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. 2023 Feb 16;12(4):892. doi: 10.3390/plants12040892

Identification, Fine Mapping and Application of Quantitative Trait Loci for Grain Shape Using Single-Segment Substitution Lines in Rice (Oryza sativa L.)

Xiaoling Wang 1,2,3,*, Xia Li 1, Xin Luo 1, Shusheng Tang 1, Ting Wu 1, Zhiquan Wang 1, Zhiqin Peng 1, Qiyu Xia 3, Chuanyuan Yu 1, Yulong Xiao 1,*
Editor: Adriana Basile
PMCID: PMC9966618  PMID: 36840239

Abstract

Quantitative trait loci (QTLs) and HQTL (heterosis QTLs) for grain shape are two major genetic factors of grain yield and quality in rice (Oryza sativa L.). Although many QTLs for grain shape have been reported, only a few are applied in production. In this study, 54 QTLs for grain shape were detected on 10 chromosomes using 33 SSSLs (single-segment substitution lines) and methods of statistical genetics. Among these, 23 exhibited significant positive additive genetic effects, including some novel QTLs, among which qTGW4-(1,2), qTGW10-2, and qTGW10-3 were three QTLs newly found in this study and should be paid more attention. Moreover, 26 HQTLs for grain shape were probed. Eighteen of these exhibited significant positive dominant genetic effects. Thirty-three QTLs for grain shape were further mapped using linkage analysis. Most of the QTLs for grain shape produced pleiotropic effects, which simultaneously controlled multiple appearance traits of grain shape. Linkage mapping of the F2 population derived from sub-single-segment substitution lines further narrowed the interval harbouring qTGW10-3 to 75.124 kb between PSM169 and RM25753. The candidate gene was identified and could be applied to breeding applications by molecular marker-assisted selection. These identified QTLs for grain shape will offer additional insights for improving grain yield and quality in rice breeding.

Keywords: rice, grain shape, quantitative trait locus (QTL), single-segment substitution line (SSSL)

1. Introduction

Rice (Oryza sativa L.), one of the world’s most important staple food crops, feeds nearly half of the global population [1]. Grain weight is a vital component of grain yield, typically evaluated based on grain shape [2,3,4,5]. With the improvement in living standards, the demand for high-quality rice has grown [6,7]. Rice grain shape, one of the most important agronomic traits, has garnered increasing attention from breeders and geneticists because it directly determines the physical appearance of grain and affects the cooking quality of rice [8,9,10,11,12,13]. The traits of grain shape are also highly heritable, rendering them valuable for genetic analyses. Therefore, yield determined by grain weight is critical to farmers because it is among the most stable components of yield to increase income [14,15]. In addition, applying heterosis loci for grain shape can also simultaneously increase yield and quality [16,17].

Grain shape development in rice has been extensively studied [18,19,20,21,22,23,24,25]. Most previous studies focused on the characterisation of mutants and expression of key genes associated with grain size, such as Lk-f, which confers long or short kernel size [13]. The utilisation of molecular markers has facilitated the investigation of the genetic bases of complex quantitative traits, such as grain shape. In the last decade, many independent studies on rice grain size and grain shape have been conducted in populations derived from crosses between divergent cultivars or accessions of rice [26,27,28,29]. Currently, at least 203 quantitative trait loci (QTLs) for grain shape have been detected in rice (Integrated Rice Science Database), and some QTLs or genes detected previously have been mapped and cloned in different populations with diverse backgrounds [26,30]. However, dissecting the interactions amongst QTLs is difficult with several target QTLs [1].

Furthermore, many other QTLs related to grain shape have been reported at similar locations, which typically produce minor effects on phenotypic variation [31]. Therefore, some QTLs must be validated in the population to make sure without the background effect. Single-segment substitution lines (SSSL) harbouring only a single chromosome segment are an effective material for eliminating the background effect. Thus far, nearly 10 genes have been cloned successfully, and hundreds of important QTLs have been mapped using SSSLs in rice [2,30,32,33,34,35,36,37,38]. Because of the advantages of SSSLs, more and more research has begun to apply them [39]. In this study, QTLs for grain shape were identified using 33 SSSLs. The novel grain shape loci which carry QTLs for grain yield or the heterosis loci identified in the present study will offer additional insights for studies on the fine mapping, cloning, and application of potential QTLs on rice breeding.

2. Results

2.1. Verification and Distribution of SSSLs

In 2016, 33 SSSLs harbouring 225 SSR (simple sequence repeat) and InDel (insertion/deletion) markers were identified and verified based on molecular markers (Supplementary Tables S1 and S2). The substituted chromosome segments of SSSLs were distributed on 10 chromosome linkage maps, except for chromosomes 9 and 11, and the length of the substituted segments varied from 1.6–87.5 cM. Six concentrated overlapping groups, with at least three overlapping SSSLs, were present on chromosomes 1, 3, 4, 6, 7, and 8. The substitution fragments cover 36% (561/1524.9) of the whole genome (Figure 1).

Figure 1.

Figure 1

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Distribution of the 33 single-segment substitution lines in chromosomal marker intervals. Substituted segments are represented by black lines. Symbols on the right of each segment indicate the abbreviations of the experimental materials. Codes on the left and right sides of each chromosome indicate the designated centimorgan (cM) and molecular markers, respectively.

2.2. Frequency Distribution of Phenotype in SSSLs

The distributions of the six grain shape traits of the 33 SSSLs are presented in Figure 2 (data were averaged from values collected during the early and late seasons of 2016). Values of the six grain shape traits for the receptor (HJX74) were as follows: grain length (GL) 8.12 mm, grain width (GW) 2.52 mm, length-to-width ratio (LWR) 3.24, grain size (GS) 16.91 mm2, grain circumference (GC) 19.40 mm, and grain roundness (GR) 0.33. Values for most SSSLs were close to those of the contrast (HJX74). In particular, the frequency distributions of grain length and grain circumference were close to normal distributions. Overall, the values of grain shape traits indicated sufficient genetic diversity (Figure 2).

Figure 2.

Figure 2

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Frequency distribution of grain shape traits in SSSLs. Phenotypes of the receptor parent (HJX74) are indicated by arrows. Data are presented as the mean of 10 plants of each line in independent seasons.

2.3. Identification of QTLs for Grain Shape

The thousand-grain weight (TGW) of homozygous HJX74 (21.10 ± 0.07 g) was significantly different from that of 21 SSSLs (p < 0.05, t-test), and 13 QTLs for TGW were detected. Among these, four QTLs for TGW were distributed on chromosomes 1, 3, and 8, and on chromosomes 4, 6, 7, and 10, each contained three QTLs. S42 and S45 overlapped with S2, whilst S8 overlapped with S43 on chromosome 1. S40 overlapped with S46 and S48 on chromosome 3. S25 overlapped with S18 and P19 on chromosome 8. S27 overlapped with S35 and S47 on chromosome 7. Two overlaps were noted between S4, S23, and S24 on chromosome 10 (Figure 1).

Similarly, the GL of HJX74 (8.12 ± 0.35 mm) was significantly different from that of 14 SSSLs, and 11 QTLs for GL were detected. Moreover, the GW of HJX74 (2.52 ± 0.19 mm) was significantly different from that of 11 SSSLs, and nine QTLs for GW were detected. The LWR of HJX74 (3.24 ± 0.27) was significantly different from that of seven SSSLs, and six QTLs for LWR were detected. The GS of HJX74 (16.91 ± 1.52 mm2) was significantly different from that of 10 SSSLs, and seven QTLs for GS were detected. The GC of HJX74 (19.40 ± 0.77 mm) was significantly different from that of seven SSSLs, and six QTLs for GC were detected. The GR of HJX74 (0.33 ± 0.02) was significantly different from that of three SSSLs, and two QTLs for GR were detected. In total, 54 QTL loci were detected for seven grain shape traits, among which 20 exhibited positive effects. Specifically, 7, 6, 2, 2, 3, 2, and 1 QTLs exhibited positive effects for TGW, GL, GW, LWR, GS, GC, and GR, respectively (Table 1). Note that the overlapping QTL were counted as a single QTL.

Table 1.

QTLs Identified for Grain Shape Using SSSLs (n = 10).

Trait Material QTL Marker Interval Mean ± SE Add Effect APVE% p-Value
TGW/g S1 qTGW1−1 RM329–RM9 20.80 ± 0.01 −0.0900 −2.12 0.004
S8 qTGW1−2 RM5–RM128 20.50 ± 0.02 −0.1500 −3.53 <0.001
S42 qTGW1−3 RM283–RM562 22.50 ± 0.03 0.1600 3.76 <0.001
S43 qTGW1−4 RM306–RM403 22.25 ± 0.02 0.2000 4.71 <0.001
S10 qTGW3−1 END–RM569 19.35 ± 0.03 −0.3800 −8.94 <0.001
S40 qTGW3−2 PSM126–RM503 23.55 ± 0.01 0.4884 11.58 <0.001
S46 qTGW3−3 PSM16–PSM128 21.75 ± 0.04 0.1000 2.35 <0.001
S48 qTGW3−4 RM135–RM448 24.40 ± 0.02 0.6300 14.82 <0.001
S28 qTGW4−1 PSM106–PSM382 22.95 ± 0.01 0.3691 8.75 <0.001
S30 qTGW4−2 RM348–RM127 23.60 ± 0.02 0.4700 11.06 <0.001
S3 qTGW5−1 PSM202–RM437 22.40 ± 0.03 0.2651 6.29 <0.001
S50 qTGW6−1 RM135–RM448 19.24 ± 0.16 −0.5700 −2.88 0.0450
S27 qTGW7−1 PSM142–RM70 22.20 ± 0.02 0.2188 5.19 <0.001
S35 qTGW7−2 RM481–RM182 20.15 ± 0.02 −0.1863 −4.42 <0.001
S47 qTGW7−3 PSM140–RM125 18.65 ± 0.02 −0.5200 −12.24 <0.001
S18 qTGW8−1 PSM394–RM80 22.10 ± 0.02 0.1700 4.00 <0.001
S19 qTGW8−2 RM308–RM281 20.45 ± 0.01 −0.1289 −3.06 <0.001
S25 qTGW8−3 RM223–RM433 19.60 ± 0.03 −0.3300 −7.76 <0.001
S4 qTGW10−1 PSM162–PSM163 20.25 ± 0.01 −0.2000 −4.71 <0.001
S23 qTGW10−2 END–RM596 22.75 ± 0.02 0.2200 5.18 <0.001
S24 qTGW10−3 RM271–PSM407 19.20 ± 0.01 −0.4100 −9.65 <0.001
GL/mm S44 qGL1−1 RM490–RM562 8.00 ± 0.11 −0.1235 −1.52 0.0023
S9 qGL2−1 RM425–END 8.49 ± 0.11 0.3715 4.57 <0.001
S10 qGL3−1 END–RM569 7.55 ± 0.10 −0.5110 −6.34 <0.001
S40 qGL3−2 PSM126–RM503 8.98 ± 0.08 1.0200 12.81 <0.001
S48 qGL3−3 RM135–RM448 9.48 ± 0.15 1.3565 16.70 <0.001
S28 qGL4−1 PSM106–PSM382 8.36 ± 0.13 0.2405 2.96 <0.001
S14 qGL4−2 PSM104–END 8.52 ± 0.02 0.0600 0.71 <0.001
S17 qGL7−1 PSM353–RM478 8.78 ± 0.11 0.7189 8.92 <0.001
S35 qGL7−2 RM481–RM182 7.54 ± 0.10 −0.5240 −6.50 <0.001
S47 qGL7−3 PSM140–RM125 7.35 ± 0.15 −0.7160 −8.88 0.0029
S25 qGL8−1 RM223–RM433 7.79 ± 0.11 −0.2780 −3.45 0.0421
S23 qGL10−1 END–RM596 8.27 ± 0.10 0.2088 2.59 <0.001
S24 qGL10−2 RM271–PSM407 7.45 ± 0.11 −0.6150 −7.63 <0.001
S31 qGL12−1 PSM192–PSM193 7.96 ± 0.03 −0.1100 −1.36 <0.001
GW/mm S42 qGW1−1 RM283–RM562 2.65 ± 0.01 0.0300 1.15 0.0370
S45 qGW1−2 RM579–RM594 2.50 ± 0.03 −0.0390 −1.54 0.0199
S10 qGW3−1 END–RM569 2.41 ± 0.05 −0.0700 −3.00 0.0257
S40 qGW3−2 PSM126–RM503 2.34 ± 0.06 −0.1980 −7.80 0.0047
S48 qGW3−3 RM135–RM448 2.46 ± 0.05 −0.0823 −3.24 <0.001
S50 qGW6−1 PSM387–RM557 2.43 ± 0.05 −0.1060 −4.17 <0.001
S17 qGW7−1 PSM353–RM478 2.34 ± 0.06 −0.2002 −7.88 <0.001
S47 qGW7−2 PSM140–RM125 2.48 ± 0.05 −0.0623 −2.47 0.0044
S25 qGW8−1 RM223–RM433 2.39 ± 0.01 −0.0400 −1.65 0.0170
S24 qGW10−1 RM271–PSM407 2.45 ± 0.02 −0.0800 −3.16 <0.001
S31 qGW12−1 PSM192–PSM193 2.72 ± 0.01 0.0400 1.49 <0.001
LWR S8 qLWR1−1 RM5–RM128 3.02 ± 0.08 −0.1533 −4.83 0.0077
S44 qLWR1−2 RM490–RM562 2.96 ± 0.06 −0.2150 −6.77 0.0153
S40 qLWR3−1 PSM126–RM503 3.64 ± 0.11 0.4650 14.64 <0.001
S48 qLWR3−2 RM135–RM448 3.88 ± 0.10 0.6330 19.51 <0.001
S17 qLWR7−1 PSM353–RM478 3.78 ± 0.10 0.6009 18.71 <0.001
S47 qLWR7−2 PSM140–RM125 2.96 ± 0.08 −0.2200 −6.92 0.0237
S31 qLWR12−1 PSM192–PSM193 2.93 ± 0.02 −0.0900 −2.98 <0.001
GS/m2 S42 qS1−1 RM283–RM562 16.75 ± 0.12 0.3000 1.82 0.0050
S45 qS1−2 RM579–RM594 14.27 ± 0.30 −1.8300 −11.37 0.0012
S40 qS3−1 RM223–RM433 17.52 ± 0.18 1.6100 10.12 <0.001
S48 qS3−2 RM135–RM448 17.91 ± 0.16 1.9100 11.94 <0.001
S50 qS6−1 PSM387–RM557 16.14 ± 0.50 −0.7710 −4.56 0.0137
S51 qS6−2 END–PSM137 15.15 ± 0.42 −1.7610 −10.42 0.0332
S47 qS7−1 PSM140–RM125 14.63 ± 0.43 −2.2750 −13.46 <0.001
S25 qS8−1 RM223–RM433 16.19 ± 0.10 −0.3000 −1.82 <0.001
S24 qS10−1 RM271–PSM407 15.46 ± 0.42 −1.4420 −8.53 0.0194
S23 qS10−2 END–RM596 17.19 ± 0.21 0.4500 2.69 0.0040
GC/mm S45 qC1−1 RM579–RM594 18.60 ± 0.23 −0.8060 −4.15 0.0132
S40 qC3−1 PSM126–RM503 20.70 ± 0.34 1.2930 6.66 0.0174
S48 qC3−2 RM135–RM448 21.46 ± 0.15 2.3900 12.53 <0.001
S17 qC7−1 PSM353–RM478 21.27 ± 0.38 1.8710 9.64 <0.001
S47 qC7−2 PSM140–RM125 17.29 ± 0.36 −1.3830 −7.41 0.0042
S24 qC10−1 RM271–PSM407 17.21 ± 0.25 −1.4630 −7.84 <0.001
S31 qC12−1 RM271–PSM407 19.06 ± 0.06 −0.2700 −1.40 <0.001
GR S40 qR3−1 PSM126–RM503 0.28 ± 0.01 −0.0480 −14.50 <0.001
S48 qR3−2 RM135–RM448 0.27 ± 0.01 −0.0550 −16.62 <0.001
S31 qR12−1 PSM192–PSM193 0.35 ± 0.00 0.0100 2.94 <0.001
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Notes: Add effect, additive effect; positive additive indicates SSSL alleles increasing the phenotypic value of grain shape. APVE%, add effect variation explained by the QTLs (i.e., the QTL was repeatedly detected in SSSL populations). The p-value indicates the significance level. All data are presented as mean ± SE. The p-values for each trait between HJX74 and SSSLs are obtained using a t-test. n represents the number of samples per SSSL material.

2.4. Identification of Heterosis Loci

TGW of 15 SSSLs was significantly different from that of their respective F1 populations. Two of the three SSSLs overlapped on chromosome 1, and three of the four SSSLs overlapped on chromosome 10 (Figure 1). In total, 10 heterosis loci for TGW were detected in 15 SSSLs, among which six exhibited positive dominant effects. In addition, the GL of seven SSSLs was significantly different from that of their respective F1 populations. Three of the four SSSLs overlapped on chromosome 3. Six heterosis loci for GL were detected, among which five exhibited positive dominant effects. The GW of four SSSLs was significantly different from that of their respective F1 populations. The heterosis loci for GW were distributed on chromosomes 3, 7, 10, and 12, and two of them exhibited negative dominant effects. The LWR of seven SSSLs was significantly different from that of their respective F1 populations. Two of the three SSSLs overlapped on chromosome 3. Six heterosis loci for LWR were detected, among which five showed positive dominant effects (Table 2). In total, 26 heterosis loci for four grain shape traits, including 17 loci with the most stable positive genetic effects, were detected and are expected to improve rice yield and quality in rice breeding applications.

Table 2.

Effects of heterosis loci on MPH/dominance for grain shape (n = 10).

Trait Material QTL Marker Interval P F1 A APVE% D DPVE% p-Value
TGW/g S1 qHTGW1−1 RM329–RM9 20.60 4.24 −0.1300 −3.60 0.0550 1.31 <0.001
S2 qHTGW1−2 RM572–RM449 21.35 4.42 0.0200 0.47 0.1600 3.76 <0.001
S8 qHTGW1−3 RM5–RM128 20.50 4.29 −0.1500 −3.53 0.1150 2.75 <0.001
S40 qHTGW3−1 PSM126–RM503 24.00 4.46 0.5580 13.15 −0.0610 −1.35 <0.001
S48 qHTGW3−2 RM135–RM448 25.30 4.63 0.8180 19.28 −0.0210 −0.45 <0.001
S28 qHTGW4−1 PSM106–PSM382 22.85 4.43 0.3524 8.36 0.0362 0.82 <0.001
S30 qHTGW4−2 RM348–RM127 23.60 4.41 0.4700 11.06 0.0750 1.67 <0.001
S17 qHTGW7−1 PSM353–RM478 21.10 4.31 0.0024 0.06 0.0912 2.16 0.0034
S20 qHTGW8−1 RM502–RM281 21.15 4.28 −0.0200 −0.47 0.0400 0.94 <0.001
S25 qHTGW8−2 RM223–RM433 19.85 4.08 −0.3800 −8.94 −0.0200 0.49 <0.001
S4 qHTGW10−1 PSM162–PSM163 20.10 4.24 −0.2000 −4.71 0.0900 2.17 <0.001
S6 qHTGW10−2 RM216–RM269 20.75 4.27 −0.1000 −2.35 0.0700 1.67 <0.001
S23 qHTGW10−3 END–RM596 22.25 4.35 0.2000 4.71 0.0000 0.00 <0.001
S24 qHTGW10−4 RM271–PSM407 19.40 3.98 −0.3700 −8.71 −0.0850 −2.09 <0.001
S31 qHTGW12−1 PSM192–PSM193 21.45 4.24 0.0724 1.72 −0.0138 −0.32 0.0201
GL/mm S29 qHGL3−1 RM293–RM227 7.81 8.02 −0.3105 −3.82 0.0548 0.69 0.0083
S40 qHPL3−2 PSM126–RM503 8.98 8.17 1.0200 12.81 −0.3000 −3.54 <0.001
S48 qHPL3−3 RM135–RM448 9.13 8.18 1.1100 13.84 −0.3950 −4.61 <0.001
S17 qHGL7−1 PSM353–RM478 8.78 8.45 0.7170 8.89 0.0285 0.34 <0.001
S35 qHGL7−2 RM481–RM182 7.54 7.83 −0.5230 −6.49 0.0285 0.37 <0.001
S24 qHGL10−1 RM271–PSM407 7.55 7.83 −0.5130 −6.36 0.0235 0.30 <0.001
S31 qHGL12−1 PSM192–PSM193 7.74 7.91 −0.3230 −4.01 0.0085 0.11 0.0090
GW/mm S48 qGW3−1 RM135–RM448 2.33 2.50 −0.1680 −6.72 0.0830 3.44 0.0036
S17 qHGW7−1 PSM353–RM478 2.22 2.25 −0.0700 −3.06 −0.0050 −0.22 <0.001
S24 qHGW10−1 RM271–PSM407 2.45 2.49 −0.0800 −3.16 0.0000 0.00 <0.001
S31 qHGW12−1 PSM192–PSM193 2.61 2.58 0.0500 1.95 −0.0050 −0.19 0.0370
LWR S29 qHLWR3−1 RM293–RM227 2.98 3.09 −0.1970 −6.20 0.0115 0.37 0.0265
S40 qLWR3−2 PSM126–RM503 3.88 3.31 0.6990 21.97 −0.2245 −6.36 0.0021
S48 qLWR3−3 RM135–RM448 3.97 3.38 0.7870 24.74 −0.1975 −5.53 <0.001
S16 qHLWR6−1 RM217–RM557 2.97 3.13 −0.2070 −6.52 0.0565 1.84 0.0037
S17 qHLWR7−1 PSM353–RM478 3.78 3.50 0.6030 18.98 0.0215 0.62 <0.001
S24 qHLWR10−1 RM271–PSM407 3.13 3.26 −0.0470 −1.48 0.1065 3.38 0.0190
S31 qHLWR12−1 PSM192–PSM193 2.92 3.08 −0.2570 −8.09 0.0315 1.03 <0.001
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Notes: P, parent; D, dominance effect; positive dominance indicated that the hybrid values are higher than the mid-parent values. F1: The hybrids derived from the SSSLs and the receptor parent HJX74. MPH: mid-parent heterosis. n represents the number of samples per SSSL material.

2.5. QTLs Linkage Analysis

QTL linkage analysis indicated that the interval PSM169–RM258 of S24 produced pleiotropic effects on six traits of grain shape, including GL, GW, LWR, GS, GC, and TGW (Table 3), contributing −18.49%, −42.62%, −9.18%, −38.32%, −23.44%, and −17.81% of the phenotypic variation in the early season of 2018 and −22.28%, −30.35%, −4.09%, −22.56%, −26.56%, and −26.78% of the phenotypic variation in the early season of 2019, respectively. Alleles from the receptor HJX74 increased the GL, GW, LWR, GS, GC, and GR, whereas parent S24 retained the QTL/HQTL for yield per plant (Table 4). In the present study, QTLs for grain shape were detected in the QTL-focus region of chromosome 3. The intervals RM633–RM16 of S40 and RM633–RM168 of S48 were detected at a similar locus as GS3.

Table 3.

QTL linkage analysis of important grain shape traits (n = 224).

Material Trait Marker Interval 2018E 2018L Marker 2019E
LOD PVE (%) LOD PVE (%) LOD PVE (%)
S24 GL PSM169–RM258 8.71 −18.49 17.36 −38.00 PSM169–RM25753 16.86 −22.28
GW PSM169–RM258 23.15 −42.62 8.22 −21.89 PSM169–RM25753 24.00 −30.35
LWR PSM169–RM258 3.95 −9.18 PSM169–RM258 2.77 −4.09
GS PSM169–RM258 20.38 −38.32 19.71 −42.31 PSM169–RM25753 16.92 −22.56
GC PSM169–RM258 11.46 −23.44 18.79 −40.33 PSM169–RM25753 20.61 −26.56
TGW PSM169–RM258 9.80 −17.81 7.67 −18.93 RM25753–RM147 20.24 −26.78
S40 GL RM633–RM16 77.51 73.33 81.82 84.11
GW RM633–RM16 18.51 34.64 13.64 30.50
LWR RM633–RM16 70.68 72.21 85.70 86.54
GS RM633–RM16 43.52 61.44 37.97 61.76
GC RM633–RM16 79.07 73.65 76.19 83.10
GR RM633–RM16 66.43 71.60 74.37 83.43
TGW RM633–RM16 25.10 44.16 9.35 22.67
S48 GL RM633–RM168 99.66 46.78 80.12 75.55
GW RM633–RM168 11.16 22.36 9.88 23.51
LWR RM633–RM168 74.75 40.38 74.05 71.12
GS RM633–RM168 47.23 68.16 34.37 57.96
GC RM633–RM168 95.79 46.55 77.34 75.73
GR RM633–RM168 64.210 37.84 68.53 70.07
TGW RM633–RM168 34.65 30.40 14.90 28.73
S50 MPL RM225–RM190 2.72 6.31 RM190–RM557 7.59 5.67
VPL RM225–RM190 6.11 11.54 RM225–RM190 8.58 8.00
TGW RM50–RM557 3.76 3.08 RM225–RM190 6.42 6.52
GW RM190–RM557 3.35 3.16
LWR RM225–RM190 2.93 3.06
GC RM190–RM557 2.99 0.00
S17 GL RM505–RM234 49.32 52.28
GW RM478–RM505 8.38 11.61
LWR RM478–RM505 99.83 37.58
GC RM505–RM234 27.27 33.92
TGW RM478–RM505 4.95 1.05
VPL RM505–RM234 10.55 14.39
MPL RM505–RM234 6.94 9.79
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Notes: GL, grain length; GW, grain width; LWR, length-to-width ratio; GS, grain size; GC, grain circumference; GR, grain roundness; TGW, 1000-grain weight; VPL, average panicle length; MPL, maximum panicle length; E, early season; L, late season. LOD values indicate significance levels. n represents the number of plants in F2. Population.

Table 4.

Phenotypes of QTLs for grain shape and yield.

Materials Yield per Plant Panicle Number The Number of Full Grains Thousand-Grain Weight Panicle Length Grain Length Grain Width Heading Day Plant Height
HJX74 22.4 6.8 1087.2 21.10 22.4 8.130 2.530 97.0 105.2
S24 21.5 6.8 1166.8 18.50 ** 21.9 7.584 * 2.402 * 103.0 * 119.2 *
S29 23.0 * 6.4 1078.6 21.55 21.1 7.813 2.559 98.0 106.4
F1 (HJX74/S24) 26.1 ** 7.7 1312.2 ** 20.02 * 22.2 7.775 * 2.407 * 100.0 * 116.1 *
D7 (S24/S29) 23.8 * 6.7 1254.1 * 18.76 * 21.8 7.673 * 2.408 * 103.0 * 110.1 *
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* and ** denote the significance levels at p < 0.05 and p < 0.01, respectively.

Two QTLs for panicle length were identified in the intervals RM225–RM190 of S50 and RM505–RM234 of S17. Interestingly, these loci were also associated with grain shape (Table 1). The interval RM505–RM234 of S17 harboring grain shape QTLs and panicle shape QTLs was semblable to the semi-dominant gene GW7 (LOC_Os07g4120), which was related to the production of more slender grains and the improvement of rice yield and grain quality [30]. Therefore, the grain shape genes had a pleiotropic effect, not only between grain shape traits but also with other yield-related traits.

2.6. Fine Mapping of QTLs for Grain Shape

SSSL-S24 was an exotic small-grain line compared with the receptor parent HJX74 (Table 1). The QTL identified in the interval RM271–PSM407 of S24 during the early and late seasons of 2016 might represent a new gene regulating grain shape. By mapping the population of sub-SSSLs and F2 populations, the interval range was narrowed from 2253.039 kb (PSM169–RM258) in 2018 to 75.124 kb (PSM169–RM25753) in 2019 (Figure 3), harbouring eight predicted genes (LOC_Os10g37850.2, LOC_Os10g37860, LOC_Os10g37870, LOC_Os10g37880, LOC_Os10g37899, LOC_Os10g37920, LOC_Os10g37940, and LOC_Os10g37950), among which novel QTL were contained for grain shape [40]. According to the RAP database (Rice Genome Annotation Project), the expression levels of the above four genes (LOC_Os10g37850.2, LOC_Os10g37860, LOC_Os10g37870, LOC_Os10g37880) were elevated in the seeds and panicles (Supplementary Table S3). Based on expression analysis, LOC_Os10g37880 might be a candidate gene by expression primers (Figure 4) (Supplementary Table S4). Cloning this gene will offer deeper insights into the molecular breeding application of rice with lesser grain weight, higher grain yield, superior quality, and positive heterosis in the future.

Figure 3.

Figure 3

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Putative location of the QTL for grain shape in S24. (Up) Mapped in 2018; (Down) mapped in 2019. Genetic distance (in cM, centimorgans) between two molecular markers is indicated on the X-axis. LOD is abbreviation of the logarithm/likelihood of odds on the Y-axis. The red symbols indicate marker sites or peak locations.

Figure 4.

Figure 4

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Expression pattern of LOC_0s10g37880. The Y-axis indicates the relative expression multiple, and the X-axis indicates the materials. W0 is short for HJX74 as the corresponding control. S24-50, S24-60, S24-70 and S24-80-2 represent the expression of genes LOC_Os10g37850.2, LOC_Os10g37860, LOC_Os10g37870, and LOC_Os10g37880 in S24, respectively.

2.7. QTLs Breeding Application for Grain Shape

In addition to the new QTLs for grain shape, S24 also carried QTLs for heading date and plant height. The yield of another substitution line, S29, was slightly higher than that of the control, but it did not carry QTLs for grain shape (Table 4). The use of this material was conducive to the study of the genetic breeding effect for the grain shape of S24. The yield of test crossing combination F1(HJX74/S24) was significantly higher than that of the control, while the GL, GW and TGW were significantly reduced. In addition, the heading date and plant height of F1(HJX74/S24) were the same as those of S24. The yield of the pyramiding line D7(S24/S29) was significantly higher than that of the control, and it also had the same QTLs for grain shape, heading date and plant height with S24. It should be noted that the difference in the number of panicles was not significant, but the TGW reduced significantly, indicating the yield was probably dominated by the significant increase in the number of grains per panicle (Figure 5).

Figure 5.

Figure 5

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Plant appearance and grain shape of the pyramid line. (a) Plant appearance, from left to right: S24, D7 (S24/S29), HJX74. Bar = 15 cm. (b) Grain length for HJX74 (up) and S24 (down). Bar = 1 cm. (c) Grain width for HJX74 (up) and S24 (down). Bar = 1 cm.

3. Discussion

3.1. Breeding Applications of Grain Shape QTLs

Pursuing grain yield is one of the most critical objectives of rice breeding. The genetic bases of grain shape have received much attention because of their importance to rice yield and quality [2,28]. QTLs for grain shape have been widely studied in rice genetics. GS3 harboured multiple alleles in the fifth exon, and each independent deletion variant caused a premature stop codon, conferring a short-seeded phenotype [31]. GW7 was correlated with the production of more slender grain owing to increased cell division in the longitudinal direction and decreased cell division in the transverse direction in the gene-upregulated expression. Moreover, the GW7TFA allele in tropical japonica rice was associated with superior grain quality without a yield penalty, providing a new strategy by manipulating the OsSPL16-GW7 module to simultaneously improve rice yield and grain quality [30]. Our study showed that S29 carried a yield QTL with a negative additive effect and a positive dominant effect on grain shape. The yield difference of S24 was not significant, but its dominance effect (F1) of yield QTL was positive and significant. The pyramiding line D7(S24/S29), which had the same QTL as S24 for small 1000-grain weight, high stalk, and late heading day, showed a significant increase in yield and the number of grains per panicle, while the number of panicles remained semblable and the 1000-grain weight decreased, indicating that the grain yield was dominated by the increase in the number of full grains per panicle. The loci RM505–RM234 and the adjacent RM633 identified in the present study were close to the previously reported QTL-focus regions harbouring genes GS3 and GW7 on chromosomes 3 and 7, respectively. Therefore, QTLs exist in hot mapping regions. In addition, pyramiding breeding of QTLs for grain shape played a pivotal role in obtaining rice with higher yield and superior quality [41]. Therefore, these QTLs for grain shape detected by the study would have ideal significance for the improvement of rice yield and quality in the future.

3.2. Pleiotropic Effects of QTLs for Grain Shape

Natural variations in the functions of genes encoding grain shape proteins result in pleiotropic effects of QTLs for grain shape and size in rice [26]. In rice, the appearance of grain shape is a result of the simultaneous regulation of multiple genes, which are either several tightly linked QTLs or pleiotropic QTLs with opposite effects on this trait. For the map-based cloning of such QTLs, interaction effects or non-genetic factors (i.e., epigenetic factors) must be removed [30,35]. According to the results, the grain shape genes also have a pleiotropic effect, not only between grain shape traits but also with panicle shape traits (Table 3).

In rice production, mono-effect QTLs can be easily used for rice improvement; however, the application of pleiotropic QTLs with opposite effects on different traits is complicated. In modern breeding, with the exception of yield, more attention should be paid to grain quality, which is mainly evaluated based on the appearance of the rice grain, as represented by the length-width-ratio (LWR) [29]; therefore, the manipulation of alleles for grain shape with pleiotropic effect is beneficial to rice yield and quality improvement. The results of this study also showed that higher plant height was not necessarily associated with longer grain shape, as in S24, which had higher plant height and produced shorter and smaller grains. Supposedly, small grains of rice may be an indicator of admirable rice quality. Nonetheless, whether QTLs for grain shape and panicle length produce pleiotropic effects warrants further research [42].

3.3. SSSL as an Excellent Material for QTL Mapping

Many QTLs are finally defined to a wide interval of approximately 10 cM in a primary mapping population because of the noise from their genetic backgrounds. Recent studies on QTL fine mapping have revealed that some tiny chromosome regions produce pleiotropic effects on TGW, SPP (spikelets per panicle) [43], and grain per the main panicle [29] and that their interactions are hardly removed in the general population. Owing to advanced mapping populations, such as CSSLs and SSSLs, the background noise can be greatly reduced, and fine mapping accuracy can be significantly improved. In these lines, regardless of the closely linked QTLs or pleiotropic QTLs in a small region, the target region harbouring genes may balance the allocation between grain weight and shape. Therefore, the same or similar intervals mapped in QTLs for several grain shape traits can be distinguished more easily in SSSLs [32].

4. Materials and Methods

4.1. Plant Materials

In 2016 and 2017, a population including 33 SSSLs derived from receptor HJX74(W0) and 8 donors (Supplementary Table S5) was selected to identify QTLs for grain shape in the Key Laboratory of Plant Molecular Breeding, Guangdong Province. All of the SSSLs were developed through three back-crossings followed by five to eight self-crossings and checked at each step by molecular marker-assisted selection. The distribution of each SSSL on the genetic linkage map is depicted in Figure 1.

4.2. Field Planting

Germinated seeds were sown on a seedling bed. Each line was transplanted with 30-day-old seedlings (late February) in the early season and 20-day-old seedlings (late July) in the late season. The seedlings were planted in two rows (with 10 plants per row) with 16.5 cm of distance between the plants and 30 cm of distance between the rows. Plantation was done following a randomised complete block design with two replicates on a homogenous field [35]. The plants were grown in paddy fields under natural long-day conditions (from March to July) during the early season and natural short-day conditions (from July to November) during the late season. The field was equipped with a bird net to prevent damage caused by birds.

4.3. Phenotypic Evaluation

Seeds collected (mid-July and late November) from primary panicles were dried for a week in a glasshouse. Fifty neat and evenly dried seeds from each line were evaluated per replicate. Six grain shape traits, including grain length (GL), grain width (GW), length-to-width ratio (LWR), grain size (GS), grain circumference (GC), and grain roundness (GR), were scanned and calculated using a Wan-Shen SC-A-type scanner (ScanMaker i800 plus); LWR was calculated as GL divided by GW, and the weight of 1000 grains was measured as 1000-grain weight (TGW). Ten plants per line from the middle of the two rows in each plot were harvested and measured, and the average of measurements was used as the phenotype of each line to determine the traits of grain shape per plant [14]. Panicle length was measured from the panicle neck section to the tip of the tallest panicle [32].

4.4. DNA Extraction and Genotyping

DNA was extracted using a micro-isolation method, as previously described [34]. Approximately 2 cm of young rice leaves at the four-leaf seedling stage were cut and ground in a GENO/TissueLyser-192 grinder for DNA extraction. Mini-scale DNA extraction was performed using the modified TE mixture. Genomic DNA used for analysing polymorphic markers was extracted using TPS solution (100 mM Tris–HCl (pH 8.0), 10 mM ethylenediaminetetraacetic acid (EDTA), and 1 M KCl).

The polymerase chain reaction (PCR) amplification conditions for all markers were the same as those previously described [36,37]. DNA fragments were amplified using PCR under the following thermal cycling conditions: denaturation for 5 min at 94 °C; followed by 38 cycles of denaturation for 30 s at 94 °C, annealing for 30 s at 55 °C, extension for 45 s at 72 °C; and a final extension for 5 min at 72 °C.

Each 15 μL reaction mixture contained 5.3 μL of master mix (Genstar 2× Taq PCR Star Mix with loading dye), 1.2 μL each of R/F primers (2–3 μM), and 2.0 μL of the extracted genomic DNA as the template, raised to 15 μL with water. The PCR products (approximately 5 μL) were separated by electrophoresis on 6% (w/v) non-denaturing polyacrylamide gel and subjected to silver staining as previously described [38].

All SSR markers were identified from the Gramene database (http://www.gramene.org/, accessed on 21 October 2013), and simple-sequence repeat (SSR) primers were designed according to the International Rice Genome Sequencing Project (http://rice.plantbiology.msu.edu/, accessed on 21 October 2013). A total of 560 SSR and InDel markers distributed throughout the genome were used to analyse the polymorphism and genetic background between the donor and receptor parents, among which 225 (40.18%) polymorphic markers were finally retained. These polymorphic markers were used to confirm the genotypes of the F1 and F2 lines, which were used as the source material to develop sub-SSSLs in this study.

4.5. Data Analysis

Phenotypic means were compared using ANOVA and Tukey’s multiple comparison tests with the Minitab software package (Release 13.1). The molecular linkage map was constructed using Mapmaker 3.0, setting the logarithm of odds (LOD) value to 3.0 [44]. The Kosambi function was used to calculate genetic distance. Composite interval mapping was performed for QTL analysis on the F2 populations using ICIM 4.1 (http://www.isbreeding.net/software/?type=detail&id=14, accessed on 21 October 2013). Marker intervals and the site at the peak value were determined, and candidate intervals were provided. The significance level to determine the candidate intervals and detect putative QTL and HQTL was set at a probability level of 0.01 and 0.05. A similar p-value was used to test the significance of the QTL effect. The length of the substitution segment was measured from sites a to d, and the estimated length of the substitution segment was measured from sites b to c in centimol units (Figure 6).

Add effect (A) = SSSL − W0
Add effect variation explained (APVE, %) = (SSSL − W0)/W0 × 100
D effect (D) = F1 − (SSSL + W0)/2
D effect variation explained (DPVE, %) = [D/(SSSL + W0)/2] × 100.

Figure 6.

Figure 6

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The measuring illustration of the length of the substitution fragment. (b and c were the median values of a and e, f and d, respectively).

4.6. Fine Mapping and Expression Analysis

During the early and late seasons of 2018 and the early season of 2019, S17, S24, S40, S48, and S50 were used for further QTL linkage analysis on a continuous F2 population for finer mapping with 280 sequentially developed plants. Of the 280 plants, 224 were subjected to linkage analysis using novel polymorphic molecular markers. The expression of candidate genes was performed as previously described [2].

5. Conclusions

A SSSL with a single donor fragment and chromosomal region harbouring the target trait may be considered a QTL. In this study, 54 QTLs for grain shape were detected, including some QTLs reported previously, such as GS3, GW8, and GW7, indicating the feasibility of identifying QTLs using SSSLs. In addition, a novel QTL qTGW10-3 for grain shape was identified in the interval RM271–PSM407 of S24. Linkage mapping revealed that the interval range of the target trait could be narrowed down from 2253.039 kb (PSM169–RM258) to 75.124 kb (PSM169–RM25753), and the candidate gene was identified as LOC_Os10g37880 which could be applied to breeding practice by molecular marker-assisted selection. Therefore, QTL identification using SSSL is feasible, convenient, and rapid. Amongst the detected QTLs, qTGW4-1, qTGW4-2 (PSM106–PSM382; RM348–RM127), qTGW5-1 (PSM202–RM437), qTGW7-1 (PSM142–RM70), and qTGW10-2 (END–RM596) showed a high positive-additive genetic effect on grain shape. qTGW10-3 (RM271-PSM407) showed a high dominant genetic effect on grain yield. In the future, these novel genes might be cloned and used in breeding to improve grain yield and the quality of rice.

Acknowledgments

We thank Guiquan Zhang, Shaokui Wang, and Haitao Zhu of the South China Agricultural University for providing materials and experimental conditions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants12040892/s1, Table S1: Polymorphic markers for SSSLs; Table S2: Polymorphic marker information of insertion and deletion for SSSLs; Table S3: Polymorphic marker information of insertion and deletion for SSSLs; Table S4: TRAP-Seq FPKM expression values; Table S5: Substitute fragment information of the biological materials of 33 SSSLs.

Click here for additional data file. (336.4KB, zip)

Author Contributions

Data curation: X.W., X.L. (Xia Li), T.W. and Z.W.; Formal analysis: Z.P. and C.Y.; Funding acquisition: Q.X., Z.P. and Y.X.; Investigation: X.L. (Xia Li) and T.W.; Methodology: X.L. (Xia Li) and T.W.; Project administration: X.L. (Xin Luo), S.T. and Q.X.; Resources: Q.X., Z.P. and Y.X.; Software: X.W., X.L. (Xin Luo) and S.T.; Supervision: C.Y.; Validation: C.Y. and Y.X.; Visualization: Y.X. and C.Y.; Writing—original draft: X.W. and X.L. (Xia Li); Writing—review & editing: X.W. and Y.X. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

All the data are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Funding Statement

This work was funded by the National Natural Science Foundation of China (No. 32060475); the Jiangxi Provincial Talent Fund–Basic Research Project (JXSNKYJCRC202312); the Basic Research and Leading Talent Nurturing Program (JXSNKYJCRC202202); the Jiangxi Modern Agricultural Scientific Research Collaborative Innovation Special Project (No. JXXTCX202201); and the Innovation Platform for Academy of Hainan Province (2021-01) and Jiangxi seed industry joint research project-Excellent germplasm creation of japonica and cultivation of new varieties of indica-japonica hybrid rice.

Footnotes

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