Introduction

With economic and social development, cultivating systems for rice (Oryza sativa L.) production have changed. At present, direct seeding rice (DSR) has been widely adopted by farmers and is just as important as the traditional transplanting rice (TTR) system, which is now performed with the assistance of mechanical tools in China. In other parts of Asia, the adoption of DSR in place of TTR has also increased because of its labour and energy savings1. DSR can significantly reduce water consumption and labour requirements, while increasing system productivity and resource use efficiency2. Moreover, it can reduce greenhouse gas emissions during rice cultivation3.

In practice, there are at least two types of cultivating systems for DSR, paddy direct seeding (PDS) and upland direct seeding (UDS), also known as aerobic direct seeding. In UDS, the lack of a ‘head start’ and the absence of a standing water layer to suppress weeds make it highly vulnerable to weeds, causing severe yield losses compared with other rice ecosystems4. Thus, seedling vigour (SV), especially early SV (ESV), which refers to the SV for seedlings less than 28 d old, is imperative for crop stand establishment and weed competitiveness4,5. In PDS, however, the control of weeds is much easier owing to the standing water layer. Thus, the late SV (LSV), 28 d after sowing (DAS) is more important for the population type and final grain yield, especially in PDS. This is similar to the rice in the TTR cultivating system. Nevertheless, the growth of rice is different for seedlings under TTR and PDS conditions owing to the transplanting recovery procedure.

Substantial genetic variation in SV exists within the rice gene pool6,7,8. A lot of research has focused on SV, especially its molecular mapping. Many QTLs for SV in rice have been identified in bi-parental-derived populations by QTL analyses9,10,11,12,13,14,15. Dynamic and conditional QTL mapping have been used for the detection of SV-related QTLs16,17. However, most of the QTLs detected in these studies were located in relatively large regions and favourable allele/haplotype mining was not performed. The mining of the germplasm diversity for important SV-related QTLs is still limited.

Recently, natural populations, with advantages over the bi-parental populations, have been used for identifying QTLs of complex traits18,19. QTLs and favourable haplotypes affecting mesocotyl length were identified in a set of 621 rice accessions by GWAS, and the combinations of superior haplotypes of OsML1 and OsML2 increase mesocotyl length by up to 4 cm20. Using general and mixed linear model approaches, 16 and 10 SSRs, respectively, were identified as significantly associated with ESV traits in 96 rice lines selected from a set of 629 rice accessions21. The 3,000 (3 K) Rice Genome Project has produced a highly diverse pool carrying favourable alleles for different traits22,23. Determining important loci with less background interference has been a challenge for GWAS. Additionally, widely used methods based on single marker analyses accumulate errors with the application of multiple tests. Thus, a multiple test correction, such a Bonferroni correction, is commonly required. A multi-locus random-single nucleotide polymorphism (SNP)-effect mixed linear model package, named mrMLM, was recently developed to overcome this shortcoming24.

Here, we adopted a traditional GAPIT method combined with mrMLM to dissect the SV variations in a sub-panel of 744 accessions from the 3 K rice genome. The aims of this study were to (1) identify QTL regions for SV at different DAS in diverse rice germplasms using GWAS, (2) to fine-map and mine favourable alleles for candidate genes in some important QTL regions, and (3) to compare the mapping efficacies and respective characteristics of the genomic association and prediction integrated tool (GAPIT) and mrMLM methods. This work could offer useful information for the molecular breeding of DSR, as well as being a reference for the joint use of GAPIT and mrMLM.

Methods

Plant materials and field experiments

A set of 744 germplasms was used in this work. Its members were randomly selected from the sequenced accessions of the 3 K Rice Genome Project25. The field work was conducted at the experimental station in Shenzhen (22.6°N, 114.4°E), Guangdong Province during the late season of 2017. Approximately 120 germinated seeds for each accession were directly sown with 20 cm between rows, 15 cm between individuals and two seeds per hill in the paddy plot. At the one-leaf stage, 60 similar seedlings for each accession, with a single seedling in each hill, were maintained. The extra seedlings were removed. The tests were arranged in randomised plots with three replications. Field management was carried out according to the local practice. Specifically, ~113 kg ha−1 of carbamide was applied at the 2.5-leaf stage to supply nitrogen. Butachlor was applied at ~1.8 kg ha−1 for weed control during paddy preparation before seeding, and it was applied again after the 3-leaf stage.

The first sampling (stage A) was carried out at 27 DAS, and the second (stage B) and third (stage C) samplings were carried out at 34 and 41 DAS, respectively. From each accession, 10 uniform individuals, except for those on the boarders, were evaluated for SV traits. The SV traits, tiller number (TN), plant height (PH), and aboveground dry weight (DW), were measured for each individual.

Genotyping by sequencing and SNP extraction

The 744 accession panel was re-sequenced, with an average depth of more than 10×25. The cleaned reads were then mapped to the reference genome of ‘Nipponbare’ (IRGSP1.0), and ~14 M of high-quality SNPs were identified25. From these SNPs, a set of 2.9 M SNPs related to potential protein-coding areas was carefully selected. To build a SNP set for association studies, a subset of 27,921 SNPs was selected from the 2.9 M SNPs by choosing one SNP per 100 counts, as described in our previous GWAS mapping work23.

QTL analysis, comparative mapping and haplotype analysis

Basic statistical analyses for SV traits were conducted with SAS26. For the graphing and plotting, both Excel and R scripts were adopted. The basic scenario of a compressed mixed linear model27 implemented in GAPIT28 was adopted for the association analysis between QTL-flanking markers and SV traits for the 744 sequenced accessions. The parameters for GAPIT were set in accordance with our previous report23. A relatively stringent threshold was adopted to identify significant correlations between the SNPs and SV traits with a −LOG10(P) value of 6.0. To minimise to the possibility of type II errors in QTL detection29, a relatively loose threshold of 3.0 was adopted for the QTL regions having supporting evidence from different traits or previous reports. The allelic effects were estimated by setting the Major.allele.zero = TRUE in GAPIT to identify the donors of favourable alleles and their effects on SV traits. We used the mrMLM package24 to confirm and complement the mapping results from GAPIT.

Comparative mapping was carried out against a reference sequence map, the GRAMENE annotation sequence map30, to compare the QTLs detected in this study with previously reported QTLs or genes known to be associated with SV-related traits in rice.

Favourable haplotypes for candidate genes were investigated jointly with the aid of Perl and R scripts as described in our previous reports22,23, with minor modifications. The procedure included the following six major steps: (1) Determine the sub-regions defined by quantitative trait nucleotides (QTNs) with supporting evidence from both GAPIT and mrMLM; (2) Fill in the sub-regions with more SNPs from the original 2.9 M sets23, and then carry out GWASs using mrMLM within these sub-regions; (3) Screen the key QTNs with evidences from different SV-related traits; (4) Search for candidate genes harbouring these key QTNs for SV traits; (5) Carry out haplotype analyses for these candidate genes for SV-related traits to confirm the GWAS mapping results; and 6) Determine the top donors carrying favourable haplotypes.

Results

Performances of SV-related traits in the sequenced accessions

As shown in Fig. 1a–c, the three measured major SV-related traits, TN, PH, and DW, were different at the three sampling stages (27, 34, and 41 DAS). For the three sampling stages, the TN had ranges of 2.4–34.2, 4–36.6, and 4.6–37.8, respectively, with mean values of 13.1, 15.6, and 16.0, respectively. For the three sampling stages, the PH had ranges of 30.3–71.8 cm, 38.2–105.2 cm, and 45.2–118.8 cm, respectively, with mean values of 49.8, 64.2, and 76.4 cm, respectively. For the three sampling stages, the DW had ranges of 0.39–4.92 g, 1.47–10.34 g, and 3.34–18.38 g, respectively, with mean values of 2.20 g, 5.37 g, and 9.77 g, respectively. The ranges of the phenotypic distributions became wider at each progressive sampling stage. One unique trait was DW, for which the distribution range increased significantly from sampling stages A to C (Fig. 1c). However, the distribution range of TN and PH almost remained almost unchanged from sampling stages A to C. The pattern change for PH based on the sampling stages was between those of the previous two traits.

Figure 1
figure 1

Distribution of three different traits at three stages of the 744 3 K-germplasms. (a) Tiller number (TN). (b) Plant height (PH). (c) Dry weight (DW). Three different colors represent sampling at 27 (blue), 34 (orange), and 41 (green) days after seeding.

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For each trait, all of the values from the different sampling stages were highly significantly correlated except for the correlations between PH and TN, and all of the correlations were in positive direction (Fig. 2). Especially, DW and TN were highly correlated having correlation coefficients greater than 0.60 at the first two sampling stages (27 and 34 DAS), but only ranged by 0.42–0.55 at the third sampling stage (41 DAS). However, the correlations between DW and PH (ranged by 0.35–0.59) were not as significant as the others.

Figure 2
figure 2

Correlations between three SV traits (TN = tiller number, PH = plant height, and DW = dry weight) at three stages (A = 27 DAS, B = 34 DAS, and C = 41 DAS) of the 744 3 K-germplasms. (1) Red = positive correlation, and blue = negative correlation; (2) the size of circle indicated the significance of correlations.

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Using the 27 K SNPs, sample clustering and a PCA were also performed. The PCA results for the 744 panel are shown in Fig. 3a, and the kinship between the 744 accessions is presented in Fig. 3b. Based on the PCA and the kinship analysis, the 744 accessions were divided into at least two large groups. Within each group, there were at least one or two subgroups. This was consistent with the two sub-species groupings of the rice accessions.

Figure 3
figure 3

Phylo-tree (a) and PCA plots (b) based on the SNP genotyping data of the 744 germplasms.

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Linkage disequilibrium (LD) decay values were calculated throughout the genome, and the average values are shown in Supplementary Fig. 1. On average, LD blocks in this rice population of 744 germplasms extend up to ~50 kb.

Identification of QTL regions controlling SV-related traits at three sampling stages

A total of 42 QTL regions (Table 1) were detected by GWAS mapping of the three SV-related traits at three sampling stages in 744 sequenced rice accessions. Among them, 28 QTL regions were responsible for TN. Among these, 15, 11, and 15 QTL regions were detected by GAPIT and 10, 7, and 12 QTL regions were detected by mrMLM at 27, 34, and 41 DAS, respectively. The averaged −LOG10(P) values for SV-related QTL regions affecting TN at 27, 34, and 41 DAS were 3.5, 3.5, and 3.7, respectively, with ranges of 3.0–4.4, 3.0–6.0, and 3.0–5.7, respectively, as determined by GAPIT/mrMLM), and 3.6, 3.6, and 3.7, respectively, with ranges of 3.1–4.7, 3.0–6.0, and 3.0–5.7, respectively, as determined by mrMLM. Three QTLs (qSV2c, qSV5a, and qSV9b) were active at all three sampling stages.

Table 1 Loci detected for seedling vigor (SV) traits.
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A total of 35 SV-related QTL regions were responsible for PH. Among these, at 27, 34, and 41 DAS, 20, 26 and 21, respectively, were detected by GAPIT and 12, 18, and 11 respectively were detected by mrMLM. Eleven QTL regions (qSV1c, qSV1d, qSV2c, qSV4a, qSV5c, qSV6a, qSV6b, qSV7a, qSV7c, qSV8c, and qSV10b) were active throughout all three sampling stages as assessed by GAPIT. Four of them (qSV2c, qSV4a, qSV5c, and qSV7c) were confirmed by mrMLM. The averaged −LOG10(P) values for SV-related QTL regions affecting PH at 27, 34, and 41 DAS were 4.2, 3.9, and 3.9, respectively, according to GAPIT, with ranges of 3.0–6.7, 3.0–6.0, and 3.0–6.0, respectively, and 4.1, 3.6, and 3.9, respectively, according to mrMLM, with ranges of 3.0–6.9, 3.0–5.2, and 3.0–5.5, respectively.

Of the 31 SV-related QTL regions responsible for DW, 18, 12, and 8, as assessed by GAPIT, and 10, 8, and 7, as assessed by mrMLM, were detected at 27, 34, and 41 DAS, respectively. Only qSV2c was active throughout all three sampling stages according to GAPIT and it was confirmed by mrMLM at 27 and 34 DAS. The averaged −LOG10(P) values for SV-related QTL regions affecting DW at 27, 34, and 41 DAS, were 4.2, 4.0, and 3.9, respectively, with ranges of 3.0–6.7, 3.0–6.0, and 3.0–6.0, respectively, GAPIT, and 4.1, 3.6, and 3.9, respectively, with ranges of 3.0–6.9, 3.0–5.2, and 3.0–5.5, respectively, according to mrMLM.

Approximately 11.5% of the SV-related QTL regions were responsible for TN throughout all three sampling stages (27, 34, and 41 DAS). When we analysed PH with a higher heritability, the percentage of stable QTL regions reached 33.3%; however, the DW value dramatically decreased to 3.3%

Mapping results from GAPIT and mrMLM

As shown in Table 2, for the 28 SV-related QTL regions responsible for TN, GAPIT detected 26 (15, 11, and 15 for the three sampling stages, respectively), while mrMLM detected 14 (10, 7, and 12 for the three sampling stages, respectively). In a comparison between the two methods, 12 QTL regions overlapped (9, 7, and 11 for the three sampling stages, respectively). For the 33 QTL regions responsible for PH, GAPIT detected all of them, with 20, 26, and 21 for the three sampling stages, respectively, while mrMLM detected 27, with 12, 18, and 11 for the three sampling stages, respectively. The overlapping QTL regions between the two methods at the three sampling stages were 12, 18, and 11, respectively. For the 31 SV-related QTL regions responsible for DW, GAPIT detected 30, with 18, 12, and 8 for 27, 34, and 41 DAS, respectively, while mrMLM detected 23, with 10, 8, and 7 for the three sampling stages, respectively. The overlapping QTL regions between the two methods were 10, 8, and 6 at the three sampling stages, respectively.

Table 2 Comparisons of QTL region/QTN for seedling vigor detected by two different methods (GAPIT and mrMLM).
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As shown in Supplementary Figs 2–4, Manhattan plots with similar patterns were obtained for both GAPIT and mrMLM. However, using the threshold settings, relatively fewer QTL regions with clearer backgrounds were obtained by mrMLM.

Haplotype analysis of candidate genes for QTL regions

Based on the above association mapping results, four QTL regions, qSV1a, qSV3e, qSV4c, and qSV7c, were selected for fine-mapping using more SNPs. The region harbouring qSV3e was split into two sub-regions, which contained candidate Os03g0799700, with multi-evidenced QTNs, and Os03g0856700, with a single SNP but large −LOG10(P) value of 13.0 based on the ‘Nipponbare’ reference genome (Supplementary Table). Similarly, the other three QTL regions were delimited and harboured multi-evidenced QTNs after a sub-region analysis. Candidate genes containing significant SNPs or exactly located SNPs having phenotypic haplotype effects, as well as their donors, were analysed and are shown in Table 3 and Figs 4–8. Os01g0166800, OS03g0799700 and Os03g0856700 (OsGA20ox1), Os04g0683600, and Os07g0600400 were determined to be candidate genes for qSV1a, qSV3e, qSV4c, and qSV7c, respectively, owing to significant differences in related traits among their different haplotypes. As shown in Fig. 4, haplotype 2 of Os01g0166800 appeared to be elite, affecting both TN and DW without affecting PH, while haplotype 5 affected all three traits. Haplotypes 1 and 6 of OS03g0799700 were recommended (Fig. 5), affecting TN and DW without significantly increasing PH, while haplotype 3 influenced all three traits. Two haplotypes of Os03g0856700 (OsGA20ox1) appeared in the 744 germplasms (Fig. 6). Phenotypic differences among the three trait values (TN, PH, and DW) between the two haplotypes were highly significant at all three sampling stages, except for marginal differences in PH at 34 and 41 DAS (Fig. 6b). Haplotype 2 behaved better than haplotype 1 for Os03g0856700. For qSV4c, haplotype 1 was better than haplotype 2, while for qSV7c, haplotypes 1 and 3 were better than haplotype 2. The favourable haplotypes of qSV4c and qSV7c affected all the three SV-related traits (Figs 6 and 7).

Table 3 Favorable haplotypes and elite donors of candidate genes for four SV loci.
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Figure 4
figure 4

Boxplot for SV phenotypic values of two haplotypes of Os01g0166800 (qSV1a) in the 744 germplasms. (a–c) TN, PH, and DW values for the three sampling stages 27, 34, and 41 DAS (left to right).

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Figure 5
figure 5

Boxplot for SV phenotypic values of two haplotypes of Os03g0799700 (qSV3e) in the 744 germplasms. (a–c) TN, PH, and DW values for the three sampling stages 27, 34, and 41 DAS (left to right).

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Figure 6
figure 6

Boxplot for SV phenotypic values of two haplotypes of OsGA20ox1 in the 744 germplasms. (a–c) TN, PH, and DW values for the three sampling stages 27, 34, and 41 DAS (left to right).

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Figure 7
figure 7

Boxplot for SV phenotypic values of two haplotypes of Os04g0683600 (qSV4c) in the 744 germplasms. (a–c) TN, PH, and DW values for the three sampling stages 27, 34, and 41 DAS (left to right).

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Figure 8
figure 8

Boxplot for SV phenotypic values of two haplotypes of Os07g0600400 (qSV7c) in the 744 germplasms. (a–c) TN, PH, and DW values for the three sampling stages 27, 34, and 41 DAS (left to right).

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Discussion

Comparison of identified SV-related QTL regions with reported genes/QTLs

Among the 42 SV-related QTL regions detected in this research, 18 (42.9%) were consistent with those of previous reports. Of these, 10 (qSV1d, qSV2a, qSV2c, qSV3e, qSV4b, qSV5c, qSV6b, qSV7c, qSV8d, and qSV11a) harboured more than one reported locus.

Among these QTLs, qSV2c was the most supported, and it was mapped with four reported loci, qGR2 for germination rate31, qSEV-2-2 for PH and plant dry weight16, qCSH2 for seedling height, and qSDW2 for SV-related traits under cold stress32. Another two QTL regions (qSV1d and qSV3e) were mapped with three reported loci. qSV1d was mapped with qSSL1b for seedling shoot length33, qRL-1 for root length34, and qLA-1 for leaf area35, while qSV3e was close to qPHS3-2/OsGA20ox1 for seedling height36, qSEV-3-4 for both PH and plant dry weight16, and qSV-3-2 for shoot length and germination rate13. Seven QTL regions (qSV2a, qSV4b, qSV5c, qSV6b, qSV7c, qSV8d, and qSV11a) were each mapped with two reported loci. The qSV2a was mapped with qFML2-1 for mesocotyl length20 and qRL-2 for root length35; qSV4b was mapped with qSDW4.2 for shoot dry weight37 and qGP-4 for germination percentage38; qSV5c was mapped with qFV-5-2 for seedling height15 and qSEV-5-2 for both PH and plant dry weight16, although these two were detected in different tests of the same population. qSV6b was close to qDW-6 for seedling dry weight34 and qLDW-6 for leaf biomass35; qSV7c was mapped with qSDW5-1 for shoot dry weight11 and qSL-7 for shoot length35; qSV8d was mapped with qSV-8-2 for germination rate, shoot length, and root length13 and with qPR-8 for biomass portioning to roots35, while qSV11a was mapped with qFML11-1 for mesocotyl length20 and with qLN-11 for leaf number35. QTL regions for the SV-related traits that were identified in different mapping populations and diverse environments could be beneficial for the marker-assisted selection-based development of varieties with high levels of SV.

Dynamic expression of SV-related QTL regions and their pleiotropy for SV-related traits

Improving SV has long been a focus of rice breeders. In particular, ESV (less than 28 DAS) is important for DSR under aerobic conditions in which they compete with weeds. In LSV, the tillering ability and biomass are the bases for the final yield components.

However, LSV is also based on the ESV. Thus, these two types of SV are highly associated. More than one third (42.9%) of the SV-related QTL regions detected in this work were mapped with reported ESV loci. However, four QTLs (qSV1d, qSV2c, qSV3e, and qSV4b) were not only mapped with more than one reported loci, but were also detected by both GAPIT and mrMLM, and all of them were responsible for PH. PH is an agronomic trait with a relatively high level of heritability. Thus, more QTL regions were responsible for PH (33 and 27 by GAPIT and mrMLM, respectively) compared with TN (26 and 23, respectively) and DW (30 and 23, respectively). In total, 11 of the loci at the QTL regions stably detected at all three sampling stages were responsible for PH. However, the number of stably detected QTL regions decreased to three and one for TN and DW traits, respectively.

One locus on chromosome 2 (qSV2c) affected all the three traits (TN, PH, and DW) throughout all three sampling stages (27, 34, and 41 DAS). This locus is also consistent with the three previously reported loci (qGR231, qCSH2, and qSDW2)32. This stably expressed locus could be very useful and will be analysed further. However, the −LOG10(P) values for qSV2c were not very high. They ranged from 3.0 to 3.4, with a mean value of 3.2, for TN, from 3.8 to 4.7, with a mean value of 4.4, for PH, and from 3.0 to 3.3, with a mean value of 3.1, for DW.

The number of SV-related QTL regions expressed under different sampling stages varied according to the studied traits. For example, there were totally 28 loci responsible for TN, 33 loci for PH, and 31 for DW. The numbers of TN-related QTL regions for the three different sampling stages varied by 16, 11, and 16, respectively, while the numbers of PH-related QTL regions varied by 20, 26, and 21, respectively for the three stages. However, the numbers of DW-related QTL regions decreased by 18, 12, and 9, respectively. A dynamic pattern was determined for the SV-related QTL regions. In total, 33.3% of PH-related QTL regions were detected in all three sampling stages, and 36.4% were responsible for PH at two stages. Only 10.7% of TN-related and 3.2% of DW-related QTL regions were detected in all three stages, and 32.1% of the former and 19.4% of the latter were detected at two stages.

Joint mapping of QTLs using GAPIT and mrMLM

GAPIT is based on a single dimension of genome scanning. We used the built-in maximum-likelihood method for the GWAS mapping of SV-related traits, while mrMLM was used because of its multi-locus nature. Thus, for trait variations controlled by multiple loci, the estimations of the loci effects by mrMLM should have greater confidence levels24.

Using a common setting, in comparison with GAPIT, mrMLM detected relatively fewer numbers of loci with a similar statistical power, indicated by −LOG10(P) values, in most cases. Additionally, as shown in Supplementary Figs 2–4, the threshold setting influenced QTL mapping. mrMLM focused on loci with high confidence levels and produced results with clearer backgrounds than GAPIT. This would be adequate for most molecular breeding or gene cloning applications. However, to determine the whole picture of a trait, a traditional package, such as GAPIT, should also be used. Conversely, as shown in this report, QTL regions can be located with GAPIT and then, supporting evidence obtained from mrMLM.

For the estimated allelic effects, of the 84 cases detected both by GAPIT and mrMLM, only 25 (29.8%) cases showed consistent effect directions. According to previous reports, with simulated data, a multi-loci method (mrMLM) is more powerful and more accurate in QTN effect estimation than single-locus methods24. All of these QTL regions were responsible for TN and PH rather than DW. Further experiments are required to clarify this inconsistency.

Additionally, as an example, we adopted SV mapping results from GAPIT with a suitable threshold setting to minimise possible type II errors. With the aid of mrMLM, the SV mapping results should have greater confidence levels. Thus, combining GAPIT and mrMLM is an option for our future GWAS mapping.

Candidate gene identification in important QTL regions

In our 744 accessions, the average LD decayed significantly to 0.3 at a physical distance of ~50 kb (Supplementary Fig. 1). This is much less than the previously reported LD distance of 75 kb for Xian/indica39, not to mention the 150-kb LD distance for tropical Geng/japonica, and the >500-kb LD distance for temperate Geng/japonica. Thus, in this population, especially for the specific QTL regions, a sub-regional analysis with higher density markers may offer more useful information.

Based on the sub-region analysis using more SNPs and the haplotype analysis, five candidate genes, Os01g0166800, OS03g0799700 and Os03g0856700 (OsGA20ox1), Os04g0683600, and Os07g0600400 were inferred for qSV1a, qSV3e, qSV4c, and qSV7c, respectively, using multi-evidenced QTNs. The candidate gene Os01g0166800 for qSV1a is an E2F transcription factors target gene. Os03g0799700 for qSV3e is a GTP1/OBG subdomain-containing protein. Both E2F- and GTP-related pathways are highly conserved in higher eukaryotes, and are involved in multiple basic functions, including cell cycle, DNA replication, and germination40,41. Os03g0856700, or OsGA20ox1, for qSV3e is a key gene responsible for seedling height, which is especially associated with ESV36, while later it is associated with spikelet number per panicle42. Thus, it is most likely the candidate gene for qSV3e. Os04g0683600 for qSV4c is similar to the H0306F12.6 protein, which is a putative LRR receptor-like serine/threonine-protein kinase in maize but its function remains unclear in Oryza43. Os07g0600400 for qSV7c codes a WD40/YVTN repeat-like domain-containing protein, which may function in the processes of signal transduction and stress adaptation44. The candidate genes for the four QTL regions need to be validated using transgenic or gene editing approaches in the future.

Implications in the development of DSR cultivars

Both ESV and LSV are important for cultivars used in DSR cultivating systems. The ESV is highly correlated to the ability of rice seedlings to compete with weeds, especially under the aerobic condition (as in UDS)45. In PDS, weed control is much easier than in UDS. The plant type, which develops during the period of LSV, including the TN, PH, and DW contribute to the final grain yield. Most alleles at important stably detected QTL regions and/or in regions with multi-evidenced QTNs, such as qSV1a, qSV3e, qSV4c, and qSV7c, increase the TN, PH, and DW (Table 1). Further validation of favourable alleles at the above four stable QTL regions detected throughout the three sampling stages will be performed.

A rice cultivar with strong SV-related traits is desirable for direct seeding. However, SV-related traits have not been selected for in crop improvement programs focused on conventional breeding owing to their complex nature and quantitative inheritances. Molecular markers are effective in increasing selection efficiency, particularly for quantitative traits that are simply inherited. In this study, except for qSV2c’s roles in all three SV-related traits, there were 2 QTL regions (qSV5a and qSV9b) responsible for TN only, and 10 QTL regions (qSV1c, qSV1d, qSV4a, qSV5c, qSV6a, qSV6b, qSV7a, qSV7c, qSV8c, and qSV10b) responsible for PH only. A simple increase in PH would result in problems in the final maturation stage and may cause lodging, which is another key trait for DSR. A balance between TN and PH is favourable for increased yields. Thus, an appropriate combination of TN and PN for both a high SV at the seedling stage and a high yield potential at harvest could be achieved by pyramiding the QTLs underlying the two traits, such as qSV5a and qSV9b for TN and qSV1c and qSV4afor PH, after QTL validation using a marker-assisted selection approach.

With a high density of SNP markers, we identified 11 favourable haplotypes for four loci. These haplotypes, defined by key SNPs, can be transformed into breeder-friendly markers in the future. In particular, haplotype 2 of qSV1a, and haplotype 1, 4, and 6 of qSV3e offer more opportunities for the development of cultivars with greater biomasses, resulting from more tillers rather than taller plants. PDR cultivars with this kind of SV would not affect lodging resistance and would have relatively wider adaptations. In addition, elite donors for these favourable haplotypes were also identified (Table 3). For example, IRIS_313-12135 can be used as the donor for haplotype 2 of qSV1a, and IRIS_313-12033, IRIS_313-11863, and IRIS_313-11807 can be used for haplotype 6 of qSV3e. IRIS_313-11453 can be used as a donor for haplotype 1 of qSV3e, while CX92 can be the donor for haplotype 4 of qSV3e. For OsGA20ox1, IRIS_313-10973 could be a good donor for the elite haplotype 2.

Conclusions

Tremendous variations for three SV-related traits, TN, PH, and DW, existed in the studied rice germplasms. Using a GWAS of GAPIT and mrMLM, 42 QTL regions, including 18 overlapping previously reported QTL regions and 24 new ones, for the SV-related traits were identified. Five candidate genes were inferred by fine-mapping using more SNPs and a haplotype analysis, including the known gene OsGA20ox1 for qSV3e, which controls ESV and spikelet number per panicle at the maturing stage. Our results indicated that combining GAPIT and mrMLM is an option for GWAS mapping. Favourable alleles at stably expressed QTLs, such as qSV1a, qSV3e, qSV4c, and qSV7c, were mined, and their corresponding accessions were also identified. The results provide useful germplasms and genetic information for the future improvement of SV in rice.