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Article

Genome-Wide Association Study Reveals the Genetic Basis of Kernel and Cob Moisture Changes in Maize at Physiological Maturity Stage

by
Minyan Zhang
†,
Chaoyang Xiangchen
†,
Jiaquan Yan
,
Yujuan Chengxu
,
Hao Liu
,
Chaoying Zou
,
Guangtang Pan
,
Yaou Shen
and
Langlang Ma
*
State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Plants 2022, 11(15), 1989; https://doi.org/10.3390/plants11151989
Submission received: 8 July 2022 / Revised: 24 July 2022 / Accepted: 28 July 2022 / Published: 30 July 2022
(This article belongs to the Special Issue Functional Genomics and Molecular Breeding of Crops)
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Abstract

:
Low moisture content (MC) and high dehydration rate (DR) at physiological maturity affect grain mechanical harvest, transport, and storage. In this study, we used an association panel composed of 241 maize inbred lines to analyze ear moisture changes at physiological maturity stage. A genome-wide association study revealed nine significant SNPs and 91 candidate genes. One SNP (SYN38588) was repeatedly detected for two traits, and 15 candidate genes were scanned in the linkage disequilibrium regions of this SNP. Of these, genes Zm00001d020615 and Zm00001d020623 were individually annotated as a polygalacturonase (PG) and a copper transporter 5.1 (COPT5.1), respectively. Candidate gene association analysis showed that three SNPs located in the exons of Zm00001d020615 were significantly associated with the dehydration rate, and AATTAA was determined as the superior haplotype. All these findings suggested that Zm00001d020615 was a key gene affecting moisture changes of maize at the physiological maturity stage. These results have demonstrated the genetic basis of ear moisture changes in maize and indicated a superior haplotype for cultivating maize varieties with low moisture content and high dehydration rates.

1. Introduction

Maize (Zea may L.) is one of the main food crops worldwide and is an important source of human nutrition, animal feed, and bioenergy. To further improve production efficiency and reduce production costs, mechanized grain harvesting has become a key technology for crop production [1]. However, ears having a high moisture content (MC) at the harvesting stage causes grain mildew and restrict mechanized harvesting [2]. In the natural drying process from physiological maturity to harvest, the initial MC at physiological maturity and the dehydration rate (DR) in the field environment jointly determine the final MC at harvest [3,4]. Therefore, breeding maize hybrids with low MC and high DR at physiological maturity can promote mechanical harvest, improve threshing efficiencies, and reduce additional drying costs [5,6,7].
The MC and DR are quantitative traits mainly controlled by genetic factors [8], although they are affected by changes in the field ambient temperature and humidity [9,10]. Currently, numerous moisture-gain-associated QTL have been detected by linkage analysis. For example, a total of 76 QTL associated with dehydration characteristic parameters were detected using a recombinant inbred line (RIL) population of 208 lines, which explained 1.03% to 15.24% of the phenotypic variation [11]. Liu et al. [12] identified seven QTL related to grain water content using an RIL population, which explained 6.92–24.78% of the phenotypic variation. However, only a few moisture-gain-associated genes were fine-mapped due to the relatively low resolution of linkage mapping [13]. Genome-wide association study (GWAS) is an effective tool for analyzing the genetic structure of complex quantitative traits, which provides a high-resolution strategy for identifying the loci of quantitative traits [14]. The genetic controls of many agronomic traits have been identified by GWAS [15,16,17,18,19,20,21,22,23,24]. For example, in recent years, several SNPs related to MC and DR were detected by GWAS at different stages of maize kernel development. Using an association panel containing 513 maize inbred lines, Li et al. [25] identified 71 SNPs affecting MC by GWAS. Combined with genetic population analysis, transcriptome analysis, and gene editing, two key genes Zm00001d020929 and Zm00001d046583 were shown to negatively regulate the MC. A total of 16 SNPs significantly associated with MC were detected by GWAS among a natural panel consisting of 310 maize inbred lines at the harvest stage [26]. Using GWAS, Li et al. [27] identified 27 SNPs related to kernel water content (KWC) and kernel dehydration rate (KDR) in maize. In addition, 334 SNPs were detected that significantly controlled the kernel moisture content (KMC) and KDR in 132 maize inbred lines before physiological maturity [28]. Despite the conduct of these studies, the genetic control of maize kernel and cob moisture changes at the physiological maturity stage remains unclear.
In this study, we measured cob moisture content (CMC), cob dehydration rate (CDR), KMC, and KDR values of 241 maize inbred lines on the 45th, 50th, and 55th days after pollination (DAP). GWAS was performed using the fixed and random circulating probability unification (FarmCPU) model to uncover the important SNPs and causal genes that control MC and DR. In addition, candidate gene association studies were conducted to elucidate the intragenic variations affecting the target traits. This study sought to (i) reveal the genetic basis of moisture change in maize kernel and cob, (ii) identify the hub genes affecting the maize moisture change, and (iii) contribute to the development of function markers for breeding low-MC and high-DR maize varieties through marker-assisted selection.

2. Results

2.1. Phenotypic Descriptions

The 12 traits investigated in the present study displayed great variation in the association panel of 241 maize inbred lines (Table 1). Most of the 12 traits followed normal distributions (Figure S1). At 45, 50, and 55 DAP, the mean values of CMC and KMC ranged from 57.01% to 62.60% and 26.98% to 35.70%, respectively (Table 1). The CMC and KMC both significantly declined from 45 to 55 DAP (Figure 1A). For DR at two successive time spans, the means of CDR and KDR were 0.95%–1.11% and 0.85%–1.16%, respectively (Table 1). The CDR and KDR variations both significantly (p < 0.05) increased from 45 to 55 DAP (Figure 1B). The coefficients of variation (CVs) of these12 traits were in the range of 0.61% to 10.68% (Table 1). In addition, the mean value of broad-sense heritability (H2) was 57.34% (Table 1), which indicated that the MC-related traits were mainly controlled by genetic factors.

2.2. SNPs and Candidate Genes Associated with Moisture Changes

GWAS was performed for MC and DR to explore the genetic basis of moisture changes. A total of four, three, and three significant (p < 1.82 × 10−6) SNP markers were detected for CDR45–55, CDR50–55, and KMC45, respectively, which distributed on chromosomes 1, 2, 3, 5, and 7 (Figure 2 and Table S1). Among these significant SNPs, one (SYN38588) was simultaneously associated with CDR50–55 and CDR45–55 and had the lowest p-value (p = 9.58 × 10−11) in CDR45–55 associations (Table S1). Based on our previous reports, the linkage disequilibrium (LD) decay was around 220 kb for this maize panel [29]. Thus, we searched the gene models in the LD regions of the significant SNPs. In total, we found 91 moisture-change-associated genes (Table 2 and Table S2). For CDR50–55 and CDR45–55, one SNP, SYN38588, was repeatedly detected (Table S1). Within the LD region of the co-detected marker (SYN38588), 15 gene models were uncovered, of which four were functionally annotated and the other 11 were unknown genes in maize (Table 2). Notably, the two genes Zm00001d020615 and Zm00001d020623 separately encode polygalacturonase (PG) and copper transporter 5.1 (COPT5.1), respectively. According to previous studies, PG and COPT5.1 proteins are involved in regulating the MC and DR of soybean and rice seeds [30,31,32,33,34]. To further understand the functions of the 11 genes, we performed a homology analysis and found six homologous genes in rice, sorghum, and Panicum virgatum (Table S3). However, the encoding proteins of these homologous genes had not been reported to be associated with moisture changes. Therefore, Zm00001d020615 and Zm00001d020623 were selected as the prioritized candidate genes that controlled the moisture changes of maize at physiological maturity.

2.3. Candidate Gene Association Analysis Revealed Intragenic Variations Affecting Moisture Changes

To identify the causal gene(s) that control MC and DR at the physiological maturity stage in maize, we performed candidate gene association analyses for Zm00001d020615 and Zm00001d020623 using 67 randomly selected lines from the maize association panel. We detected 29 (27 SNPs and two indels) and nine (eight SNPs and one indel) polymorphic sites in the promoter (upstream 2000 bp), UTR, and gene body regions of Zm00001d020615 and Zm00001d020623, respectively (Table S4). Candidate gene association analyses showed that three significant SNPs (p < 0.05/19 = 2.63 × 10−3) in Zm00001d020615 were identified, involving two SNPs (S7_125166053 and S7_125166495) related to KDR45–50, and one CDR50–55-associated SNP (S7_125165191) (Figure 3A, Table 3). However, no significant variation loci were identified for Zm00001d020623.
Among the three significant SNPs detected in Zm00001d020615, one missense variation (C/A) occurred in the fifth exon, and the other two synonymous variations (G/A, C/T) were located in the third and fifth exons, respectively (Figure 3A, Table 3). The 67 inbred lines were classified into three haplotypes (Hap1: AATTAA, Hap2: GGCCAA and Hap3: GGCCCC) based on the three variations. The lines with Hap1 had a significantly higher CDR50–55 value than those with Hap 3 (p < 0.05), whereas no significant difference in moisture-change-related phenotypes was observed between Hap2 and each of Hap1 and Hap3 (Figure 3B). As such, Hap1 was considered as the superior haplotype in this study. All these findings suggest that Zm00001d020615 is a key gene affecting moisture changes of maize at the physiological maturity stage.

2.4. Distributions of Superior Alleles in Elite Lines

Among the association panel, 30 elite lines with excellent agronomic traits have been widely used as the parent lines for cultivating commercial maize varieties [29]. Evaluation of the utilization of superior alleles for the significant SNPs controlling the moisture-change-related traits is urgently needed for the breeding of varieties with low MC and high DR. Herein, the alleles that were associated with higher DR and lower MC values were defined as the superior alleles; conversely, the alleles associated with lower DR and higher MC values were designated as the inferior alleles. For each SNP, the ratio of superior to inferior alleles in the elite inbred lines was defined as the number of inbred lines containing superior alleles divided by the total number of inbred lines. Among the 30 elite inbred lines, the superior allele ratio for the nine significant SNPs ranged from 10% (SYN11290 and SYN30412) to 90% (PZE-102053209) (Figure 4). For seven SNPs associated with CDR traits, the superior allele ratios of three loci (PZE-103097076, PZE-102053209, and PZE-101122473) exceeded 50%, and the ratios of superior alleles for the other three loci (SYN38588, SYN11290, and SYN30412) were less than 20% (Figure 4). In addition, three SNPs (SYN15586, PZB01400.1, and SYN8680) associated with KMC all had low superior allele ratios, which were less than 20% (Figure 4). These results indicated that the superior alleles of CDR and KMC at physiological maturity were not widely used in maize breeding among the 30 elite inbred lines. Therefore, more superior alleles related to high DR and low MC need to be integrated into these elite inbred lines, which would be of practical significance for improving the dehydration characteristics of maize at physiological maturity.

3. Discussion

3.1. Dissecting the Genetic Basis of Moisture Changes at Physiological Maturity in Maize Using GWAS

Abundant phenotypic variation is an important factor for the successful analysis of the genetic basis of target traits. Population structure analysis showed that the association panel used in this study had high genetic diversity, including a tropical group, a stiff stalk (SS) group and a non-stiff stalk (NSS) group [35]. The MC and DR values ranged from 11.46% to 78.75% and 0.05% to 4.78% (with an average of 0.92%), respectively, suggesting that this panel was appropriate for GWAS. In addition, the mean value of H2 among 12 traits was 57.34%, which indicated that genetic factors were primary for these traits. Thus, it is feasible to decompose the genetic control of moisture changes at physiological maturity stage using a GWAS strategy. Moreover, a total of nine SNPs were associated with CDR45–50, CDR50–55, and KMC45, among which five SNPs were located within (or were close to) the kernel dehydration-associated QTL in maize reported in previous studies (Table S5). For instance, SYN38588 and PZE-101122473 identified in this study were situated in the intervals of QTL (PZE-107014666/PZE-107082484 and PZE-101120411/PZE-101128157, respectively) that controlled dehydration initial moisture and initial time in maize kernels [11]. PZB01400.1 and SYN8680 were both contained within kernel dehydration initial moisture-associated QTL (PZE-101235343/SYN25670) [11]. The distance between the significant SNP SYN30412 identified in this study and the kernel final moisture-associated QTL (SYN30432/PZE-105040767) reported by Yin et al. was 0.08 Mb [11]. For the significant SNP PZB01400.1 identified in this study, a nearby (312 bp) significant SNP_286449826 associated with KMC at 40 DAP was detected by Li et al. [25]. These findings suggested that the genetic loci detected in this study were reliable. Notably, the lead SNP SYN38588 (p = 9.58 × 10−11) was repeatedly detected for traits CDR45–55 and CDR50–55. Based on this SNP, we identified two further priority genes controlling moisture changes. A candidate gene association study revealed that three significant SNPs located in the coding sequence of Zm00001d020615 were associated with CDR50–55. In addition, we identified the favorable haplotype AATTAA, which should be given priority in marker-assisted selection breeding to cultivate maize varieties with low MC and high DR.

3.2. Candidate Genes Involved in MC and DR at Physiological Maturity Stage

At physiological maturity, the MC and DR of maize are mainly determined by two factors: stress dehydration caused by field environmental conditions and physiological dehydration caused by seed development and maturation [25]. Some specialized proteins that inhibit early embryo germination and avoid damages to embryo development play crucial roles in the process of grain physiological dehydration. In the present study, combining GWAS and candidate gene association analysis, we identified a key gene Zm00001d020615, which encodes a PG. Based on previous research, most of the PGs in plants belong to cell-wall localized pectin degrading enzymes and are expressed in different tissues and development stages [36]. PGs are involved in seed development, fruit softening, organ shedding, pollen ripening, and adversity stress [36]. To control water inflow and exit in soybean seeds, the PG encoding gene PG031 affects seed coat permeability by regulating the intracellular space of parenchyma tissue and maintaining the integrity of the osteosclerosis layer [30]. In tomato, the PG gene SlPG has been confirmed as a key gene for improving fruit firmness. After mutagenesis of the SlPG gene, the hydrolysis of cell wall polymers decreased, which led to water loss reduction and delay in the softening of tomato fruit [37]. In rice, the PSL1 gene functions as a PG that plays an important role in modifying cell wall biosynthesis of root and leaf and reducing water loss under drought stress [38].

3.3. Application of Superior Alleles in Breeding Maize Varieties with Low MC and High DR

In our study, SNP markers were provided for breeding new maize varieties with low MC and high DR at physiological maturity. By analyzing the utilization of superior alleles in 30 elite maize inbred lines, the superior allele ratios of three SNPs were found to be greater than 50%—one, PZE-102053209, was 90% (Figure 4). This indicated that the superior alleles of the three SNP were well maintained by artificial selection. A possible reason is that these alleles are closely linked with the agronomic traits of interest to breeders. Owing to natural variation in the association panel, researchers can develop molecular markers for breeding ideal maize varieties. In addition, only six lines contained more than five superior alleles (Figure 4), indicating that breeders have probably paid little attention to maize moisture changes at the physiological maturity stage. Therefore, the proportion of favorable alleles in 30 elite lines should be improved in cultivating maize varieties with low MC and high DR by marker-assisted selection breeding. The lines Mo17, Chuan273, Zheng22, and SCLM202, containing six superior alleles, are considered as excellent resources for breeding maize varieties with low MC and high DR through backcross breeding. In future research, the function of Zm00001d020615 needs, firstly, to be verified. Then, gene Zm00001d020615 can be overexpressed/knocked out to create maize lines with high DR.

4. Materials and Methods

4.1. Plant Materials

The association panel consisted of 241 diverse maize inbred lines (Table S6), which were collected from the southwest China breeding program [35]. These lines were planted in greenhouses using a randomized complete block design with three replicates. The greenhouse parameters were set as follows: light/darkness = 16/8 h; temperature under light/temperature in darkness = 25/22 °C; relative humidity = 65%. In each replicate, every line was grown in one row with 14 plants. The distance between two rows was 0.7 m, and the row length was 3 m. To ensure the consistency of sampling, we selected the plants that grew uniformly for investigating the target traits.

4.2. Phenotypic Collection and Data Analysis

For each line, the fresh weights (FWs) of the cob and kernel were measured separately on the 45th, 50th, and 55th DAP. Then, the dry weights (DWs) of the cob and kernel were measured after drying at 80 °C in an oven for 72 h. Three individuals were weighed for every line at each stage. The MC was calculated as follows: moisture content (%) = (FW − DW)/FW × 100%. According to the above method, cob moisture content (CMC) and kernel moisture content (KMC) were calculated on the 45th, 50th, and 55th DAP, respectively. Based on the MC values at two successive stages, DR was calculated as follows: dehydration rate (45–50 d) (%) = (MC on 45 d-MC on 50 d)/5 × 100%; dehydration rate (50–55 d) (%) = (MC on 50 d-MC on 55 d)/5 × 100%; dehydration rate (45–55 d) (%) = (MC on 45 d-MC on 55 d)/10 × 100%. The mean value across three replicates was used as the final phenotype value. The KMCs on 45, 50, and 55 DAP were designated as KMC45, KMC50, and KMC55. The CMCs on 45, 50, and 55 DAP were designated as CMC45, CMC50, and CMC55. The kernel DRs for the three time spans (namely, 45–50, 50–55, and 45–55 DAP) were denoted as KDR45–50, KDR50–55, and KDR45–55. The cob DRs for the three time spans (namely, 45–50, 50–55, and 45–55 DAP) were denoted as CDR45–50, CDR50–55, and CDR45–55. Descriptive statistical analysis was performed using SPSS25 software. The broad-sense heritability (H2) was calculated as follows [39]: H2 = σG2/σP2, σG2 = (MSG-MSE)/rep, σP2 = (MSG-MSE)/rep + MSE. Herein, σG2, σP2, MSG, MSE, and rep represent genotypic variance, phenotypic variance, mean square of genotype, mean square of error, and number of replicates, respectively.

4.3. Genome-Wide Association Study

In our previous study, genotyping of the maize panel was performed by Illumina Maize SNP50K Bead Chip, and a total of 56,110 SNPs were detected [35]. According to filtration criteria of missing rate >20% and minor allele frequency (MAF) ≤ 0.05, 46,603 SNPs were retained for GWAS. Cluster analysis revealed that the 241 inbred lines could be divided into three types: a tropical group (population 1), a non-stiff stalk (NSS) group (population 2), and a stiff stalk (SS) group (population 3) based on the 46,603 SNPs (Figure S2). As the FarmCPU model was superior to other models (general linear model and mixed linear model) in balancing false positives/negatives [40], we used the FarmCPU to detect the associations for moisture changes. The model was executed by the R Studio ver.4.0.3 (Allaire, Boston, MA, USA) with a FarmCPU package [41]. The simpleM program in R Studio ver.4.0.3 (Allaire, Boston, MA, USA) [42] was applied to calculate the effective marker number of independent tests (Meff_G = 27,507) [42,43,44]. The significance threshold was calculated as p = 0.05/Meff_G = 1.82 × 10−6 [26]. In addition, the gene models that were located in the linkage disequilibrium (LD) region of each SNP were considered the candidate genes. Combined with the gene functional annotations from NCBI (NCBI, National Center for Biotechnology Information, https://www.ncbi.nlm.nih.gov/, accessed on 24 February 2022) databases, we determined the hub genes which were related to moisture changes.

4.4. Candidate Gene Association Study

We amplified the hub gene sequences, including the promoter (upstream 2000 bp), UTR, and gene body regions in 67 inbred lines, randomly selected from three subgroups of the association panel (Figure S3). The PCR-amplified sequences were aligned with the B73 (v4) genome using DNAMAN ver.5.2.2 (Reachsoft, Beijing, China) [45]. The variations (SNPs and InDels) with MAF ≥ 5%, and the trait phenotypes, were used as the inputs for a candidate gene association study based on a general linear model in TASSEL ver.5.0 (Buckler lab, Cornell university, New York, NY, USA) software [23]. The significant threshold was set as: p = 0.05/n (n represents the number of SNPs). The LD decay between pairwise SNPs was calculated using Haploview.JRE ver.18.0.2 (The Broad Institute of MIT and Harvard, Cambridge, MA, USA) software [46]. Phenotypic differences between haplotypes were analyzed using a t-test.

4.5. Analysis of Superior Alleles

In our study, alleles associated with low MC and high DR were considered as superior alleles. The ratio of superior alleles for each significant SNP was calculated as the number of lines with superior alleles divided by the total number of lines [19]. According to the rate of superior alleles in each line, we generated a visualized heat map using the heatmap package in R Studio ver.4.0.3 (Allaire, Boston, MA, USA) software.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11151989/s1, Figure S1: The phenotypic distributions of 12 collected traits at different time points. The CMC45, CMC50, and CMC55 represent cob moisture content on the 45th, 50th, and 55th days after pollination, respectively. The CDR45–50, CDR50–55, and CDR45–55 represent cob dehydration rate of three time spans: 45–50, 50–55, and 45–55 days after pollination, respectively. The KMC45, KMC50, and KMC55 represent kernel moisture content on the 45th, 50th, and 55th days after pollination, respectively. The KDR45–50, KDR50–55, and KDR45–55 represent kernel dehydration rate of three time spans: 45–50, 50–55, and 45–55 days after pollination, respectively. * p = 0.05, ** p = 0.01, *** p = 0.001; Figure S2: Population structure of 241 maize inbred lines estimated from 46,603 SNPs. Population 1, population 2, and population 3 represent tropical group, non-stiff stalk (NSS) group, and stiff stalk (SS) group, respectively; Figure S3: Phylogenetic tree of 67 maize inbred lines. The group 1, group 2, and group 3 represent tropical group, non-stiff stalk (NSS) group and stiff stalk (SS) group, respectively; Table S1: Significant SNPs detected by GWAS using FarmCPU. CDR45–55 and CDR50–55 represent cob dehydration rate on the 45–55 and 50–55 days after pollination, respectively. KMC45 represent kernel moisture content on the 45th, 50th, and 55th days after pollination, respectively; Table S2: Annotations of candidate genes based on B73 (RefGen_v4) genome. CDR45–55 and CDR50–55 represent cob dehydration rate on the 45–55 and 50–55 days after pollination, respectively. KMC45 represent kernel moisture content on the 45th, 50th, and 55th days after pollination, respectively; Table S3: Homologous annotations of 11 unknown genes. ‘-’ represents no homologous or annotations; Table S4: Details of 38 variations detected in two genes. ‘-’ represents deletion; Table S5: Significant SNPs overlapping with QTL for kernel moisture change identified in previous studies. CDR45–55 and CDR50–55 represent cob dehydration rate on the 45–55 and 50–55 days after pollination, respectively. KMC45 represents kernel moisture content on the 45th day after pollination. mini, dehydration initial moisture. mfni, final moisture. tini, dehydration initial time (DAP). KMC 40 DAP, kernel moisture content on the 40th day after pollination. “–” represents SNP located within the QTL; Table S6: Information of 241 maize inbred lines in the association panel.

Author Contributions

Conceptualization, L.M. and Y.S.; methodology, M.Z.; software, L.M. and M.Z.; validation, M.Z., C.X. and J.Y.; formal analysis, Y.C.; investigation, M.Z., C.X. and H.L.; resources, Y.S. and G.P.; data curation, C.Z.; writing—original draft preparation, M.Z.; writing—review and editing, L.M. and Y.S.; visualization, M.Z.; supervision, L.M. and Y.S.; project administration, L.M.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by the National Key Research and Development Program of China (2021YFF1000303).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The genotype data used in this study were described in a previous study. All datasets are available from the corresponding author on reasonable request.

Acknowledgments

We would like to thank the Maize Research Institute of Sichuan Agricultural University for providing the platform. We also extend thanks to other graduate students who attended this project.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wang, G.; Xu, Z.; Wang, F.; Huang, Y.; Xin, Y.; Liang, S.; Li, B.; Si, H.; Sun, L.; Wang, Q.; et al. Development of an efficient and precise adenine base editor (ABE) with expanded target range in allotetraploid cotton (Gossypium hirsutum). BMC Biol. 2022, 20, 45. [Google Scholar] [CrossRef]
  2. Qian, Y.L.; Zhang, X.Q.; Wang, L.F.; Chen, J.; Chen, B.R.; Lv, G.H.; Wu, Z.C.; Guo, J.; Wang, J.; Qi, Y.C.; et al. Detection of QTLs controlling fast kernel dehydration in maize (Zea mays L.). Genet. Mol. Res. GMR 2016, 15, gmr.15038151. [Google Scholar] [CrossRef]
  3. Kang, M.S.; Zhang, S. Narrow-Sense Heritability for and Relationship between seed imbibition and grain moisture loss rate in maize. J. New Seeds 2001, 3, 1–16. [Google Scholar] [CrossRef]
  4. Shaw, R.H.; Loomis, W.E. Bases for the prediction of corn yields. Plant Physiol. 1950, 25, 225–244. [Google Scholar] [CrossRef] [Green Version]
  5. Capelle, V.; Remoué, C.; Moreau, L.; Reyss, A.; Mahé, A.; Massonneau, A.; Falque, M.; Charcosset, A.; Thévenot, C.; Rogowsky, P.; et al. QTLs and candidate genes for desiccation and abscisic acid content in maize kernels. BMC Plant Biol. 2010, 10, 2. [Google Scholar] [CrossRef] [Green Version]
  6. Gao, S.; Ming, B.; Li, L.; Yin, X.; Xue, J.; Wang, K.; Xie, R.; Li, S. Relationship and distribution of in-field dry-down and equilibrium in maize grain moisture content. Agric. For. Meteorol. 2021, 304–305, 108409. [Google Scholar] [CrossRef]
  7. Xiang, K.; Reid, L.M.; Zhang, Z.-M.; Zhu, X.-Y.; Pan, G.-T. Characterization of correlation between grain moisture and ear rot resistance in maize by QTL meta-analysis. Euphytica 2012, 183, 185–195. [Google Scholar] [CrossRef]
  8. Zhang, F.; Wang, Y.; Ding, Y.; Zhang, J.; Zhao, X.; Zhao, F.; Tang, B. Corn kernel dehydration rate: Research progress. J. Agric. 2018, 8, 4–8. [Google Scholar]
  9. Magari, R.; Kang, M.S.; Zhang, Y. Genotype by environment interaction for ear moisture loss rate in corn. Crop Sci. 1998, 37, 774–779. [Google Scholar]
  10. Schmidt, J.L.; Hallauer, A.R. Estimating harvest date of corn in the field. Crop Sci. 1966, 6, 227–231. [Google Scholar] [CrossRef]
  11. Yin, S.; Liu, J.; Yang, T.; Li, P.; Xu, Y.; Fang, H.; Xu, S.; Wei, J.; Xue, L.; Hao, D.; et al. Genetic analysis of the seed dehydration process in maize based on a logistic model. Crop J. 2020, 8, 182–193. [Google Scholar] [CrossRef]
  12. Liu, J.; Yu, H.; Liu, Y.; Deng, S.; Liu, Q.; Liu, B.; Xu, M. Genetic dissection of grain water content and dehydration rate related to mechanical harvest in maize. BMC Plant Biol. 2020, 20, 118. [Google Scholar] [CrossRef] [PubMed]
  13. Holland, J.B. Genetic architecture of complex traits in plants. Curr. Opin. Plant Biol. 2007, 10, 156–161. [Google Scholar] [CrossRef]
  14. Sukumaran, S.; Dreisigacker, S.; Lopes, M.; Chavez, P.; Reynolds, M.P. Genome-wide association study for grain yield and related traits in an elite spring wheat population grown in temperate irrigated environments. Theor. Appl. Genet. 2015, 128, 353–363. [Google Scholar] [CrossRef] [PubMed]
  15. Liu, M.; Tan, X.; Yang, Y.; Liu, P.; Zhang, X.; Zhang, Y.; Wang, L.; Hu, Y.; Ma, L.; Li, Z.; et al. Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping. Plant Biotechnol. J. 2020, 18, 207–221. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Zhang, X.; Guan, Z.; Li, Z.; Liu, P.; Ma, L.; Zhang, Y.; Pan, L.; He, S.; Zhang, Y.; Li, P.; et al. A combination of linkage mapping and gwas brings new elements on the genetic basis of yield-related traits in maize across multiple environments. Theor. Appl. Genet. 2020, 133, 2881–2895. [Google Scholar] [CrossRef]
  17. Li, C.; Huang, Y.; Huang, R.; Wu, Y.; Wang, W. The genetic architecture of amylose biosynthesis in maize kernel. Plant Biotechnol. J. 2018, 16, 688–695. [Google Scholar] [CrossRef]
  18. Tian, F.; Bradbury, P.J.; Brown, P.J.; Hung, H.; Sun, Q.; Flint-Garcia, S.; Rocheford, T.R.; McMullen, M.D.; Holland, J.B.; Buckler, E.S. Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat. Genet. 2011, 43, 159–162. [Google Scholar] [CrossRef]
  19. Ma, L.; Zhang, M.; Chen, J.; Qing, C.; He, S.; Zou, C.; Yuan, G.; Yang, C.; Peng, H.; Pan, G.; et al. GWAS and WGCNA uncover hub genes controlling salt tolerance in maize (Zea mays L.) seedlings. Theor. Appl. Genet. 2021, 134, 3305–3318. [Google Scholar] [CrossRef]
  20. Ma, L.; An, R.; Jiang, L.; Zhang, C.; Li, Z.; Zou, C.; Yang, C.; Pan, G.; Lübberstedt, T.; Shen, Y. Effects of ZmHIPP on lead tolerance in maize seedlings: Novel ideas for soil bioremediation. J. Hazard. Mater. 2022, 430, 128457. [Google Scholar] [CrossRef]
  21. Wu, X.; Feng, H.; Wu, D.; Yan, S.; Zhang, P.; Wang, W.; Zhang, J.; Ye, J.; Dai, G.; Fan, Y.; et al. Using high-throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance. Genome Biol. 2021, 22, 185. [Google Scholar] [CrossRef] [PubMed]
  22. Zhang, Y.; Liu, P.; Zhang, X.; Zheng, Q.; Chen, M.; Ge, F.; Li, Z.; Sun, W.; Guan, Z.; Liang, T.; et al. Multi-locus genome-wide association study reveals the genetic architecture of stalk lodging resistance-related traits in maize. Front. Plant Sci. 2018, 9, 611. [Google Scholar] [CrossRef] [PubMed]
  23. Pace, J.; Gardner, C.; Romay, C.; Ganapathysubramanian, B.; Lübberstedt, T. Genome-wide association analysis of seedling root development in maize (Zea mays L.). BMC Genom. 2015, 16, 47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Vanous, A.; Gardner, C.; Blanco, M.; Martin-Schwarze, A.; Lipka, A.E.; Flint-Garcia, S.; Bohn, M.; Edwards, J.; Lübberstedt, T. Association mapping of flowering and height traits in germplasm enhancement of maize doubled haploid (GEM-DH) lines. Plant Genome 2018, 11, 170083. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Li, W.; Yu, Y.; Wang, L.; Luo, Y.; Peng, Y.; Xu, Y.; Liu, X.; Wu, S.; Jian, L.; Xu, J.; et al. The genetic architecture of the dynamic changes in grain moisture in maize. Plant Biotechnol. J. 2021, 19, 1195–1205. [Google Scholar] [CrossRef] [PubMed]
  26. Zhang, Y.; Hu, Y.; Guan, Z.; Liu, P.; He, Y.; Zou, C.; Li, P.; Gao, S.; Peng, H.; Yang, C.; et al. Combined linkage mapping and association analysis reveals genetic control of maize kernel moisture content. Physiol. Plant. 2020, 170, 508–518. [Google Scholar] [CrossRef]
  27. Li, S.; Zhang, C.; Lu, M.; Yang, D.; Qian, Y.; Yue, Y.; Zhang, Z.; Jin, F.; Wang, M.; Liu, X.; et al. QTL mapping and GWAS for field kernel water content and kernel dehydration rate before physiological maturity in maize. Sci. Rep. 2020, 10, 13114. [Google Scholar] [CrossRef]
  28. Li, S.; Zhang, C.; Yang, D.; Lu, M.; Qian, Y.; Jin, F.; Liu, X.; Wang, Y.; Liu, W.; Li, X. Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS. Sci. Rep. 2021, 11, 1764. [Google Scholar] [CrossRef]
  29. Luo, X.; Wang, B.; Gao, S.; Zhang, F.; Terzaghi, W.; Dai, M. Genome-wide association study dissects the genetic bases of salt tolerance in maize seedlings. J. Integr. Plant Biol. 2019, 61, 658–674. [Google Scholar] [CrossRef] [Green Version]
  30. Wang, F.; Sun, X.; Liu, B.; Kong, F.; Pan, X.; Zhang, H. A polygalacturonase gene PG031 regulates seed coat permeability with a pleiotropic effect on seed weight in soybean. Theor. Appl. Genet. 2022, 135, 1603–1618. [Google Scholar] [CrossRef]
  31. Klaumann, S.; Nickolaus, S.D.; Fürst, S.H.; Starck, S.; Schneider, S.; Ekkehard Neuhaus, H.; Trentmann, O. The tonoplast copper transporter COPT5 acts as an exporter and is required for interorgan allocation of copper in Arabidopsis Thaliana. New Phytol. 2011, 192, 393–404. [Google Scholar] [CrossRef] [PubMed]
  32. Xiong, Z.T.; Wang, H. Copper toxicity and bioaccumulation in chinese cabbage (Brassica pekinensis Rupr.). Environ. Toxicol. 2005, 20, 188–194. [Google Scholar] [CrossRef] [PubMed]
  33. Ahsan, N.; Lee, D.G.; Lee, S.H.; Kang, K.Y.; Lee, J.J.; Kim, P.J.; Yoon, H.S.; Kim, J.S.; Lee, B.H. Excess copper induced physiological and proteomic changes in germinating rice seeds. Chemosphere 2007, 67, 1182–1193. [Google Scholar] [CrossRef] [PubMed]
  34. Mir, A.R.; Pichtel, J.; Hayat, S. Copper: Uptake, toxicity and tolerance in plants and management of Cu-contaminated soil. Biometals: Int. J. Role Met. Ions Biol. Biochem. Med. 2021, 34, 737–759. [Google Scholar] [CrossRef]
  35. Zhang, X.; Zhang, H.; Li, L.; Lan, H.; Ren, Z.; Liu, D.; Wu, L.; Liu, H.; Jaqueth, J.; Li, B.; et al. Characterizing the population structure and genetic diversity of maize breeding germplasm in southwest China using genome-wide SNP markers. BMC Genom. 2016, 17, 697. [Google Scholar] [CrossRef] [Green Version]
  36. Dou, X. Advances in studies on the function of plant polygalacturonase. J. Shanxi Agric. Univ. Nat. Sci. Ed. 2004, 24, 5. [Google Scholar]
  37. Nie, H.; Shi, Y.; Geng, X.; Xing, G. CRISRP/Cas9-mediated targeted mutagenesis of tomato polygalacturonase gene (SlPG) delays fruit softening. Front. Plant Sci. 2022, 13, 729128. [Google Scholar] [CrossRef]
  38. Zhang, G.; Hou, X.; Wang, L.; Xu, J.; Chen, J.; Fu, X.; Shen, N.; Nian, J.; Jiang, Z.; Hu, J.; et al. Photo-sensitive leaf rolling 1 encodes a polygalacturonase that modifies cell wall structure and drought tolerance in rice. New Phytol. 2021, 229, 890–901. [Google Scholar] [CrossRef]
  39. Liang, T.; Qing, C.; Liu, P.; Zou, C.; Yuan, G.; Pan, G.; Shen, Y.; Ma, L. Joint GWAS and WGCNA uncover the genetic control of calcium accumulation under salt treatment in maize seedlings. Physiol. Plant. 2022, 174, e13606. [Google Scholar] [CrossRef]
  40. Ma, L.; Liu, M.; Yan, Y.; Qing, C.; Zhang, X.; Zhang, Y.; Long, Y.; Wang, L.; Pan, L.; Zou, C.; et al. Genetic dissection of maize embryonic callus regenerative capacity using multi-locus genome-wide association studies. Front. Plant Sci. 2018, 9, 561. [Google Scholar] [CrossRef] [Green Version]
  41. Kaler, A.S.; Gillman, J.D.; Beissinger, T.; Purcell, L.C. Comparing different statistical models and multiple testing corrections for association mapping in soybean and maize. Front. Plant Sci. 2019, 10, 1794. [Google Scholar] [CrossRef] [PubMed]
  42. Liu, X.; Huang, M.; Fan, B.; Buckler, E.S.; Zhang, Z. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet. 2016, 12, e1005767. [Google Scholar] [CrossRef] [PubMed]
  43. Gao, X.; Becker, L.C.; Becker, D.M.; Starmer, J.D.; Province, M.A. Avoiding the high Bonferroni penalty in genome-wide association studies. Genet. Epidemiol. 2010, 34, 100–105. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Johnson, R.C.; Nelson, G.W.; Troyer, J.L.; Lautenberger, J.A.; Kessing, B.D.; Winkler, C.A.; O’Brien, S.J. Accounting for multiple comparisons in a genome-wide association study (GWAS). BMC Genom. 2010, 11, 724. [Google Scholar] [CrossRef] [Green Version]
  45. Gao, X.; Starmer, J.; Martin, E.R. A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet. Epidemiol. 2008, 32, 361–369. [Google Scholar] [CrossRef]
  46. Zhang, X.; Guan, Z.; Wang, L.; Fu, J.; Zhang, Y.; Li, Z.; Ma, L.; Liu, P.; Zhang, Y.; Liu, M.; et al. Combined GWAS and QTL analysis for dissecting the genetic architecture of kernel test weight in maize. Mol. Genet. Genom. MGG 2020, 295, 409–420. [Google Scholar] [CrossRef]
Plants 11 01989 g001 550
Figure 1. Phenotypes of moisture-content-related traits of the association panel at physiological maturity stages. (A) Phenotypic values of CMC and KMC measured at each stage. MC, moisture content. (B) Phenotypic values of CDR and KDR measured for the two time spans. DR, dehydration rate. * Significant at p < 0.05. ** Significant at p < 0.01.
Figure 1. Phenotypes of moisture-content-related traits of the association panel at physiological maturity stages. (A) Phenotypic values of CMC and KMC measured at each stage. MC, moisture content. (B) Phenotypic values of CDR and KDR measured for the two time spans. DR, dehydration rate. * Significant at p < 0.05. ** Significant at p < 0.01.
Plants 11 01989 g001
Plants 11 01989 g002 550
Figure 2. Significant SNPs detected by GWAS using FarmCPU model. (A) Manhattan diagram of GWAS results for moisture changes. The blue, red, and turquoise dots represent the significant SNPs associated with CDR50–55, CDR45–55, and KMC45, respectively. CDR50–55 and CDR45–55 represent cob dehydration rate at 50–55 and 45–55 days after pollination, respectively; KMC45 represents kernel moisture content on the 45th day after pollination. The red broken line represents the significant threshold of 1.82 × 10−6. (B) Quantile–quantile (Q–Q) plot of GWAS results for moisture changes. The blue, green, and yellow dots represent the SNPs associated with CDR50–55, CDR45–55, and KMC45, respectively. CDR50–55 and CDR45–55 represent cob dehydration rate at 50–55 and 45–55 days after pollination, respectively. KMC45 represents kernel moisture content on the 45th day after pollination.
Figure 2. Significant SNPs detected by GWAS using FarmCPU model. (A) Manhattan diagram of GWAS results for moisture changes. The blue, red, and turquoise dots represent the significant SNPs associated with CDR50–55, CDR45–55, and KMC45, respectively. CDR50–55 and CDR45–55 represent cob dehydration rate at 50–55 and 45–55 days after pollination, respectively; KMC45 represents kernel moisture content on the 45th day after pollination. The red broken line represents the significant threshold of 1.82 × 10−6. (B) Quantile–quantile (Q–Q) plot of GWAS results for moisture changes. The blue, green, and yellow dots represent the SNPs associated with CDR50–55, CDR45–55, and KMC45, respectively. CDR50–55 and CDR45–55 represent cob dehydration rate at 50–55 and 45–55 days after pollination, respectively. KMC45 represents kernel moisture content on the 45th day after pollination.
Plants 11 01989 g002
Plants 11 01989 g003 550
Figure 3. Association analysis of Zm00001d020615. (A) Significant SNPs associated with CDR50–55 and KDR45–50. CDR50–55 represent cob dehydration rate at 50–55 days after pollination; KDR45–50 represent kernel dehydration rate at 45–50 days after pollination. Red line shows the significant threshold of markers. The boxes with gray, white, red, and black colors represent promoter (upstream 2000 bp), UTR, exons, and introns, respectively. Bottom plot shows the pairwise linkage disequilibrium between the target SNP markers. (B) Comparison of three haplotypes for CDR50–55. CDR50–55 represent cob dehydration rate at 50–55 days after pollination. Hap1, haplotype1; Hap2, haplotype2; Hap3, haplotype3. * Significant at p < 0.05. ns, no significant.
Figure 3. Association analysis of Zm00001d020615. (A) Significant SNPs associated with CDR50–55 and KDR45–50. CDR50–55 represent cob dehydration rate at 50–55 days after pollination; KDR45–50 represent kernel dehydration rate at 45–50 days after pollination. Red line shows the significant threshold of markers. The boxes with gray, white, red, and black colors represent promoter (upstream 2000 bp), UTR, exons, and introns, respectively. Bottom plot shows the pairwise linkage disequilibrium between the target SNP markers. (B) Comparison of three haplotypes for CDR50–55. CDR50–55 represent cob dehydration rate at 50–55 days after pollination. Hap1, haplotype1; Hap2, haplotype2; Hap3, haplotype3. * Significant at p < 0.05. ns, no significant.
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Plants 11 01989 g004 550
Figure 4. Superior allele distributions of nine SNPs in 30 elite inbred lines. Red and white colors represent superior and inferior alleles, respectively.
Figure 4. Superior allele distributions of nine SNPs in 30 elite inbred lines. Red and white colors represent superior and inferior alleles, respectively.
Plants 11 01989 g004
Table
Table 1. Phenotypic variations of 12 traits in 241 maize inbred lines.
Table 1. Phenotypic variations of 12 traits in 241 maize inbred lines.
TraitMean (%)Max (%)Min (%)SDCV (%)H2 (%)
CMC4562.6078.7546.370.380.6162.38
CMC5061.2677.2842.300.480.7973.69
CMC5557.0171.8035.520.711.2565.25
KMC4535.7073.5022.520.491.3729.79
KMC5032.5965.5514.460.491.5035.15
KMC5526.9846.7211.460.612.2668.71
CDR45–500.954.780.010.088.1592.16
CDR50–551.114.530.020.109.3588.41
CDR45–550.723.410.010.0810.6860.41
KDR45–500.851.840.060.047.5122.20
KDR50–551.162.560.050.088.7524.52
KDR45–550.751.360.060.046.3365.41
Max, maximum; Min, minimum; SD, standard deviation; CV, coefficient of variation; H2, broad-sense heritability. CMC45, CMC50, and CMC55 represent cob moisture content on the 45th, 50th, and 55th days after pollination, respectively; KMC45, KMC50, and KMC55 represent kernel moisture content on the 45th, 50th, and 55th days after pollination, respectively; CDR45–50, CDR50–55, and CDR45–55 represent cob dehydration rate at 45–50, 50–55, and 45–55 days after pollination, respectively; KDR45–50, KDR50–55, and KDR45–55 represent kernel dehydration rate at 45–50, 50–55, and 45–55 days after pollination, respectively.
Table
Table 2. Annotations of candidate genes scanned from co-localized significant SNP (SYN38588) detected by GWAS.
Table 2. Annotations of candidate genes scanned from co-localized significant SNP (SYN38588) detected by GWAS.
Associated TraitsCandidate GenesAnnotations
Zm00001d020618-
Zm00001d020610-
Zm00001d020609-
Zm00001d020612-
Zm00001d020622-
Zm00001d020615polygalacturonase
CDR45–55Zm00001d020626-
CDR50–55Zm00001d020613-
Zm00001d020617exocyst complex component EXO70B1
Zm00001d020614-
Zm00001d020623copper transporter 5.1
Zm00001d020616-
Zm00001d020627-
Zm00001d020628trimethyltridecatetraene synthase
Zm00001d020620-
CDR45–55 and CDR50–55 represent cob dehydration rate at 45–55 and 50–55 days after pollination, respectively. ‘-’ represents no functional annotations.
Table
Table 3. Gene-based association studies detected significantly associated variants within Zm00001d020615.
Table 3. Gene-based association studies detected significantly associated variants within Zm00001d020615.
TraitMarkerPositionp-ValueAlleleVariation Region
CDR50–55S7_1251651911251651910.00259G/Athe third exon (synonymous)
KDR45–50S7_1251660531251660530.00246C/Tthe fifth exon (synonymous)
S7_1251664951251664950.00108C/Athe fifth exon (missense)
CDR50–55 represent cob dehydration rate at 50–55 days after pollination; KDR45–50 represent kernel dehydration rate at 45–50 days after pollination.
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Zhang, M.; Xiangchen, C.; Yan, J.; Chengxu, Y.; Liu, H.; Zou, C.; Pan, G.; Shen, Y.; Ma, L. Genome-Wide Association Study Reveals the Genetic Basis of Kernel and Cob Moisture Changes in Maize at Physiological Maturity Stage. Plants 2022, 11, 1989. https://doi.org/10.3390/plants11151989

AMA Style

Zhang M, Xiangchen C, Yan J, Chengxu Y, Liu H, Zou C, Pan G, Shen Y, Ma L. Genome-Wide Association Study Reveals the Genetic Basis of Kernel and Cob Moisture Changes in Maize at Physiological Maturity Stage. Plants. 2022; 11(15):1989. https://doi.org/10.3390/plants11151989

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Zhang, Minyan, Chaoyang Xiangchen, Jiaquan Yan, Yujuan Chengxu, Hao Liu, Chaoying Zou, Guangtang Pan, Yaou Shen, and Langlang Ma. 2022. "Genome-Wide Association Study Reveals the Genetic Basis of Kernel and Cob Moisture Changes in Maize at Physiological Maturity Stage" Plants 11, no. 15: 1989. https://doi.org/10.3390/plants11151989

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