Introduction

Wheat is an economically important crop that is a major energy and nutrient source for humans. Against the background of a growing global population and climate change, human food demands are increasing rapidly. Wheat breeders are currently focused on developing varieties with high and stable yields to meet the increase in food demand (Eltaher et al. 2021). However, it is difficult to increase wheat yields quickly, which jeopardizes future food security (Li et al. 2016).

Previous studies have indicated that wheat yield is mainly determined by three traits: spike number per area, kernels per spike, and thousand-kernel weight (TKW) (Yang et al. 2016). Thousand-kernel weight is mainly controlled by grain morphology and grain-filling degree (Okamoto et al. 2013). Generally, grain morphology mainly affects TKW through grain length (GL), grain width (GW), grain length–width ratio (GR), grain circumference (GC), and grain surface area (GSA), and ultimately impacts on wheat yield (Wang et al. 2012; Gao et al. 2021; Cristina et al. 2016; Liu et al. 2017). Therefore, research on grain morphology has important applications for improvement of the grain weight in wheat. Identification of the key genes associated with grain morphology is crucial for further improvement of wheat yield. Many quantitative trait loci (QTLs) that contribute to grain morphology or grain weight traits have been identified in durum wheat (Varshney et al. 2000) and in hexaploid wheat distributed on all 21 chromosomes Huang et al. 2003 and 2006; Li et al. 2022; Williams et al., 2014; Okamoto et al. 2013; Patil et al. 2013; Simmonds et al. 2014; Zhang et al. 2015; Kumar et al. 2019; Cabral et al. 2018; Liu et al. 2017). Some of the genes that influence grain morphology have been cloned. For instance, TaGW2 negatively regulates wheat GW and TKW (Su et al. 2011; Wang et al. 2018). The gene DA1-RELATED 1 (TaDA1) is a negative regulator of grain size and can interact with TaGW2 to affect grain weight in wheat (Liu et al. 2020). TaPGS1 is a transcription factor that regulates wheat grain size and grain weight. Overexpression of TaPGS1 significantly increases grain weight, GL, and GW. Further investigation showed that TaPGS1 binding to the FI3 promoter to knockdown FI3 expression significantly reduces grain weight, GL, and GW (Guo et al. 2022). In addition, many genes, such as TaCKX, TaGS5, TaAGP-L, TaSDIR1, TaCYP78A3, TaGS3, TaGW7, and TaGW8, have been confirmed to be involved in the regulation of wheat grain morphology or grain weight (Jabłoński et al. 2020; Wang et al. 2015, 2020; Zhang et al. 2019; Ma et al. 2015, 2017; Zhang et al. 2019; Yan et al. 2019).

Chuanmai104 (CM104), an elite synthetic hexaploid wheat-derived variety bred by the Crop Research Institute, Sichuan Academy of Agricultural Sciences, exhibits numerous desirable traits, including high yield, good grain quality, broad adaptability, and resistance to stripe rust, powdery mildew, and pre-harvest sprouting under the unique ecological conditions of Sichuan Province in China (Li et al. 2014). From 2010 to 2020, CM104 consistently maintained the highest yield increase recorded in the southwestern China wheat production region, and set a record yield of 10,947 kg per hectare for southwestern China in 2020. Recently, CM104 has been widely used as an elite breeding material in the wheat breeding program in southwestern China, and a total of 21 varieties are derived from CM104 in this growing region (Liu et al. 2021). However, the genetic mechanism for the high and stable yield of CM104 remains uncertain.

In this study, CM104 grains were sampled in different years and from different regions to analyze the genetic basis for the high yield and its stability. The grain morphology and grain weight phenotypes were recorded for a population of 251 recombination inbred lines (RILs; F9–10) derived from the cross of CM104 and the wheat landrace Baimaomai (BMM) grown in three environments. The results provide a foundation for analysis of the genetic mechanism for the high yield and stability of CM104, and provide valuable information for pyramiding of multiple genes associated with grain traits for future improvement of wheat varieties.

Materials and methods

Plant materials and mapping population

Nineteen samples of CM104 grains were collected from five provinces in China during 2018–2022, and three BMM samples were collected from Sichuan in 2021–2022 (Fig. 1, Table 1). Recombination inbred lines were derived from the cross between CM104 and BMM (Fig. 2), comprising 251 RILs-F9 (2021) and RILs-F10 (2022) lines. The RILs were grown at Guanghan in 2021 (2021GH) and 2022 (2022GH), and at Shifang in 2022 (2022SF). Approximately 30 seeds of each line were uniformly sown in a single 1-m-long row with 25 cm spacing between rows. Standard field management practices were applied.

Fig. 1
figure 1

CM104 grain samples collection location in 2018–2022

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Table 1 CM104 and BMM planting environment and year
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Fig. 2
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Comparison of grain phenotype of Baimaomai and Chuanmai104

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Evaluation of grain traits and statistical analysis

Thousand-kernel weight, GL, GW, grain length–width ratio (GR), GC, and GSA of each accession from different environments were recorded using an automatic seed test analyzer (Mini1600, Jie Lai Mei Technology Co., Ltd., Chengdu, China). Regression analysis of the CM104 grain traits was performed with Excel software (Microsoft, Redmond, WA, USA). The correlations among the grain traits were analyzed using the two-tailed Pearson method implemented in IBM SPSS Statistics 25 (IBM Corporation, Armonk, NY, USA). Analysis of variance (ANOVA) was conducted, and then, the accessions were ranked with Duncan’s test and plotted using GraphPad Prism V8.0.2.263. R software (i386 4.1.0) and the lme4 package were used to calculate the broad-sense heritability (H2; Smith et al. 1998) of the wheat grain traits. High-quality images of wheat grains were captured with a Leica M205 FA stereomicroscope (Leica, Germany).

QTL mapping

Genotyping of the CM104 × BMM RILs was performed using the wheat 50 K SNP (single nucleotide polymorphism) genotyping array developed by CapitalBio Corporation (Beijing, China) and synthesized by Affymetrix (California, America). In a previous study, we constructed genetic linkage maps with 3779 polymorphic SNP markers (Liu et al. 2017). The QTL analysis was conducted with inclusive composite interval mapping using GAHP software (Zhang et al. 2022). The QTLs were mapped at a logarithm of odds threshold of 4 based on 1000 permutations and a walking speed of 1.0 cM, with P = 0.001 in a stepwise regression. The QTL effects were estimated as the proportion of phenotypic variance explained by the QTL. The QTLs for a trait with identical, overlapping, or adjacent marker intervals in a linkage group were treated as identical and given a common name. Only QTLs identified in two or three environments were considered. The physical distance between the marker interval of a QTL was analyzed using the International Wheat Genome Sequencing Consortium (http://www.wheatgenome.org/) and EnsemblPlants (http://plants.ensembl.org/info/website/ftp/index.html) databases based on the flanking sequences.

Results

Phenotypic variation and diversity analysis

Nineteen samples of CM104 grains from different environments in China were collected to evaluate phenotypic variation in grain morphology and grain weight (Table 2). Of these samples, 14 samples were harvested in Sichuan Province during 2018–2022 (SC group) and five samples were harvested outside Sichuan Province in 2022 (SW group) (Fig. 3). Differences in the means for the grain morphology and grain weight traits between the SC and SW groups were analyzed by performing an ANOVA. The TKW, GW, GC, and GSA traits in the SC group were significantly higher than those in the SW group (P < 0.05; Fig. 3a), suggesting that Sichuan is the most suitable production zone for CM104. Furthermore, ANOVA and a regression analysis were used to evaluate the stability of the grain morphology and grain weight traits. Two CM104 samples (2018CZ and 2021GH) were compared with 12 CM104 samples collected in 2022 from different environments in Sichuan Province, but no significant differences were observed between the two groups (Fig. 3b).

Table 2 Phenotypic performance of CM104 in diverse environments for thousand-kernel weight (TKW), grain length (GL), grain width (GW), grain length–width ratio (GR), grain circumference (GC), and grain surface area (GSA). The environment is designated by the trait, year, and location
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Fig. 3
figure 3

Variance analysis of grain morphology and grain weight traits in CM104 in multiple environments. a the grain phenotypes were compared between groups Sichuan (SC) and outside Sichuan (SW); b the grain phenotypes were compared between groups 2022 and other years (2018 and 2021). *means P < 0.05

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Regression analysis showed that the grain morphology and grain weight traits of CM104 grown in Sichuan Province were highly stable. A highly significant correlation was observed between grain morphology and grain weight traits for all 14 environments (R2 = 0.88–0.95; Fig. 4).

Fig. 4
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Regression analysis of grain morphology and grain weight traits in CM104 in Sichuan during 2018–2022

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QTL mapping of grain traits

Among the three environments for the RILs, significant differences in TKW, GL, GC, and GSA between CM104 and BMM were observed (P < 0.05; Fig. 5a). The frequency distributions of the investigated traits revealed continuous variation in the RILs population, suggesting that the phenotypic data for the four grain traits were normally distributed and these traits were controlled by multiple QTLs (Fig. 5b).

Fig. 5
figure 5

Grain phenotypes were analyzed in RILs populations and parents. A) variance analysis of grain morphology and grain weight traits in CM104 and BMM; B) phenotypic distribution for TKW, GL, GC, and GSA in CM104XBMM RILs populations. *means P < 0.05, ***means P < 0.001, ****means P < 0.0001

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The estimated H2 values for TKW, GL, GC, and GSA were greater than 0.5 (range 0.52–0.84) (Table 3). Thus, it was assumed that the traits were influenced by genetic factors. Pearson correlation coefficients for the four grain morphologies and grain weight traits in the three environments ranged from 0.58 to 0.96 (Fig. 6).

Table 3 Grain phenotype of the parents and RILs in this study. H2 broad-sense heritability
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Fig. 6
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Pearson’s correlation coefficients (r) between thousand-kernel weight (TKW), grain length (GL), grain width (GW), grain length–width ratio (GR), grain circumference (GC), and grain surface area (GSA). a Grain phenotypic correlation analysis of CM104 was performed in nineteen environments; b grain phenotypic correlation analysis of CM104 × BMM RILs population in three environments

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In the three environments, 15 QTLs were identified for GC, GL, GSA, and TKW. Three detected QTLs for TKW explained 6.08–6.84% of the phenotypic variation, 11 QTLs for grain morphology explained 3.35–12.13%, and one QTL for GSA and TKW explained 4.33–5.35%. Three stable putative QTLs (QTL10, QTL11, and QTL12) for grain morphology associated with GC, GL, and GSA were detected in this study. These QTLs were located on chromosomes 5B and 5D, and explained 3.35–12.13% of the phenotypic variation (Table 4, Fig. 7). The additive effect of the individual QTLs indicated that CM104-positive alleles contributed to the three traits.

Table 4 QTLs for grain morphology and grain weight traits by inclusive composite interval mapping in RIL population of Chuanmai104/Baimaomai in three environments, the physical position of markers was referenced to the Chinese Spring genome (IWGSCv1.0)
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Fig. 7
figure 7

Locations of the detected QTL that related the grain morphology in the CM104 × BMM RILs population. A mega base (Mb) scale is shown on the left. Red QTLs mean donor of positive alleles come from CM104 and blue QTLs mean donor of positive alleles come from BMM

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Discussion

Grain traits of CM104 showed high stability in different environments

Thousand-kernel weight is positively correlated with grain morphology and is an important trait owing to its phenotypic stability and high heritability (Kuchel et al. 2007). Selection for a higher TKW has been conducted during domestication, and therefore, it has been argued that the grain morphology of cereals is a component of the domestication syndrome (Brown et al. 2009). Larger grains generally favor TKW, seedling vigor, and flour yield characteristics (Chastain et al. 1995). Stable wheat production is crucial to global food security (Macholdt and Honermeier 2017). Many factors, such as the phenotypic stability of wheat varieties and genotype × environment interactions, affect wheat yield to different extents (Nielsen and Vigil 2018). Regression analysis and ANOVA are widely used to evaluate the relationship between wheat yield and environment (Chen et al. 2018; Pepó and Győri 2005; Ayalneh et al. 2014; van Frank et al. 2020; Ji et al. 2023). In the present study, CM104 samples were collected from different environments; grain traits in the SC group were significantly higher than those in the SW group. The highest TKW was recorded in the 2022_JY sample (59.62 g/1000 grains), which indicated that JY was the most suitable production location for CM104. As the highest yielding wheat variety grown in Sichuan, CM104 exhibits broad adaptability and stable high yield. However, its adaptability is generally poor outside of Sichuan Province, with the exception of the 2022_XX sample (51.14 g/1000 grains) in Henan Province, which suggested that CM104 can maintain favorable grain traits in specific environments. Correlation analysis showed that the TKW of CM104 was positively correlated with GL, GW, GC, and GSA, but was negatively correlated with GR (Fig. 6). This result indicated that modern breeders prefer rounded seeds, consistent with previous findings (Cheng et al. 2020; Zhang et al. 2022).

Stable QTLs associated with grain morphology and grain weight traits

Grain morphology and grain weight are complex quantitative traits, and are influenced by many main and micro-effect QTLs (Slafer et al. 2014). In previous studies, more than 100 QTLs for TKW, GL, GW, GC, and GSA in wheat have been identified and are distributed on all 21 chromosomes. In addition, wheat TKW is significantly positively correlated with grain morphology, and many QTLs associated with grain morphology have been co-localized with TKW QTLs (Börner et al., 2002; Huang et al. 2003; Huang et al. 2006; Li et al. 2022; Williams et al., 2014; Okamoto et al. 2013; Simmonds et al. 2014; Zhang et al. 2015; Kumar et al. 2019; Cabral et al. 2018; Liu et al. 2017). However, many QTLs for grain morphology are not detectable in different environments and explain only a small proportion of the phenotypic variation for grain weight. Taken together, these factors mean that such QTLs are unsuitable for use in marker-assisted selection for wheat breeding. However, such QTLs are strongly affected by the environment and not all major QTLs are repeatedly detected in all environments (Akram et al., 2020; Li et al. 2019; Suliman et al. 2021; Alemu et al. 2020; White et al. 2022; Gao et al. 2021). Therefore, molecular marker-assisted breeding for wheat grain traits is challenging. The present RIL populations were used for QTL analysis in multiple environments, which is a beneficial approach for breeding to select loci that can be detected in different environments. The present study detected 15 QTLs for grain morphology. With reference to previous studies, four QTLs for TKW identified in previous reports were detected on chromosomes 1A (1), 2A (2), and 2D (1). The co-localized GL and TKW locus QTL1 (537.54–544.74 Mb) is located at a similar position to that of the TKW QTL on chromosome 1A reported by Yang et al. (2020), and three QTLs located on chromosomes 2A and 2D have been reported in multiple studies. Interestingly, all of the favorable alleles associated with TKW were inherited from CM104 (Table 4). Among the 15 QTLs associated with grain morphology and grain weight identified in the present study, 14 QTLs have been located in previous studies except for QTL15. However, the physical location of QTL15 is near that of QTL14, and thus, they may represent a single QTL (Akram et al. 2021; Li et al. 2019; Suliman et al. 2021; Alemu et al. 2020; White et al. 2022; Gao et al. 2021; Okamoto et al. 2013; Tyagi et al. 2015; Gahlaut et al. 2021; Yang et al. 2020; Chidzanga et al. 2022; Sun et al. 2009; Azadi et al. 2015). In the current study, three QTLs associated with grain morphology were identified in multiple environments on chromosomes 5B and 5D, namely QTL10, QTL11, and QTL12, and their favorable alleles were all derived from CM104. Among these three stable QTLs, QTL10 and QTL11 explained 3.34–5.06% and 5.32–6.50% of the phenotypic variation, respectively, whereas QTL12 explained 6.00–12.13% of the phenotypic variation, which was significantly higher than that for QTL10 and QTL11. These results indicated that QTL12 was an important QTL for the grain morphology of CM104 (Table 4). These stable QTLs (QTL10, QTL11, and QTL12) detected in multiple environments provide a foundation for fine-mapping of TKW and grain morphology-related genes and improvement of the yield traits of CM104 by molecular marker-assisted selection. Additionally, the major and minor QTLs presented the excellent genetic basis to ensure the stable phenotype of grain traits of CM104 in multiple environments. It was also an effective strategy to cultivate high and stable yield wheat varieties with widely adaptation by pyramiding major and minor QTLs and utilizing the synergistic effect of major and minor QTLs.

Conclusion

The high-yielding and phenotypically stable wheat variety CM104 harbored favorable alleles for TKW and grain morphology. Eight previously reported QTLs were identified in CM104, but only three stable QTLs (QTL10, QTL11, and QTL12) were detected in multiple environments. Therefore, these three QTLs may be the genetic basis for the phenotypic stability of the TKW and grain morphology of CM104 in multiple environments. Pyramiding major and minor QTLs will be also useful to improve wheat varieties and contribute to the yield stability in multiple environments in future.