Phenotypic Variations of GWC in Hybrid Population
To evaluate the dynamic variant of GWC and corresponding AUDDC from maturity to harvest in maize hybrids, the GWC were determined in five periods—35 days after silking (M35), M42, M49, M56, and M63—in two environments. The analysis of variance components showed that the genotype and genotype-environment interaction of GWC and AUDDC were significantly different (p < 0.001) for GMC and AUDDC in all periods (Table 1). In particular, the effects of both parents were significant (p < 0.001) for GWC and AUDDC in all periods, and the genotype interaction effect between bi-parents was significant in most periods, except for M49. Yet, the interaction effect between the bi-parent and environment was significant only in M42, M63, and A4.
Along with the kernel development, the average GWC was decreased from 40.28–27.65%, and the coefficient of variation (CV) of them was increased from 1.99–8.31%. The same phenomenon also happened for AUDDC, where the average varied from 277.72 to 209.30 and CV from 2.27 to 6.56%. This result indicates that the differences between materials became increasingly apparent at later times due to the different dehydration rates. The heritability for GWC ranged from 0.42 to 0.77 at different stages and from 0.64 to 0.83 for AUDDC during different periods (Table 1). In addition, we found that GWC and AUDDC were extremely significantly positively correlated at different stages (Fig. 1), and the grain yield (GY, t/ha), grain moisture (GM) at harvest, ear leaf width (ELW), and ear leaf area (ELA) were positively correlated with GWC and GDR at later stages (Figure S1).
Table 1
Analysis of variance components (σ2) and descriptive Statistics of BLUEs for GWC and AUDDC in hybrid population
Source | M35 | M42 | M49 | M56 | M63 | A1 | A2 | A3 | A4 | AT |
\({\sigma }_{Genotype}^{2}\) | 14.66*** | 30.12*** | 13.53*** | 22.19*** | 13.55*** | 32.31*** | 25.64*** | 25.87*** | 25.62*** | 37.27*** |
\({\sigma }_{female}^{2}\) | 6*** | 13.82*** | 7.63*** | 16.6*** | 9.72*** | 13.91*** | 13.39*** | 18.58*** | 19.44*** | 26.27*** |
\({\sigma }_{male}^{2}\) | 7.38*** | 14.39*** | 4.68*** | 4.31*** | 2.76*** | 16.41*** | 10.58*** | 5.87*** | 5*** | 9.39*** |
\({\sigma }_{female\times male}^{2}\) | 1.28** | 1.91*** | 1.22* | 1.28** | 1.07 | 1.99*** | 1.67*** | 1.42*** | 1.18* | 1.61*** |
\({\sigma }_{Genotype\times Environments}^{2}\) | 6.9*** | 8.68*** | 5.92*** | 6.3*** | 16.65*** | 10.54*** | 7.66*** | 6.72*** | 13.81*** | 7.54*** |
\({\sigma }_{ female\times Environment}^{2}\) | 4.46*** | 5.48*** | 3.17*** | 3.01*** | 12.8*** | 7.47*** | 4.67*** | 3.51*** | 9.99*** | 4.01*** |
\({\sigma }_{male\times Environment}^{2}\) | 1.61*** | 1.94*** | 1.84*** | 2.23*** | 2.6*** | 2.06*** | 1.93*** | 2.12*** | 2.7*** | 2.44*** |
\({\sigma }_{female\times male\times Environment}^{2}\) | 0.83 | 1.26** | 0.91 | 1.06 | 1.25** | 1.01 | 1.06 | 1.09 | 1.12* | 1.09 |
Range | 37.67–42.51 | 35.90–42.60 | 32.90-40.73 | 26.54–39.31 | 20.50-35.75 | 258.39-295.34 | 243.37-290.47 | 210.26–280.10 | 165.52-253.23 | 884.89-1106.63 |
Mean ± SD | 40.28 ± 0.80 | 39.07 ± 1.15 | 37.19 ± 1.42 | 33.06 ± 2.19 | 26.75 ± 2.22 | 277.72 ± 6.31 | 266.90 ± 8.30 | 245.86 ± 11.33 | 209.30 ± 13.74 | 999.78 ± 34.71 |
CV | 1.99 | 2.95 | 3.82 | 6.63 | 8.31 | 2.27 | 3.11 | 4.61 | 6.56 | 3.47 |
Heritability(H2) | 0.66 | 0.77 | 0.70 | 0.77 | 0.42 | 0.74 | 0.76 | 0.79 | 0.64 | 0.83 |
Probability level: *, **, and *** means significantly different for zero at P < = 0.1, P < = 0.01, and P < = 0.001, respectively, not assigned not significant. |
Parental Effect of GWC in Hybrid Population
To verify the effect of different test parents on the GWC and AUDDC in the F1 generation, we compared the differences in hybrids from different tester lines using the Fisher LSD method with a significance level of 0.05 (Fig. 2). The decline of GWC was apparently faster in the later than in the earlier stage (Fig. 2A). When we constructed the individual trend for the F1s from the same test lines, the results showed that GWC of the combination from the test lines A008, A009, and B137 was at a high level in all periods, while the combinations from A021 and B018 were always at low levels (Fig. 2B,C). This significant differential between the combinations from the same test lines indicated that the test lines make a contribution to the combination. Among them, A021 and B018 can be used to improve the germplasm by improving the dehydration rate during the breeding process.
Genome-Wide Association Analysis of GWC and AUDDC
By conducting a genome-wide association analysis of GWC and AUDDC using BLUE values of 442 F1 hybrid combinations in two environments (Yangling and Yulin), 48 independently associated SNPs were identified, including 22 SNPs for GWC and 26 for AUDDC, with ten common SNPs identified for GWC and AUDDC (Table 2, Fig. 3A and Figures S2 and S3). Finally, 26 unique SNPs were left by deleting the association SNP with a higher P value according to the R2 between the SNPs, and 3 of them were simultaneously associated with more than four traits from GWC and AUDDC. One was chr4:150010129, which was simultaneously associated with M35, M42, M49, A1, A2, A3, and AT; the other two SNPs were chr2:14522675 and chr8:165108325, both of which were associated with M56, A3, A4, and AT. In addition, five associated SNPs with more than 10% phenotypic variant explanation (PVE) were detected: chr1:4535637 for M56 with 16.55% PVE and A3 with 17.65% PVE, chr2:5320161 for AT with 21.07% PVE, chr2:14522675 for A4 with 10.04% PVE, and chr6:2463271 for A4 with 13.09% PVE (Table 2).
In addition, 64 pairs of interaction SNPs were detected for GWC and 77 pairs for AUDDC, which contained 162 independent SNPs (Table S1). Among them, 28 common pairs of interaction SNPs were associated with more than two individual traits, such as chr1:156254070_chr5:26130738 associated with M49, A3, and AT, or chr1:261928509_chr8:114505826 related to M35, M42, and A1. For GWC and AUDDC at different stages, the total PVE of GWC (11.39–68.2%) and AUDDC (41.07–67.02%) were widely different (Fig. 3B). Epistatic effects played an important role, especially M49 and M63, accounting for 28.05% and 11.39%, respectively. This suggested that GWC and AUDDC might be controlled by several major genes with multiple minor genes. The additive and epistatic effects might be the main genetic effects.
Table 2
Significant SNP of GWC and AUDDC identified by genome wide association analysis
SNP No. | SNP Position | Trait | SNP name | Model | Effect | -log10 P v | PVE (%) |
●*SNP1 | chr1:4535637 | M56 | M56_SNP1 | Add | 1.38 | 3.98 | 16.55 |
A3 | A3_SNP1 | ADD | 7.41 | 4.06 | 17.65 |
●*SNP2 | chr2:5320161 | M56 | M56_SNP6 | ADD | -1.16 | 4.48 | 5.41 |
AT | AT_SNP3 | ADD | -18.68 | 4.15 | 13.95 |
●SNP3 | chr2:9065055 | M42 | M42_SNP1 | ADD | -0.62 | 4.11 | 13.01 |
●**SNP4 | chr2:14522675 | M56 | M56_SNP3 | ADD | -1.24 | 4.63 | 1.82 |
A3 | A3_SNP4 | ADD | -6.37 | 4.47 | 8.47 |
A4 | A4_SNP1 | ADD | -7.92 | 4.64 | 10.04 |
AT | AT_SNP2 | ADD | -21.02 | 4.85 | 5.65 |
*SNP5 | chr2:217195604 | M35 | M35_SNP1 | ADD | 0.7 | 4.74 | 1.83 |
Dom | -0.7 | 4.74 |
A1 | A1_SNP1 | ADD | 5.6 | 4.23 | 2.07 |
Dom | -5.6 | 4.23 |
SNP6 | chr2:222189635 | M56 | M56_SNP4 | ADD | 1.79 | 4.15 | 7.49 |
*SNP7 | chr2:222392187 | M56 | M56_SNP5 | ADD | 2.27 | 5.29 | 0.63 |
A3 | A3_SNP5 | ADD | 10.47 | 4.1 | 4.39 |
SNP8 | chr3:174099983 | M35 | M35_SNP2 | ADD | -0.7 | 4.07 | 2.73 |
Dom | 0.7 | 4.07 |
SNP9 | chr3:231877073 | A4 | A4_SNP2 | Dom | 7.82 | 3.95 | 0.11 |
SNP10 | chr3:233093482 | M56 | M56_SNP7 | ADD | 1.04 | 4.24 | 1.98 |
SNP11 | chr4:49310536 | M35 | M35_SNP5 | ADD | 0.5 | 4.13 | 0.96 |
SNP12 | chr4:50008878 | M35 | M35_SNP6 | ADD | 0.5 | 4.13 | 2.7 |
SNP13 | chr4:51159825 | M35 | M35_SNP7 | ADD | 0.52 | 4.32 | 0.12 |
**SNP14 | chr4:150010129 | M35 | M35_SNP3 | ADD | -0.5 | 4.91 | 4.99 |
M42 | M42_SNP2 | ADD | -0.69 | 4.19 | 1.09 |
M49 | M49_SNP1 | ADD | -0.68 | 3.97 | 1.8 |
Dom | 0.64 | 4.24 |
A1 | A1_SNP2 | ADD | -4.33 | 4.8 | 3.47 |
A2 | A2_SNP1 | ADD | -4.97 | 4.92 | 1.93 |
Dom | 4.03 | 4.69 |
A3 | A3_SNP6 | ADD | -5.81 | 4.13 | 0.32 |
AT | AT_SNP4 | ADD | -20.29 | 4.9 | 0.79 |
*SNP15 | chr4:151643385 | M35 | M35_SNP4 | ADD | 0.53 | 4.54 | 3.98 |
A1 | A1_SNP3 | ADD | 4.42 | 4.03 | 2.81 |
SNP16 | chr4:163086906 | A4 | A4_SNP3 | ADD | -13.25 | 4.01 | 2.65 |
Dom | 13.25 | 4.01 |
AT | AT_SNP5 | ADD | -36.29 | 4.42 | 2.46 |
Dom | 36.29 | 4.42 |
SNP17 | chr5:34601200 | A3 | A3_SNP7 | ADD | -9.48 | 4.28 | 1.1 |
Dom | 9.48 | 4.28 |
SNP18 | chr5:40003874 | M35 | M35_SNP8 | ADD | -0.56 | 4.34 | 0.93 |
Dom | 0.54 | 4.26 |
SNP19 | chr5:43036650 | M35 | M35_SNP9 | ADD | -0.62 | 4.68 | 0.36 |
Dom | 0.62 | 4.68 |
●SNP20 | chr6:2463271 | A4 | A4_SNP4 | ADD | 12.41 | 3.99 | 21.07 |
SNP21 | chr8:10440687 | A4 | A4_SNP5 | Dom | -6.58 | 4.4 | 2.27 |
*SNP22 | chr8:141262609 | M35 | M35_SNP10 | ADD | -0.47 | 3.96 | 1.27 |
M42 | M42_SNP3 | ADD | -0.72 | 3.96 | 6.23 |
A1 | A1_SNP4 | ADD | -4.33 | 4.24 | 7.52 |
**SNP23 | chr8:165108325 | M56 | M56_SNP8 | ADD | 1.44 | 4.75 | 0.68 |
A3 | A3_SNP8 | ADD | 7.48 | 4.63 | 0.71 |
AT | AT_SNP6 | ADD | 23.3 | 4.5 | 5.8 |
A4 | A4_SNP6 | ADD | 8.85 | 4.39 | 1.59 |
SNP24 | chr9:154763539 | A4 | A4_SNP7 | ADD | 10.96 | 3.98 | 0.11 |
Dom | -10.96 | 3.98 |
SNP25 | chr10:58675816 | AT | AT_SNP1 | ADD | -26.69 | 4.17 | 5.34 |
A3 | A3_SNP2 | ADD | -8.39 | 4.24 | 3.12 |
*SNP26 | chr10:97858855 | A3 | A3_SNP3 | Dom | 10.17 | 4.67 | 1.01 |
M56 | M56_SNP2 | Dom | 1.91 | 4.45 | 1.15 |
ADD: additive effect, Dom: dominance effect |
Relationship Between Significant Loci and Phenotype
To dissect the effect of the loci on the related phenotype, we studied the phenotypic differences between different alleles of the associated SNPs. We found that the favorite allele of the co-located SNPs associated with more than three traits or major SNPs, with PVE > 10%, was always a homozygous genotype, such as CC in chr1:4535637, GG in chr2:14522675, TT in chr2:5320161, TT in chr2:9065055, AA in chr4:150010129, CC in chr6:2463271, and GG in chr8:165108325(Fig. 4A–G). For the epistatic SNP pairs, the individual with a double homozygous genotype (TT-CC) for the SNP pair chr1:156254070_chr5:26130738 showed a lower trend in GWC and AUDDC (Fig. 4H). The individuals with double homozygous (TT-CC) and single heterozygous (TT-CT and CT-CC) genotypes in the co-located epistatic SNP pair chr1:261928509_chr8:114505826 performed significantly worse in terms of GWC and AUDDC than double heterozygous genotypes (CT-CT) (Fig. 4I). For the co-located epistatic SNP pair chr1:267007323_chr2:1263421, the lowest GWC and AUDDC occurred in the double homozygous individuals (CC-GG) (Fig. 4J). These results suggest that a low GWC mainly requires the accumulation of homozygous genotypes and the interaction of a small portion of the heterozygous genotypes with other loci. In the future, we can explore KASP markers using these favorite homozygous alleles, to select a low GMC germplasm for mechanized harvested varieties’ breeding.
Distribution of Favorite Allele in Group A and Group B
To clarify the distributions of the favorite alleles in group A and group B, we calculated the dominant allele enrichment of the parent materials at different stages of GWC and AUDDC by considering the homozygous genotype with the lowest phenotypic level in the heterozygous generation as the dominant allele type. The results showed that for all materials, M35 (71.1%) and A1 (72.5%) had the highest enrichment of dominant alleles, while the enrichment period of other stages was relatively close, from 41.1–56.9%), except for M63 (11.39%) (Figures S4, Figures S5, Table S2). Furthermore, the enrichment of dominant alleles in group A and group B only showed a significant difference in M35 and A1 but not in other traits. Overall, we recommend some promising inbred lines, including B056(KB106), B018(KB024), B151(KB228), B022(KB025), and A021(2014KA60), which have the potential to reduce maize GWC through the enrichment of favorable alleles.
Candidate Genes Annotated by Bioinformatics
To further infer the candidate genes for GWC and AUDDC, we screened the putative genes around the associated SNPs within a 150 kb distance, which was the LD decay distance in the Shaan A and Shaan B populations. In total, 727 and 860 protein-coding genes were located in the confidence region for GWC and AUDDC, respectively. Combined with the RNA-seq data for kernels at a later development stage in two inbred lines (A034:KA225 and B008:KB020) (Figure S6A), where the GWC and AUDDC were evidently different(Qu et al. 2021), we detected 398 GWC-related genes and 457 AUDDC-related genes, with 137 common genes expressed differentially in later kernel development (Tables S3 and S4, Figure S6 B–E)..
Through GO enrichment analysis, we found that the 398 putative genes for GWC were enriched in the vitamin E metabolic pathway (GO:0046136, GO:1904965, GO:1904966), leukotriene process (GO:0006691, GO:0019370), and hydroxyl acid oxidase activities (GO:0008891, GO:0003973, GO:0052852, GO:0052853, GO:0052854), which participate in photorespiration and regulate plant development and senescence (Fig. 5A). For the 459 putative genes for AUDDC, they were enriched in the pathway of zeaxanthin epoxidase activity (GO:0052662, GO:0052663), the metabolic processes of salicylic acid (GO:0046244) and cinnamic acid (GO:0009800, GO:0009803), and the molecular means of antheraxanthin epoxidase and zeaxanthin epoxidase activity (Fig. 5B). Most of them were related to abscisic acid synthesis and would promote seed development and fruit maturation.
Interaction Network Predicted by the Candidate Genes
To gain an understanding of the regulation network for GWC and AUDDC, we constructed the interaction network of proteins encoded by these candidate genes (Fig. 6, Tables S3 and S4). This revealed that 76 of them interacted with each other and formed a big network, including NAD7 (NADH dehydrogenase subunit 7), PPI1 (microtubule-associated protein futsch-like isoform X1), ENO1 (enolase 1), and H3 (histone H3), which are encoded as the structural proteins or key enzymes that maintain the normal function of cells. Meanwhile, apoptosis-related autophagy family proteins (ATG4B, ATG8C, and ATG18A), auxin-related proteins (ABP1, ARF4, ARF23 GRF10, and IAA31), and transcript factors (MADS5, WRKY3, GATA4, and GATA12) were involved in the regulation network.
Beyond this, to explore the interactions between the identified epistatic effect pairs, SNPs with multiple epistatic effects were screened out in an interaction network analysis of candidate gene-encoded proteins. For a group of 13 SNPs involved in epistasis, the proteins encoded by the candidate genes of these sites definitely interacted, while another group of 12 SNPs was also observed to interact (Figure S7). The results affirmed the reliability of the epistatic interaction between SNPs identified by GWAS in this study.