Genome-Wide Association Study Identiﬁed Novel SNPs Associated with Chlorophyll Content in Maize

: Chlorophyll is an essential component that captures light energy to drive photosynthesis. Chlorophyll content can affect photosynthetic activity and thus yield. Therefore, mining candidate genes of chlorophyll content will help increase maize production. Here, we performed a genome-wide association study (GWAS) on chlorophyll content and its dynamic changes in 378 maize inbred lines with extensive natural variation. Our phenotypic assessment showed that chlorophyll content and its dynamic changes were natural variations with a moderate genetic level of 0.66/0.67. A total of 19 single-nucleotide polymorphisms (SNPs) were found associated with 76 candidate genes, of which one SNP, 2376873-7-G, co-localized in chlorophyll content and area under the chlorophyll content curve (AUCCC). Zm00001d026568 and Zm00001d026569 were highly associated with SNP 2376873-7-G and encoded pentatricopeptide repeat-containing protein and chloroplastic palmitoyl-acyl carrier protein thioesterase, respectively. As expected, higher expression levels of these two genes are associated with higher chlorophyll contents. These results provide a certain experimental basis for discovering the candidate genes of chlorophyll content and ﬁnally provide new insights for cultivating high-yield and excellent maize suitable for planting environment.


Introduction
Maize (Zea mays L.) is an important source used for animal feed and bioenergy and has become the top crop produced in China, with production estimated up to 272.6 million tons (MT) in 2021 (http://www.stats.gov.cn, accessed on 20 April 2022). High yield based on photosynthesis has always been the focus topic in crop breeding [1]. Chlorophyll is essential for photosynthesis and promotes light energy absorption and assembly with photosynthetic protein complexes [2]. Chlorophyll content is one of the most important physiological traits, which is closely related to leaf photosynthesis and crop yield potential [3]. In a certain range, chlorophyll content is positively correlated with photosynthetic rate, which directly determines the yield of crops [4]. Therefore, maintaining high chlorophyll content in leaves can improve photosynthetic activity and increase yield [5,6]. At present, chlorophyll content has been used to evaluate the photosynthetic capacity and yield potential of leaves in rice [7]. Therefore, a better mining of the genes of chlorophyll content in maize leaves is of great value to speed up maize high-yield breeding.
Chlorophyll content is controlled by nuclear genes [8], and chlorophyll metabolism can be divided into four major steps [9]. The first step is the synthesis of chlorophyll-a

Field Experiments
The 378 inbred lines were grown in Fushun City, Liaoning Province, China (121 • 74 E, 42 • 14 N) in May 2017 (17FS) and Ledong city, Hainan Province, China (108 • 39 E, 18 • 24 N) in November 2017 (17LD). The field experiment was designed as random blocks with 2 replicates. Fifteen plants were planted in a 2.5 m long row with 0.6 m row distance, and the planting density was approximately 45,000 plants/ha. The inbred lines were labeled before pollination for standard field management. Chlorophyll content was measured using a portable chlorophyll meter (SPAD-502, plus Konica Minolta, Tokyo, Japan), which was non-destructive, fast and cheap. SPAD-502 readings were taken from five plants per plot on five dates at a 5 d interval starting 0 d after silking. There were three measurements in the middle of the ear leaf for each plant, and the average value was used for the statistical analysis. AUCCC was calculated based on SPAD-502 readings on all measure dates. Larger AUCCC values represent higher chlorophyll content, and lower AUCCC values represent lower chlorophyll content. The formula used to calculate AUCCC was modified from the AUCCC formula [30]: in which n is the number of assessment times, γ is the meter reading, i is the ith rating date, and t is time (in days).

Statistical Analysis of Phenotypes
The "PROC MIXED" program in SAS software was used to analyze the variance heritability of the phenotypic values at the two places. The mixed linear model (MLM) was used for the analysis, and the model yijk = µ+e l + rk(l) + fi + (fe)il + εlik, where yijk was the phenotypic value of the attenuation rate ijk in this test, µ denoted the average value of attenuation rate, el was the influence of two environments, rk(l) was the repeated effect in the environment, fl was the genetic effect of the ith family, (fe)il was the interaction between genetic and environmental effects, and εlik was a random error.
The generalized heritability formula is: h 2 = σg 2 /(σg 2 + σge 2 /e + σε 2 /re) [31], where h 2 represents the generalized heritability of the trait, σg 2 is the genetic variance of the trait, σge 2 represents the variance of the interaction between genetic and environmental effects, σε 2 denotes the residual error, and e and r denote the number of environments and the number of repetitions of the trait in each environment. The total phenotype of the two trials was predicted and expressed by the best linear unbiased prediction (BLUP) value so as to minimize the environmental impact.

Genome-Wide Association Mapping
The 96,726 SNPs (MAF ≥ 0.05) were used to conduct GWAS by combining the data from two genotyping platforms (RNA-seq and SNP array). The mixed linear model (MLM) calculation method was used to analyze the association of chlorophyll content in R 4.0.3, where population structure and kinship were fitted to control false positives [32,33]. We used the standard Bonferroni correction threshold α = 1 as the significance node. The p value was calculated as 1/n (n = 96,726), and we obtained p < 1.03 × 10 −5 as significant nodes.

Prediction of Candidate Genes
The most significant SNP was selected to represent the locus associated with chlorophyll content in the same LD block (r 2 < 0.2). The physical locations of significant SNPs were determined using the B73 RefGen v4 database. The annotated genes were searched within the 100 kb region around (50 kb upstream and 50 kb downstream) the detected significant SNP and were identified based on functional domains. The function of annotation genes referred to the MaizeGDB (https://maizegdb.org/), NCBI (https: //www.ncbi.nlm.nih.gov/gene) and the homologous genes of Arabidopsis.

Heat Map of Candidate Genes Expression
The expression amount of candidate genes in leaves of different days after maize silking was obtained from the Sequence Read Archive database of NCBI, and the mapping method was as described in [34].

RNA Extraction and RT-qPCR
The SNP allele effect was analyzed using R 4.0.3, and leaves of 6 varieties (Liao7980, A801, 29MIBZ2, PHVA9, LX9311 and Dan330) were selected during maize V4 stage. A leaf segment of 2 cm in length was excised from the middle of the fourth leaf and stored at −80 • C. The RNAprep Pure Plant Kit was used to extract the RNA (TIANGEN). cDNA was synthesized from 2 µg of total RNA using the FastKing RT Kit (TIANGEN). RT-qPCR reactions were performed using a Bio-Rad (Hercules, CA, USA) real-time PCR system using the SuperReal PreMix Plue (SYBR Green) (TIANGEN). Transcript levels were analyzed using the comparative CT (2 − CT ) method [29]. ZmTubulin1 (Zm00001d033850) was used as an internal control for data normalization. All data were measured in three independent biological replicates. The primers are listed in Table S6.

Chlorophyll Content Diversity and Heritability at Silking
Chlorophyll content was investigated at silking stage in 17FS and 17LD, and the BLUP values were calculated according to the phenotypic values of two places. The chlorophyll content of 17FS, 17LD and BLUP showed a normal distribution ( Figure 1)  The heritability of chlorophyll content is 0.67, which is medium (Table 1). There were significant and positive correlations between 17FS:17LD, 17FS:BLUP, 17LD:BLUP (p < 0.01; Figure 1). The Genotype × Environment interaction effects were not significant for the chlorophyll content, suggesting that the Genotype × Environment interaction effects were small and had good stability and adaptability of varieties (Table 1). These results suggested that the population's phenotypic variations of chlorophyll content are largely affected by genetic factors, and therefore, the association panel can be used for further association mapping. Table 1. Variance composition and broad-sense heritability of chlorophyll content and AUCCC in the maize association population in two environments (17FS and 17LD).  The heritability of chlorophyll content is 0.67, which is medium (Table 1). There were significant and positive correlations between 17FS:17LD, 17FS:BLUP, 17LD:BLUP (p < 0.01; Figure 1). The Genotype × Environment interaction effects were not significant for the chlorophyll content, suggesting that the Genotype × Environment interaction effects were small and had good stability and adaptability of varieties (Table 1). These results suggested that the population's phenotypic variations of chlorophyll content are largely affected by genetic factors, and therefore, the association panel can be used for further association mapping.

AUCCC and Heritability after Silking
The chlorophyll content was measured at 0, 5, 10, 15, and 20 days after silking in 17FS, and at 0, 5, and 10 days after silking in 17LD ( Figure 2). We found that the chlorophyll content was the highest at silking (day 0) and gradually decreased and showed the lowest at 20 days after silking during measurement in 17FS and 10 days after silking in 17LD ( Figure 2). In general, the chlorophyll content showed a downward trend. After silking, the plant enters the reproductive phase of growth. Nitrogen, phosphorus, potassium, and other nutrients are rapidly transferred to the kernel [35], and this nutrient redistribution phenomenon is most obvious from the filling stage to the mature stage of maize, which leads to the aging of maize and the decrease in chlorophyll content [36].
The AUCCC showed a normal distribution (Figure 3), and the heritability of AUCCC was 0.66, which is medium (Table 1). These results show that the AUCCC is controlled by genetics and is suitable for further GWAS. content was the highest at silking (day 0) and gradually decreased and showed the lowest at 20 days after silking during measurement in 17FS and 10 days after silking in 17LD ( Figure 2). In general, the chlorophyll content showed a downward trend. After silking, the plant enters the reproductive phase of growth. Nitrogen, phosphorus, potassium, and other nutrients are rapidly transferred to the kernel [35], and this nutrient redistribution phenomenon is most obvious from the filling stage to the mature stage of maize, which leads to the aging of maize and the decrease in chlorophyll content [36]. The AUCCC showed a normal distribution (Figure 3), and the heritability of AUCCC was 0.66, which is medium (Table 1). These results show that the AUCCC is controlled by genetics and is suitable for further GWAS.  ( Figure 2). In general, the chlorophyll content showed a downward trend. After silking, the plant enters the reproductive phase of growth. Nitrogen, phosphorus, potassium, and other nutrients are rapidly transferred to the kernel [35], and this nutrient redistribution phenomenon is most obvious from the filling stage to the mature stage of maize, which leads to the aging of maize and the decrease in chlorophyll content [36]. The AUCCC showed a normal distribution (Figure 3), and the heritability of AUCCC was 0.66, which is medium (Table 1). These results show that the AUCCC is controlled by genetics and is suitable for further GWAS.

Correlations of Chlorophyll Content with Other Plant Developmental Processes
The correlation coefficients (Pearson's) were calculated between chlorophyll content of ear leaf and 10 agronomic traits, including hundred kernel weight (HKW), flowering time (FT), ear rows number (ERN), kernel number per row (KNR), ear length (EL), ear perimeter (EP), kernel length (KL), kernel width (KW), kernel thickness (KT) and kernel area (KA). As shown in Figure 4, chlorophyll content is positively correlated with HKW at the 0.05 level. Previous studies also showed that chlorophyll content was positively correlated with kernel weight in rye and barley, whereas the correlation was significant at the level of 0.01 [37,38]. time (FT), ear rows number (ERN), kernel number per row (KNR), ear length (E perimeter (EP), kernel length (KL), kernel width (KW), kernel thickness (KT) and area (KA). As shown in Figure 4, chlorophyll content is positively correlated with at the 0.05 level. Previous studies also showed that chlorophyll content was positiv related with kernel weight in rye and barley, whereas the correlation was signifi the level of 0.01 [37,38].

Genome-Wide Association Analysis
To ascertain the candidate genes of chlorophyll content, we conducted a GW two environments of 378 lines using each environment and BLUP values at silking phenotype ( Figure 5). The GWAS was carried out using the MLM method with a thr of p < 1.03 × 10 −5 (Figures 5A-C and S1), and a total of 15 SNPs were identified. A them, three significant SNPs were located on chromosome 1, 7 and 10, respectiv 17FS ( Figure 5A and Table 2). Ten significant SNPs were found in 17LD, of whic were located on chromosome 1, one was mapped on chromosome 5, and chromos 4, 10 harbored 2 SNPs respectively ( Figure 5B and Table 2). In BLUP, two significan were located on chromosome 2 and 5 ( Figure 5C and Table 2). These results indica chlorophyll content in maize was controlled by multiple genetic loci in this crossnation.

Genome-Wide Association Analysis
To ascertain the candidate genes of chlorophyll content, we conducted a GWAS in two environments of 378 lines using each environment and BLUP values at silking as the phenotype ( Figure 5). The GWAS was carried out using the MLM method with a threshold of p < 1.03 × 10 −5 ( Figures 5A-C and S1), and a total of 15 SNPs were identified. Among them, three significant SNPs were located on chromosome 1, 7 and 10, respectively, in 17FS ( Figure 5A and Table 2). Ten significant SNPs were found in 17LD, of which three were located on chromosome 1, one was mapped on chromosome 5, and chromosome 2, 4, 10 harbored 2 SNPs respectively ( Figure 5B and Table 2). In BLUP, two significant SNPs were located on chromosome 2 and 5 ( Figure 5C and Table 2). These results indicate that chlorophyll content in maize was controlled by multiple genetic loci in this crosscombination.
Manhattan plots of GWAS were conducted on AUCCC after silking (Figures 6 and S2). There were four SNPs identified, among which two were for 17FS ( Figure 6A) and two were for 17LD ( Figure 6B), and there were no significant SNPs in the BLUP ( Figure 6C). Notably, we identified the same SNP 2376873-7-G on chromosome 10 by GWAS for chlorophyll content at silking and AUCCC. Manha an plots of GWAS were conducted on AUCCC after silking (Figures 6 and  S2). There were four SNPs identified, among which two were for 17FS ( Figure 6A) and two were for 17LD ( Figure 6B), and there were no significant SNPs in the BLUP ( Figure  6C). Notably, we identified the same SNP 2376873-7-G on chromosome 10 by GWAS for chlorophyll content at silking and AUCCC.   Manha an plots of GWAS were conducted on AUCCC after silking (Figures 6 and  S2). There were four SNPs identified, among which two were for 17FS ( Figure 6A) and two were for 17LD ( Figure 6B), and there were no significant SNPs in the BLUP ( Figure  6C). Notably, we identified the same SNP 2376873-7-G on chromosome 10 by GWAS for chlorophyll content at silking and AUCCC.

Candidate Genes
A total of 76 candidate genes were identified from 19 SNPs within the 50 kb flanking regions (Tables 2 and S2), of which 41 genes had functional annotations. The annotations for the candidate genes consisted of transmembrane protein, kinase, phosphatase, signal transduction protein, and transcription factors that may be involved in photosynthesis, redox, chloroplast development, and plant growth. The co-located SNP, 2376873-7-G, harbored Zm00001d026563, Zm00001d026569 and Zm00001d026574 encoding APETALA2/ethylene-responsive element binding protein (AP2/EREBP), chloroplastic

Candidate Genes
A total of 76 candidate genes were identified from 19 SNPs within the 50 kb flanking regions (Tables 2 and S2) , of which 41 genes had functional annotations. The annotations for the candidate genes consisted of transmembrane protein, kinase, phosphatase, signal transduction protein, and transcription factors that may be involved in photosynthesis, redox, chloroplast development, and plant growth. The co-located SNP, 2376873-7-G, harbored Zm00001d026563, Zm00001d026569 and Zm00001d026574 encoding APETALA2/ethyleneresponsive element binding protein (AP2/EREBP), chloroplastic palmitoyl-acyl carrier protein thioesterase and UDP-D-galacturonate, respectively (Table 2). We discovered that the genomic region containing SNP, Marker.247949, includes the gene Zm00001d007012, which encodes chloroplast RNA binding protein and directly participates in chloroplast morphogenesis ( Table 2). In addition, ATP synthase encoded by Zm00001d007011 can directly affect photosynthesis. Together, these results further demonstrate that the candidate genes were reliable.

Expression Pattern of Candidate Gene
To determine the candidate genes' expression pattern, an in silico profiling was compiled using the published RNA-Seq datasets (Figure 7). The results showed that the expression patterns of different candidate genes varied in seven different tissues ( Figure 7A and Table S3). The color scale bar at the top of the heat map represents log10transformed FPKM value, which represents low and high expression. Zm00001d023314, Zm00001d034534, Zm00001d003404, Zm00001d012982 and Zm00001d014126 had a relatively high level of expression with tissue specificity in the leaf relative to other tissues. Contrarily, Zm00001d039221 had a relatively low level of expression in leaf ( Figure 7A). Zm00001d007011 and Zm00001d034528 showed high expression tendencies in all tissues compared to the other candidate genes ( Figure 7A). Other genes showed moderate or low expression levels in leaf, such as Zm00001d026563 and Zm00001d007479, which encoded AP2/EREBP transcription factors and BSD-transcription factor, respectively ( Figure 7A, Tables 2 and S2).
To confirm the expression pattern of the candidate genes in leaves, we performed a heat map of the candidate gene expression at 0,6,12,18,24,30 d after pollination ( Figure 7B and Table S4). Zm00001d021162 had a low expression level at day 0, moderate on days 6 and 12, and high on days 18 and 24. Zm00001d026563 showed high expression only on day 6. Zm00001d003403 was lowly expressed on days 0 and 6 and moderate on days 12-30. Zm00001d003405 was moderate on day, and low on days 6-30. Zm00001d027601 was lowly expressed on day 0, moderate on day 6, and high on days 12-30. For Zm00001d039221, the expression increased gradually after pollination during measurement.
Expression of Zm00001d003403, encoding AMINO ACID PERMEASE (AAP), gradually increased after pollinating. AAP belongs to the amino acid/auxin permease family, which is involved in transportation of the principal nitrogen assimilates amino acid [39,40]. When the AAP2 gene was mutated in Arabidopsis thaliana, the mutant leaf had more chlorophyll content than the wild-type plant [41]. The leaf chlorophyll decreased (Figure 2) with an increase in Zm00001d003403 expression in maize. These results are in line with previous reports.
The allele variations of SNP 2376873-7-G were identified, and A allele genotypes showed higher chlorophyll content at the population level ( Figure 8A and Table S5). Zm00001d026568 and Zm00001d026569 genes were associated with the same SNP 2376873-7-G. The relative expressions of Zm00001d026568 and Zm00001d026569 were higher in the genotypes carrying the SNP A allele ( Figure 8B,C). In contrast, the relative expressions of Zm00001d026568 and Zm00001d026569 were lower in lines carrying the G genotype ( Figure 8B,C). As expected, higher chlorophyll content in the A genotypes was associated with higher expression of these two candidate genes in the selected lines shown in this study.

Discussion
Improvements in maize yield are very important to ensure world food security. Enhancing crop yield potential by enhancing photosynthesis is a major focus of modern crop breeding [42,43]. Chlorophyll content is an indicator of photosynthesis activity, which can directly affect photosynthetic efficiency [44][45][46]. A large number of functional genes related to chlorophyll have been identified by analysis of many mutants in maize, rice, and other species [47][48][49]. However, chlorophyll content is a quantitative trait controlled by multiple genes, which requires further study on its function. In particular, these genes have more functional variation, and many genomic regions remain largely elusive [50,51]. Therefore, analyzing the candidate genes of chlorophyll content and identifying its important variation sites will contribute to the improvement of maize breeding.
Compared with the traditional linkage analysis, GWAS can directly use the existing natural population as the material that has the advantage of saving time and effort [52]. However, it also has shortcomings in specific research, such as population structure that is prone to lead to false positives, linkage disequilibrium level that is affected by a variety of genetic or non-genetic factors [53,54], and the accuracy of candidate gene determination that is not enough. In order to obtain more accurate research results, we can reduce false positives as much as possible by increasing the population size. We use a mixed linear model (MLM) to take the individual kinship (K) and group structure (Q) as covariates (Q + K) at the same time, which can also effectively reduce the false positive rate [55]. Currently, GWAS approaches based on MLM are widely employed in both plant and animal systems [56]. GWAS is more and more widely used in crop research, for example, the genetic basis of several kernel-related traits and charcoal rot resistance has been reported in maize, and some candidate genes have been predicted by the GWAS [57,58].
The 378 maize inbred lines were used to construct an association population, and the candidate genes of chlorophyll content at silking and AUCCC after silking were mined by using high-density markers and phenotypic data. In this study, the heritability of the two methods was medium, and both of them conformed to normal distribution, indicating that chlorophyll content was contributed to by various types of genetic variation effects. Thee area under the curve to study dynamic processes has been widely used in the medical field; for example, the area under the curve was used to represent the dynamic distribution of the age composition of patients [59]. The AUCCC is a simple tool for breeders to evaluate the chlorophyll content. Although the heritability of AUCCC was similar to chlorophyll content in this study, Yang et al. [30] found that the heritability of the area under the curve could be effectively improved, indicating that the results of this method were reliable. As an indicator, AUCCC not only provides sufficient chlorophyll content information but can also accurately compare genotypes [60]. Furthermore, this simple manipulation method can be used to integrate applied and basic maize research, providing a good foundation for follow-up gene function studies [61].
The chlorophyll content of ear leaves was measured in two different sites, which have significant environmental differences between them; therefore, it could result in that the same SNP was not identified in 17FS and 17LD. Teng et al. [25] revealed the genetic basis of chlorophyll content of the first leaves at the seedling stage by GWAS, which identified nine SNPs. However, no same SNP was found in our study. In addition, positive correlation was detected for chlorophyll content and HKW. This is because the abundance and stability of chlorophyll in leaves significantly affect grain filling, and increasing the chlorophyll content of crop leaves can improve biomass yield and grain yield [62][63][64][65]. In general, a breeding strategy is mainly dependent on phenotypic selection of the best genotype by environmental interactions and the heritability level [66,67]. A total of 19 significant SNPs were associated with chlorophyll content at silking and the AUCCC after silking. To realize good prospects in the improvement of maize, a number of SNPs needs to be selected in order to reduce the overall time and cost.
A total of 76 candidate genes were identified for the chlorophyll content and the dynamic changes of chlorophyll content according to the position of significantly associated SNPs. In addition, Li et al. [6] previously identified gene overlap with these candidate genes, such as cytochrome c reductase. They compared transcriptome related to the physiological changes of yellow-green leaf mutant of maize and found that cytochrome c reductase participates in the tricarboxylic acid cycle. The chlorophyll content of yellow green leaves is different, which results in the difference of cytochrome c reductase. The role of cytochrome c reductase is to catalyze the transfer of electrons from coenzyme Q to cytochrome c [68], which is an important electron transporter in biological oxidation and is related to programmed cell death [69,70]. Programmed cell death leads to leaf senescence, which in turn leads to a decrease in chlorophyll content [71,72]. In addition, the co-located SNPs include Zm00001d026563, Zm00001d026569, Zm00001d026568, etc., which encode AP2/EREBP transcription factor 40, chloroplastic palmitoyl-acyl carrier protein thioesterase and pentatricopeptide repeat-containing protein, respectively. The AP2/EREBP superfamily is one of the largest and specific transcription factor (TF) families in plants that is involved in biotic/abiotic stress, compound storage and plant growth and development [73][74][75][76]. Chlorophyll content is an important physiological index used to measure plant growth and development [77,78]. Palmitoyl acyl carrier protein thioesterase is a key gene for de novo fatty acid synthesis [79]. Fatty acids are one of the main components of the cell membrane, and insufficient fatty acid synthesis triggers programmed cell death [80,81]. Leaf senescence is an organ-level programmed death process during plant growth and development, resulting in a decrease in chlorophyll content. In the inferior allele G, the expression of palmitoyl acyl carrier protein thioesterase is low, and fatty acid metabolism is accelerated, thus leading to a reduction in chlorophyll content. Pentatricopeptide repeatcontaining protein is one of the largest protein families in plants that affects chloroplast development [82,83]. Therefore, in the dominant allele A, the chlorophyll content is high, and the expression of pentatricopeptide repeat-containing protein is high.
To explore the potential roles of candidate genes in regulating chlorophyll content, we performed expression profiling analysis on RNA-seq data from different tissues at 0, 6, 12, 18, 24, and 30 days after pollination. The expressions of AP2/EREBP transcription factor 40 and chloroplast palmitoyl carrier protein thioesterase in leaves were low, and the expressions were moderately high on the 6th day after pollination. These results indicate that they have direct or indirect effects on chlorophyll content. Thus, selection of Zm00001d026563, Zm00001d026569, Zm00001d026568 or this SNP may help to regulate chlorophyll content in maize breeding. Taken together, the discovery of candidate genes provides help for further analyzing the molecular regulatory network of chlorophyll content on maize ear leaves. The identification of SNP will promote marker-assisted selection in maize molecular breeding.

Conclusions
In this study, we discovered candidate genes of natural variation in maize ear chlorophyll content by GWAS. Chlorophyll content appeared to be of moderate heritability and showed extensive variation in the association panel, indicating that chlorophyll content is a quantitative trait suitable for GWAS. GWAS showed that the natural changes regulating chlorophyll content had manymicro effect loci. We found 19 SNPs containing 76 candidate genes that may participate in leaf senescence, photosynthesis, and plant developmental processes. One of the SNPs was co-localized in AUCCC and chlorophyll content that was associated with five genes. Zm00001d026568 and Zm00001d026569 contained significant SNP and encoded pentatricopeptide repeat-containing protein and chloroplastic palmitoylacyl carrier protein thioesterase, respectively. They were found to be highly expressed in lines with the A allele as determined by RT-qPCR. These candidate genes provide valuable resources for further study of the molecular network regulating chlorophyll content in maize. In addition, the prediction of SNPs with chlorophyll content may help devise effective breeding plans and selection strategies to improve maize yield.

Supplementary Materials:
The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/genes14051010/s1. All datasets generated in this study are included in the article/supplementary material, and for further inquiries, the corresponding authors can be directly contacted. Table S1: Phenotypic description of chlorophyll content in the maize association population; Table S2: Single-nucleotide polymorphism (SNP) chromosomal positions and candidate genes significantly associated with chlorophyll content and AUCCC identified by genomewide association study; Table S3: Expression of different candidate genes in seven different tissues; Table S4: Expression of candidate genes in leaves at 0, 6, 12, 18, 24 and 30 days after pollinating; Table S5: Chlorophyll content in 6 different varieties; Table S6: Primers used in this study; Figure S1. Quantile-quantile plot for chlorophyll content of maize ear leaf; Figure S2

Data Availability Statement:
The original data for this article can be found online at: https://wp. me/p80aHo-L5.

Conflicts of Interest:
The authors declare no conflict of interest.