Identification of quantitative trait loci and candidate genes underlying kernel traits of wheat (Triticum aestivum L.) in response to drought stress

doi:10.21203/rs.3.rs-4494648/v1

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Identification of quantitative trait loci and candidate genes underlying kernel traits of wheat (Triticum aestivum L.) in response to drought stress

https://doi.org/10.21203/rs.3.rs-4494648/v1

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Kernel traits are the most important yield components in wheat. The use of genetic loci and candidate genes that respond to drought stress without yield loss improves the productivity of wheat in arid regions. In this study, the 90K iSelect Infinium SNP assay for wheat was used to generate a high-density genetic map and identify quantitative trait loci (QTL) for kernel traits in a set of recombinant inbred lines (RILs). A total of 85 additive QTLs, including 16 for thousand-kernel weight (TKW), 14 for kernel length (KL), 16 for kernel width (KW), 11 for kernel thickness (KT), 18 for kernel size (KS), and 10 for kernel diameter ratio (KDR), were detected under drought stressed and well-watered conditions. Among them, 12 QTLs were identified as environmentally stable QTLs and refined into 10 QTL clusters, in which a total of 3738 candidate genes were extracted from the confidence interval of these QTL clusters. We discovered a QTL cluster interval (C3) on chromosome 5A, in which we found a potential candidate gene TraesCS5A02G288000 (TaCYP71E1-5A) for KS and KL and was annotated as “cytochrome P450”. The KASP marker for TaCYP71E1-5A was developed and further validated in 220 wheat varieties. These results provide a useful insight into the genetic factors underlying the kernel traits response to drought stress and will thus facilitate the improvement of wheat productivity under drought stress conditions in breeding programs.

wheat

kernel traits

drought stress

QTL mapping

candidate gene

A total of 10 QTL clusters on eight chromosomes with pleiotropic effects on kernel traits were detected under drought stress and well-watered conditions across six environments. A kompetitive allele-specific PCR (KASP) marker for TaCYP71E1-5A was developed and verified in 220 wheat varieties.

Bread wheat (Triticum aestivum L.) is a widespread crop grown under a wide range of environmental conditions in terms of climatic factors, water regimes and soil types especially in the regions of the world most affected by drought (Langridge and Reynolds 2021). As current changes in global climate have increased the variability of precipitation (Trenberth 2011), wheat production in semi-arid and arid regions is increasingly constrained and exhibits high variability due to highly erratic drought events (Lesk et al. 2016; Langridge and Reynolds 2021). In this case, improving the drought tolerance and productivity of wheat is considered the best strategy to achieve an annual yield increase of 2.4% to meet the food needs of the ever-growing population, extreme climate changes and the reduction of arable land in the future (Ray et al. 2013; Mohammadi 2018).

Wheat yield is a complex trait composed of three main components: thousand-kernel weight (TKW), number of kernels per ear (KN) and number of spikes per unit area (SN) (Brinton and Uauy 2019). TKW is mainly affected by kernel size (KS) traits including kernel length (KL), kernel width (KW), kernel thickness (KT) and kernel diameter ratio (KDR) (Guan et al. 2020; Li et al. 2022; Ji et al. 2023). These traits are closely correlated (Dholakia et al. 2003; Su et al. 2018; Chen et al. 2020a) and are relatively more strongly and stably inherited than KN, SN and general yield traits (Kuchel et al. 2007; Su et al. 2018; Chen et al. 2020a). For these reasons, increasing TKW and KS are major objectives in wheat breeding programs, while larger grains with higher TKW can improve seedling vigor and thus plant establishment, leading to higher yields (Botwright et al. 2002; Qin et al. 2015). Despite all this, both TKW and KS are significantly influenced by drought stress, which can disrupt the source-sink balance, shorten grain filling duration and lead to smaller and shrunken seeds (Zahra et al. 2021). Drought stress also frequently alters the expression of genes involved in wheat grain development due to complex polygenic inheritance and large genotype-environment (G×E) interactions (Zahra et al. 2021; Rabieyan et al. 2023). Therefore, elucidating the genetic basis for controlling grain yield components through identifying, validating and incorporating loci crucial for kernel weight and size is a promising approach to facilitate genetic improvement of high-yielding varieties under drought stress conditions.

High-density genetic linkage maps play an important role in genetic and genomic studies, including QTL mapping, map-based cloning and marker-assisted selection (MAS) (Brinton and Uauy 2019). With the advent of advanced wheat genome sequencing and high-throughput genotyping technologies, genotyping tests for single nucleotide polymorphisms (SNP), including 35K (Allen et al. 2017), 55K, 660k and 820K (Winfield et al. 2016) and 90K SNP array (Wang et al. 2014) have led to the generation of saturated genetic maps and the identification of a large number of QTL associated with kernel traits (Su et al. 2016; Hassan et al. 2018; Xu et al. 2019; Cao et al. 2020a; Yang et al. 2020; Onishi et al. 2021; Zhao et al. 2021; Song et al. 2022; Ji et al. 2023), and many stable QTLs (Cao et al. 2020b) on all wheat chromosomes. Up to date, the QTL (Qtgw-jic.6A), discovered and validated using near-isogenic lines, was considered a robust QTL that significantly increased TKW by 5.1% (Simmonds et al. 2014). However, only a few QTLs for kernel traits have been validated (Cao et al. 2020b), and only a few related genes such as TaCOL (Zhang et al. 2022) and TaACTIN7-D (Li et al. 2023) have been verified by map-based cloning (Brinton and Uauy 2019). In addition, many genes related to wheat kernel weight and size have been cloned and identified by homology-based cloning (Cao et al. 2020b), including TaSus2 (Jiang et al. 2011), TaGW2 (Su et al. 2011; Hong et al. 2014; Jaiswal et al. 2015), TaCwi-A1 (Ma et al. 2012), TaCKX6-D1 (Zhang et al. 2012), TaSAP1-A1 (Chang et al. 2013), TaGASR7-A1 (Dong et al. 2014), TaTGW6 (Hanif et al. 2015; Hu et al. 2016), TaGS5-3A (Ma et al. 2016), TaGW8 (Yan et al. 2019) and TaGL3-5A (Yang et al. 2019).

Marker-assisted selection (MAS) has been deployed widely in crop breeding programs to accelerate and enhance cultivar development, via selection during the juvenile phase and parental selection prior to crossing (Mercati and Sunseri 2020). The 90K SNP array for wheat (Wang et al. 2014) can be effectively converted into the Kompetitive Allele-Specific PCR Marker (KASP), a flexible, breeder-friendly marker platform for SNP genotyping of medium- to high-throughput breeding material (Liu et al. 2023). KASP markers have been successfully developed for many genes and QTLs in wheat, e.g. for yield (Wang et al. 2018; Irshad et al. 2021; Xiong et al. 2021; Zhang et al. 2021) and abiotic stress (Liu et al. 2023), which greatly facilitates the identification and pyramiding of these genes/QTLs in new wheat varieties (Kaur et al. 2020). However, few of KASP markers for grain traits under drought stress have been validated in modern cultivars. Moreover, the functions of most known genes involved in the regulation of kernel weight and size are categorized into different groups, including transcription factors, G-protein, ubiquitin-proteasome degradation, mitogen-activated protein kinase (MAPK), and hormone metabolism and signaling pathways (Orozco-Arroyo et al. 2015; Nadolska-Orczyk et al. 2017). Despite this knowledge, the identification of the molecular basis of QTL/genes in the regulation of kernel weight is still largely unknown and remains a long-term task.

In the present study, a population of recombinant inbred lines (RILs) of bread wheat was used to identify QTLs for kernel weight and size- related traits. The objectives were: (1) to identify QTLs for kernel weight and size under drought stress (DS) and well-watered (WW) conditions, (2) to evaluate the interaction effects between genetics and environment, (3) to identify candidate genes for kernel weight and size, and (4) to identify the favorable haplotypes.

Plant material

A population of recombinant inbred lines (RILs) with 160 lines was developed by crossing two winter wheat cultivars, GW011 and Jinmai47. GW011 is an elite winter wheat with relatively low kernel weight and size that is sensitive to water, while Jinmai47 was used as a control and is widely grown in Northern China with excellent drought tolerance (Song et al. 2017) and a larger kernel weight and size than GW011. The RILs was further developed into the F₈ generation using the single seed descent method.

Field trials and trait evaluation

Field experiments were conducted under drought-stressed (DS) and well-watered (WW) conditions at Yuzhong Farm Station, Gansu, China (104°07′E, 35°51′N, 1900m ASL) during wheat growing seasons in 2014–2015 (E1), 2015–2016 (E2), 2016–2017 (E3) and 2017–2018 (E4), and Tongwei Farm Station, Gansu, China (105°19′E, 35°11′N, 1750m ASL) in 2019–2020 (E5) and 2020–2021 (E6). The DS condition corresponded to rainfed cropping in each growing season, while the WW condition was irrigated with a water supply of 75 m³ at the spike emergence (Zadoks 55) and grain filling (Zadoks 71) stages (Zadoks 1974). The two growing sites are characterized by typical dry inland environmental conditions in Northwest China, where the annual average temperature is about 7.0 ℃, the annual precipitation during wheat growth is less than 300 mm, and the annual evapotranspiration capacity is more than 1500 mm (Ma et al. 2022; Miao et al. 2022) (Figure S1). All progenies and parents were sown in late September and harvested in early July of the following year. A randomized complete block design (RCBD) experiment was conducted with three replicates for each line and parent. Each plot consisted of six 1 m long rows with a spacing of 0.2 m and 30 seeds per row. Field management followed local wheat cultivation practices. In addition, a total of 220 wheat varieties were planted in Tongwei Farm Station in 2021–2022 (E7) and 2022–2023 (E8), Zhuanglang Farm Station, Gansu, China (35°34′N, 105°96′E, 2131m ASL) in 2022–2023 (E9) and used to identify significant SNP.

After harvest, two hundred seeds for each line and parents were randomly selected and used to evaluate TKW, KL, KW, KS and KDR traits using the SC-G image analysis system for wheat grain quality (Hangzhou WSeen Detection Technology Co., Ltd., Hangzhou, China). KT was measured using a caliper with three biological replicates.

Statistical analysis

The best linear unbiased prediction (BLUP) was calculated with the function “lmer” in R package, with both genotype and environment as random factors and treated as additional environment. The mean phenotypic values across three replicates in each environment were used for statistical analysis (P < 0.05) using the statistical package SPSS version 22.0 (California State University Information Services, Los Angeles, CA, USA). Analysis of variance (ANOVA) based on a combined linear mixed model was used to estimate the total and residual variances between the RILs and the parents for the kernel weight and size under different water conditions. Basic statistics and Pearson correlation analyses were performed with the mean values and the BLUP dataset under DS and WW conditions. Broad-sense heritability (h²_B) was estimated using the formula described by Toker (2004) for all environments:

\({h}_{B}^{2}={\sigma }_{g}^{2}/\left({\sigma }_{g}^{2}+{\sigma }_{ge}^{2}/r+{\sigma }_{e}^{2}/re\right)\) here, \({\sigma }_{g}^{2}\), \({\sigma }_{ge}^{2}\) and \({\sigma }_{e}^{2}\) represent genotype, genotype by environment interaction and residual error variances, respectively, and e and r are the numbers of environments and replicates per environment, respectively.

Genotyping

High-quality genomic DNA of parents and RILs was extracted using cetyltrimethylammonium bromide (CTAB) method (Schenk et al. 2023) and verified by agarose gel electrophoresis. The parents and RILs were genotyped using the 90K SNP Illumina iSelect array to detect polymorphic SNP sites between Jinmai47 and GW011 and the RIL population. SNP clustering and genotype calling were conducted using GenomeStudio 2.0 software (Illumina) (Wang et al. 2014). The SNP was retained if it met the following criteria: (i) polymorphism between two parents; (ii) missing rate less than 5% (the heterozygous SNP was also considered as a missing); and (iii) unique physical map against the reference genome of IWGSC RefSeq v1.0.

Linkage analysis and genetic map

IciMapping V4.2 (Meng et al. 2015) was used for genetic map construction. The “BIN” function was used to remove redundant markers based on their segregation patterns in the mapping population, with the “Missing Rates” and “Distortion Value” parameters set to 20% and 0.01, respectively. The linkage map was created using the “MAP” function, and the ordered groups algorithm was “nnTwoOpt”. Kosambi mapping function was applied to calculate the map distances obtained from the recombination frequencies. The criterion and window size for rippling were “SARF” and “5”, respectively.

QTL mapping

QTL analysis was performed using the Inclusive Composite Interval Mapping (ICIM) method with the Biparental Population (BIP) and Multiple Environments (MET) modules in QTL IciMapping V4.2 for determination of the positions and effects of QTLs (Meng et al. 2015). The walking step for all QTL was 1.0 cM, and the P-value inclusion threshold was 0.001 in which QTLs with LOD values determined by 1000 permutation tests were accounted for the presence of QTL. The genetic model included significant main effects of QTL (a) as well as QTL × water condition (QEI) effects (ae). QTL were named based on the International Rules for Genetic Nomenclature (http://wheat.pw.usda.gov/ggpages/wgc/98/intro.htm). QTLs detected in more than five environments were considered as stable QTLs. QTLs for a trait identified with common flanking markers or overlapping confidence intervals were treated as one QTL. The chromosomal regions harboring co-localized QTLs for different traits were considered as QTL clusters, and the flanking markers of the co-localized QTL with the longest genetic distances were considered as the genetic distance of the QTL cluster.

Prediction of candidate genes

According to the mapping results, the physical position of the QTL flanking markers was determined by alignment (E-value of 1e-10) to the Chinese spring reference genome (IWGSC RefSeq v1.0). The genes in the high confidence interval of physical positions were obtained from WheatOmics 1.0 (http://202.194.139.32/). The functional annotations of the predicted genes were determined based on UniProt (http://www.uniprot.org/). Gene expression data in different tissues and development stages were extracted from expVIP (http://www.wheat-expression.com/) (Borrill et al. 2016). The expression patterns in different tissues were normalized using the ZerotToOne method and then plotted in the HeatMap of TBtools (Chen et al. 2020b). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify genes within the high confidence interval of QTL clusters and their biological pathway. Gene annotations against the GO database (http://geneontology.org/) were performed using the Blast2Go program. The gene that included in grain development pathways or highly expressed in kernel were selected for SNP analysis.

KASP marker development and Haplotype analysis of CGs promoter

The SNP variations in promoter (± 2000 bp) and exon region of candidate genes were obtained from SnpHub website (http://wheat.cau.edu.cn/WheatUnion/) and the significant SNP site were identified with a published kernel phenotypic data delivered by Ma et al. (2016b). After that, the KASP marker were developed for significant SNPs and verified with 220 wheat varieties. The KASP marker designed for significant SNP and the verification implemented according to the following steps: Genomic sequences with a length of 200bp on the 5' and 3' strand surrounding the significant SNP were extracted from the wheat reference genome v1.0 for developing allele-specific KASP markers in PolyMarker (Ramirez-Gonzalez et al. 2015). The markers were then used to screen the 220 wheat varieties to validate the polymorphism. The KASP assays were performed in OMEGA F Reader in 384-well compatible plate format according to the protocol of LGC Genomics. Reaction mixes consisted of final volumes of 5 µL containing 2 µL of genomic DNA (50–100 ng), 2 µL of 2X KASP master mix (V4.0, LGC Genomics), 1 µL of primer mix (12 µM of each allele-specific primer and 30 µM of the common primer). An OMEGA F Reader 384-well compatible plate (LGC, Middlesex, UK) was used with the following cycling conditions: denaturation at 94℃ for 10 minutes, 10 cycles of 94 ℃ for 20 seconds, cooling from 61 ℃ for 45 seconds (decreasing by 0.6 ℃ per cycle), followed by 30–40 cycles of amplification (94℃ for 20 seconds; 55℃ for 45 seconds). Endpoint fluorescence data were analyzed using LGC KlusterCaller software (LGC, Middlesex, UK). A total of 548 Chinese wheat landraces and cultivars, obtained from SnpHub website (333) and nature population (215), were used to identify geography distribution in China.

Construction of a high-density genetic map

Genotyping of RILs and parental lines with 81,423 SNPs revealed 23,226 polymorphic markers (28.5%) of which 10,294 markers were distributed over 6,428 bins spanning 3635.29 cM on wheat 21 chromosomes. The average interval distance was 0.56 cM/bin and 0.35 cM/marker (Figure S2), with bin marker of 3442 (33.43%) on sub-genome A, 4151 (40.32%) on sub-genome B, and 2701 (26.24%) on sub-genome D and a genetic length of 1170.61 cM, 1295.16 cM, and 1169.52 cM, respectively (Table S1). The mapped bin markers on each chromosome ranged from 223 on 4D to 1,118 on 5B. Chromosome 4D (113.77 cM) had the shortest genetic length, while chromosome 7A (232.58 cM) had the longest genetic length. The average genetic distance between neighboring bins ranged from 0.50 cM for 4A to 0.64 cM for 4B. The average distances between markers ranged from 0.18 cM/marker (5B) to 0.51 cM/marker (4D) (Table S1). The genetic positions of the bins and markers were in a good alignment with that in the physical maps, were reported in wheat 90K array and the IWGSC RefSeq v1.0 (Table S2 and Figure S3).

Phenotypic evaluations

The mean values of six kernel traits in six environments and BLUP values were used to evaluate the phenotypic variations of the RILs and parents in response to DS and WW conditions. A large phenotypic variation and transgressive segregation was observed in this population (Fig. 1). All six kernel traits (TKW, KL, KW, KT, KS and KDR) assessed at Jinmai47 were significantly higher than GW011 in six environments and under two water conditions (Table S3) and the mean values of the RILs were intermediate between those of the parents (Table S3). The coefficients of variation (CV) ranged from 1.93–10.81%, and most absolute values of skewness and kurtosis were less than 1.0, suggesting that kernel traits exhibit continuous variation in the RILs population (Table S3). The value of the traits for RILs and the parents were significantly higher under WW conditions than under DS water conditions (Table S3). The F-tests showed that there were highly significant differences between the RILs and the parents under DS and WW conditions in the different environments, while no significant differences were observed under the same water conditions for the kernel traits (Table S4). Analysis of variance (ANOVA) showed highly significant phenotypic variances between genotypes (G), environments (E) and G×E for kernel traits evaluated in the RILs under DS and WW conditions (P < 0.01) (Table S5). The h²_B for most traits (except TKW and KDR) was slightly higher under DS than under WW, varying from 0.61 to 0.74 in DS and from 0.56 to 0.80 in WW (Table S5). The KT showed the lowest h²_B under WW (0.56) water conditions.

Phenotypic correlations

The phenotypic correlation coefficients (r) between the kernel traits evaluated in the RILs under DS and WW conditions are shown in Fig. 2. Most of the traits were positively correlated under the two water conditions, and some correlations were highly significant. Under the DS condition, TKW was significantly correlated with KL, KW, KT and KS (0.47–0.69, P < 0.01) while KL was significantly correlated with KW, KS and KDR (0.54–0.68, P < 0.01). KW also significantly correlated with KT and had the highest correlation with KS (0.48–0.91, P < 0.01). A significant positive correlation was identified between KT and KS (0.62, P < 0.01), while a significant negative correlation was observed between KT and KDR (-0.38, P < 0.01). At the same time, the correlation coefficients of the individual traits were higher under DS conditions (-0.38-0.91) than under WW conditions (-0.24-0.84).

Identification of QTLs for kernel traits

The phenotypic data of six kernel traits were collected in environments E1-E6 under DS and WW conditions, and the BLUP dataset was used for QTL mapping with ICIM method in IciMapping v4.2. The BLUP dataset was regarded as an additional environment. A total of 85 additive (a) QTLs were detected for TKW, KL, KW, KT, KS, and KDR and mapped on chromosomes 1A, 1B, 2B, 3B, 4B, 5A, 5B, 5D, 6A, 6D, 7A, and 7D, respectively (Fig. 3). Among them, 14 QTLs were identified as stable QTLs, which were detected in over four environments and with an individual PVE ranging from 1.73–12.01% (Table S6). Most stable QTLs had positive additive (a) value and negative QTL × water condition (ae) effect, suggesting that the Jinmai47 allele has an increasing effect on the traits and drought stress has a negative effect on the corresponding traits (Table S6).

QTL for QEI

Under drought stress, 68 QTLs were detected and had interactions with water conditions, explained 1.71–14.01% of the phenotypic variances for the kernel traits (Table S6). There are 55 QTLs identified with negative ae values with decreasing effects, while 13 QTLs showed positive ae values with increasing effects on the traits. Interestingly, all stable QTLs were detected with a negative ae effect on the kernel traits evaluated under DS conditions (Table S6).

QTL for TKW

We detected a total of 11 QTLs and five stable QTLs for TKW on chromosomes 1A, 1B, 3B, 5A, 5B, 5D, 6A, 6D, 7A and 7D (Fig. 3). These QTLs individually explained 1.39–12.01% of the phenotypic variations. The stable QTL Qtkw.acs-1A.1 explained 3.49–12.01% of the phenotypic variations, with LOD values ranging from 2.9 to 17.32. The GW011 alleles of the stable QTLs Qtkw.acs-1A.1 and Qtkw.acs-1B.1 reduced TKW. The stable QTLs Qtkw.acs-5B.1, Qtkw.acs-5D.2 and Qtkw.acs-7D had a positive effect on TKW due to favorable alleles of Jinmai47 (Table S6).

QTL for KL

A total of 11 QTLs and three stable QTLs were identified for KL accounting for 2.47–10.96% of the PVE on chromosomes 1A, 1B, 2B, 4B, 5A, 5B, 5D, 6D, 7A and 7D. Negative effects on KL by the GW011 allele were observed for the stable QTL Qkl.acs-1B, while the stable QTL Qkl.acs-6D.2 and Qkl.acs-7A resulted in an increase effect in KL by the Jinmai47 allele.

QTL for KW

For KW, 16 QTLs were detected on chromosomes 1A, 1B, 2B, 3B, 5A, 5B, 5D, 6A, 7A and 7D, with PVE ranging from 0.51–17.33%. Among them, stable QTLs Qkw.acs-5B.1 and Qkw.acs-7D were identified on chromosomes 5B and 7D, showing a positive effect of the Jinmai47 alleles and strongly affected by the DS condition.

QTL for KT

A total of 11 QTLs for KT were detected on chromosomes 1B, 2B, 3B, 4B, 5A, 5B, 5D, 6A, 6D and 7D (Fig. 3), with PVE ranging from 2.02–10.81% (Table S6). Of these, one stable QTL, Qkt.acs-5A, was detected on chromosome 5A, with a PVE between 5.3% and 10.81%. Stable QTL, Qkt.acs-5A had a positive effect conferred by favorable alleles of Jinmai47 (Table S6).

QTL for KS

We detected a total of 18 QTLs for KS on chromosomes 1A, 1B, 2B, 3B, 4B, 5A, 5B, 5D, 6A, 6A, 6D, 7A and 7D (Fig. 3), with PVE ranging from 2.21–10.75%. Of these, stable QTLs Qks.acs-2B, Qks.acs-5A and Qks.acs-7A were detected on chromosomes 2B, 5A and 7A, respectively (Table S6). Two stable QTL Qks.acs-5A and Qks.acs-7A had positive effect conferred by favorable alleles of Jinmai47, while stable QTL Qks.acs-2B had negative effects of GW011 (Table S6). All stable QTLs for this trait were strongly affected by DS condition (Table S6).

QTL for KDR

For KDR, 10 QTLs were detected on chromosomes 1B, 2B, 3B, 5A, 5D, 6A, 6D, 7A and 7D (Fig. 3), with PVE ranging from 1.18–12.67% and no stable QTL detected for KDR. Most QTLs for KDR with a negative effect were from GW011 (Table S6). For KDR, four out of nine QTLs with a negative ae effect on were observed under DS conditions (Table S6).

QTL clusters

We detected 10 QTL clusters for kernel traits which were distributed on chromosomes 1B, 2B, 5A, 5B, 5D, 6D, 7A and 7D (Table 1). They harbored 34 QTLs for all kernel traits and exhibited pleiotropic effects (Table 1). All 10 QTL clusters also harbored stable QTLs for TKW, KL, KW and KS. These QTL clusters had genetic distances ranging from 0.64 (C10) to 3.70 (C6) cM and physical distances ranging from 2.83 (C1) to 29.68 (C6) Mb. Of these, three clusters (C3, C4 and C7) were positively affected by the superior allele of Jinmai47, while only C1 was negatively affected by the allele of GW011. In addition, the DS condition contributed to a negative effect on six clusters (C1, C2, C3, C4, C5 and C7). Interestingly, the QTLs for KDR in clusters 6, 8 and 10 and the QTLs for KL and KS in clusters 9 and 10, respectively, were identified to have a positive effect under DS condition. The identified QTL clusters used as target regions for candidate gene screening and marker development.

Table 1

Stable QTLs and QTL clusters for kernel traits located in the physical intervals of less than 30Mb identified in the RILs population.
Cluster	Chrom.	Flanking markers	Genetic position		Physical position		Trait (number of environments)*	Number of QTLs
Cluster	Chrom.	Flanking markers	Interval (cM)	Distance (cM)	Interval (bp)	Distance (Mb)	Trait (number of environments)*	Number of QTLs
C1	1B	JD_c954_2440 ~ BS00094237_51	8.48–9.36	0.88	638015155–640850078	2.83	TKW(4, BLUP), KL(4, BLUP), KDR(2, BLUP), KW(1)	4
C2	2B	Ku_c2936_1987 ~ Excalibur_c59657_405	53.58–55.77	2.19	782154304–797956550	15.80	KS(4, BLUP), KW(1), KT(1), KDR(1)	4
C3	5A	IAAV52 ~ GENE-3211_99	17.96–19.07	1.11	484432128–495883712	11.45	KS(4, BLUP), KL(1, BLUP)	2
C4	5B	RAC875_c13164_695 ~ IAAV7226	57.17–58.10	0.94	526589846–542953054	16.36	TKW(5, BLUP), KW(4, BLUP), KL(2)	3
C5	5D	Excalibur_c4699_215 ~ IACX6116	175.95-176.64	0.69	521398999–537675200	16.28	TKW(5, BLUP), KDR(2, BLUP), KS(2), KL(1), KW(1),	5
C6	6D	Kukri_c23046_253 ~ TA001847-0566	121.21-124.91	3.70	24338456–54015106	29.68	KL(5, BLUP), TKW(3), KDR(3)	3
C7	7A	wsnp_Ex_c14219_22169892 ~ Excalibur_c57869_162	11.47–12.34	0.87	15090040–20774498	5.68	KL(4, BLUP), TKW(1)	2
C8	7A	TA001117-0895 ~ wsnp_Ex_c35_77935	161.52-163.68	2.16	33364972–41960766	8.60	KS(4, BLUP), KDR(3, BLUP), KW(1)	3
C9	7D	Excalibur_rep_c101576_466 ~ Kukri_c9068_918	69.86–71.50	1.65	604982550–621264083	16.28	TKW(5, BLUP), KL(1), KS(1)	3
C10	7D	Tdurum_contig59440_711 ~ CAP11_c846_221	119.47-120.11	0.64	486057904–496154171	10.10	KW(5, BLUP), KS(2), KDR(1, BLUP), KL(1), KT(1)	5
* Trait names in bold indicate that stable QTLs for these traits were detected in more than four environments and in the BLUP dataset.

Candidate genes for QTLs controlling kernel traits

The sequences of the flanking markers of each QTL cluster were anchored to their physical location in the Chinese Spring Reference Genome v1.0 (IWGSC RefSeq v1.0) to identify candidate genes. A total of 3738 candidate genes were extracted from 10 QTL clusters with the number of candidate genes ranging from 82 genes in the C1 interval to 587 genes in the C9 interval (Table S7). The candidate genes were then subjected to in-silico expression analysis using online RNAseq data from expVIP. There are 50 candidate genes belonging to seven QTL clusters (except C1, C2 and C10) were highly and specifically expressed in the spike and grain developmental stages (Fig. 4). These genes were involved in various metabolic pathways, such as protein processing in the endoplasmic reticulum and ribosome pathways.

In addition, the potential candidate genes were further screened and annotated based on the Chinese Spring Wheat Reference Genome in the IWGSC RefSeq V1.0 version (Table S7). The GO terms associated with biological processes belonged to metabolic processes and cellular processes (680 and 550 potential candidate genes, respectively) (Fig. 5). GO terms associated with cellular components were related to membranes and membrane components (379 and 324 potential candidate genes, respectively). GO terms associated with molecular function were related to binding and catalytic activity (1,062 and 666 potential candidate genes, respectively). The KEGG analysis revealed that most potential candidate genes fall into two important groups, which play a role in metabolic pathways and the biosynthesis of secondary metabolites (Fig. 6). In summary, a total of 223 candidate genes were included in kernel development pathways.

SNPs in the CG promoter is closely associated with the wheat kernel weight

An SNP variation (chromosome 5A, 495818646bp) in TraesCS5A02G288000 (TaCYP71E1-5A) were regarded as significant SNP (Fig. 7A). Thus, a KASP marker (Table S8) for significant SNP was developed and verified with 220 wheat varieties (Table S9). The KASP marker divided these wheat accessions into three categories, including 116 varieties with allele C/C, 101 varieties with allele T/T, and four varieties without any allele (Fig. 7B). Association analysis indicated that the TKW, KL, and KS for those varieties with allele C/C (Hap-C) were significantly higher than varieties with T/T allele (Hap-T) (Fig. 7C). Therefore, the Hap-C was considered as superior haplotype. Geographic distribution analysis showed that the wheat accessions (Table S10) with the Hap-C allele were common in low-latitude areas in China, while the Hap-T allele was common in high-latitude areas in China (Fig. 7D).

In this study, 23,226 SNP markers (28.5%) were polymorphic between two parental lines. A total of 10,294 SNP markers representing 6,428 bins were mapped on 21 chromosomes with a marker density of 0.35 cM/marker and 0.56/bin, which significantly improved the resolution of the genetic maps compared to most previous maps used to study kernel traits (Sun et al. 2009; Ramya et al. 2010; Tsilo et al. 2010; Gegas et al. 2010; Prashant et al. 2012; Williams and Sorrells 2014; Avni et al. 2014; Kumar et al. 2016; Wen et al. 2017; Xu et al. 2019; Isham et al. 2021).

It is known that kernel weight and yield are largely determined by traits related to kernel size (Lizana et al. 2010; Xie et al. 2015). However, the relationships between kernel traits under different water conditions are largely unclear due to the complex interactions between genotype and environment. In this study, we found a similar trend in the relationships between kernel traits in response to both water conditions (Fig. 2). TKW was strongly correlated with KW under DS (0.69, P < 0.01) and WW (0.58, P < 0.01) conditions, suggesting that KW is the crucial and stable factor for determining kernel weight (Breseghello and Sorrells 2007; Li et al. 2019; Ma et al. 2019; Schierenbeck et al. 2021; Xie et al. 2022). Therefore, the improvement of KW is of great importance for increasing kernel weight and yield under different growing conditions. While drought stress has a negative effect on kernel size and kernel weight (Bennett et al. 2012; Dong et al. 2017; Ma et al. 2019; Wang et al. 2019), we observed that the correlations between kernel traits were higher under DS conditions (-0.38–0.91) than under WW conditions (-0.24–0.84), indicating the important influence of these traits in responding to environmental stress.

Table S5 shows that the heritability of kernel traits was generally higher than 0.6 in all environments, but KW (0.59) and KT (0.56) were lower under DS conditions. Previous studies (Tsilo et al. 2010; Yan et al. 2017; Qu et al. 2021; Ma et al. 2022) have showed that the kernel traits are strongly inherited and closely correlated with each other. Since TKW is the most significant and heritable trait among yield components, improving kernel weight and identifying the underlying mechanisms controlling these components are crucial for wheat breeding (Quarrie et al. 2005; Liu et al. 2014; Savadi 2018; Ma et al. 2022; Miao et al. 2022).

We identified a total of 85 additive and 14 stable QTLs for kernel traits (Table S6). Among them, 51 of 85 QTLs and 10 of 14 stable QTLs were detected with positive additive effects of Jinmai47 (Table S6), suggesting that the development and utilization of elite parental lines such as Jinmai47 play an important role in breeding wheat cultivars for higher production. We found that 68 of 85 QTLs for kernel traits showed an interaction between water conditions in one or more environments. Of these, only 13 QTLs were positively affected by the WW condition, while the others were negatively affected by the DS condition. In particular, 14 stable QTLs were found to have a negative effect on the DS condition, and six QTL clusters showed negative QEI effects on the DS condition, while other four QTL clusters were mainly affected by the DS condition. These results suggest a significant effect of kernel traits on environment and genotype by environment interaction under different water conditions.

Recently, Smith (2023) reported that floral traits often show correlated variation, both within and between species. One explanation for this pattern of floral integration is that different elements of the floral phenotype are controlled by the same genes, i.e. that genetic architecture is pleiotropic, and therefore genetic correlations are an inherent source of phenotypic correlations. Accordingly, in our study we found ten QTL clusters harboring 34 QTLs for different kernel traits on chromosomes 1B, 2B, 5A, 5B, 5D, 6D, 7A and 7D (Table 1). Significant and positive correlations were previously observed between TKW, KL, KW and KS under DS and WW conditions (0.47–0.69 and 0.36–0.58, respectively) (Fig. 2). Compared with the QTL mapping results, four stable QTLs were clustered in the C1 (Qtkw.acs-1B.1 and Qkl.acs-1B) and C4 (Qtkw.acs-5B.1 and Qkw.acs-5B.1) clusters on chromosomes 1B and 4B, respectively (Table S6), along with other QTLs with larger or smaller effect for other kernel traits such as KDR. In addition, QTLs for TKW were co-localized with QTLs for KL within six clusters (C1, C4, C5, C6, C7 and C9) (Table 1). Significant positive correlations were also previously observed between KL, KW and KS, and QTLs for these traits tended to be located in the same QTL clusters (C1, C2, C3, C4, C5, C8, C9 and C10). Combined with the phenotypic correlation results, the co-localized QTLs for different kernel traits indicated that TKW was significantly influenced by KS and comprehensively increased KL and KW, which ultimately can lead to yield increase in wheat under DS conditions.

As Li and Li (2016) summarized, there are complex factors in the regulation of kernel development including phytohormones and transcriptional regulatory pathways, various signaling pathways such as the ubiquitin-proteasome pathway, the mitogen-activated protein kinase (MAPK) signaling pathway and the G-protein signaling pathway with effect on kernel size. In addition to the seed size control pathways summarized by Li and Li (2016), the cytochrome P450 (CYP) family has been implicated as a regulator in crop improvement (Nelson 2006; Nelson and Werck-Reichhart 2011) and kernel size. For example, the CYP78A pathway plays an important role in regulating kernel size in plants (Adamski et al. 2009; Chakrabarti et al. 2013; Wang et al. 2015; Xu et al. 2015; Ma et al. 2015; Suzuki et al. 2015; Qi et al. 2017; Guo et al. 2022). In wheat, TaCYP78A3 (Ma et al. 2015) and TaCYP78A5 (Guo et al. 2022) influence the final kernel size and weight by regulating cell number and auxin accumulation, respectively. In addition to the CYP78A subfamily, the other CYP subfamilies are also involved in the development of kernel size. SMG11 encodes a cytochrome P450 (CYP90D2) and is involved in the biosynthesis of brassinosteroids (BR), which control kernel size by promoting cell expansion in the kernel coat (Fang et al. 2016). GNS4 has been identified as a positive regulator of kernel number and size in rice and encodes a key enzyme (CYP724B1) in BR biosynthesis, which belongs to the cytochrome P450 family (Zhou et al. 2017). CYP71 is the largest CYP clan and family in plants and its role in kernel development remains enigmatic (Nelson and Werck-Reichhart 2011). Of the 3738 candidate genes for kernel traits identified in this study, only 50 candidate genes belonging to seven QTL clusters were highly and specifically expressed in developing ears and kernels (Fig. 4). Interestingly, we discovered a QTL cluster interval (C3) on chromosome 5A, in which we identified the candidate gene TraesCS5A02G288000 (TaCYP71E1-5A) for KS and KL, homologous with the rice gene LOC_Os12g32850 (OsCYP71E5) and was annotated as “cytochrome P450”. This gene, which encodes 4-hydroxyphenylacetaldehyde oxime monooxygenase, could be proposed as a potential candidate gene for kernel traits. A gene-based KASP marker was validated in the panel of 220 wheat varieties, demonstrated that SNP variation (Hap-C) in (495818646 bp) site significantly contribute to TKW, KS, and KL variation (Fig. 7A and 7C). The results demonstrate that natural variations in TaCYP71E1-5A contribute to grain size diversity in a wide range of wheat varieties. However, further research is required to confirm the mode of action of this candidate and to demonstrate its role in the control of kernel traits so that it can be converted into a functional and predictive marker for application in MAS.

Overall, our results will contribute to the understanding of the function and complex mechanisms underlying QTLs for kernel traits and cloning of QTLs for yield, and assist wheat breeders in selecting traits that maintain yield more efficiently under drought conditions.

ANOVA, analysis of variance; BLUP, best linear unbiased prediction; DS, drought-stressed; GWAS, genome-wide association study; RIL, recombinant inbred line; KASP, kompetitive allele-specific PCR; KDR, kernel diameter ratio; KL, kernel length; KN, number of kernel per ear; KS, kernel size; KT, kernel thickness; KW, kernel width; MAS, marker-assisted selection; PVE, phenotypic variance; QEI, QTL × drought-stressed environment; QTL, quantitative trait loci; RCBD, randomized complete block design; SN; spike number per unit; SNP, single nucleotide polymorphisms; TKW, thousand-kernel weight; WW, well-watered.

Acknowledgements

This work was financially supported by the Key Sci & Tech Special Project of Gansu Province (22ZD6NA009), the Key Cultivation Project of University Research and Innovation Platform of Gansu Province (2024CXPT-01), the National Natural Science Foundation of China (32260520, 32360518, 32160487), the Industrial Support Plan of Colleges and Universities in Gansu Province (2022CYZC-44), the Development Fund Project of National Guiding Local Science and Technology (23ZYQA0322).

Author contribution statement

JM, TT, PW and YL were responsible for the creation of the mapping population and the development of markers. JM, TT and PW performed phenotyping and genotyping. PZ and LG contributed to the writing process and genotyping. YZ and YW were responsible for data analysis and preparation of the figures. JM, TT, PW and TC analyzed the data and drafted the manuscript. DY and FS conceived and supervised the project and edited the manuscript. All authors approved the final version of the manuscript for submission.

Compliance with ethical standards

Conflict of interests

The authors declare that they have no competing interests.

Ethical standards

I declare on behalf of my co-authors that the work described is original, previously unpublished research, and not under consideration for publication elsewhere. The experiments in this study comply with the current laws of China.

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FigS1.tif
Figure S1: The monthly precipitation in E1 to E6 environment (A); and the total precipitation in each environment during the growing season of the wheat (B).
FigS2.tif
Figure S2: The distribution of SNPs on the wheat chromosome.
FigS3.tif
Figure S3: Comparative analysis between the position of the markers on the genetic map and their physical position in the wheat reference genome IWGSC RefSeq v1.1
SupplementalInformation.xlsx
Table S1. General information on the genetic map of the RILs population. Table S2. Comparison of the genetic and physical positions of the mapped markers. Table S3. Variation of the kernel traits of the RILs and the parental lines under different water conditions. Table S4. Significant differences between the RILs and the parents evaluated for kernel traits under drought stress (DS) and well-watered (WW) conditions in different environments. Table S5. ANOVA for kernel traits evaluated in the RILs population under drought-stressed (DS) and well-watered (WW) conditions across different environments. Table S6. Additive QTLs and QTL × drought stress interactions identified for six kernel traits in the RILs population under different environmental conditions. Table S7. The information on candidate genes predicted within ten QTL clusters for kernel traits. Table S8. The primer sequence of KASP marker. Table S9. The information of wheat variety genotyped with KASP marker. Table S10. The information of wheat varieties used in geography distribution analysis.

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Editor assigned by journal
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First submitted to journal
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Identification of quantitative trait loci and candidate genes underlying kernel traits of wheat (Triticum aestivum L.) in response to drought stress

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Abstract

Figures

Key message

Introduction

Materials and methods

Plant material

Field trials and trait evaluation

Statistical analysis

Genotyping

Linkage analysis and genetic map

QTL mapping

Prediction of candidate genes

KASP marker development and Haplotype analysis of CGs promoter

Results

Construction of a high-density genetic map

Phenotypic evaluations

Phenotypic correlations

Identification of QTLs for kernel traits

QTL for QEI

QTL for TKW

QTL for KL

QTL for KW

QTL for KT

QTL for KS

QTL for KDR

QTL clusters

Candidate genes for QTLs controlling kernel traits

SNPs in the CG promoter is closely associated with the wheat kernel weight

Discussions

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1