Rapid identification and characterization of genetic loci for defective kernel in bread wheat
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Wheat is a momentous crop and feeds billions of people in the world. The improvement of wheat yield is very important to ensure world food security. Normal development of grain is the essential guarantee for wheat yield formation. The genetic study of grain phenotype and identification of key genes for grain filling are of great significance upon dissecting the molecular mechanism of wheat grain morphogenesis and yield potential.
Here we identified a pair of defective kernel (Dek) isogenic lines, BL31 and BL33, with plump and shrunken mature grains, respectively, and constructed a genetic population from the BL31/BL33 cross. Ten chromosomes had higher frequency of polymorphic single nucleotide polymorphism (SNP) markers between BL31 and BL33 using Wheat660K chip. Totally 783 simple sequence repeat (SSR) markers were chosen from the above chromosomes and 15 of these were integrated into two linkage groups using the genetic population. Genetic mapping identified three QTL, QDek.caas-3BS.1, QDek.caas-3BS.2 and QDek.caas-4AL, explaining 14.78–18.17%, 16.61–21.83% and 19.08–28.19% of phenotypic variances, respectively. Additionally, five polymorphic SNPs from Wheat660K were successfully converted into cleaved amplified polymorphic sequence (CAPS) markers and enriched the target regions of the above QTL. Biochemical analyses revealed that BL33 has significantly higher grain sucrose contents at filling stages and lower mature grain starch contents than BL31, indicating that the Dek QTL may be involved in carbohydrate metabolism. As such, the candidate genes for each QTL were predicated according to International Wheat Genome Sequence Consortium (IWGSC) RefSeq v1.0.
Three major QTL for Dek were identified and their causal genes were predicted, laying a foundation to conduct fine mapping and dissect the regulatory mechanism underlying Dek trait in wheat.
KeywordsDefective kernel Grain filling Rapid QTL mapping SNP array Triticum aestivem L
adenosine diphosphate glucose pyrophosphorylase
analysis of variance
cleaved amplified polymorphic sequence
cell wall invertase
days after flowering
grain filling duration
grain filling rate
genome-wide association study
inclusive composite interval mapping
International Wheat Genome Sequence Consortium
kompetitive allele specific polymerase chain reaction
logarithm of odds
phenotype variation explained
quantitative real-time PCR
single nucleotide polymorphism
simple sequence repeat
thousand grain weight
water soluble carbohydrates
Wheat (Triticum aestivum L.) supplies staple food for more than 35% of population worldwide (http://www.wheatinitiative.org/sites/default/files/attached_file/wheat_facts.pdf). The increase of population, decline of cultivated land and frequent occurrence of extreme climate posed large challenges to meet the consumption of wheat. Identification and mining of genetic loci for grain yield is important to improve the yield potential in wheat. Yield is determined by multiple components, such as grain weight and size. Grain plumpness is an important parameter affecting grain weight and morphological quality . Many genetic loci controlling grain weight have been identified in previous studies , whereas genetic analyses on grain plumpness associated with grain filling is rarely reported in wheat. The defective kernel (Dek) mutants in gramineous crops are directly reflected in grain plumpness and usually characterized by shriveled kernel and low grain weight. Thus, identification of genetic loci for Dek is important to improve yield potential and appearance quality. Quite a few Dek mutants were identified in maize . Of them, the genes of dek1, dek35, dek38 and dek39 have been isolated, and all involved in the synthesis and accumulation of starches and/or proteins in the endosperm [4, 5, 6, 7]. Many Dek mutants have been generated in wheat, but their genetic information remains largely unknown. SSR markers are characterized with high polymorphisms, good repeatability, co-dominance, and distribution throughout whole genome, and favored for QTL mapping and molecular breeding in wheat [8, 9, 10]. With rapid advancements in sequencing technology, high-throughput sequencing or chip-based SNP genotyping platforms have been used for QTL mapping [11, 12, 13, 14]. Additionally, the wheat reference genome was assembled and released recently . The availability of high-quality wheat genome data and high-throughput SNP genotyping systems is very important to improve wheat genetics and breeding. We identified a Dek line in a spontaneous variation population and found that its kernels are severely shrunken and sunken. The objective of this study was to rapidly achieve QTL mapping and causal gene prediction for Dek through multiple genotyping systems, wheat reference genome and biochemical analysis.
Statistical analysis of Dek rates for F2 and F2:3 populations across three environments
Mean ± SDa
0.33 ± 0.32
0.24 ± 0.32
0.22 ± 0.29
Analysis of variance of Dek rates for F2:3 lines derived from BL33/BL31
Source of variation
Degrees of freedom
Sum of square
Genotype × Environment
Chromosomal locations of Dek loci and construction of linkage maps
QTL for Dek trait detected in the F2 and F2:3 populations across different environments
Physical interval (Mb)a
Marker enrichment in the target regions of QTL
To further narrow down the genetic intervals of target QTL, the polymorphic SNPs identified from the Wheat660K chips were selected according to the above physical regions defined by QTL preliminary mapping. Totally 60 polymorphic SNPs were used to develop site-specific CAPS markers and five of these, AX-109027972, AX-109516011, AX-110042240, AX-109301653 and AX-108996126, were successfully mapped in the linkage groups (Additional file 4; Additional file 5; Additional file 6). Subsequently, QDek.caas-3BS.2 and QDek.caas-4AL were narrowed to the genetic intervals of 14.01 cM and 13.18 cM, respectively (Table 3; Fig. 2). Accordingly, the physical intervals of QDek.caas-3BS.2 and QDek.caas-4AL are reduced to 1.16 Mb and 1.13 Mb, respectively (Table 3). Although the polymorphic loci AX-108996126 and AX-110042240 did not further diminish the genetic interval of QDek.caas-3BS.1 and QDek.caas-4AL, they saturated the target regions (Fig. 2). Dek trait displays abnormal development of grain and thus it is usually considered to affect grain weight. To verify this case, we analyzed the effect of the resultant Dek QTL on thousand grain weight (TGW). Compared with BL31, the TGW of BL33 significantly decreased 24.12–28.81% (Additional file 7). QTL analyses based on the linkage maps above showed that only one QTL for TGW, designated as QTGW.caas-3BS, was detected between Xcfd79 and AX-109027972, explaining 19.18 to 23.94% of phenotypic variances (Additional file 8). QTGW.caas-3BS overlapped QDek.caas-3BS.2 on the linkage map, suggesting that the Dek QTL QDek.caas-3BS.2 may affect TGW. No significant effect on TGW was detected in the genetic intervals of the other two QTL, QDek.caas-3BS.1 and QDek.caas-4AL.
Predication of candidate genes for the Dek QTL
The cloned dek genes in maize are involved in the grain growth and development, especially the synthesis and storage of starches and/or proteins in the endosperm [4, 5, 6, 7]. Our biochemical analysis also showed that the mature grain starch contents of BL33 were significantly lower than those of BL31 (Additional file 9). In contrast, BL33 had higher sucrose contents and slower sucrose reduction across the filling stages than BL31 (Additional file 10). Accordingly, we assumed that the causal genes for the three QTL are involved in carbohydrate metabolism. In this study, three major QTL was mapped into the genetic intervals of less than 15 cM. The physical region of QDek.caas-3BS.1 is 11.24 Mb, whereas those of QDek.caas-3BS.2 and QDeg.caas-4AL are only 1.16 and 1.13 Mb, respectively, according to IWGSC RefSeq v1.0 (Table 3). Totally 233 genes were present in the physical intervals of the three QTL (Additional file 11). We found nine genes (Gene ID: TraesCS3B01G025200, TraesCS3B01G028100, TraesCS3B01G028200, TraesCS3B01G028300, TraesCS3B01G028500, TraesCS3B01G028700, TraesCS3B01G039100, TraesCS3B01G039800, and TraesCS4A01G446700) involving carbohydrate metabolism or grain development in or near the target regions of the Dek QTLs (Additional file 11). To further identify candidate genes for the Dek QTL, the spatio-temporal expression patterns of the nine genes were analyzed using the WheatExp (https://wheat.pw.usda.gov/WheatExp/). Of these, four genes, TraesCS3B01G025200, TraesCS3B01G028700, TraesCS3B01G039800, and TraesCS4A01G446700, had an observable expression during grain development (Additional file 12) . Based on functional annotation in IWGSC RefSeq v1.0, the four genes encoded fructose-bisphosphate aldolase (Fba), β-fructofuranosidase, abscisic acid-deficient 4 (ABA4), and sucrose synthase (Sus), respectively, tentatively designated as TaFba-3B, TaBff-3B, TaABA4-3B, and TaSus-4A. Their expression patterns were confirmed in the immature grains harvested at three different stages (5, 15, and 25 DAF) using qPCR (Additional file 13). The results showed that all of the above genes expressed in grain and had the lowest expression level at 5 DAF. TaBff-3B and TaSus-4A was mainly detected at 15 DAF, while TaFba-3B and TaABA4-3B had the highest expression activity in 25-DAF grains.
A rapid and cost-effective QTL mapping strategy through multiple genotyping systems in combination with wheat reference genome
Comparisons of the Dek QTL with previously reported Dek-related genetic loci
The comparisons of genetic loci for Dek-related traits can not only improve the understanding of genetic effects of the resultant QTL in the present study but also supply with genetic information for QTL fine mapping. QDek.caas-3BS.1 is linked with Xbarc133 (Table 3; Fig. 2). Two QTL, QGfrmax.nfcri-3B and QGfd.nfcri-3B, controlling grain filling rate (GFR) and grain filling duration (GFD), respectively, were linked to Xgwm533 with a physical distance of 1 Mb from Xbarc133 . QTgw.nfcri-3B for TGW was also linked to Xgwm533 . QDek.caas-3BS.1 and QDek.caas-3BS.2 were identified in adjacent regions and both are linked to Xcfd79 (Table 3; Fig. 2). A QTL for TGW linked with Xcfd79 was detected by Groos et al. . Additionally, QMswscf.acs-3B.1 for water soluble carbohydrates (WSC) of main stem at the early anthesis stage, were linked to Xcfd79 . QDek.caas-4AL is flanked by Xwmc500 and AX-109301653 (Table 3). The physical distance between QDek.caas-4AL and Wx-B1 is 16 Mb approximately. So far, many QTL for the contents of starch, protein and amylose and amylopectin, and grain yield have been reported on chromosome 4AL close to Wx-B1 [40, 41, 42, 43, 44, 45, 46, 47]. QGsc4A, a QTL for grain starch contents, is closely linked to Wx-B1 . Qftsc4A for total grain starch content and Qfams4A for grain amylose content were also linked to Wx-B1 . To identify the genetic relationship between QDek.caas-4AL and Wx-B1, the gene-specific functional marker developed by Nakamura et al.  was used to genotype the parents, BL31 and BL33. The result showed that no polymorphism was detected between them (Additional file 14), suggesting that Wx-B1 had little effect on Dek trait.
Candidate genes analysis
Candidate genes of Dek QTLs
Abscisic acid-deficient 4
In the study, we detected three DEK QTLs, QDek.caas-3BS.1, QDek.caas-3BS.2 and QDek.caas-4AL, each of which could explain more than 10% phenotypic variation. Additionally, their candidate genes, TaFba-3B, TaBff-3B, TaABA4-3B, and TaSus-4A, were predicated according to biochemical analysis and IWGSC RefSeq v1.0 annotation. These findings provide important information for QTL fine mapping and molecular mechanistic dissection underlying Dek in wheat.
A number of Dek lines were generated in Wheat Research Institute, Gansu Academy of Agricultural Science (GAAS). Through multi-generation self-crossing, we identified a pair of isogenic lines, BL31 and BL33, with plump and shrunken mature grains, respectively. Genetic populations comprising 146 F2 plants and their corresponding F2:3 lines from the cross between BL31 and BL33 were created for QTL mapping.
Field trials and phenotype evaluation
The F2 population and parents were planted at Gaoyi (37°33′N, 114°26′E) in Hebei province during the 2016–2017 cropping season. The F2:3 lines were sown at Xinxiang (34°53′N, 113°23′E) in Henan province and Gaoyi during the 2017–2018 cropping season. Field trials were arranged in randomized complete blocks with two replicates at each location. Each plot comprised a single 2 m row with 20 plants and spaced 30 cm apart. Grains were harvested in individual plants or lines. Grain phenotype of F2 plants was scored using the percentages of defective kernels (defined as Dek rate) in single plants. For F2:3 lines, ten representative plants in each line were selected from the middle part of each plot to figure out Dek rate, then the average Dek rate of each plot was used for subsequent statistical and genetic analysis.
Preliminary mapping of QTL on target chromosomes using SNP chip
High-quality genomic DNA was extracted from fresh leaves following Guillemaut and Laurence . The Wheat660K SNP array containing 630,517 SNPs, a high-throughput genotyping tool developed by Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (https://wheat.pw.usda.gov/ggpages/topics/Wheat660_SNP_array_developed_by_CAAS.pdf), was used to test two isogenic lines BL31 and BL33 at CapitalBio Corporation (Beijing, China; http://www.capitalbio.com/). The frequency distribution of polymorphic SNPs in each chromosome was analyzed using Microsoft Excel 2016 (https://www.microsoft.com/zh-cn/). The target chromosomes with potential QTL were determined based on the frequency distribution of polymorphic SNPs in each chromosome.
Genotyping by SSR markers
Polymerase chain reaction (PCR) for SSR markers was performed in a reaction mixture of 15 μl containing 7.5 μl of 2 × Taq PCR Mix (Beijing HT-biotech Co., Ltd., Beijing; http://www.ht-biotech.net/), 5 pmol of each primer and 50–100 ng of genomic DNA using the ABI Applied Biosystems Veriti 96 Well Thermal Cycler (Gene Co., Ltd., Shanghai; http://www.genecompany.com/). PCR amplification was performed at 95 °C for 5 min, followed by 35 cycles (94 °C for 30s, 55–60 °C for 30s and 72 °C for 30s) and a final extension at 72 °C for 7 min. The PCR products were separated in 8% polyacrylamide gels at 250 V using the JY 600HE Electrophoretic Apparatus (Beijing Junyi Electrophoresis Co., Ltd., Beijing; http://www.bjjunyi.com/) or 2% agarose gels at 220 V using the DYY-7C Electrophoresis Apparatus (Beijing Liuyi Biotechnology Co., Ltd.; http://www.ly.com.cn/).
Converting chip-based polymorphic SNPs into CAPS markers
To enrich the target genetic region, polymorphic SNPs between BL31 and BL33, are needed to convert into flexible markers, such as kompetitive allele specific PCR (KASP) and CAPS markers. CAPS technology is an efficient tool to genotype SNPs , combining PCR with restriction enzymes digestion to differentiate the target alleles with polymorphic SNPs. The polymorphic SNPs between parents in the target region were identified by alignments with DNAMAN software (https://www.lynnon.com/index.html) for designing chromosome-specific CAPS markers . The primers and restriction enzymes used for CAPS markers were listed in Additional file 4.
The analysis of variance (ANOVA) was conducted using the SAS 9.2 software (http://www.sas.com) with PROC MIXED procedure. Broad-sense heritability (hB2) was calculated using the following formula : hB2 = σ2g / (σ2g + σ2ge / r + σ2ε / re).
in which, σ2g, σ2ge and σ2ε were estimates of genotypes, genotype × environment interaction and residual error variances, respectively; r and e were replicates per environment and the number of environments, respectively. The correlation coefficients of traits among three different environments were computed using the PROC CORR procedure with the SAS 9.2 software.
Linkage map construction and QTL analysis
Joinmap 4.0 software (http://www.kyazma.nl) was used for linkage map construction  and genetic distances between markers were estimated based on the Kosambi mapping function . QTL analysis was conducted by inclusive composite interval mapping (ICIM) based on the Dek rate using IciMapping 4.0 software (http://www.isbreeding.net) [65, 66]. The logarithm of odds (LOD) threshold score of 2.5 was determined for declaring significant QTL based on 1000 permutations with a type I error of P < 0.05. The phenotypic variance explained (PVE) were calculated through stepwise regression to estimate individual QTL effects.
Assays for grain starch and sucrose contents
Six mature grains were grinded to fine powder in a mortar. The resultant powder (0.1 g) was used to measure starch contents with Starch Assay Kit (Beijing Solarbio Science & Technology Co., Ltd., Beijing, http://www.solarbio.net/). Then, the following formula was used to convert mg/g to mg/grain for scoring the grain starch content: Grain starch content (mg/grain) = starch contents (mg/g) * TGW / 1000.
TGW data was shown in Additional file 7. Six grains of two parents were collected at 5, 10, 15, 20, 25, and 30 days after flowering (DAF) to validate the dynamic trend of grain sucrose contents during the filling stage. The dried grain were grinded to fine powder in mortar, and 0.1 g powder was used to measure grain sucrose contents using Plant Sucrose Assay kit (Beijing Solarbio Science & Technology Co., Ltd., Beijing, http://www.solarbio.net/).
Quantitative real-time PCR
Total RNA was extracted from fresh grain 5, 15, and 25 days after flowering (DAF) using RNAprep pure Plant Kit (Tiangen Biotech Co., Ltd., http://www.tiangen.com/). Reverse transcription was performed using the reverse transcriptase M-MLV (RNase H−) (Takara Biomedical Technology Co., Ltd., http://www.takara.com). The iTaq™ Universal SYBR Green Supermix on CFX Real-Time System (Bio-Rad) was used to perform quantitative real-time PCR (qPCR). The profile of qPCR was as follows: 95 °C for 2 min, followed by 40 cycles of 95 °C for 5 s and 58 °C for 20 s. A final dissociation stage was run to generate a melting curve for judgement of amplification specificity. Quantification of qPCR was determined using the 2-ΔΔCt method . Elongation factor 1 α (EF1α) gene was used as the internal control to calibrate the expression level of genes of interest. An additional file shows the primers used in qPCR (see Additional file 15).
The authors are grateful to Prof. R. A. McIntosh, at Plant Breeding Institute, University of Sydney, for critical review of this manuscript.
FC, TXL, FLP, XDA, WY and XXT performed the experiments and data analysis. DJY created and provided DEK lines for the research. CSH and ZY designed the experiments. FC and CSH wrote the draft. XXC and HZH revised the manuscript. All authors read and approved the final manuscript.
This work was funded by the National Key Research and Development Programs of China (2016YFD0100502), National Natural Science Foundation of China (31571663) and CAAS Science and Technology Innovation Program. The planting and phenotyping of materials were funded by National Natural Science Foundation of China (31571663) and CAAS Science and Technology Innovation Program, whereas the genotyping, data analysis and manuscript writing were funded by National Key Research and Development Programs of China (2016YFD0100502).
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Consent for publication
The authors declare that they have no competing interests.
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