Meta-GWAS analyses
Meta-analyses of GWAS were performed based on four datasets’ (two locations for each population) GWAS results (Additional file 1: Figures S1-S4). Manhattan plots and quantile–quantile plots of meta-GWAS are shown in Fig. 1. A total of 3592 significant loci were identified (Additional file 2: Table S1), including 2450, 1116, 23 and 3 significant associated loci were separately detected for GPP, KGW, TP and YD, which were distributed on all of the rice chromosomes except for chromosome 10. According to the information of RAP-DB (http://rapdb.dna.affrc.go.jp/), candidate genes were searched in a genomic region of 200 KB around the associated SNPs (Additional file 1: Table S2). We discovered 7, 7, and 3 cloned genes separately associated with GPP, KGW and TP. A total of three candidate genes associated with different traits, among which OsBZR1 (Zhu et al. 2015) and OsSPL14 (Jiao et al. 2010) have been reported previously and Os02g0106966 was novelty discovered. Both OsBZR1 and OsSPL14 were detected in KGW and GPP, Os02g0106966 was detected in KGW and TP. In this study, only 3 significant loci for YD were detected, but 3589 significant loci for the component traits were detected. It may be because rice yield has a low heritability which mainly affected by many minor-effect genes, the low heritability of rice yield is also showed in our previous study (Xu et al. 2018). These results suggested that selecting the component traits of yield as a complementary route to improve the rice production is recommended.
Causal Relationship Between Yield And Its Components
MR analyses were performed to estimate the causal relationship between rice yield and its component traits in the study. The results of MR analyses were shown in Table 1. The IVW method results showed both GPP and TP have a positive causal relationship with YD (P < 0.05), and there was no directional causal relationship between KGW and YD. In sensitivity analyses, the results of the weighted median method are also confirmed the results of IVW method. The intercept term obtained by MR-Egger regression analysis indicated no evidence of directional pleiotropic (P > 0.05). The MR analyses provided some evidence that rice yield probably enhanced by pyramiding the superior alleles of genes controlling GPP or TP, and the superior alleles of genes controlling TP should be priority to pyramid, because TP has a greater effect (Beta = 1.865) on yield than KGW (Beta = 0.456) and GPP (Beta = 0.086).
Table 1
MR results of a causal relationship between yield and its component traits
Trait | Methods | Beta | 95% CI | P |
GPP | IVW | 0.086 | 0.030 ~ 0.141 | 0.003 |
Weighted median | 0.081 | 0.009 ~ 0.152 | 0.028 |
MR-Egger | -0.029 | -0.160 ~ 0.103 | 0.668 |
MR-Egger(intercept) | 1.387 | -0.063 ~ 2.836 | 0.061 |
KGW | IVW | 0.456 | -0.119 ~ 1.031 | 0.120 |
Weighted median | 0.035 | -0.758 ~ 0.828 | 0.931 |
MR-Egger | -1.002 | -2.858 ~ 0.855 | 0.290 |
MR-Egger(intercept) | 1.115 | -0.236 ~ 2.466 | 0.106 |
TP | IVW | 1.865 | 1.035 ~ 2.694 | < 0.0001 |
Weighted median | 1.540 | 0.353 ~ 2.727 | 0.011 |
MR-Egger | 1.797 | -1.633 ~ 5.228 | 0.304 |
MR-Egger(intercept) | 0.046 | -2.165 ~ 2.256 | 0.968 |
Note: CI confidence intervals, P statistically significant associations with a P < 0.05. |
Causal Relationship Between Gpp And Yd
As required for MR analysis, a total of 2450 SNPs reached genome-wide significance for GPP (P < 1E-06) in meta-analyses of GWAS, among which six SNPs were selected as instrumental variables to estimate the causal relationship between GPP and YD (Table 2). The six SNPs were not associated with KGW or TP (P > 0.05), and no evidence of LD between them (all pairwise r2 < 0.01). In MR analysis, a positive causal relationship between GPP and YD were observed with the IVW method (Table 1, Fig. 2a). One standard deviation (SD) genetic higher GPP was associated with a 0.086 SD higher YD (Beta = 0.086, 95% CI: 0.030 ~ 0.141, P = 0.003). In sensitivity analyses, the Cochran's Q-test illustrated no obvious heterogeneity (I2 = 5%, P = 0.38). The weighted median method also showed GPP had a positive effect on YD (Beta = 0.081, 95% CI: 0.009 ~ 0.152, P = 0.028). MR-Egger regression indicated no evidence of directional pleiotropy for the associations of GPP with YD (intercept = 1.387,P = 0.061). It is worth noting that some cloned genes were detected in the meta-GWAS on GPP, the phenotype of transgenic plants with these genes had a similar phenomenon. For example, the OsSPL14 mutant produced more grain number per panicle, enhanced rice yield (Jiao et al. 2010). Compared with the control non-transgenic plants, the over-expression of OsBZR1 plants showed the 1000-grain weigh was increased about 3.4% and the spikelet number per panicle was increased 21.9%, that resulting in enhanced yield (Zhu et al. 2015). The cd1 mutant exhibited a variety of phenotypic traits, such as reduction in grain number and panicle length, the biomass was lower than that of the wild type (Luan et al. 2011).
Table 2
Information about instrumental variables
SNP | Chromosome | Position | GPP | | YD |
Beta | P-value | | Beta | P-value |
chr03_29979498 | 3 | 29979498 | -18.724 | 1.12E-07 | | -0.633 | 0.543 |
chr03_898774 | 3 | 898774 | 18.953 | 2.92E-07 | | 1.254 | 0.299 |
chr05_7226049 | 5 | 7226049 | -7.242 | 2.25E-08 | | -1.329 | 0.005 |
chr08_25257522 | 8 | 25257522 | -16.559 | 8.58E-07 | | 1.178 | 0.553 |
chr09_12464309 | 9 | 12464309 | -7.538 | 4.49E-07 | | -0.974 | 0.066 |
chr12_22633431 | 12 | 22633431 | 15.738 | 3.01E-07 | | 1.439 | 0.158 |
Note: All the SNP markers are named after the chromosome _ position |
Causal Relationship Between Kgw And Yd
As required for MR analysis, a total of 1116 SNPs reached genome-wide significance for KGW (P < 1E-06) in meta-analyses of GWAS, among which eleven SNPs were selected as instrumental variables to estimate the causal relationship between KGW and YD (Table 3). These SNPs were not associated with GPP or TP (P > 0.05), and no evidence of LD between them (all pairwise r2 < 0.01). In MR analysis, no significant associations between KGW and YD were observed with the IVW method (Table 1, Fig. 2b) (Beta = 0.456, 95% CI: -0.119 ~ 1.031, P = 0.120). In sensitivity analyses, the Cochran's Q-test illustrated no obvious heterogeneity (I2 = 0%, P = 0.67). The weighted median method also showed no evidence of causal relationship between KGW and YD (Beta = 0.035, 95% CI: -0.758 ~ 0.828, P = 0.931). MR-Egger regression indicated no evidence of directional pleiotropy for the associations of KGW with YD (intercept = 1.115, P = 0.106). Some regulate KGW genes were detected in the meta-GWAS on KGW. Relationship between KGW and yield is inconsistent in the transgenic plants with these genes. In Chen et al.’s study, the KGW of FUWA plants was 16.7% higher than that of wild type, but their yield was comparable (Chen et al. 2015). Song et al. (2007) reported loss of GW2 function accelerated the grain milk filling rate, resulting in increased grain weight and yield.
Table 3
Information about instrumental variables
SNP | Chromosome | Position | KGW | | YD |
Beta | P-value | | Beta | P-value |
chr01_3547491 | 1 | 3547491 | 0.706 | 7.14E-08 | | -0.036 | 0.948 |
chr01_5524333 | 1 | 5524333 | 1.018 | 6.56E-07 | | 0.310 | 0.840 |
chr02_1118809 | 2 | 1118809 | 0.786 | 1.56E-09 | | 1.030 | 0.082 |
chr02_334316 | 2 | 334316 | -1.474 | 2.38E-07 | | 0.948 | 0.505 |
chr02_7792121 | 2 | 7792121 | -1.256 | 9.21E-08 | | 0.029 | 0.980 |
chr03_17810847 | 3 | 17810847 | 0.651 | 4.74E-07 | | 0.711 | 0.394 |
chr03_33060865 | 3 | 33060865 | -0.505 | 2.03E-08 | | -0.978 | 0.027 |
chr04_13785932 | 4 | 13785932 | 0.739 | 3.16E-07 | | 0.623 | 0.406 |
chr05_16393143 | 5 | 16393143 | 0.466 | 4.71E-07 | | 0.029 | 0.955 |
chr07_23215227 | 7 | 23215227 | 0.952 | 1.09E-07 | | -0.308 | 0.715 |
chr08_25464238 | 8 | 25464238 | -0.875 | 2.01E-09 | | -0.004 | 0.997 |
Causal Relationship Between Tp And Yd
As required for MR analysis, a total of 23 SNPs reached genome-wide significance for TP (P < 1E-06) in meta-analyses of GWAS, among which three SNPs were selected as instrumental variables to estimate the causal relationship between TP and YD (Table 4). These SNPs were not associated with KGW or GPP (P > 0.05), and no evidence of LD between them (all pairwise r2 < 0.01). In MR analysis, a positive causal relationship between TP and YD were observed with the IVW method (Table 1, Fig. 2c), 1 SD genetic higher TP was associated with a 1.865 SD higher YD (Beta = 1.865, 95% CI: 1.035 ~ 2.694, P < 0.0001). In sensitivity analyses, Cochran's Q-test illustrated no obvious heterogeneity (I2 = 0%, P = 0.43), The weighted median method also showed TP had a positive effect on YD (Beta = 1.54, 95% CI: 0.353 ~ 2.727, P = 0.011). MR-Egger regression indicated no evidence of directional pleiotropy for the associations of TP with YD (intercept = 0.046, P = 0.968). The cloned gene OsPIN2 were detected in the meta-GWAS on TP. Chen et al. (2012) found that the OsPIN2 transgenic plants had more effective tiller number, lower 1000-grain weight and higher yield.
Table 4
Information about instrumental variables
SNP | Chromosome | Position | TP | | YD |
Beta | P-value | | Beta | P-value |
chr02_21604477 | 2 | 21604477 | -1.279 | 8.25E-07 | | -1.977 | 0.043 |
chr06_1578700 | 6 | 1578700 | -0.639 | 4.52E-07 | | -1.691 | 3.21E-04 |
chr11_26492375 | 11 | 26492375 | -0.507 | 7.04E-08 | | -0.720 | 0.043 |
Loci for component traits had an indirect effect on yield
We identified four significant loci that had an indirect effect on yield by MR analyses (Fig. 2, Additional file 1: Table S3). Among them, the SNP chr05_7226049 (Fig. 2a) for GPP had an indirect effect on yield, and located nearby the cloned gene OsPYL11. Kim et al. (2014) reported that compared with the control plants, the transgenic plants over expressing OsPYL11 showed no significant difference in tiller number, but the yield was severely reduced. Our study indicated the yield severely reduced may be caused by the number of grains decreased. The SNP chr06_1578700 (Fig. 2c) for TP closed to the D62 (a gene regulating tillers). Li et al. (2010) found that the tiller number of D62 mutant rice was less than that of wild type. The SNPs chr02_21604477 and chr11_26492375 for TP also had indirect effects on yield (Fig. 2c, Table S3), which were first detected in our research. These finding provided new information for further improve rice yield potential.
Pyramiding Superior Alleles Of Significant Loci
The average yield performance of F1 lines with different superior allele number of significant loci with direct effect, indirect effect and direct plus indirect effect were showed in Table 5 and Fig. 3. Three loci had an direct effect on yield were detected in the meta-GWAS on YD (Additional file 1: Table S3), among them, the average yield of the lines without superior alleles was 41.29 g, and the average yield of the lines with one superior alleles was 44.26 g (Fig. 3a, Table 5). The superior alleles of four loci that had an indirect effect on yield were also pyramided in the study. The results showed that the average yield of F1 lines with 0 to 4 superior alleles was: 42.75 g, 42.52 g, 43.32 g, 45.34 g, 52.60 g, respectively. In general, pyramiding superior alleles of loci revealed enhanced yield (Fig. 3b, Table 5). A similar phenomenon also found in pyramiding the direct plus indirect loci, and the yield probably further increased (Fig. 3c, Table 5). Other research reported that the phenotype performance improved by pyramiding the superior alleles of loci associated with agronomic traits (Huang et al. 2015), our results suggested the yield enhanced also by pyramiding the superior alleles of loci that had indirect effect on yield. A combination of direct and indirect effects may better contribute to the yield potential of rice.
Table 5
The average yield performance with different number of superior alleles
Effect | No. of superior alleles |
0 | 1 | 2 | 3 | 4 |
Direct | 41.29 | 44.26 | | | |
Indirect | 42.75 | 42.52 | 43.32 | 45.34 | 52.60 |
Direct plus indirect | 40.71 | 43.53 | 43.16 | 44.98 | 52.91 |