Skip to main content
NCBI home page
Frontiers in Plant Science logoLink to Frontiers in Plant Science
. 2022 Nov 30;13:1072904. doi: 10.3389/fpls.2022.1072904

Genetic architecture and candidate gene identification for grain size in bread wheat by GWAS

Haitao Yu 1,2,†, Yongchao Hao 3,†, Mengyao Li 3,†, Luhao Dong 3, Naixiu Che 3, Lijie Wang 2, Shun Song 2, Yanan Liu 2, Lingrang Kong 3,*, Shubing Shi 1,*
PMCID: PMC9748340  PMID: 36531392

Abstract

Grain size is a key trait associated with bread wheat yield. It is also the most frequently selected trait during domestication. After the phenotypic characterization of 768 bread wheat accessions in three plots for at least two years, the present study shows that the improved variety showed significantly higher grain size but lower grain protein content than the landrace. Using 55K SNP assay genotyping and large-scale phenotyping population and GWAS data, we identified 5, 6, 6, and 6 QTLs associated with grain length, grain weight, grain area, and thousand grain weight, respectively. Seven of the 23 QTLs showed common association within different locations or years. Most significantly, the key locus associated with grain length, qGL-2D, showed the highest association after years of multi-plot testing. Haplotype and evolution analysis indicated that the superior allele of qGL-2D was mainly hidden in the improved variety rather than in landrace, which may contribute to the significant difference in grain length. A comprehensive analysis of transcriptome and homolog showed that TraesCS2D02G414800 could be the most likely candidate gene for qGL-2D. Overall, this study presents several reliable grain size QTLs and candidate gene for grain length associated with bread wheat yield.

Keywords: wheat, grain size, mapping, GWAS, yield

Introduction

Bread wheat is one of the major crops, accounting for nearly 20% of calories in our diet (http://faostat.fao.org). Improvement of yield remains a challenge under heavy population pressure and projected global change (Ray et al., 2013). Grain size is a major determinant of grain weight, besides the number of panicles per plant and the number of grains per panicle (Fan et al., 2006). In wheat breeding, grain size is usually evaluated by grain weight, which is positively correlated with grain length, grain width and grain thickness (Evans, 1972; Fan et al., 2006). Thus, it is vital to identify and introduce favorable genes or alleles controlling grain traits to improve the grain yield in bread wheat breeding.

Using linkage mapping, hundreds of grain size quantitative trait loci (QTLs) have been identified in the past few years (Zhang et al., 2018; Mora-Ramirez et al., 2021; Guo et al., 2022). Recently, multiple signals associated with grain size were detected in different populations via genome-wide association study (GWAS) (Breseghello and Sorrells, 2006a; Breseghello and Sorrells, 2006b; Pang et al., 2020). These QTLs are distributed on all the 21 chromosomes of bread wheat. However, the real genes underlying these QTLs have yet to be identified due to the complexity of parental mapping, QTL effect, QTL × genotype and QTL × QTL interactions. Using homology cloning, several orthologous genes associated with grain traits have been isolated and characterized in bread wheat. For instance, TaGW2 and TaGS5 were isolated in wheat based on OsGW2 and OsGS5 orthologs in rice (Wang et al., 2016; Zhai et al., 2018). TaGW2 is involved in regulation of grain weight and grain number in bread wheat (Zhai et al., 2018). TaGS5 is associated with thousand grain weight (Wang et al., 2016), TaGW8 is related to grain size in bread wheat (Yan et al., 2019). It is still hard to determine the variation in natural elite alleles of these known genes that can be used in marker assisted selection (MAS) of bread wheat. Therefore, it is still very important to explore and identify new QTLs and their natural allelic variation in wheat breeding.

In this study, we constructed a GWAS panel with 768 bread wheat accessions. After phenotypic evaluation in multiple plots for several years, we performed GWAS to the identify grain size of QTLs. A total of 23 grain size QTLs were identified. For a major grain length QTL qGL-2D, we investigated the signatures of natural variation via comprehensive analysis of haplotype and evolutionary features. Finally, one candidate gene associated with qGL-2D was identified. The results suggest that grain size QTLs and grain length candidate genes as well as information may facilitate MAS of these loci/genes in breeding high-yield wheat in the future.

Materials and methods

Materials

A total of 768 bread wheat accessions were used to identify QTLs of grain size, including 683 Chinese resources (560 improved varieties and 123 landraces) and 85 introduced accessions. Field experiments were performed at three locations, i: the Shandong Agricultural University Agronomy Experimental Station in Tai’an from 2016 to 2019, ii: Weifang Academy of Agricultural Sciences in Weifang in 2019, and iii: Jining Academy of Agricultural Sciences in Jining in 2019. Each accession was planted in five-row plots with 5 cm distance between plants and 25 cm distance between rows. The interval between adjacent plots was 50 cm. At the mature stage, we harvest 10 spikes without any mechanical damage, disease or insect infestation. After threshing, we measured thousand grain weight (TGW), grain length (GL), grain width (GW), grain area (GA), grain perimeter (GP), grain roundness (GR), grain diameter (GD), length-to-width ratio (LWR), grain protein content (GPC) and grain starch content (GSC) for each accession using a Crop Grain Appearance Quality Scanning Machine (SC-E, Wanshen Technology Company, Hangzhou, China).

Genotyping

Genomic DNA was extracted from the seedling leaves of all 768 wheat accessions, followed by further genotyping via an Illumina 55K assay. Finally, a total of 47,743 of 53,063 SNPs were identified in the wheat panel. We estimated the whole-genome distribution and minor allele frequency (MAF) of these SNPs using an in-house Python script. Additionally, we performed quality control of SNPs to exclude those with high missing rate (> 50%) and low MAF (< 5%) for further analysis.

Population structure

We first extracted 45,298 SNPs with miss rate ≤ 0.5 and MAF ≥ 0.05 from 53,063 SNPs using an in-house Python script. Using PLINK (window size 50, step size 50, r 2 ≥ 0.3), a total of 4,360 independent SNPs were further screened out based on r 2 of LD ≤ 0.3 (Purcell et al., 2007). The software STRUCTURE was used to calculate varying levels of K (K = 1-20) (Pritchard et al., 2000). We also performed principal component analysis (PCA) and kinship analysis using these independent SNPs and GAPIT software (Lipka et al., 2012; Tang et al., 2016). The phylogenetic analysis of qGL-2D was performed by generating a neighbor-joining tree using Mega 7 (Kumar et al., 2016).

Association mapping

Only 45,298 un-imputed SNPs with miss rate ≤ 0.5 and MAF ≥ 0.05 were used to conduct GWAS for GL, GW, GA and TGW, respectively. The first three PCs were used to construct the PC matrix. We performed GWAS with a Compressed Mixed Linear Model (CMLM) via PCA and kinship analysis using default settings of GAPIT (Lipka et al., 2012; Tang et al., 2016). Additionally, the threshold to determine significant association was set at 1.0 × 10-5 after Bonferroni-adjusted correction (Pang et al., 2020).

Expression analysis and epidermal cell observation

Gene expression data from different wheat cultivars were used to analyze the gene expression profiles of the candidate region. Expression data were download from wheat-URGI website (https://wheat-urgi.versailles.inra.fr/Seq-Repository/Expression). Then the transcriptomic information of candidate genes were exacted by a custom python script. Epidermal tissues were peeled off using tweezers under a stereomicroscope. Then, the cell layers were stained with safranin and mounted on glass slides (Matsunami Glass Ind., Japan). The tissue specimens were subjected to observation with a light microscope (BX50F Olympus Optical Co., Ltd, Japan).

Screening of candidate genes for qGL-2D

In order to identify candidate genes for qGL-2D, LD heatmaps surrounding peaks were constructed using the R package “LD heatmap” (Shin et al., 2006). Using pairwise LD correlation (r 2 > 0.6), we mined the candidate regions of qGL-2D (Yano et al., 2016). We further investigated the expression of these candidate genes in bread wheat grain using typical materials belonging to different haplotypes.

Results

Population structure and grain characterization of 768 bread wheat accessions

To identify genetic loci associated with grain weight, a panel of 768 bread wheat accessions were constructed, including 560 improved varieties, 123 landraces and 85 introduced accessions. Using a 55K SNP assay, we obtained 47,743 SNPs of the panel. Subsets of these data were further filtered and used in additional analyses ( Figure S1 ). A reasonable assessment of population structure facilitates the identification of real marker-trait associations (Crowell et al., 2016; Juliana et al., 2019). Therefore, we calculated varying levels of K means using un-imputed SNPs and STRUCTURE software (Golbeck, 1987). Landrace, improved and introduced varieties appeared clearly at K = 3 ( Figure 1A ). Further PCA indicated that top three PCs accounted for 17.09%, 6.15% and 3.38% of genetic variation within the bread wheat panel ( Figure 1B ). The results suggested obvious genetic differentiation between landrace and improved varieties of bread wheat.

Figure 1.

Figure 1

Open in a new tab

Genetic architecture and characteristic of grain size and grain quality of 768 bread wheat accessions. (A) Genetic structure of the panel analyzed using the program STRUCTURE. Landrace (LD), improved variety (IV) and introduced variety (IA) groups appeared at K = 3. (B) Principle components analysis reveals that the first 3 principle components explain 17.09%, 6.15% and 3.38% of the genetic variance within the panel. Comparison of grain size traits (C) and grain quality traits (D) among LD, IA, IV. Different letters above the boxes indicate significant differents (p < 0.05) when analyzed by Duncan’s test.

A total of 10 traits were identified in three different plots for two years, including eight grain shape components (TGW, GL, GW, GA, GP, GR, GD, and LWR) and two grain quality components (GPC and GSC). All traits showed high heritability from 89.30% (GSC) to 95.27% (TGW) ( Table S1 ). After obtaining the best linear unbiased prediction (BLUP) of each accession with respect to each trait across all traits, the coefficient of variation (CV) of all traits ranged from 1.44% GSC to 15.48% TGW ( Table S1 ). GPC was proved to be negatively correlated with the eight grain size components, suggesting that larger, heavier and longer bread wheat grains usually had lower GPC ( Figure S2 ). During the domestication of landrace to improved variety, bread wheat grains increased in size, weight, and length, but their GPC decreased ( Figures 1C, D , 2C ).

Figure 2.

Figure 2

Open in a new tab

Identification and haplotype analysis of grain length QTL qGL-2D. Quantile-quantile (Q-Q) plot (A) and manhattan plot (B) using 7 groups of GL data of multi-year and multi-plots. qGL-2D is an association signal detected in all tests. (C) qGL-2D haplotype analysis and comparisons of grain length (GL) among four qGL-2D haplotypes. (D) Comparison of grain area (GA), grain perimeter (GP), grain width (GW), length-width ratio (LWR), grain diameter (GD), grain roundness (GR), hundred grain weight (HGW) and grain protein content (GPC) among four qGL-2D haplotypes. For better chart presentation, TGW is replaced by HGW. (E) Phylogenetic tree of the four qGL-2D haplotypes. The number of landrace (LD), improved variety (IV) and introduced variety (IV) are marked for four haplotypes.

Identification of grain shape QTLs by GWAS

Focusing on four key grain shape traits (GL, GW, GA and TGW), GWAS was performed to identify QTLs based on their respective multi-year and multi-location data and BLUP. A total of 23 QTLs were detected on 12 chromosomes, including 5, 6, 6 and 6 QTLs for GL, GW, GA and TGW, respectively ( Table 1 and Figures S3 , S4 ). Seven of 23 QTLs showed common association within different locations or years, including qGW-2B, qGL-2D, qGW-2D.1, qTGW-4A, qTGW-5A.1, qGA-6D, qTGW-6D and qTGW-7D. Consistent with the positive correlations between GL, GW, GA and TGW ( Figure S2 ), close linkage, and overlapping or one-factor-to-many-effects (pleiotropy) were detected on chromosome 2D (for qGA-2D and qGL-2D), chromosome 5A (for qGA-5A, qTGW-5A.1 and qGL-5A.1), chromosome 6D (for qGA-6D and qTGW-6D), and chromosome 7D (for qGA-7D, qGW-7D and qTGW-7D ( Table 1 ).

Table 1.

QTL identified for grain weight or shape by combined analysis of six environments and BLUP.

Chr. QTL Trait Environments Peak SNP Position -log10(p) QTL reported
1D qGA-1D.1 GA 18T AX-109817000 79999712 5.13 –
qGA-1D.2 19T AX-86164003 95559483 5.79 –
2A qGW-2A GW 19T AX-109994744 721725535 5.56 Wang et al. (2012)
BLUP AX-109994744 721725535 5.65
2B qGW-2B 18T AX-108936154 720581605 5.45 Zanke et al. (2015)
19T AX-108936154 720581605 5.53
BLUP AX-108936154 720581605 5.89
2D qGA-2D GA 20W AX-108767381 528101770 5.29 –
BLUP AX-108767381 528101770 5.27 –
qGL-2D GL 17T AX-110982403 525904353 6.41 –
18T AX-108767381 528101770 6.60 –
19T AX-108767381 528101770 8.63 –
20J AX-108767381 528101770 6.99 –
20T AX-108767381 528101770 7.00 –
20W AX-108767381 528101770 6.09 –
BLUP AX-108767381 528101770 8.31 –
qGW-2D.1 GW 17T AX-109910122 587284788 6.01 Ramya et al. (2010)
18T AX-94632592 593270570 6.13
19T AX-109464110 585470933 5.87
20J AX-109449735 590677250 6.79
20T AX-94632592 593270570 6.70
20W AX-111098468 593217154 5.99
BLUP AX-111098468 593217154 7.06
qGW-2D.2 18T AX-111956072 34428803 6.10 Wang et al. (2019a)
3D qGW-3D 20T AX-111624595 572830156 5.18 Ma et al. (2019)
4A qTGW-4A TGW 18T AX-108908317 681180867 5.13 Zanke et al. (2015)
19T AX-108908317 681180867 5.22
BLUP AX-108908317 681180867 5.30
4B qGL-4B GL 20T AX-110919438 643312159 5.69 –
5A qGA-5A GA 18T AX-111136203 430037627 5.34 Cheng et al. (2017); Wu et al. (2015).
qTGW-5A.1 TGW 19T AX-110508884 428416559 5.02
20W AX-110508884 428416559 5.15
qGL-5A.1 GL 18T AX-111136203 430037627 5.14
qGL-5A.2 20J AX-108762108 595372901 5.32 Wang et al. (2019b)
qTGW-5A.2 TGW 20J AX-109504344 704583912 5.25 Zanke et al. (2015)
5B qTGW-5B 20T AX-110427093 34285686 5.09 Yang et al. (2020)
5D qGL-5D GL 20W AX-110985437 404832095 5.13 –
6D qGA-6D GA 17T AX-110007215 93614544 5.12 Lopes et al. (2013), McCartney et al. (2005), Shi et al. (2017)
18T AX-110007215 93614544 6.82
20J AX-110007215 93614544 5.60
20W AX-110007215 93614544 5.87
BLUP AX-110007215 93614544 5.79
qTGW-6D TGW 17T AX-110007215 93614544 5.52
18T AX-110007215 93614544 8.46
19T AX-110007215 93614544 6.49
20W AX-110007215 93614544 6.03
BLUP AX-110007215 93614544 6.26
7D qGA-7D GA 20T AX-110826147 65503524 5.60 Liu et al. (2014), Tang et al. (2017)
qGW-7D GW 20T AX-110826147 65503524 5.55
qTGW-7D TGW 20T AX-110826147 65503524 5.87
18T AX-111843581 67448018 5.25
Open in a new tab

To validate the results of GWAS, we compared the localization of the QTLs identified in this study with previously detected QTLs associated with bi-parental mapping population. Twelve of 23 QTLs in this study were co-localized with previously reported QTLs, including 1, 6, 3 and 6 QTLs for GL, GW, GA, and TGW, respectively ( Table 1 ). The qGA-6D and qTGW-6D were detected most frequently (five times), followed by qGA-5A, qTGW-5A.1, qGL-5A.1, qGL-5A.2, qGA-7D, qGW-7D and qTGW-7D (twice), whereas qGW-2A, qGW-2B, qGW-2D.1, qGW-2D.2, qGW-3D, qTGW-4A, qGL-5A.2, qTGW-5A.2 and qTGW-5B were detected rarely (once). Additionally, we also identified six new grain size QTLs, including qGA-1D.1, qGA-1D.2, qGA-2D, qGL-2D and qGL-4B.

Haplotype analysis of qGL-2D

The qGL-2D was a key locus for GL, as it was detected using the data for each location every year and BLUP ( Figures 3A, B and Figure S3 ). Using BLUP of GL yielded five significant SNPs (-log(p) > 5) representing qGL-2D. Thus, the five SNPs were identified via qGL-2D haplotype analysis. A total of seven haplotypes were detected, including two high-frequency haplotypes (HAP1 and HAP4, 36.6% and 56.4%), two low-frequency haplotypes (HAP2 and HAP3, 3.6% and 2.8%) and three rare haplotypes (HAP5-7, < 1%) ( Figure 3C ). Among them, GL was the shortest in HAP1 (6.56 mm), followed by HAP2 (6.57 mm) and HAP3 (6.70 mm), whereas HAP4 had the longest GL ( Figure 3C ). For other five traits were related to grain shape (GA, GW, GD and HGW) and grain quality (GPC). The HAP4 exhibited the greatest GA, GW, GD, and HGW, and the lowest GPC ( Figure 3D ). The results suggested that qGL-2D was widely involved in grain shape and grain quality.

Figure 3.

Figure 3

Open in a new tab

Determination of candidate genes within qGL-2D. (A) Association signals (top) and LD heatmap (bottom) of qGL-2D. Triangular block shows region with strong local LD (r 2 > 0.6). (B) Grain length of 04chu122, Aikang58 and Chinese Spring. Scale bar, 10 mm. (C) Epidermal cell length of 04chu122, Aikang58 and Chinese Spring. Scale bars, 200 um. (D) Expression level of TraesCS2D02G414800 in 04chu122, Aikang58, Chinese Spring and NILs carried 04chu122 qGL-2D or Aikang58 qGL-2D. (E) Comparison of expression level of Chinese Spring (HAP1), and the other accessions including 04chu122 (HAP5), Aikang58 (HAP4) and NILs carried 04chu122 qGL-2D and Aikang58 qGL-2D.

To determine the evolutionary features of qGL-2D, we conducted a phylogenetic analysis of the seven haplotypes. Two major clades were formed ( Figure 3E ). One clade contained a widely divergent group, including HAP4, HAP3, HAP2 and HAP7, the most prevalent haplotypes associated with improved varieties of bread wheat. Another major haplotype in bread wheat landrace, HAP1, was clustered in the other clade ( Figure 3E ). In summary, the qGL-2D allele associated with improved varieties of bread wheat showed substantial genetic differences compared with bread wheat landrace, which could be attributed to selective effects on large grain during the process of modern bread wheat improvement.

Determination of candidate genes within qGL-2D

To analyze the candidate gene within qGL-2D, we defined the QTL region based on local LD. As indicated in the LD heatmap, an interval from 522,544,495 to 533,987,666 bp on chromosome 2D was an LD block with r 2 > 0.6 ( Figure 2A ). The qGL-2D contains 125 annotated genes. To further reduce the candidate number, we performed transcriptome analysis using one short-grain accession (Chinese Spring (HAP1)), two long-grain bread wheat accessions (Aikang 58 (HAP4), 04chu122 (HAP5)) and 5 BC2 near isogenic lines (NILs) carrying qGL-2D 04chu122 or aikang58 segment ( Figure 2B ). A total of 29 expressed genes were identified in eight accessions mentioned above ( Table S2 ), and only TraesCS2D02G414800 showed higher expression within two long-grain and eight NILs than in one short-grain accession ( Figures 2D , E ). Homology analysis showed that TraesCS2D02G414800 encodes oleosin, which is involved in seed maturation and germination. Taken together, the results provide possible key candidates for further investigation of the molecular mechanism underlying GL within bread wheat.

Discussion

Grain size is one of the most frequently selected traits during domestication (Meyer and Purugganan, 2013; Zuo and Li, 2014). Among the many yield-related traits, increased grain size is the main factor associated with increased grain yield at a certain stage of domestication (Zheng et al., 2011). The grains of wild relatives are usually small and round in shape, and domestication has greatly increased the diversity of grain shape and size together with other changes (Fan et al., 2006). Grain size is predominantly determined by genetic factors, whereas grain filling is controlled by both genetic and environmental factors (Sakamoto and Matsuoka, 2008). Our study validated the significant changes in grain size of landrace to improved variety of bread wheat, and also suggested further accumulation of large-size alleles within improved variety rather than landrace. The most significant finding of the present study was the key locus for GL, qGL-2D, which showed the highest association after years of multi-plot testing. Haplotype and evolution analysis indicated that the superior allele of qGL-2D was mainly hidden in the improved variety rather than in landrace, which may result in significant difference in GL. Identification of the differential expression yielded a single candidate gene of qGL-2D. The results provide the opportunity for the delineation of the regulatory mechanism and related processes during grain development.

The coordination of grain size (weight) and grain quality is a major goal in breeding, as the increased grain size often reduces grain quality (Sakamoto and Matsuoka, 2008; Wang et al., 2012). Correlations between traits are a common biological phenomenon, especially those associated with determination of spike, growth duration, yield, and root and shoot (Crowell et al., 2016; Li et al., 2018; Zhao et al., 2019; Zhao et al., 2021). The present study indicated that the grain size increased while the GPC of bread wheat decreased from landrace to improved variety. The long-grain allele of qGL-2D showed a lower GPC, while the short-grain allele of qGL-2D showed a higher GPC. Pleiotropy and LD in natural population are usually considered as the main factors underlying this phenomenon, which is a major challenge in future breeding programs (Chen and Lübberstedt, 2010; Crowell et al., 2016). The role of two complementary genes associated with grain yield and grain quality requires further analysis (Zuo and Li, 2014).

Data availability statement

The data presented in the study are deposited in the OMIX repository (https://ngdc.cncb.ac.cn/omix/), accession number OMIX002373.

Author contributions

S.B.S. and L.R.K. designed and supervised the work; H.T.Y., M.Y.L., L.H.D., N.X.C., L.J.W., S.S. and Y.N.L. performed the research; H.T.Y. and Y.C.H. analyzed the data; H.T.Y. and S.B.S. wrote the paper. All authors read and approved the final manuscript.

Funding

This work was supported by the Natural Science Foundation of Shandong Province (ZR2020MC096 and ZR2021ZD31).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.1072904/full#supplementary-material

Click here for additional data file. (14.2KB, xlsx)
Click here for additional data file. (1.9MB, docx)

References

  1. Breseghello F., Sorrells M. E. (2006. a). Association analysis as a strategy for improvement of quantitative traits in plants. Crop Sci. 46, 1323–1330. doi:  10.2135/cropsci2005.09-0305 [DOI] [Google Scholar]
  2. Breseghello F., Sorrells M. E. (2006. b). Association mapping of kernel size and milling quality in wheat (Triticum aestivum l.) cultivars. Genetics 172, 1165–1177. doi:  10.1534/genetics.105.044586 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Chen Y., Lübberstedt T. (2010). Molecular basis of trait correlations. Trends Plant Sci. 15, 454–461. doi:  10.1016/j.tplants.2010.05.004 [DOI] [PubMed] [Google Scholar]
  4. Cheng R., Kong Z., Zhang L., Xie Q., Jia H., Yu D., et al. (2017). Mapping QTLs controlling kernel dimensions in a wheat inter-varietal RIL mapping population. Theor. Appl. Genet. 130, 1405–1414. doi:  10.1007/s00122-017-2896-2 [DOI] [PubMed] [Google Scholar]
  5. Crowell S., Korniliev P., Falcão A., Ismail A., Gregorio G., Mezey J., et al. (2016). Genome-wide association and high-resolution phenotyping link oryza sativa panicle traits to numerous trait-specific QTL clusters. Nat. Commun. 7, 10527. doi:  10.1038/ncomms10527 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Evans L. T. (1972). Storage capacity as a limitation on grain yield. Rice Breeding., 499–511. [Google Scholar]
  7. Fan C., Xing Y., Mao H., Lu T., Han B., Xu C., et al. (2006). GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theor. Appl. Genet. 112, 1164–1171. doi:  10.1007/s00122-006-0218-1 [DOI] [PubMed] [Google Scholar]
  8. Golbeck J. H. (1987). Structure, function and organization of the photosystem I reaction center complex. Biochim. Biophys. Acta 895, 167–204. doi:  10.1016/S0304-4173(87)80002-2 [DOI] [PubMed] [Google Scholar]
  9. Guo X., Fu Y., Lee Y. J., Chern M., Li M., Cheng M., et al. (2022). The PGS1 basic helix-loop-helix protein regulates Fl3 to impact seed growth and grain yield in cereals. Plant Biotechnol. J. 20, 1311–1326. doi:  10.1111/pbi.13809 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Juliana P., Poland J., Huerta-Espino J., Shrestha S., Crossa J., Crespo-Herrera L., et al. (2019). Improving grain yield, stress resilience and quality of bread wheat using large-scale genomics. Nat. Genet. 51, 1530–1539. doi:  10.1038/s41588-019-0496-6 [DOI] [PubMed] [Google Scholar]
  11. Kumar S., Stecher G., Tamura K. (2016). MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874. doi:  10.1093/molbev/msw054 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Lipka A. E., Tian F., Wang Q., Peiffer J., Li M., Bradbury P. J., et al. (2012). GAPIT: Genome association and prediction integrated tool. Bioinformatics 28, 2397. doi:  10.1093/bioinformatics/bts444 [DOI] [PubMed] [Google Scholar]
  13. Li F., Xie J., Zhu X., Wang X., Zhao Y., Ma X., et al. (2018). Genetic basis underlying correlations among growth duration and yield traits revealed by GWAS in rice (Oryza sativa l.). Front. Plant Sci. 9. doi:  10.3389/fpls.2018.00650 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Liu G., Jia L., Lu L., Qin D., Zhang J., Guan P., et al. (2014). Mapping QTLs of yield-related traits using RIL population derived from common wheat and Tibetan semi-wild wheat. Theor. Appl. Genet. 127, 2415–2432. doi:  10.1007/s00122-014-2387-7 [DOI] [PubMed] [Google Scholar]
  15. Lopes M. S., Reynolds M. P., Mcintyre C. L., Mathews K. L., Jalal Kamali M. R., Mossad M., et al. (2013). QTL for yield and associated traits in the Seri/Babax population grown across several environments in Mexico, in the West Asia, north Africa, and south Asia regions. Theor. Appl. Genet. 126, 971–984. doi:  10.1007/s00122-012-2030-4 [DOI] [PubMed] [Google Scholar]
  16. Ma J., Zhang H., Li S., Zou Y., Li T., Liu J., et al. (2019). Identification of quantitative trait loci for kernel traits in a wheat cultivar Chuannong16. BMC Genet. 20, 77. doi:  10.1186/s12863-019-0782-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Mccartney C. A., Somers D. J., Humphreys D. G., Lukow O., Ames N., Noll J., et al. (2005). Mapping quantitative trait loci controlling agronomic traits in the spring wheat cross RL4452x'AC domain'. Genome 48, 870–883. doi:  10.1139/g05-055 [DOI] [PubMed] [Google Scholar]
  18. Meyer R. S., Purugganan M. D. (2013). Evolution of crop species: genetics of domestication and diversification. Nat. Rev. Genet. 14, 840–852. doi:  10.1038/nrg3605 [DOI] [PubMed] [Google Scholar]
  19. Mora-Ramirez I., Weichert H., Wirén N., Frohberg C., Bodt S., Schmidt R. C., et al. (2021). Theda1 mutation in wheat increases grain size under ambient and elevated CO2 but not grain yield due to trade-off between grain size and grain number. Plant-Environment Interact. 2, 61–73. doi:  10.1002/pei3.10041 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Pang Y., Liu C., Wang D., St. Amand P., Bernardo A., Li W., et al. (2020). High-resolution genome-wide association study identifies genomic regions and candidate genes for important agronomic traits in wheat. Mol. Plant 13, 1311–1327. 10.1016/j.molp.2020.07.008 [DOI] [PubMed] [Google Scholar]
  21. Pritchard J. K., Stephens M., Donnelly P. (2000). Inference of population structure using multilocus genotype data. Genetics 155, 945–959. doi:  10.1093/genetics/155.2.945 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M. A. R., Bender D., et al. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575. doi:  10.1086/519795 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Ramya P., Chaubal A., Kulkarni K., Gupta L., Kadoo N., Dhaliwal H. S., et al. (2010). QTL mapping of 1000-kernel weight, kernel length, and kernel width in bread wheat (Triticum aestivum l.). J. Appl. Genet. 51, 421–429. doi:  10.1007/BF03208872 [DOI] [PubMed] [Google Scholar]
  24. Ray D. K., Mueller N. D., West P. C., Foley J. A. (2013). Yield trends are insufficient to double global crop production by 2050. PloS One 8, e66428. doi:  10.1371/journal.pone.0066428 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Sakamoto T., Matsuoka M. (2008). Identifying and exploiting grain yield genes in rice. Curr. Opin. Plant Biol. 11, 209–214. doi:  10.1016/j.pbi.2008.01.009 [DOI] [PubMed] [Google Scholar]
  26. Shi S., Azam F. I., Li H., Chang X., Jing R. (2017). Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes. Euphytica 213, 246. doi:  10.1007/s10681-017-2002-5 [DOI] [Google Scholar]
  27. Shin J.-H., Blay S., Mcneney B., Graham J. (2006). LDheatmap: An r function for graphical display of pairwise linkage disequilibria between single nucleotide polymorphisms. J. Stat. Software 16, 1–9. doi:  10.18637/jss.v016.c03 [DOI] [Google Scholar]
  28. Tang Y., Liu X., Wang J., Li M., Wang Q., Tian F., et al. (2016). GAPIT version 2: An enhanced integrated tool for genomic association and prediction. Plant Genome 9, 1–9. doi:  10.3835/plantgenome2015.11.0120 [DOI] [PubMed] [Google Scholar]
  29. Tang H., Wang H., He M., Zhang M., Hu Y., Li Z., et al. (2017). QTL analysis for wheat falling number in a recombinant inbred line population segregated with 1BL/1RS translocation in a rainfed agricultural area of China. Euphytica 213, 235. doi:  10.1007/s10681-017-2028-8 [DOI] [Google Scholar]
  30. Wang S., Kun W., Yuan Q., Liu X., Liu Z., Lin X., et al. (2012). Control of grain size, shape and quality by OsSPL16 in rice. Nat. Genet. 44, 950–954. doi:  10.1038/ng.2327 [DOI] [PubMed] [Google Scholar]
  31. Wang S., Yan X., Wang Y., Liu H., Cui D., Chen F. (2016). Haplotypes of the TaGS5-A1 gene are associated with thousand-kernel weight in chinese bread wheat. Front. Plant Sci. 7. doi:  10.3389/fpls.2016.00783 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Wang X., Dong L., Hu J., Pang Y., Hu L., Xiao G., et al. (2019. b). Dissecting genetic loci affecting grain morphological traits to improve grain weight via nested association mapping. Theor. Appl. Genet. 132, 3115–3128. doi:  10.1007/s00122-019-03410-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Wang J., Li X., Do Kim K., Scanlon M. J., Jackson S. A., Springer N. M., et al. (2019. a). Genome-wide nucleotide patterns and potential mechanisms of genome divergence following domestication in maize and soybean. Genome Biol. 20, 74. doi:  10.1186/s13059-019-1683-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Wu Q. H., Chen Y. X., Zhou S. H., Fu L., Chen J. J., Xiao Y., et al. (2015). High-density genetic linkage map construction and QTL mapping of grain shape and size in the wheat population Yanda1817 × Beinong6. PloS One 10, e0118144. doi:  10.1371/journal.pone.0118144 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Yang L., Zhao D., Meng Z., Xu K., Yan J., Xia X., et al. (2020). QTL mapping for grain yield-related traits in bread wheat via SNP-based selective genotyping. Theor. Appl. Genet. 133, 857–872. doi:  10.1007/s00122-019-03511-0 [DOI] [PubMed] [Google Scholar]
  36. Yano K., Yamamoto E., Aya K., Takeuchi H., Lo P.-C., Hu L., et al. (2016). Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat. Genet. 48, 927–934. doi:  10.1038/ng.3596 [DOI] [PubMed] [Google Scholar]
  37. Yan X., Zhao L., Ren Y., Dong Z., Cui D., Chen F. (2019). Genome-wide association study revealed that the TaGW8 gene was associated with kernel size in Chinese bread wheat. Sci. Rep. 9, 2702. doi:  10.1038/s41598-019-38570-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Zanke C., Ling J., Plieske J., Kollers S., Ebmeyer E., Korzun V., et al. (2015). Analysis of main effect QTL for thousand grain weight in European winter wheat (Triticum aestivum L.) by genome-wide association mapping. Frontiers in Plant Science 6. doi: 10.3389/fpls.2015.00644 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Zhai H., Feng Z., Du X., Song Y., Liu X., Qi Z., et al. (2018). A novel allele of TaGW2-A1 is located in a finely mapped QTL that increases grain weight but decreases grain number in wheat (Triticum aestivum l.). Theor. Appl. Genet. 131, 539–553. doi:  10.1007/s00122-017-3017-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Zhang Y., Li D., Zhang D., Zhao X., Cao X., Dong L., et al. (2018). Analysis of the functions of TaGW2 homoeologs in wheat grain weight and protein content traits. Plant J. 94, 857–866. doi:  10.1111/tpj.13903 [DOI] [PubMed] [Google Scholar]
  41. Zhao Y., Jiang C., Rehman R. M. A., Zhang H., Li J., Li Z. (2019). Genetic analysis of roots and shoots in rice seedling by association mapping. Genes Genomics 41, 95–105. doi:  10.1007/s13258-018-0741-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Zhao Y., Yin Z., Wang X., Jiang C., Aslam M. M., Gao F., et al. (2021). Genetic basis and network underlying synergistic roots and shoots biomass accumulation revealed by genome-wide association studies in rice. Sci. Rep. 11, 13769. doi:  10.1038/s41598-021-93170-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Zheng T. C., Zhang X. K., Yin G. H., Wang L. N., Han Y. L., Chen L., et al. (2011). Genetic gains in grain yield, net photosynthesis and stomatal conductance achieved in henan province of China between 1981 and 2008. Field Crops Res. 122, 225–233. doi:  10.1016/j.fcr.2011.03.015 [DOI] [Google Scholar]
  44. Zuo J., Li J. (2014). Molecular genetic dissection of quantitative trait loci regulating rice grain size. Annu. Rev. Genet. 48, 99–118. doi:  10.1146/annurev-genet-120213-092138 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Click here for additional data file. (14.2KB, xlsx)
Click here for additional data file. (1.9MB, docx)

Data Availability Statement

The data presented in the study are deposited in the OMIX repository (https://ngdc.cncb.ac.cn/omix/), accession number OMIX002373.

Articles from Frontiers in Plant Science are provided here courtesy of Frontiers Media SA

RESOURCES