,46,47,48], while our mutation alleles affecting MT and ET were located on chr. 4A, 6B, 7B, and 7D and did not colocalize with these previously mapped QTLs.

Favorable mutation alleles and their potential application in future breeding programs

The candidate mutation alleles especially those of favorable alleles identified in the paper could enrich wheat genetic diversity. As different alleles and haplotypes of the target gene may have different effects on the phenotype [20, 49], breeders usually pyramid favorable alleles to improve target traits [30], such as plant architecture-related traits [33], so the identified favorable mutation alleles could be used in future wheat breeding programs. One potentially favorable allele is the G allele of AX-109900989, which reduced PH and FLA. Due to its effect on photosynthesis, changing FLA results in changes in yield [50]. For example, a leaf angle less than 25° enhances yield in wheat [51]. Thus, the G allele of AX-109900989 could potentially be used to decrease FLA and increase yield. Similarly, the T allele of AX-110371706, which increases SD, could also be used to increase yield. At the same time, unfavorable mutation alleles affecting TGW (G of AX-109326075 and T of AX-109947280) and tiller number (G of AX-89425861) should be avoided.

The relationship between the terminal ends of chr. 3B and 5DL with plant height and thousand grain weight

Chromosome 3B is the largest chromosome in wheat, and multiple QTLs affecting plant architecture and yield-related traits have been identified in this chromosome [19, 39, 52] that are distributed on the entire chromosome. The 11 SNPs identified on chr. 3B (Fig. 1) were significantly associated with TGW, GNS, SD, PH, ET, or MT and, with the exception of SNP AX-109438215, which affects GNS, all colocalized with or mapped very near four previously reported meta-QTLs for yield-related traits. SNP AX-109326075 is located near the meta-QTL F6 (26.1–66.4 Mb) [53]. SNPs AX-109655447 to AX-111563435 colocalize with MQTL3B-3 (150.8–398.1 Mb), and SNPs AX-111029728 and AX-110536382 are both located near MQTL3B-4 (535.9–576.3 Mb) [39]. MQTL3B-3 and MQTL3B-4 are located in the same region as MQTL28 (116.8–737.0 Mb), which affects PH and yield-related traits [19].

Physical positions of both TGW and PH related SNPs identified on chr. 5DL in the study were colocalized with previously reported genes and QTLs. The TGW-associated SNP AX-109947280 identified in our study located at 409 Mb/566 Mb, while the cell wall invertase gene TaCWI, which is significantly associated with TGW, is located in this region [20]. This gene has been under strong selection during modern wheat breeding, and its genetic diversity has declined dramatically [20]. A QTL and a favorable allele affecting TGW are also located in this region (155.4 cM/170.7 cM and 421 Mb/566 Mb respectively) [41, 54]. Five SNPs associated with PH are located downstream of this region (414–419 Mb). The 42 genes located in this region included the Aux-IAA transcription factor, and a mutation in the auxin response gene resulted in decreased PH [55]; the PH of lines carrying these mutation SNPs were significantly lower compared with that of WT (Fig. 2a). Although the mutant SNPs did not occur in the genes, the five SNPs were very closely linked and the phenotypic variation might be caused by the genes in this region.

The six lncRNAs located near the four SNPs from AX-108930866 to AX-108907798 (about 6.6 Mb) (Fig. 4), are expressed in stems, leaves, or roots and share sequence similarity with CKX genes. CKX genes play important roles during plant development, and the level of CKX activity in barley, Arabidopsis, and tobacco results in variation in PH [56,57,58,59]. The dwarf gene Rht23 is also located at the terminus of chr. 5DL [60]. So, it deduce that the terminus of chr. 5DL might play a critical role in wheat PH and TGW, but the relationship between our mutation alleles and Rht23, the CKX genes, and TaCWI needs to be further studied.

Combining GWAS with t-tests could effectively reduce the false negative rate

Each advanced phenotypic mutants used in the study carried numerous mutations, it should think about how to rapidly identify linkage markers and mutation alleles affecting target traits. It is very difficult to use only a single mutant to identify linkage markers and mutation alleles affecting target traits, an alternative strategy is to use multiple mutants, as the probability of three individual mutants sharing a mutation in the same gene is less than 1E-05, and this probability is even lower when including more lines [61]. More than 67,000 qualified SNPs were used in the current study, and most of them should have no relationship with the target traits. It is very time consuming and unnecessary to analyze the association between each SNP and trait one by one, so TASSEL software was used to filter out most of the insignificant markers and to reserve as many potentially significant markers as possible by reducing the P value threshold from 7.4E-07 to 0.001. All potential markers were then further analyzed by t-tests to eliminate false positive markers.

Through the analysis of 10 plant architecture- and yield-related traits in 190 individual mutant lines, genome-wide association analysis and t-tests only gave similar results for PH, while for other traits, fewer than 40% of the markers were verified by t-test. This indicates that the set P threshold was appropriate, with fewer positive markers for PH or other traits omitted. This also means that if it only used genome-wide association analysis, the false negative rate would be very high, even with a relatively higher threshold. At the same time, the genetic effects analysis is based on the use of multiple mutant lines. Because mutation alleles present in only one or two mutant lines would be very difficult to identify, this would result in the identification of a certain number of false negatives. Some of the mutation alleles significantly associated with traits were identical with those identified in previous studies, and the favorable alleles could be used for wheat improvement.

Conclusion

One hundred ninety advanced phenotypic wheat mutants were genotyped by 660 K SNP assay, and 10 agronomic traits were investigated under 5 environments. By using GWAS and t-tests methods, 32 SNPs distributed on 12 chromosomes were identified as mutation alleles associated with plant architecture and yield related traits, and chromosome 5DL clustered more alleles on PH and TGW. Among them, G allele of AX-109900989 could reduce PH and FLA, and T allele of AX-110371706 increased SD, those were favorable alleles. Five SNPs, AX-110409382, AX-109041501, AX-109446470, AX-111556361 and AX-89425861, were novel alleles associated with PH and tiller abilities. The mutants carrying favorable alleles could be further used in future breeding practice as diverse germplasm, and the SNPs could be converted into KASP markers and used to assist breeding selection.

Methods

Plant materials

Up to 20,000 seeds of an elite winter wheat (Triticum aestivum L.) cultivar Jing 411 were treated with 1.0 or 1.5% ethyl methanesulfonate solution according to the reported protocol [11] and 200 Gy or 250 Gy ɣ-rays every year. The germination percentage in the field ranged from about 60–70% by 1.0% EMS, 45–55% by 1.5% EMS, 50–60% by 200 Gy ɣ-rays, and 30–40% by 250 Gy ɣ-rays batch by batch. Each M1 plant was bagged at the heading and flowering stages to avoid hybridization and to strictly maintain self-crossing. The M2 populations were developed using both single-seed descent and mixed descent methods and each M2 population was kept at about 20,000 individual plants. Mutants affecting plant architecture- and yield-related traits were screened in the M2 generation in the field. Screening was continued in the next selfing generations until the phenotypes were stable. After a decade of continuous production in M1 and M2 populations, field selection, and identification, a mutant resource was constructed, which contained more than 4000 individual genotypes with phenotypic mutations. Within the mutant resource, a total of 190 advanced and stable independent individual mutant lines showing polymorphism in PH, tiller number, FLA, SL, NSL, SD, GNS, and/or TGW were selected for subsequent analysis, among them, 181 were induced by EMS and nine were induced by ɣ-rays.

Phenotyping and data analysis

The mutants and the WT Jing 411 were planted at the headquarter (H) and Changping (C) experimental stations of the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences in the 2014–2015, 2015–2016, and 2016–2017 growing seasons. Each genotype was planted in three rows with a row length of 2 m and an interplant distance of 5 cm. Two rows of plants that grew uniformly were selected for phenotyping. PH, tiller number, SL, and TGW were investigated in five different environments (2015H, 2015C, 2016H, 2016C, and 2017H); FLA, NSL, and SD were determined in four different environments (2015H, 2015C, 2016H and 2016C); and GNS was measured in two environments (2016H and 2016C).

Tiller number: PWT, MT, and ET were counted for all plants in each row at the end of November before the elongation stage and after the heading stage, respectively, and average PWT, MT, and ET per plant were calculated by dividing the total tiller number in a row by the seedling number of that row. The average PWT, MT, and ET values were used for subsequent analysis. Each genotype and environment had two replications. FLA between the flag leaf and its stem was measured 15 days after flowering using a protractor. Each genotype had 10 replications. NSL and GNS of the main spike for each genotype were counted in the field 20 days after flowering with five replications. PH, SL, and TGW were measured or calculated after harvest with five replications. SD was calculated by dividing NSL by SL.

Phenotypic data (Additional file 11: Table S8) were analyzed with QTL IciMapping v4.1 (http://www.isbreeding.net/) using the default settings to estimate the best linear unbiased estimate (BLUE) values for each environment and across environments. For calculating BLUE values, error variance of each environment is firstly calculated using model y = genotype+block+error, and then the phenotypic values of the individual in each environment are weighted and averaged by using the reciprocal of the error variance in each environment as the weight.

Statistical analysis

Analysis of correlations between the investigated traits was performed using the BLUE values from QTL IciMapping v4.1. Calculation of standard deviations, t-tests, and average values of agronomic traits were performed using Microsoft Excel 2010.

Genotyping

The 660 K SNP microarray was used to genotype the WT and 190 individual mutants. Microarray analysis was performed by China Golden Marker (Beijing) Biotech Co. Ltd. (CGMB, http://www.cgmb.com.cn), and the quality of the genotyping data was assessed. SNPs with a minor allele frequency (MAF) < 5% and a failed missing test (call rate < 97%) were excluded. A total of 67,402 SNPs were included in subsequent genome-wide association analysis.

Linkage disequilibrium (LD) and population structure analysis

The LD across the chromosomes of the WT and 190 mutant lines was estimated using TASSEL 5.2 (https://tassel.bitbucket.io/) with 67,402 SNP markers (Additional file 12: Table S9). The squared allele frequency correlation (r2) was used for evaluation of LD [62] and the average r2 of 559,830 pairs of SNP markers at P ≤ 0.001 was calculated. PCA was conducted using TASSEL 5.2 for assessment of the population structure.

Screening of candidate mutation SNPs

First, filtered SNPs and BLUE values for each trait were used in genome-wide association analysis performed using the mixed linear model (PCA + K) model of TASSEL v5.2 software [63] with the default settings. In order to reduce the risk of omitting positive candidate association markers, a less strict uniform threshold of P ≤ 0.001 was used to estimate additional potential significantly associated SNP markers. Manhattan plots were constructed using R 3.4.1 software (http://www.r-project.org/).

Secondly, mutant lines were classified into two groups based on the allele of a significant SNP identified by genome-wide association analysis, and t-tests were performed to test for significant differences (P ≤ 0.05) in the phenotypes of the two groups. When significant differences in phenotype were observed in more than 50% of environments, the SNPs were designated as candidate SNPs.

Analysis of mutation allele effects

To identify target SNPs and mutation alleles leading to significant phenotypic variation, mutants carrying the mutant allele of a specific candidate SNP were identified, and variance between the phenotypes of WT and these mutants was further analyzed using t-tests.

Sequence BLAST searches and gene annotation

The flanking sequences of the candidate alleles and reported QTLs, markers, and alleles were used as queries in BLAST searches against the IWGSC RefSeq V1.0 database (http://www.wheatgenome.org/) to determine their physical positions on chromosomes. Each candidate gene sequence was used as a query in a further BLAST search against the NCBI database (https://www.ncbi.nlm.nih.gov/) to annotate its function. Chromosome diagrams were drawn using mapdrawer [64].

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Abbreviations

BLUE:

Best linear unbiased estimate

DArT:

Diversity array technology

ET:

Effective tiller numbers

FLA:

Flag leaf angle

GNS:

Grain number per spike

GWAS:

Genome-wide association studies

KASP:

Kompetitive allele specific PCR

LD:

Linkage disequilibrium

MT:

Maximum tiller numbers

NILs:

Near isogenic lines

NSL:

Spikelet number per spike

PCA:

Principal component analysis

PH:

Plant height

PVE:

Percent variance explained

PWT:

Pre-winter tiller numbers

Rht:

Dwarfing genes

RILs:

Recombinant inbred lines

SD:

Spikelet density

SL:

Spike length

SNP:

Single nucleotide polymorphism

TGW:

Thousand grain weight

TILLING:

Targeting induced local lesions in genomes

WT:

Wild type

References

  1. Krasileva KV, Vasquez-Gross HA, Howell T, Bailey P, Paraiso F, Clissold L, Simmonds J, Ramirez-Gonzalez RH, Wang X, Borrill P, et al. Uncovering hidden variation in polyploid wheat. Proc Natl Acad Sci U S A. 2017;114(6):E913–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Belfield EJ, Gan X, Mithani A, Brown C, Jiang C, Franklin K, Alvey E, Wibowo A, Jung M, Bailey K, et al. Genome-wide analysis of mutations in mutant lineages selected following fast-neutron irradiation mutagenesis of Arabidopsis thaliana. Genome Res. 2012;22(7):1306–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Du Y, Luo S, Li X, Yang J, Cui T, Li W, Yu L, Feng H, Chen Y, Mu J, et al. Identification of substitutions and small insertion-deletions induced by carbon-ion beam irradiation in Arabidopsis thaliana. Front Plant Sci. 2017;8:1851.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bovina R, Brunazzi A, Gasparini G, Sestili F, Palombieri S, Botticella E, Lafiandra D, Mantovani P, Massi A. Development of a TILLING resource in durum wheat for reverse- and forward-genetic analyses. Crop Pasture Sci. 2014;65:112–24.

    Article  Google Scholar 

  5. McCallum CM, Comai L, Greene EA, Henikoff S. Targeting induced local lesions IN genomes (TILLING) for plant functional genomics. Plant Physiol. 2000;123:439–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Rawat N, Sehgal SK, Joshi A, Rothe N, Wilson DL, McGraw N, Vadlani PV, Li W, Gill BS. A diploid wheat TILLING resource for wheat functional genomics. BMC Plant Biol. 2012;12:205.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Slade AJ, McGuire C, Loeffler D, Mullenberg J, Skinner W, Fazio G, Holm A, Brandt KM, Steine MN, Goodstal JF, et al. Development of high amylose wheat through TILLING. BMC Plant Biol. 2012;12:69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Guo H, Liu Y, Li X, Yan Z, Xie Y, Xiong H, Zhao L, Gu J, Zhao S, Liu L. Novel mutant alleles of the starch synthesis gene TaSSIVb-D result in the reduction of starch granule number per chloroplast in wheat. BMC Genomics. 2017;18(1):358.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Chen A, Dubcovsky J. Wheat TILLING mutants show that the Vernalization gene VRN1 Down-regulates the flowering repressor VRN2 in leaves but is not essential for flowering. PLoS Genet. 2012;8(12):e1003134.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Dhaliwal AK, Mohan A, Sidhu G, Maqbool R, Gill KS. An Ethylmethane Sulfonate mutant resource in pre-green revolution Hexaploid wheat. PLoS One. 2015;10(12):e0145227.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Guo H, Yan Z, Li X, Xie Y, Xiong H, Liu Y, Zhao L, Gu J, Zhao S, Liu L. Development of a high-efficient mutation resource with phenotypic variation in hexaploid winter wheat and identification of novel alleles in the TaAGP.L-B1 gene. Front Plant Sci. 2017;8:1404.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Rakszegi M, Kisgyörgy BN, Tearall K, Shewry PR, Láng L, Phillips A, Bedő Z. Diversity of agronomic and morphological traits in a mutant population of bread wheat studied in the Healthgrain program. Euphytica. 2010;174(3):409–21.

    Article  Google Scholar 

  13. Tian X, Wen W, Xie L, Fu L, Xu D, Fu C, Wang D, Chen X, Xia X, Chen Q, et al. Molecular mapping of reduced plant height gene Rht24 in bread wheat. Front Plant Sci. 2017;8:1379.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Yang S, Zhang X, He Z, Xia X, Zhou Y. Distribution of dwarfing genes Rht-B1b and Rht-D1b in Chinese bread Wheats detected by STS marker. Sci Agric Sin. 2006;39(8):1680–8.

    Google Scholar 

  15. Zhou Y, He Z, Zhang G, Xia L, Chen X, Zhang L, Chen F. Rht8 dwarf gene distribution in Chinese wheats identified by microsatellite marker. Acta Agron Sin. 2003;29(6):810–4.

    Google Scholar 

  16. Peng J, Richards DE, Hartley NM, Murphy GP, Devos KM, Flintham JE, Beales J, Fish LJ, Worland AJ, Pelica F, et al. Green revolution genes encode gibberellin response modulators. Nature. 1999;400:256–61.

    Article  CAS  PubMed  Google Scholar 

  17. Li A, Yang W, Lou X, Liu D, Sun J, Guo X, Wang J, Li Y, Zhan K, Ling HQ, et al. Novel natural allelic variations at the Rht-1 loci in wheat. J Integr Plant Biol. 2013;55(11):1026–37.

    Article  CAS  PubMed  Google Scholar 

  18. Zanke CD, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Hinze M, Neumann K, et al. Whole genome association mapping of plant height in winter wheat (Triticum aestivum L.). PLoS One. 2014;9(11):e113287.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Zhang L-Y, Liu D-C, Guo X-L, Yang W-L, Sun J-Z, Wang D-W, Zhang A. Genomic distribution of quantitative trait loci for yield and yield-related traits in common wheat. J Integr Plant Biol. 2010;52(11):996–1007.

    Article  PubMed  Google Scholar 

  20. Jiang Y, Jiang Q, Hao C, Hou J, Wang L, Zhang H, Zhang S, Chen X, Zhang X. A yield-associated gene TaCWI, in wheat: its function, selection and evolution in global breeding revealed by haplotype analysis. Theor Appl Genet. 2015;128:131–43.

    Article  CAS  PubMed  Google Scholar 

  21. Guo J, Shi W, Zhang Z, Cheng J, Sun D, Yu J, Li X, Guo P, Hao C. Association of yield-related traits in founder genotypes and derivatives of common wheat (Triticum aestivum L.). BMC Plant Biol. 2018;18(1):38.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Lu Q, Zhang M, Niu X, Wang S, Xu Q, Feng Y, Wang C, Deng H, Yuan X, Yu H, et al. Genetic variation and association mapping for 12 agronomic traits in indica rice. BMC Genomics. 2015;16:1067.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Wang H, Li K, Hu X, Liu Z, Wu Y, Huang C. Genome-wide association analysis of forage quality in maize mature stalk. BMC Plant Biol. 2016;16(1):227.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Parra-Londono S, Fiedler K, Kavka M, Samans B, Wieckhorst S, Zacharias A, Uptmoor R. Genetic dissection of early-season cold tolerance in sorghum: genome-wide association studies for seedling emergence and survival under field and controlled environment conditions. Theor Appl Genet. 2017;131(3):581–95.

    Article  PubMed  Google Scholar 

  25. Liu Y, Lin Y, Gao S, Li Z, Ma J, Deng M, Chen G, Wei Y, Zheng Y. A genome-wide association study of 23 agronomic traits in Chinese wheat landraces. Plant J. 2017;91(5):861–73.

    Article  CAS  PubMed  Google Scholar 

  26. Muqaddasi QH, Brassac J, Borner A, Pillen K, Roder MS. Genetic architecture of anther extrusion in spring and winter wheat. Front Plant Sci. 2017;8:754.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Ain QU, Rasheed A, Anwar A, Mahmood T, Imtiaz M, Mahmood T, Xia X, He Z, Quraishi UM. Genome-wide association for grain yield under rainfed conditions in historical wheat cultivars from Pakistan. Front Plant Sci. 2015;6:743.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Sukumaran S, Dreisigacker S, Lopes M, Chavez P, Reynolds MP. Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theor Appl Genet. 2015;128(2):353–63.

    Article  CAS  PubMed  Google Scholar 

  29. Winfield MO, Allen AM, Burridge AJ, Barker GL, Benbow HR, Wilkinson PA, Coghill J, Waterfall C, Davassi A, Scopes G, et al. High-density SNP genotyping array for hexaploid wheat and its secondary and tertiary gene pool. Plant Biotechnol J. 2016;14(5):1195–206.

    Article  CAS  PubMed  Google Scholar 

  30. Li G, Bai G, Carver BF, Elliott NC, Bennett RS, Wu Y, Hunger R, Bonman JM, Xu X. Genome-wide association study reveals genetic architecture of coleoptile length in wheat. Theor Appl Genet. 2017;130(2):391–401.

    Article  CAS  PubMed  Google Scholar 

  31. Sukumaran S, Lopes M, Dreisigacker S, Reynolds M. Genetic analysis of multi-environmental spring wheat trials identifies genomic regions for locus-specific trade-offs for grain weight and grain number. Theor Appl Genet. 2018;131:985–98.

    Article  CAS  PubMed  Google Scholar 

  32. Ogbonnaya FC, Rasheed A, Okechukwu EC, Jighly A, Makdis F, Wuletaw T, Hagras A, Uguru MI, Agbo CU. Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments. Theor Appl Genet. 2017;130(9):1819–35.

    Article  PubMed  Google Scholar 

  33. Sun C, Zhang F, Yan X, Zhang X, Dong Z, Cui D, Chen F. Genome-wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the yellow and Huai Valley of China. Plant Biotechnol J. 2017;15(8):953–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Liller CB, Neuhaus R, von Korff M, Koornneef M, van Esse W. Mutations in barley row type genes have pleiotropic effects on shoot branching. PLoS One. 2015;10(10):e0140246.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Liu Y, Tao Y, Wang Z, Guo Q, Wu F, Yang X, Deng M, Ma J, Chen G, Wei Y, et al. Identification of QTL for flag leaf length in common wheat and their pleiotropic effects. Mol Breed. 2018;38:11.

    Article  CAS  Google Scholar 

  36. Liu K, Sun X, Ning T, Duan X, Wang Q, Liu T, An Y, Guan X, Tian J, Chen J. Genetic dissection of wheat panicle traits using linkage analysis and a genome-wide association study. Theor Appl Genet. 2018;131(5):1073–90.

    Article  CAS  PubMed  Google Scholar 

  37. Shi W, Hao C, Zhang Y, Cheng J, Zhang Z, Liu J, Yi X, Cheng X, Sun D, Xu Y, et al. A combined association mapping and linkage analysis of kernel number per spike in common wheat (Triticum aestivum L.). Front Plant Sci. 2017;8:1412.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Ellis MH, Rebetzke GJ, Azanza F, Richards RA, Spielmeyer W. Molecular mapping of gibberellin-responsive dwarfing genes in bread wheat. Theor Appl Genet. 2005;111(3):423–30.

    Article  CAS  PubMed  Google Scholar 

  39. Choulet F, Alberti A, Theil S, Glover N, Barbe V, Daron J, Pingault L, Sourdille P, Couloux A, Paux E, et al. Structural and functional partitioning of bread wheat chromosome 3B. Science. 2014;345(6194):1249721.

    Article  PubMed  CAS  Google Scholar 

  40. Griffiths J, Simmonds M, Leverington Y, Wang L, Fish L, Sayers L, Alibert S, Orford L, Wingen JS. Meta-QTL analysis of the genetic control of crop height in elite European winter wheat germplasm. Mol Breed. 2012;29:159–71.

    Article  Google Scholar 

  41. Arora S, Singh N, Kaur S, Bains NS, Uauy C, Poland J, Chhuneja P. Genome-wide association study of grain architecture in wild wheat Aegilops tauschii. Front Plant Sci. 2017;8:886.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Cui F, Zhao C, Ding A, Li J, Wang L, Li X, Bao Y, Li J, Wang H. Construction of an integrative linkage map and QTL mapping of grain yield-related traits using three related wheat RIL populations. Theor Appl Genet. 2014;127(3):659–75.

    Article  PubMed  Google Scholar 

  43. Jia H, Wan H, Yang S, Zhang Z, Kong Z, Xue S, Zhang L, Ma Z. Genetic dissection of yield-related traits in a recombinant inbred line population created using a key breeding parent in China’s wheat breeding. Theor Appl Genet. 2013;126(8):2123–39.

    Article  CAS  PubMed  Google Scholar 

  44. Kato K, Miura H, Sawada S. Mapping QTLs controlling grain yield and its components on chromosome 5A of wheat. Theor Appl Genet. 2000;101:114–1121.

    Google Scholar 

  45. Wang Z, Liu Y, Shi H, Mo H, Wu F, Lin Y, Gao S, Wang J, Wei Y, Liu C, et al. Identification and validation of novel low-tiller number QTL in common wheat. Theor Appl Genet. 2016;129(3):603–12.

    Article  CAS  PubMed  Google Scholar 

  46. Borras-Gelonch G, Rebetzke GJ, Richards RA, Romagosa I. Genetic control of duration of pre-anthesis phases in wheat (Triticum aestivum L.) and relationships to leaf appearance, tillering, and dry matter accumulation. J Exp Bot. 2012;63(1):69–89.

    Article  CAS  PubMed  Google Scholar 

  47. Kuraparthy V, Sood S, Dhaliwal HS, Chhuneja P, Gill BS. Identification and mapping of a tiller inhibition gene (tin3) in wheat. Theor Appl Genet. 2007;114(2):285–94.

    Article  CAS  PubMed  Google Scholar 

  48. Spielmeyer W, Richards RA. Comparative mapping of wheat chromosome 1AS which contains the tiller inhibition gene (tin) with rice chromosome 5S. Theor Appl Genet. 2004;109(6):1303–10.

    Article  CAS  PubMed  Google Scholar 

  49. Huang X-Q, Brûlé-Babel A. Sequence diversity, haplotype analysis, association mapping and functional marker development in the waxy and starch synthase IIa genes for grain-yield-related traits in hexaploid wheat (Triticum aestivum L.). Mol Breed. 2012;30(2):627–45.

    Article  CAS  Google Scholar 

  50. Liu Y, Li M, Li J, Li X, Yang X, Tong Y, Zhang A, Li B, Lin J, Kuang T, et al. Dynamic changes in flag leaf angle contribute to high photosynthetic capacity. Chin Sci Bull. 2009;54(17):3045–52.

    Article  Google Scholar 

  51. Du Y, Wang Y, Wang X-H, Liu H, Yang C, Yang J. Plant-type characteristics of super-high-yield wheat in Rice-wheat cropping system. J Triticeae Crops. 2008;28(6):1075–9.

    Google Scholar 

  52. Huang XQ, Coster H, Ganal MW, Roder MS. Advanced backcross QTL analysis for the identification of quantitative trait loci alleles from wild relatives of wheat ( Triticum aestivum L.). Theor Appl Genet. 2003;106(8):1379–89.

    Article  CAS  PubMed  Google Scholar 

  53. Mao S-L, Wei Y-M, Cao W, Lan X-J, Yu M, Chen Z-M, Chen G-Y, Zheng Y-L. Confirmation of the relationship between plant height and Fusarium head blight resistance in wheat (Triticum aestivum L.) by QTL meta-analysis. Euphytica. 2010;174:343–56.

    Article  Google Scholar 

  54. Cheng X, Chai L, Chen Z, Xu L, Zhai H, Zhao A, Peng H, Yao Y, You M, Sun Q, et al. Identification and characterization of a high kernel weight mutant induced by gamma radiation in wheat (Triticum aestivum L.). BMC Genet. 2015;16:127.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Strader LC, Monroe-Augustus M, Bartel B. The IBR5 phosphatase promotes Arabidopsis auxin responses through a novel mechanism distinct from TIR1-mediated repressor degradation. BMC Plant Biol. 2008;8:41.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Ashikari M, Sakakibara H, Lin S, Yamamoto T, Takashi T, Nishimura A, Angeles ER, Qian Q, Kitano H, Matsuoka M. Cytokinin oxidase regulates rice grain production. Science. 2005;309(5735):741–5.

    Article  CAS  PubMed  Google Scholar 

  57. Galuszka P, Frebortova J, Werner T, Yamada M, Strnad M, Schmulling T, Frebort I. Cytokinin oxidase/dehydrogenase genes in barley and wheat: cloning and heterologous expression. Eur J Biochem. 2004;271(20):3990–4002.

    Article  CAS  PubMed  Google Scholar 

  58. Mameaux S, Cockram J, Thiel T, Steuernagel B, Stein N, Taudien S, Jack P, Werner P, Gray JC, Greenland AJ, et al. Molecular, phylogenetic and comparative genomic analysis of the cytokinin oxidase/dehydrogenase gene family in the Poaceae. Plant Biotechnol J. 2012;10(1):67–82.

    Article  CAS  PubMed  Google Scholar 

  59. Werner T, Motyka V, Laucou V, Smets R, Van Onckelen H, Schmulling T. Cytokinin-deficient transgenic Arabidopsis plants show multiple developmental alterations indicating opposite functions of cytokinins in the regulation of shoot and root meristem activity. Plant Cell. 2003;15(11):2532–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Chen S, Gao R, Wang H, Wen M, Xiao J, Bian N, Zhang R, Hu W, Cheng S, Bie T, et al. Characterization of a novel reduced height gene (Rht23) regulating panicle morphology and plant architecture in bread wheat. Euphytica. 2014;203:583–94.

    Article  Google Scholar 

  61. Uauy C, Wulff BBH, Dubcovsky J. Combining traditional mutagenesis with new high-throughput sequencing and genome editing to reveal hidden variation in Polyploid wheat. Annu Rev Genet. 2017;51:435–54.

    Article  CAS  PubMed  Google Scholar 

  62. Flint-Garcia SA, Thornsberry JM, Buckler ES. Structure of linkage disequilibrium in plants. Annu Rev Plant Biol. 2003;54:357–74.

    Article  CAS  PubMed  Google Scholar 

  63. Bradbury P, Zhang Z, Kroon D, Casstevens T, Ramdoss Y, Buckler E. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23:2633–5.

    Article  CAS  PubMed  Google Scholar 

  64. Liu R, Meng J. MapDraw: a microsoft excel macro for drawing genetic linkage maps based on given genetic linkage data. Heraditas. 2003;25:317–21.

    Google Scholar 

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Acknowledgements

The authors thank Dr. Jiankang Wang and Dr. Luyan Zhang (Institute of Crop Sciences, Chinese Academy of Agricultural Sciences) for their valuable suggestions on data analysis. We thank Dr. Guangyao Zhao (Institute of Crop Sciences, Chinese Academy of Agricultural Sciences) for providing the requested genetic information of SNP markers.

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Funding

This work is supported by the National Key Research and Development Program (2016YFD0102100), the National 973 Program (2014CB138101), and the China Agriculture Research System (CARS-03) of the P. R. of China. The funders had no role in the design, data collection, analysis or interpretation, decision to writing or publication of the manuscript.

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Authors and Affiliations

  1. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, National Engineering Laboratory of Crop Molecular Breeding, National Center of Space Mutagenesis for Crop Improvement, Beijing, China

    Huijun Guo, Hongchun Xiong, Yongdun Xie, Linshu Zhao, Jiayu Gu, Shirong Zhao, Yuping Ding & Luxiang Liu

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Contributions

HG and LL designed the experiment and wrote the manuscript. HG, HX, and YX analyzed data. HG, HX, YX, LZ, JG, SZ, and YD performed the experiments, and all authors have read and approved the manuscript.

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Correspondence to Luxiang Liu.

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Supplementary information

Additional file 1:

Figure S1. The principal component analysis with the variation partitioned between the first and the second principal components. The green circles indicate each subpopulation.

Additional file 2:

Table S1. Significantly associated SNPs in each environment or across environments identified by genome wide association analysis.

Additional file 3:

Figure S2. Manhattan plots showing the -log10(p) values from genome-wide association analysis of the investigated traits across environments.

Additional file 4:

Figure S3. The QQ plots from genome-wide association analysis of the investigated traits across environments.

Additional file 5:

Table S2. Plant height of mutant lines carrying mutation alleles of candidate SNPs.

Additional file 6:

Table S3. Thousand grain weight of mutant lines carrying mutant alleles of candidate SNPs.

Additional file 7:

Table S4. Maximum tiller number of mutant lines carrying mutant alleles of candidate SNPs.

Additional file 8:

Table S5. Effective tiller number of mutant lines carrying mutant alleles of candidate SNPs.

Additional file 9:

Table S6. Flag leaf angle of mutant lines carrying mutant alleles of candidate SNPs.

Additional file 10:

Table S7. Spikelet density of mutant lines carrying mutant alleles of candidate SNPs.

Additional file 11:

Table S8. Phenotypic raw data of PH, tiller number, SL, TGW, FLA, NSL, SD and GNS under each environment.

Additional file 12:

Table S9. The alleles of WT and mutant lines with 67,402 SNP markers.

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Guo, H., Xiong, H., Xie, Y. et al. Functional mutation allele mining of plant architecture and yield-related agronomic traits and characterization of their effects in wheat. BMC Genet 20, 102 (2019). https://doi.org/10.1186/s12863-019-0804-2

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BMC Genomic Data

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