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Article

Genome-Wide Association Analysis of Rice Leaf Traits

by
Nansheng Wang
1,†,
Xingmeng Wang
1,†,
Yingzhi Qian
1,†,
Di Bai
1,
Yaling Bao
1,
Xueyu Zhao
1,
Peng Xu
1,
Keyang Li
1,2,
Jianfeng Li
1,
Kang Li
1,
Dewen Zhang
2,* and
Yingyao Shi
1,*
1
College of Agronomy, Anhui Agricultural University, Hefei 230000, China
2
Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230031, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(11), 2687; https://doi.org/10.3390/agronomy13112687
Submission received: 31 August 2023 / Revised: 24 October 2023 / Accepted: 24 October 2023 / Published: 25 October 2023
(This article belongs to the Special Issue Recent Advances in Bioinformatics for Plant Genetic Traits)
Browse Figures
Versions Notes

Abstract

:
Yield-related traits have always been a research hotspot in rice breeding, and functional leaves directly affect the photosynthetic efficiency and yield of rice. Therefore, it is of great significance to explore the genes related to rice leaf size and shape to improve rice leaf morphology, photosynthesis efficiency, and yield. This study detected the quantitative trait loci (QTLs) for the length, width, length–width ratio, and area of rice flag leaf and second leaf in 393 accessions from the 3000 Rice Genome Project (3KRGP) by high-density single-nucleotide polymorphism genotyping. As a result, 91 QTLs were detected, among which 5 candidate genes (LOC_Os03g29170, LOC_Os06g17285, LOC_Os04g35060, LOC_Os03g27450, and LOC_Os09g16280) were identified. In addition, the epistatic interactions affecting leaf-related traits were also investigated, resulting in the identification of 134 significant QQIs (QTL by QTL interactions) pairs. The results of this study provide an important genetic basis for mining genes associated with rice leaf shape and valuable genetic information for rice breeding.
Keywords:
rice; leaf; GWAS; QTLs

1. Introduction

Rice is one of the most important food crops in the world and plays a critical role in human food security [1,2,3]. As an important source organ for the formation of photosynthetic products in rice, the leaf accounts for more than 90% of the total photosynthetic capacity of the rice plant, providing necessary photosynthetic assimilation products and energy substances for plant growth and development [4]. Previous studies have reported that more than 50% of the carbohydrates in rice grains are derived from the photosynthesis of flag leaves, and the photosynthetic products synthesized by flag leaves and second leaves contribute to more than 80% of rice grain yield [5,6,7,8]. An appropriate flag leaf size is conducive to the increase in photosynthetic products after heading as well as improvement of the heading and grain filling rate [9,10]. The flag leaf mainly affects rice yield by regulating the photosynthetic leaf area, light transmittance, photosynthetic efficiency, and net assimilation rate [11]. Flag leaf traits mainly include leaf length, leaf width, leaf inclination, leaf curling, and chlorophyll content. The length and width of flag leaf directly determine the length-width ratio and flag leaf area and affect the net photosynthetic rate and light transmittance. Generally, for high-yield varieties, the second leaf is the longest and the flag leaf has the greatest width, area, and weight [12].
The traits related to rice flag leaf are complex quantitative traits controlled by multiple genes, and they are susceptible to environmental and genetic background [13,14,15]. To date, many quantitative trait loci (QTLs) for flag leaf traits have been detected using different genetic populations, which are distributed on 12 pairs of chromosomes in rice [16,17,18,19,20]. For instance, Chen et al. [17] identified a major QTL, qFLW4, which controls rice flag leaf width on chromosome 4. Wang et al. [21] fine mapped a major QTL qFL1 controlling flag leaf size on chromosome 1 using the backcross population of Zhenshan 97 and 9311, which affects flag leaf length, width, area, and multiple yield-related traits. The flag leaf length, width, and area showed high heritability in the F1 generation, and the gene effect on leaf width is mainly an additive effect, while that on leaf length and area is mainly manifested as dominant effects [22]. Narrow leaf is one of the typical mutant traits in rice. Fujita et al. found a narrow leaf gene nal1 (narrow leaf 1) located on chromosome 4 of rice, which encodes a plant-specific protein with unknown biochemical function. SPIKE is an allele of NAL1 derived from a unique haplotype of tropical japonica rice, which can significantly increase the number of grains per panicle in rice [23]. NAL1 is a pleiotropic gene simultaneously controlling rice root volume, flag leaf width, and spikelet number per panicle [24,25].
With the genetic populations used for the QTL mapping of leaf shape, researchers often detect yield-related QTLs and QTL intervals with significant effects on leaf shape and yield. The co-localization of these source-sink QTLs indicates that leaf size is closely related to yield. Tang et al. [26] obtained 14 QTLs for leaf length and 19 QTLs for leaf width using a CSSL population of 143 individuals. In another study, genome-wide association analysis and high-throughput leaf scoring were performed with a natural population of 532 individuals, resulting in the mapping of 73 QTLs related to rice leaves [27]. Cui et al. identified five QTLs related to flag leaf length using recombinant inbred lines from two indica rice varieties [28]. Wang et al. analyzed the inheritance of leaf length and leaf width of upper three leaves in rice by using the recombinant inbred lines of Shanyou 63 [29]. In a study of a double-haploid (DH) population produced by another culture of a typical indica–japonica cross (Chunjiang 06/Taizhongdi 1) F1 population, it was found that the leaf shape characteristics of adjacent functional leaves are significantly correlated, and they are quantitative traits controlled by multiple genes [30].
Leaf size is controlled by the complex coordination of cell division and expansion, and a reduced number of veins is a distinct feature of rice leaf mutants [31]. Several auxin-related genes have been cloned in rice, such as TDD1 [32], OsGH3.5, OsARF19 [33], OsSAUR45 [34], and OsCHR4 [35]. The expansin genes OsEXPA8 and OsEXPB2 affect leaf width through cell expansion and have been isolated in rice [36,37]. In addition, OsFLW7 regulates the width of basal leaves and increases photosynthetic leaf area in rice [38].
Here, to explore more genes controlling rice leaf shape, we used 393 rice accessions from the 3000 Rice Genome Project (3KRG) to perform a genome-wide association study (GWAS) with the mixed linear model (MLM) and detected genome-wide QTLs and QQIs for four traits related to leaf shape. The findings provide a basis for the further cloning of relevant genes and the improvement of the plant type of hybrid rice.

2. Materials and Methods

2.1. Plant Materials and Field Experiment

In this study, 393 accessions from the 3000 Rice Genome Project (3KRGP) [39] were selected for the mapping of QTLs by GWAS (Table S1), which were derived from Bangladesh (1), Brazil (7), China (242), Colombia (1), Cote d‘Ivoire (8), Egypt (1), Ghana (1), India (30), Indonesia (4), Iran (6), Japan (6), Malaysia (9), Mali (2), Myanmar (5), Nepal (3), North Korea (2), Pakistan (2), Peru (1), the Philippines (29), Portugal (1), Sri Lanka (4), Thailand (1), the USA (3), Vietnam (11), and _no_info (13). All the accessions could be divided into five subpopulations (Xian, Geng, Admix, Bas, and Aus). Since there were no more than ten accessions for Bas and Aus, we focused on the Xian, Geng, and Admix subpopulations in the following analysis.
The 393 accessions were planted in the experimental field of Nanbin Farm, Sanya, 112 Hainan (18.3° N, 109.3° E), Institute of Crop Science, Chinese Academy of Agricultural Sciences. From December 2021 to May 2022, fourteen plants per accession were planted in two rows with seven plants per row. The spatial distances between two rows and two plants were 25 and 20 cm, respectively, and the management followed the local standard practices [40]. At the heading stage, considering the marginal effect of plant planting, the middle six plants in each plot were measured. Flag leaf length (FLL), flag leaf width (FLW), second leaf length (SLL), and second leaf width (SLW) were measured in the same way. Flag leaf length–width ratio (FLR), flag leaf area (FLA), second leaf length–width ratio (SLR), and second leaf area (SLA) were calculated as follows:
FLR = FLL/FLW
FLA = FLL × FLW × 0.75
SLR = SLL/SLW
SLA = SLL × SLW × 0.75
In the formula, 0.75 is a correction factor in the calculation of leaf area at heading stage [41].

2.2. Statistical Analysis of Phenotypic Data

The phenotypic data of the plant leaf shape were measured and analyzed. Microsoft Excel 2018 was used for data collation and compilation, and the mean value, standard deviation, and coefficient of variation of each trait were calculated, and the correlation coefficient used was the Pearson correlation coefficient. The correlation coefficient matrix and box plots of the phenotypic data were done using the R packages ‘corrploof’ and ‘ggplot2’.

2.3. Genotyping

The resequencing data of 393 rice accessions have been published in NCBI, and the information on the variation sites is available in the SNP-Seek database (https://snp-seek.irri.org/index.zul, accessed on 1 July 2023.) [42,43]. We further screened the SNPs using PLINK (version 1.9) [44], MAF (0.05), and GENO (0.2), and we obtained 1,872,276 SNPs with minor allele frequency (MAF) > 5% and data loss rate (MDR) < 0.2 (Figure S2).

2.4. Genome-Wide Association Study

In this study, we obtained 1,872,276 SNPs (MAF > 0.05) and 8 sets of phenotypic data. The mixed linear model (MLM) implemented in TASSEL [45] was used to correlate the phenotypic data with the corresponding SNP loci. A Manhattan map was generated by using the R language. In order to reduce the influence of genetic false-positive error rate, we used the method of GEC (Version 1.0) [46] to calculate Significant_p_Value as the threshold of MLM. By calculation, p = 1.22 × 10−6 was selected as the threshold for a significant association of SNP markers with traits. In addition, a 200 kb region was chosen as the overlap recognition marker-trait-associated signal range [45]. If there was one or more significant SNP in the 200 kb region, these SNPs were considered as one QTL. The smallest SNP was defined as the lead SNP.

2.5. Identification of Candidate Genes and Haplotype Analysis

To identify the candidate genes in QTLs for FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA, the rice genome annotation project (http://rice.plantbiology.msu.edu, accessed on 26 April 2023) was used to search for candidate genes within the candidate area. The SNP data of the candidate genes were screened with MAF > 0.05. Non-synonymous SNPs causing amino acid changes in GWAS results were focused on, and the haplotypes with a sample size smaller than 10 were ignored. R language was used to perform haplotype analysis on non-synonymous SNPs in the coding region, and Student’s t test was used to determine whether this locus results in changes in FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA.

2.6. Epistatic Interaction Analysis

We selected 2753, 4326, 1899, 3653, 1689, 2358, 1328, and 1938 SNPs from GWAS (−Log10 (p) > 3.0) of FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA under MLM for interaction analysis. The epistasis method of 3V multi-locus random SNP effect mixed linear model (3VmrMLM) [47] was used to detect the epistatic interaction of the main effect QTLs. The parameters of IIIVmrMLM function were set as ‘SearchRadius = [0, 1], svpal = [0.10, 0.10] and sblgwast = −2.50’, ‘num_Threads = 8’. The threshold of significant epistasis was set to LOD = 3.0.

3. Results

3.1. Phenotypic Variations of Leaf Shape in Different Rice Subpopulations

To explore new genes related to leaf phenotype, four leaf-shape-related traits (FLL, FLW, FLR, FLA, SLL, SSLW, SLR, and SLA) were measured in 393 rice accessions. Table 1 shows that the FLL of 393 accessions ranged from 12.42 to 121.5 cm, with an average of 29.78 cm. Among them, Admix had significantly higher FLL than the other two subpopulations, and Geng showed the shortest FLL. FLW ranged from 1.02 to 3.40 cm, with the value of Geng being significantly lower than that of Admix and Xian. FLR ranged from 6.92 to 68.03 cm, with an average of 17.14 cm. FLA was 12.17~155.84 cm2. In addition, the SLL of 393 accessions ranged from 13.85 to 84.25 cm, with an average of 41.04 cm. SLW ranged from 0.68 to 2.78 cm, with Geng having a significantly lower value than Admix and Xian. SLR ranged from 10.78 to 78.16 cm, with an average of 30.36 cm, and SLA ranged from 9.09 to 95.32 cm2. Among different subpopulations, Admix had significantly higher FLL and FLA than other two subpopulations, and Geng had the lowest value. Geng showed significantly lower FLW, FLR, SLL, SLR, and SLA than Admix and Xian, as well as significantly lower SLW than Xian. These results indicated that these traits have great genetic variations in the 393 rice accessions. The coefficients of variation of FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA were 30.23%, 18.02%, 34.13%, 35.80%, 28.17%, 20.22%, 30.67%, and 37.18%, respectively (Table 1, Figure 1).
Among the four traits, FLW and FLR, FLW and SLR, FLR and SLW, and SLW and SLR were negatively correlated with each other, particularly FLR and SLW, which had the highest correlation coefficient (−0.44). Positive correlations were found among the remaining pairs of traits, especially FLW and SLW, which showed the highest correlation coefficient (0.89) (Figure 1).

3.2. GWAS of FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA

In this study, a total of 1,872,276 high-quality SNPs (Figure S1) were identified from the genomic sequences of 393 rice accessions, and the phenotypic data of four leaf traits (FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA) were measured. MLM was used to combine SNPs and phenotypic data for GWAS. With the association threshold p = 1.22 × 10−6, a total of 503 significant SNP loci (Tables S2 and S3) related to leaf shape were identified. Considering the attenuation distance of rice LD, adjacent SNPs with a span of less than 200 kb were defined as one single QTL, and the SNP with the lowest p-value was used as the lead SNP. Finally, a total of 91 QTLs were identified, including 6, 30, 6, 8, 1, 34, 4, and 2 significant QTLs for FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA, respectively. Moreover, a total of 1157 candidate genes were obtained. For FLL, one QTL was detected on chromosome 2, one QTL was detected on chromosome 3, one QTL was detected on chromosome 7, one QTL was detected on chromosome 9, and two QTLs were detected on chromosome 6. The phenotypic variation coefficients were 19.43%, 19.30%, 20.85%, 18.58%, 9.61%, and 8.55%, respectively. For FLW, five QTLs were detected on chromosome 4; four on chromosomes 1 and 8; three on chromosomes 3 and 7; two on chromosomes 3, 6, 11, and 12; and one on chromosomes 5, 9, and 10. The greatest phenotypic variation coefficient was found for qFLW1.1 (16.32%) on chromosome 1, and the smallest phenotypic variation was detected for qFLW3.2 (7.11%) on chromosome 3. For FLR, one QTL was detected on chromosomes 2, 3, 4 and 5, respectively, and two on chromosome 6, whose phenotypic variations were 13.04%, 13.20%, 7.58%, 7.32%, 14.07%, and 13.32%, respectively. For FLA, two QTLs were detected on chromosomes 2, 6, and 7, and one on chromosomes 2 and 8, whose phenotypic variation coefficients were 15.37%, 9.22%, 14.12%, 15.76%, 13.59%, 10.12%, 7.80%, and 7.62%, respectively. For SLL, only one QTL was detected on chromosome 3. As for SLW, four QTLs were on chromosomes 1, 3, 4, 7, and 8; five on chromosome 2; two on chromosomes 6 and 12; one on chromosomes 9 and 10; and three on chromosome 11. The maximum phenotypic variation coefficient was found for qSLW7.2 (10.80%) on chromosome 7, and the minimum phenotypic variation coefficient was found for qSLW7.4 (7.14%) on chromosome 7. For SLR, one QTL was detected on chromosomes 1, 3, 5, and 12, and the phenotypic variation coefficients were 8.95%, 9.37%, 7.23%, and 7.59%, respectively. As for SLA, two QTLs were detected on chromosome 9, and the phenotypic variations coefficients were 7.38% and 7.21%, correspondingly (Table 2, Figure 2).

3.3. Identification of Candidate Genes for FLL, FLR, and FLA

FLL, FLR, and FLA had four identical QTLs, which were located at 26,002,540 on chromosome 2, 16,565,193 on chromosome 3, and 55,174,327 and 9,918,953 on chromosome 6, while FLL and FLA had one identical QTL located at 11,319,457 on chromosome 7. We searched the Nipponbare genome reference sequence (http://rice.plantbiology.msu.edu/gi-bin/gbrowse/rice/, accessed on 15 January 2022) to screen the candidate genes that may affect the flag leaf shape. After removal of genes encoding hypothetical proteins and retrotransposon and transposon proteins, we predicted the possible candidate genes in the QTL region.
For the 16.46–16.66 Mb interval on chromosome 3 (qFLL3.1, qFLR3.1, and qFLA3.1), seven genes were found, and LOC_Os03g29170 was annotated as sterol-4-alpha-carboxylate 3-dehydrogenase. This gene was located at 6 kb upstream of the significant QTL, and there were one SNP on the promoter, one SNP on the exon, and five SNPs on the intron. LOC_Os03g29170 had four haplotypes, with HapA, HapB, HapC, and HapD corresponding to CCCCGAT, CCCCGGA, CCTCGGA, and TTCAAGA, respectively. The average FLL in HapA–D were 24.28, 36.66, 31.12, and 29.79 cm, the average FLR were 15.4, 21.24, 17.28, and 17.93, and the average FLA were 28.74, 47.43, 41.91, and 37.24 cm2, respectively. In terms of FLL, FLR, and FLA, HapB had significantly higher values than other three haplotypes, and HapA had the lowest values (Figure 3). These results indicated that LOC_Os03g29170 may be a candidate gene for FLL, FLR, and FLA.
qFLL6.2, qFLR6.2, and qFLA6.2 were identical QTLs co-located for FLL, FLR, and FLA. A total of 14 genes were found in the 9.81–10.01 Mb interval on chromosome 6. LOC_Os06g17285 was annotated as a protein kinase located at 90 kb upstream of the significant QTL, and there were two SNPs on the promoter, four SNPs on the exon, and two SNPs on the intron. This gene had six haplotypes corresponding to AAATTACC, AAGTTACC, AGGTCACC, AGGTCACT, AGGTCGCT, and CGGACATT, correspondingly. HapB had the highest FLL and FLR (33.35 and 21.07, respectively). HapA had the highest value of FLA (41.10 cm2). HapC had significantly lower values of FLL, FLR, and FLA than other five haplotypes (Figure S2). These results indicated that LOC_Os06g17285 may be a candidate gene involved in FLL, FLR, and FLA.

3.4. Identification of Candidate Genes for FLW and SLW

For the 21.27–21.47 Mb interval on chromosome 4 (qFLW4.4 and qSLW4.4), 23 genes were found, and LOC_Os04g35060 was annotated as a nicotinate phosphoribosyltransferase-family-domain-containing protein. The gene was located at 63 kb downstream of the significant QTL, with one SNP on the promoter, one SNP on the exon, and nine SNPs on the intron. LOC_Os04g35060 had a total of five haplotypes, with HapA, HapB, HapC, HapD, and HapE corresponding to ATCCTCCGAAA, ATCTCATGAAA, ATCTCCTGAAA, H GCTCTCTCCCTGG, and GTCCTCCGAAA, respectively. For HapA–E, the FLW were 1.75, 1.85, 1.96, 1.70, and 1.63 cm, and the SLW were 1.33, 1.44, 1.50, 1.33, and 1.26 cm, respectively. HapE had significantly lower FLW and SLW than HapB and HapC (Figure 4). Therefore, it can be speculated that LOC_Os04g35060 is a functional gene in this locus and is involved in regulating FLW and SLW in rice.
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Figure 3. Identification of candidate genes for FLL, FLR, and FLA. (a) Haplotype region of LOC_Os03g29170. The promoter is represented by the white box, the exon is represented by the blue box, and the intron and the intergenic region are represented by the thin blue line that together constitutes the LOC_Os03g29170 gene structure map. The physical location of each labelled SNP in the segment is marked with a thin black line. Only haplotypes of more than 10 accessions are shown in the table. (b–d) Local Manhattan plot (top) for FLL, FLR, and FLA. (e–g) Box plots for FLL, FLR, and FLA in the four haplotypes of LOC_Os03g29170 in all accessions in 2022. Differences between haplotypes were statistically analyzed using Tukey’s test. (h) LD heat map of the local Manhattan map. ‘a’, ‘b’ and ‘c’ are based on whether the t-test is significant between each other.
Figure 3. Identification of candidate genes for FLL, FLR, and FLA. (a) Haplotype region of LOC_Os03g29170. The promoter is represented by the white box, the exon is represented by the blue box, and the intron and the intergenic region are represented by the thin blue line that together constitutes the LOC_Os03g29170 gene structure map. The physical location of each labelled SNP in the segment is marked with a thin black line. Only haplotypes of more than 10 accessions are shown in the table. (b–d) Local Manhattan plot (top) for FLL, FLR, and FLA. (e–g) Box plots for FLL, FLR, and FLA in the four haplotypes of LOC_Os03g29170 in all accessions in 2022. Differences between haplotypes were statistically analyzed using Tukey’s test. (h) LD heat map of the local Manhattan map. ‘a’, ‘b’ and ‘c’ are based on whether the t-test is significant between each other.
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3.5. Identification of Candidate Genes for SLL and SLR

qSLL3.1 and qSLR3.1 were identical QTLs co-located for SLL and SLR. A total of 14 genes were found in the 15.64–15.84 Mb interval of chromosome 3. LOC_Os03g27450 was annotated as an ADP-ribosylation factor located at 1 kb upstream of the significant QTL, and there were four SNPs on the promoter, one SNP on the exon, and nine SNPs on the intron, with three haplotypes. HapA was CCGAATACCGAATT; HapB was GCGAATACCGAATT; and HapC was GTATGCTTTTTGGC. The average SLL were 45.44, 52.70, and 29.31 cm, and the average SLR were 32.47, 37.79 and 24.81 cm, respectively. HapA had significantly lower SLL and SLR than HapB and significantly higher SLL and SLR than HapC (Figure 5). Therefore, LOC_Os03g27450 is involved in the regulation of SLL and SLR in rice.

3.6. Identification of Candidate Genes for SLA

For the 9.84–10.04 Mb interval on chromosome 9 (qSLA9.1), seven genes were detected, and LOC_Os09g16280 was annotated as a hydroxyproline-rich glycoprotein family protein. This gene was located at 13 kb downstream of the significant QTL, with nine SNPs on the promoter and 12 SNPs on the exon. LOC_Os09g16280 had four haplotypes with average SLA of 50.19, 41.05, 42.84, and 26.18 cm2, correspondingly. HapD had significantly lower SLA than the other three haplotypes (Figure 6). These results suggested that LOC_Os09g16280 may be a candidate gene for SLA.

3.7. QQIs for Rice Leaf Shape Traits

We used the 3VmrMLM (Version 1.0) software package to perform epistatic interaction analysis on the GWAS (−log10 (p) > 3) results of MLM. A total of 77 significant QQIs pairs were identified for leaf shape traits, among which three pairs were associated with the GWAS results. The identified QQIs pairs (explained total phenotypic variance) were 13 (46.15%) for FLL, 10 (16.93%) for FLW, 11 (42.96%) for FLR, 14 (65.43%) for FLA, 7 (40.61%) for SLL, 9 (29.94%) for SLW, 7 (34.16%) for SLR, and 6 (34.1%) for SLA. Detection of QQIs pairs between rs1_9643654 and rs5_882398, rs10_520002 and rs11_17671926, rs2_27980795 and rs3_31906741, rs1_26975608 and rs8_21236494, and rs10_16508641 and rs10_21870405 revealed that FLL, FLR, FLA, SLL, and SLL had the highest PVE, which were 15.10%, 11.67%, 20.00%, 23.5%, and 20.30%, respectively (Table S4). These results suggested that epistatic interaction is an important component of GWAS and may facilitate understanding the heritability of complex traits in GWAS.
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Figure 5. Identification of candidate genes for FLW and SLW. (a) Haplotype region of LOC_Os03g27450. The promoter is represented by the white box, the exon is represented by the blue box, and the intron and the intergenic region are represented by the thin blue line that together constitutes the LOC_Os03g27450 gene structure map. The physical location of each labelled SNP in the segment is marked with a thin black line. Only haplotypes of more than 10 accessions are shown in the table. (b,c) Local Manhattan plot (top) for FLW and SLW. (d,e) Box plots for FLW and SLW in the four haplotypes of LOC_Os03g27450 in all accessions in 2022. Differences between haplotypes were statistically analyzed using Tukey’s test. (f) LD heat map of the local Manhattan map. ‘a’, ‘b’ and ‘c’ are based on whether the t-test is significant between each other.
Figure 5. Identification of candidate genes for FLW and SLW. (a) Haplotype region of LOC_Os03g27450. The promoter is represented by the white box, the exon is represented by the blue box, and the intron and the intergenic region are represented by the thin blue line that together constitutes the LOC_Os03g27450 gene structure map. The physical location of each labelled SNP in the segment is marked with a thin black line. Only haplotypes of more than 10 accessions are shown in the table. (b,c) Local Manhattan plot (top) for FLW and SLW. (d,e) Box plots for FLW and SLW in the four haplotypes of LOC_Os03g27450 in all accessions in 2022. Differences between haplotypes were statistically analyzed using Tukey’s test. (f) LD heat map of the local Manhattan map. ‘a’, ‘b’ and ‘c’ are based on whether the t-test is significant between each other.
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Figure 6. Identification of candidate genes for SLA. (a) Haplotype region of LOC_Os09g16280. The promoter is represented by the white box, the exon is represented by the blue box, and the intron and the intergenic region are represented by the thin blue line that together constitute the LOC_Os09g16280 gene structure map. The physical location of each labelled SNP in the segment is marked with a thin black line. Only haplotypes of more than 10 accessions are shown in the table. (b) Local Manhattan plot (top) for SLA. (c) Box plots for SLA in the four haplotypes of LOC_Os09g16280 in all accessions in 2022. Differences between haplotypes were statistically analyzed using Tukey’s test. (d) LD heat map of the local Manhattan map. ‘a’, ‘b’ and ‘c’ are based on whether the t-test is significant between each other.
Figure 6. Identification of candidate genes for SLA. (a) Haplotype region of LOC_Os09g16280. The promoter is represented by the white box, the exon is represented by the blue box, and the intron and the intergenic region are represented by the thin blue line that together constitute the LOC_Os09g16280 gene structure map. The physical location of each labelled SNP in the segment is marked with a thin black line. Only haplotypes of more than 10 accessions are shown in the table. (b) Local Manhattan plot (top) for SLA. (c) Box plots for SLA in the four haplotypes of LOC_Os09g16280 in all accessions in 2022. Differences between haplotypes were statistically analyzed using Tukey’s test. (d) LD heat map of the local Manhattan map. ‘a’, ‘b’ and ‘c’ are based on whether the t-test is significant between each other.
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4. Discussion

Rice is one of the world’s major food crops [48]. As the main organ of seed plants to produce organic nutrients, leaves are important for plants to carry out various physiological and biochemical activities, such as photosynthesis, respiration, and transpiration [49]. This study involved 393 rice accessions from all over the world, which have rich genetic variations and are representative for GWAS mapping to reveal the genetic basis of rice leaf shape. Among different subpopulations, the measured flag leaf and second leaf related traits showed extensive phenotypic variations. Generally, Xian rice has higher length, width, length–width ratio, and area of flag leaf and second leaf than Geng rice. These significant phenotypic variations may be related to genetic diversity. Therefore, the development of subpopulation variations can be utilized to improve the leaf shape of rice.
GWAS is an effective method to understand the genetic structure of many complex traits in crops [50,51]. Moreover, the development of MLM effectively reduces the impact of false positives. Here, genotyping was performed on 393 rice accessions, and genetic structure analysis and GWAS were performed using 1,872,276 SNPs. A large number of QTLs significantly associated with the flag leaf and second leaf of rice were mapped, indicating genetic complexity in controlling rice leaf shape. The cloned genes of ONI3 [52], GLR1 [53], and OsVPE3 [54] related to leaf growth were identified, indicating reliability of the phenotypic data and genes or QTLs identified by association analysis in this study.
Five candidate genes related to leaf shape were identified in the candidate regions. In addition, the LOC_Os03g29260 gene in qFLL3.1, qFLR3.1, and qFLA3.1 was also co-detected for FLL, FLR, and FLA. LOC_Os03g29260 encodes an elongation factor protein, and its expression level is related to cell growth and proliferation rate. It may be involved in cell division during the formation of flag leaf shape, affecting the length, width, and area of flag leaves. LOC_Os06g17285 was co-located in QTLs (qFLL6.2, qFLR6.2, and qFLA6.2) detected in FLL, FLR, and FLA, and its encoded protein kinase. Protein kinases are enzymes that change the activity of their substrates by changing the conformation, stability, and localization of these proteins, playing a crucial role in leaf senescence. Therefore, we speculate that LOC _ Os06g17285 may have an important effect on the shape of plant flag leaves.
It has been reported that LOC_Os04g35060 controls the expression of nicotinate phosphoribosyltransferase-family-domain-containing protein [55]. Nicotinamide adenine dinucleotide (NAD) plays critical roles in cellular redox reactions and remains at a sufficient level in the cell to prevent cell death. It is the basis of almost all metabolic pathways in cells. In the NAD remedial synthesis pathway, LOC_Os04g35060 is transcriptionally regulated, affecting the content of nicotinamide and the expression of some senescence-related genes and controlling leaf cell senescence. We speculate that LOC_Os04g35060 also affects the size of flag leaf and second leaf by influencing the metabolic rate of cells. Therefore, in the two traits of FLW and SLW, the LOC_Os04g35060 gene located in the qFLW4.4 and qSLW4.4 regions may be a candidate gene affecting the leaf width. For the two traits of SLL and SLR, we focused on LOC_Os03g27450 in qSLL3.1 and qSLR3.1. The ADP-ribosylation factor encoded by LOC_Os03g27450 controls the formation of Golgi vesicles in cells, promotes the fusion of vesicles and target membranes, and is a key component in the vesicle transport pathway [56]. Therefore, from the perspective of material exchange and synthesis, we speculate that LOC_Os03g27450 plays an important role in regulating the length and area of the second leaf. In addition, LOC_Os09g16280 is located on chromosome 9 of qSLA9.1. In this candidate region, LOC_Os09g16280 may be a candidate gene for SLA. It encodes the hydroxyproline-rich glycoprotein family protein, which is a main structural protein in the cell wall of higher plants and plays a role in maintaining cell traits and controlling cell growth [57]. In addition, it is also involved in intercellular adhesion and cell wall adhesion and plays an important role in leaf growth and development. In the formation process of the second leaf of plants, the cell wall increases the mechanical strength of the cell and bears the swelling pressure of the internal protoplast due to vacuolar water absorption, maintaining the inherent morphology of the organ and the plant, thus partially regulating the second leaf area.
In summary, we detected 6, 30, 6, 8, 1, 34, 4, and 2 QTLs associated with FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA, respectively. The genes in the candidate regions of the GWAS scan results were functionally annotated, and the candidate genes related to leaf shape were screened according to the biological function or expression pattern of the gene, followed by further haplotype analysis. We also revealed the candidate gene haplotypes and the dominant haplotypes combined with the phenotypic data. By combining haplotype block structure analysis and gene function annotation, candidate genes for the associated traits can be effectively selected in a candidate region with a large range of physical locations, and the accuracy of candidate gene screening for target traits can be significantly enhanced. The candidate genes associated with leaf shape discovered in this study can be functionally verified by using CRISPR-cas9 knockout mutants. The verified genes can be used for marker-assisted introgression to improve the adaptation of plants to the environment and have a great impact on plant yield.

5. Conclusions

Through high-density SNPs, a large rice natural population, and GWAS, we performed a robust and efficient genetic analysis of rice leaf shape. There are considerable genetic variations in the four rice leaf traits. A total of 91 QTLs are associated with four leaf shape traits. Haplotype difference analysis and functional annotation of the candidate genes were performed, and five candidate genes (LOC_Os03g29170, LOC_Os06g17285, LOC_Os04g35060, LOC_Os03g27450, and LOC_Os09g16280) were identified. It also provides valuable information for elucidating the molecular mechanism of these traits. The results provide important resources for the breeding and improvement of rice leaf shape.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy13112687/s1. Figure S1: Distribution of single-nucleotide polymorphisms (SNPs) and nucleotide diversity across the rice Nipponbare genome in the rice association panel. Table S1: Information of 393 rice accessions. Figure S2: Identification of candidate genes for FLL, FLR, and FLA. Table S2: Significant SNPs associated with leaf shape. Table S3: List of genes associated with leaf. Table S4: Significant epistasis loci of FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA in 393 rice accessions.

Author Contributions

X.W., Y.Q., D.B. and N.W., conducted the experiments and collected the data; N.W., Y.Q., D.B., X.W., J.L., X.Z., K.L. (Keyang Li), K.L. (Kang Li), P.X., Y.B. and Y.S., collated and statistically analyzed the data; N.W., K.L. (Keyang Li), K.L. (Kang Li) and Y.Q., constructed the graphics; N.W. and Y.S., wrote the paper; Y.S. and D.Z., designed the experiment, provided intellectual guidance, and reviewed the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Scientific Research Plan Major Projects of Anhui Province (grant number 2022AH040126), the Science and Technology Major Project of Anhui Province (grant number 2021d06050002), the Improved Varieties Joint Research (Rice) Project of Anhui Province (the 14th five-year plan), and the National Natural Science Foundation of China (grant number U21A20214).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

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Figure 1. Phenotypic variations of leaf shape traits under natural conditions. (a) Flag leaf length; (b) flag leaf width; (c) flag leaf length-width ratio; (d) flag leaf area; (e) second leaf length; (f) second leaf width; (g) second leaf length-width ratio; (h) second leaf area; (i) proportion of each subpopulation of the 393 rice accessions used in this study; (j) correlations between the leaf traits, in which the numbers in the cells are positive and negative correlation coefficients, ‘a’, ‘b’ and ‘c’ are based on whether the t-test is significant between each other, and ‘*’ and ‘***’ refer to significant correlations (p < 0.05, and p < 0.001).
Figure 1. Phenotypic variations of leaf shape traits under natural conditions. (a) Flag leaf length; (b) flag leaf width; (c) flag leaf length-width ratio; (d) flag leaf area; (e) second leaf length; (f) second leaf width; (g) second leaf length-width ratio; (h) second leaf area; (i) proportion of each subpopulation of the 393 rice accessions used in this study; (j) correlations between the leaf traits, in which the numbers in the cells are positive and negative correlation coefficients, ‘a’, ‘b’ and ‘c’ are based on whether the t-test is significant between each other, and ‘*’ and ‘***’ refer to significant correlations (p < 0.05, and p < 0.001).
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Figure 2. Genome-wide association plots of FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA in 393 accessions plotted using the mixed linear model. The Manhattan map shows the mapping of −log10 (p) values across all 12 chromosomes of rice. The significance threshold of p = 1.22 × 10−6 in the correlation analysis is indicated by the solid red line. (a) Flag leaf length; (b) flag leaf width; (c) flag leaf length–width ratio; (d) flag leaf area; (e) second leaf length; (f) second leaf width; (g) second leaf length–width ratio; (h) second leaf area.
Figure 2. Genome-wide association plots of FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA in 393 accessions plotted using the mixed linear model. The Manhattan map shows the mapping of −log10 (p) values across all 12 chromosomes of rice. The significance threshold of p = 1.22 × 10−6 in the correlation analysis is indicated by the solid red line. (a) Flag leaf length; (b) flag leaf width; (c) flag leaf length–width ratio; (d) flag leaf area; (e) second leaf length; (f) second leaf width; (g) second leaf length–width ratio; (h) second leaf area.
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Figure 4. Identification of candidate genes for FLW and SLW. (a) Haplotype region of LOC_Os04g35060. The promoter is represented by the white box, the exon is represented by the blue box, and the intron and the intergenic region are represented by the thin blue line that together constitutes the LOC_Os04g35060 gene structure map. The physical location of each labelled SNP in the segment is marked with a thin black line. Only haplotypes of more than 10 accessions are shown in the table. (b,c) Local Manhattan plot (top) for FLW and SLW. (d,e) Box plots for FLW and SLW in the four haplotypes of LOC_Os04g35060 in all accessions in 2022. Differences between haplotypes were statistically analyzed using Tukey’s test. (f) LD heat map of local Manhattan map. ‘a’, ‘b’ and ‘c’ are based on whether the t-test is significant between each other.
Figure 4. Identification of candidate genes for FLW and SLW. (a) Haplotype region of LOC_Os04g35060. The promoter is represented by the white box, the exon is represented by the blue box, and the intron and the intergenic region are represented by the thin blue line that together constitutes the LOC_Os04g35060 gene structure map. The physical location of each labelled SNP in the segment is marked with a thin black line. Only haplotypes of more than 10 accessions are shown in the table. (b,c) Local Manhattan plot (top) for FLW and SLW. (d,e) Box plots for FLW and SLW in the four haplotypes of LOC_Os04g35060 in all accessions in 2022. Differences between haplotypes were statistically analyzed using Tukey’s test. (f) LD heat map of local Manhattan map. ‘a’, ‘b’ and ‘c’ are based on whether the t-test is significant between each other.
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Table 1. Descriptive statistics of flag leaf and second leaf traits in 393 rice accessions.
Table 1. Descriptive statistics of flag leaf and second leaf traits in 393 rice accessions.
Mean ± SDRangeCV (%)
FLL (cm)29.78 ± 9.0012.42~121.530.23%
FLW (cm)1.79 ± 0.321.02~3.4018.02%
FLR17.14 ± 5.856.92~68.0334.13%
FLA (cm2)38.86 ± 13.9112.17~155.8435.80%
SLL (cm)41.04 ± 11.5613.85~84.2528.17%
SLW (cm)1.39 ± 0.280.68~2.7820.22%
SLR30.36 ± 9.3110.78~78.1630.67%
SLA (cm2)42.16 ± 15.679.09~95.3237.18%
Table 2. Ninety-one regions with significant signals related to FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA in the genome-wide association study of 393 accessions.
Table 2. Ninety-one regions with significant signals related to FLL, FLW, FLR, FLA, SLL, SLW, SLR, and SLA in the genome-wide association study of 393 accessions.
TraitQTLChrPospR2 (%)
FLLqFLL2.1226,002,5402.39 × 10−1419.43%
qFLL3.1316,565,1934.42 × 10−1519.30%
qFLL6.165,174,3271.04 × 10−1420.85%
qFLL6.269,918,9534.52 × 10−1518.58%
qFLL7.1711,319,4578.92 × 10−89.61%
qFLL9.196,191,4284.23 × 10−78.55%
FLWqFLW1.111,352,4869.11 × 10−1416.32%
qFLW1.2125,377,1406.58 × 10−1012.19%
qFLW1.3130,864,6573.60 × 10−78.01%
qFLW1.4140,433,7736.87 × 10−78.44%
qFLW2.128,917,5253.24 × 10−1114.51%
qFLW2.2215,799,2287.39 × 10−77.74%
qFLW2.3225,868,6252.28 × 10−77.98%
qFLW3.134,632,8231.20 × 10−1113.47%
qFLW3.2313,925,7121.20 × 10−67.11%
qFLW4.14445,3882.33 × 10−78.39%
qFLW4.246,394,6602.23 × 10−1113.49%
qFLW4.3414,832,5771.23 × 10−78.36%
qFLW4.4421,374,3413.07 × 10−77.87%
qFLW4.5431,166,7401.03 × 10−67.27%
qFLW5.1522,973,1543.83 × 10−77.99%
qFLW6.1610,419,7691.54 × 10−1113.49%
qFLW6.2625,181,6185.97 × 10−78.08%
qFLW7.171,688,2252.16 × 10−89.33%
qFLW7.2712,686,4251.37 × 10−1113.82%
qFLW7.3724,417,1321.84 × 10−1215.03%
qFLW8.1811,722,5128.90 × 10−88.82%
qFLW8.2814,808,7374.05 × 10−78.21%
qFLW8.3822,800,9965.12 × 10−78.06%
qFLW8.4826,300,3411.02 × 10−67.48%
qFLW9.1912,020,2572.00 × 10−89.40%
qFLW10.1102,085,3032.11 × 10−1113.42%
qFLW11.11118,438,6191.19 × 10−67.11%
qFLW11.21124,306,0139.61 × 10−88.62%
qFLW12.1124,125,4073.17 × 10−78.95%
qFLW12.21212,654,0929.67 × 10−99.73%
FLRqFLR2.1226,002,5402.94 × 10−1013.04%
qFLR3.1316,565,1934.95 × 10−1113.20%
qFLR4.1421,778,7035.24 × 10−77.58%
qFLR5.15868,5807.78 × 10−77.32%
qFLR6.165,174,3271.09 × 10−1014.07%
qFLR6.269,918,9531.66 × 10−1113.32%
FLAqFLA2.1226,002,5401.12 × 10−1115.37%
qFLA2.2232,594,5703.82 × 10−89.22%
qFLA3.1316,565,1931.41 × 10−1114.12%
qFLA6.165,174,3271.63 × 10−1115.76%
qFLA6.269,918,9531.99 × 10−1113.59%
qFLA7.1711,319,4573.51 × 10−810.12%
qFLA7.2721,694,0688.54 × 10−77.80%
qFLA8.1819,560,7057.03 × 10−77.62%
SLLqSLL3.1315,745,4886.39 × 10−77.61%
SLWqSLW1.114,521,7393.19 × 10−77.81%
qSLW1.2114,583,6675.53 × 10−810.00%
qSLW1.3137,817,5284.46 × 10−89.34%
qSLW1.4142,097,9172.18 × 10−78.60%
qSLW2.122,562,3391.08 × 10−67.26%
qSLW2.228,230,9609.44 × 10−77.47%
qSLW2.3214,725,9427.03 × 10−078.13%
qSLW2.4225,868,6251.83 × 10−89.39%
qSLW2.5234,189,5246.95 × 10−78.14%
qSLW3.13225,7745.10 × 10−77.88%
qSLW3.238,650,8679.44 × 10−89.81%
qSLW3.3318,180,6531.37 × 10−78.41%
qSLW3.4332,198,7769.94 × 10−810.16%
qSLW4.141,877,2769.83 × 10−77.29%
qSLW4.244,919,8824.84 × 10−78.78%
qSLW4.3413,199,9524.26 × 10−77.72%
qSLW4.4421,374,3413.84 × 10−77.79%
qSLW6.1612,283,5501.13 × 10−78.46%
qSLW6.2629,426,6893.87 × 10−77.87%
qSLW7.17695,8902.91 × 10−77.96%
qSLW7.275,323,6102.25 × 10−810.80%
qSLW7.3717,459,2896.01 × 10−78.45%
qSLW7.4728,704,1731.13 × 10−67.14%
qSLW8.18802,2192.46 × 10−78.08%
qSLW8.2811,722,5127.01 × 10−77.76%
qSLW8.3814,808,7372.68 × 10−78.32%
qSLW8.4825,582,9478.51 × 10−77.45%
qSLW9.1912,012,3346.11 × 10−77.48%
qSLW10.1102,448,5693.27 × 10−89.12%
qSLW11.1114,473,9577.78 × 10−78.52%
qSLW11.21118,438,6191.21 × 10−67.15%
qSLW11.31122,216,5478.60 × 10−77.29%
qSLW12.11223,955,8114.49 × 10−78.56%
qSLW12.21218,285,6691.05 × 10−67.38%
SLRqSLR1.1126,877,4702.28 × 10−78.95%
qSLR3.1315,745,4882.11 × 10−89.37%
qSLR5.15996,3501.12 × 10−67.23%
qSLR12.11224,428,4465.82 × 10−77.59%
SLAqSLA9.199,946,8731.12 × 10−67.38%
qSLA9.2912,012,3341.21 × 10−67.21%
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MDPI and ACS Style

Wang, N.; Wang, X.; Qian, Y.; Bai, D.; Bao, Y.; Zhao, X.; Xu, P.; Li, K.; Li, J.; Li, K.; et al. Genome-Wide Association Analysis of Rice Leaf Traits. Agronomy 2023, 13, 2687. https://doi.org/10.3390/agronomy13112687

AMA Style

Wang N, Wang X, Qian Y, Bai D, Bao Y, Zhao X, Xu P, Li K, Li J, Li K, et al. Genome-Wide Association Analysis of Rice Leaf Traits. Agronomy. 2023; 13(11):2687. https://doi.org/10.3390/agronomy13112687

Chicago/Turabian Style

Wang, Nansheng, Xingmeng Wang, Yingzhi Qian, Di Bai, Yaling Bao, Xueyu Zhao, Peng Xu, Keyang Li, Jianfeng Li, Kang Li, and et al. 2023. "Genome-Wide Association Analysis of Rice Leaf Traits" Agronomy 13, no. 11: 2687. https://doi.org/10.3390/agronomy13112687

APA Style

Wang, N., Wang, X., Qian, Y., Bai, D., Bao, Y., Zhao, X., Xu, P., Li, K., Li, J., Li, K., Zhang, D., & Shi, Y. (2023). Genome-Wide Association Analysis of Rice Leaf Traits. Agronomy, 13(11), 2687. https://doi.org/10.3390/agronomy13112687

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