Skip to main content
Download PDF

Genetic characterization of inbred lines from Shaan A and B groups for identifying loci associated with maize grain yield

BMC Genetics volume 19, Article number: 63 (2018) Cite this article

Abstract

Background

Increasing grain yield is a primary objective of maize breeding. Dissecting the genetic architecture of grain yield furthers genetic improvements to increase yield. Presented here is an association panel composed of 126 maize inbreds (AM126), which were genotyped by the genotyping-by-sequencing (tGBS) method. We performed genetic characterization and association analysis related to grain yield in the association panel.

Results

In total, 46,046 SNPs with a minor allele frequency (MAF) ≥0.01 were used to assess genetic diversity and kinship in AM126. The results showed that the average MAF and polymorphism information content (PIC) were 0.164 and 0.198, respectively. The Shaan B group, with 11,284 unique SNPs, exhibited greater genetic diversity than did the Shaan A group, with 2644 SNPs. The 61.82% kinship coefficient in AM126 was equal to 0, and only 0.15% of that percentage was greater than 0.7. A total of 31,983 SNPs with MAF ≥0.05 were used to characterize population structure, LD decay and association mapping. Population structure analysis suggested that AM126 can be divided into 6 subgroups, which is consistent with breeding experience and pedigree information. The LD decay distance in AM126 was 150 kb. A total of 51 significant SNPs associated with grain yield were identified at P < 1 × 10− 3 across two environments (Yangling and Yulin). Among those SNPs, two loci displayed overlapping regions in the two environments. Finally, 30 candidate genes were found to be associated with grain yield.

Conclusions

These results contribute to the genetic characterization of this breeding population, which serves as a reference for hybrid breeding and population improvement, and demonstrate the genetic architecture of maize grain yield, potentially facilitating genetic improvement.

Background

Maize (Zea mays L.), the most widely grown crop in the world, plays an essential role in global food security and industrial products [1]. As a cross-pollinated crop, genomic divergence is nearly 1.42% between two maize inbred lines, which is greater than the divergence of 1.34% between humans and chimpanzees [2]. This great genomic diversity has resulted in considerable phenotypic variety. Moreover, maize is an important model plant for studying genome evolution, heterosis and the genetic architecture of complex quantitative traits [3]. According to statistical data from the FAO, the predicted worldwide population of 9 billion by 2050 will require 70% more food than today’s population [4]. It is estimated that more than half of the increased demand for cereals will come from maize, which is the crop with the largest planted area and highest total production. The necessary increase in maize production will require substantial changes in agronomic practices and methods of genetic improvement [5]. Previously, yield improvement has occurred at the expense of environmental pollution from increased fertilizer use [6, 7]. Along with the increasing focus on green production, more work has been aimed at increasing yield through genetic improvements, through which several QTLs and genes associated with grain yield and yield-related traits had been validated. For example, five QTLs showing a high genetic relationship with the phenotypic variance of yield component traits [8] and one large-effect QTL influencing kernel row number located on chromosome 7 was identified using linkage mapping [9]. Additionally, a stable locus related to kernel shape, PKS2, was identified through linkage and association analysis in 240 maize inbreds [1].

In the last several decades, the power and resolution of QTL mapping for complex quantitative traits, such as flowering, drought resistance, the contents of fatty acids and minor elements (carotenoids and tocopherols), metabolome features and kernel rows, has greatly increased because of the development of association analyses, including candidate gene association mapping and GWAS in maize and other species [10,11,12]. However, the increasingly wide application of association mapping is due to the rapid development of genotyping techniques, which has produced effective high-throughput molecular technology. A kind of molecular markers were developed by different genotyping technologies, among SSR makers were used to evaluate the polymorphisms of Dwarf 8 associated with flowering time in maize in the early days of development [13]. Later, association analyses using SSR markers were performed in plants to dissect complex quantitative traits [14, 15]. At present, SNP markers are widely utilized in association analyses of spring wheat, rice, Arabidopsis thaliana and maize [16,17,18,19], because of the advantages of biallelic markers and their higher content in the genome. Large numbers of these markers have been exploited through SNP chip and genome sequencing technology.

For maize, a variety of SNP chips based on SNP genotyping platforms have been designed by sequencing known genes for genotyping. These chips include the Illumina® SNP1536 chip, the MaizeSNP50 BeadChip with a high resolution, and a higher-density 600 k SNP genotyping array based on 57 M SNPs and small indels determined from 30 representative temperate maize lines in comparison with B73 AGP_v3 [20,21,22]. However, using SNP chip analysis as a genotyping method is expensive and fixed. In addition, the ability to detect ectopic exchange points is very weak. In comparison, genotyping-by-sequencing (GBS) is a recently developed simple sequencing procedure that can provide a large number of markers across the genome at low cost per sample and can be applied to maize, which exhibits high diversity and a large genome [23]. The GBS method does not rely on previous knowledge of SNPs and greatly expands the number of individuals and markers that can be studied, which increases the chance of discovering more uncommon or rare variants [24, 25].

Increasing grain yield is the primary target for meeting the food demand of the growing population, and dissecting the genetic architecture of grain yield is helpful for achieving this goal. Due to the complexity of the genetic architecture of grain yield and the difference between association populations, some researchers have aimed to uncover the genetic architecture of grain yield through association mapping; however, these studies are far from sufficient. Therefore, in this study, 126 maize inbred lines from the Shaan A and Shaan B groups were selected for genotyping with tGBS sequencing technology. Our aims were 1) to perform detailed characterization of the association mapping panel, including relationships, population structure, and genetic diversity; and 2) to dissect the genetic basis of grain yield in the association mapping panel.

Methods

Plant materials and field experiments

The association mapping panel consisted of 126 diverse inbred lines (AM126) selected from Shaan A and Shaan B group inbreds cultivated at Northwest A&F University. According to the theory of domestic and international maize breeding, we simplified heterotic model and adopted the breeding strategy of two divergent heterotic groups to build Shaan A and Shaan B heterotic groups, in which superior varieties are employed as basic materials to adapt maize production to Shaanxi Province [26]. High-density planting, drought, low fertilizer use and multiple environments were carried out to the expansion, improvement and utilization of the germplasm. From 2007 to 2008, basic groups were constructed over three generations. From 2009 to 2015, the Shaan A group and Shaan B group were optimized and upgraded through 7 rounds of selection in 30 departments in seven provinces (Shaanxi, Gansu, Henan, Hebei, Neimenggu, Sichuan and Xinjiang). Ultimately, we explored a technical approach for continuous improvement of maize germplasm and successfully built the Shaan A and Shaan B groups. In AM126, 94 different inbred lines belonged to the Shaan B group, and the others belonged to the Shaan A group. Detailed information about the 126 inbred lines is provided in Additional file 1. These inbred lines were planted in Yangling (34°16′N, 108°40′E) and Yulin (38°30′N, 109°77′E) in Shaanxi Province in 2017. At each location, all inbred lines were planted in a two-row plot using a randomized experimental design with two replications, with a row length of 5 m and a distance of 0.6 m between adjacent rows. The planting density was 67,500 plants/ha. During growth, field management followed normal field operations.

Phenotypic evaluation and analysis

Upon harvest, all ears were harvested by hand threshing, and corresponding data, including the grain water content, total grain weight and weight of ten panicles, were recorded to calculate the grain yield per mu (kg) by multiplying the grain yield per panicle by the total number of plants in the plot and adjusting to a 14% moisture content. Then, the mean grain yield of two replicates was calculated for subsequent analysis (Additional file 2).

Phenotypic data analyses, which included basic descriptive statistical analyses, ANOVA and Pearson correlation analysis, were carried out using SPSS v.22 software (IBM crop. Armonk, NY, USA). According to the method described by Knapp et al. [27], the broad-sense heritability (h2) of yield is estimated with the formula: \( {h}^2={\upsigma}_{\mathrm{g}}^2/\left({\upsigma}_{\mathrm{g}}^2+{\upsigma}_{\mathrm{g}\mathrm{e}}^2/\mathrm{n}+{\upsigma}_3^2/n\mathrm{k}\right) \), where σg2 is the genetic variance; σge2 is the interaction variance between the genotype and environment; σз2 represents the residual error variance; and n and k represent the environment and number of replications, respectively.

Genotyping

Total genomic DNA was extracted from leaf samples of each inbred line based on the CTAB procedure [28]. Fundamental qualities were evaluated by gel electrophoresis and spectrophotometry (Nanodrop2000, Thermo Scientific) in our laboratory. More stringent DNA quality testing and sequencing were completed by Data2Bio (D2B; LLC, Ames, IA, USA). The tGBS protocols followed by Data2Bio were described previously [29]. Briefly, 299,598,955 raw reads were generated from the 126 maize samples through six Ion Proton runs. Prior to alignment, the nucleotides of each raw read were scanned for low-quality bases. Bases with PHRED quality scores of < 15 out of 40 (≤3% error rate) were trimmed [30, 31]. Subsequently, the trimmed reads from each sample were aligned to GenomeB73_RefGenV4 using GSNAP [32], and confidently mapped reads were filtered if they mapped uniquely (≤2 mismatches every 36 bp and < 5 bases for every 75 bp as tails). Finally, 46,046 SNPs were filtered according to the following criteria (Additional file 3): 1. minimum calling rate ≥ 50%; 2. minor allele frequency (MAF) ≥0.01; 3. allele number = 2; 4. genotype ≥2; and 5. heterozygosity rate of 0% ~ (2 x Frequencyallele1 x Frequencyallele2 + 20%) from the TASSEL-GBS Pipeline [33].

Genetic diversity

The polymorphism information content (PIC) and MAF, which can be used to evaluate the genetic diversity of the population, were calculated with Powermarker v3.25 [34] using 46,046 SNPs. The PIC can reflect the degree of DNA mutation in a population and can be estimated as follows:

$$ {\overset{\hat{\mkern6mu} }{\mathrm{P}\mathrm{IC}}}_{\mathrm{l}}=1-\sum \limits_{\mathrm{u}=1}^{\mathrm{k}}{\overset{\sim }{\mathrm{P}}}_{\mathrm{l}\mathrm{u}}^2-\sum \limits_{\mathrm{u}=1}^{\mathrm{k}-1}\sum \limits_{\mathrm{v}=\mathrm{u}+1}^{\mathrm{k}}2{\overset{\sim }{\mathrm{P}}}_{\mathrm{l}\mathrm{u}}^2{\overset{\sim }{\mathrm{P}}}_{\mathrm{l}\mathrm{v}}^2 $$

where Plu and Plv refer to the frequency of the uth and vth alleles of marker l, respectively. A PIC value from 0 to 0.25 indicates low polymorphism; a PIC value from 0.25 to 0.5 indicates intermediate polymorphism; and a PIC value from 0.5 to 1 indicates high polymorphism. The MAF was used to quantify the degree of genetic differentiation in the maize population. The ratio of the number of SNPs with less variation to the total number of SNPs at each SNP locus was calculated. To avoid the influence of sample size, a re-sampling strategy was adopted in this study. The distribution of the SNPs unique to the Shaan A inbred lines or the Shaan B group inbred lines on ten chromosomes was determined with the ggplot R package.

Population structure and relative relationships

The relative kinship matrix between inbred lines i and j was calculated with TASSEL v.5.0 software to explore the pairwise relationships of the 126 inbred lines. The results were illustrated with the Genomic Association and Prediction Integrated Tool-R (GAPIT) package [35]. All negative values between pairwise lines were set to 0 [36].

In addition, to rapidly investigate population structure, 31,983 high-quality SNPs were screened with stringent criteria (missing rate ≤ 0.05, MAF ≥0.05) using TASSEL v.5.0 software and imported into Admixture software version 1.23 for cross validation [37]. The optimal partitioning of subgroups (K) was determined from the mixed cross validation error values, which were computed from the number of subpopulations (K), ranging from 1 to 15. The output of the Admixture software was imported into R to create a stacked bar chart.

SNP-based genome-wide association mapping and gene annotation

A total of 31,983 SNPs with MAF ≥0.05 and missing rate ≤ 50% were filtered for the genome-wide association study (GWAS). The GWAS of the grain yield data from the two locations was accomplished in the TASSEL v.5.0 software with a mixed linear model (MLM), controlling for population structure and relative kinship (K + Q) to avoid spurious associations [33]. When using the Bonferroni correction threshold for GWAS, we found no significant association between the grain yields from the two locations. Therefore, P < 1 × 10− 3 was chosen to determine significant SNPs for the trait. Thereafter, the LD decay distance in AM126 was estimated using TASSEL v.5.0 software. Finally, we confirmed the unique candidate genes underlying the association signals with SNP markers based on the LD decay distance of this population and annotated the candidate genes according to the information available in the Maize Sequence (http://ensembl.gramene.org/Zea_mays/Info/Index) and the MaizeGDB (http://www.maizegdb.org/gbrowse) databases. Because only a version 3 gene annotation file exists, all v4 gene IDs were converted to v3 gene IDs and then annotated.

Results

Basic SNP statistics of AM126 based on tGBS sequencing

Through tGBS sequencing, 299,598,955 raw reads were generated from AM126 and uniquely aligned to the reference genome (http://ensembl.gramene.org/Zea_mays/Info/Index, AGPV4). Ultimately, 1,133,188 sites were identified, among which 46,046 SNPs were polymorphic in AM126, with a missing rate of less than 50% and MAF of more than 0.01. For the 46,046 SNPs, the number of SNPs per chromosome ranged from 3235 SNPs on chromosome 10 to 6513 SNPs on chromosome 1 (Table 1). Chromosome 3 showed the lowest average marker density and chromosome 5 the greatest. The average marker density across the ten chromosomes was found to be approximately 45.7 kb. For the 31,983 high-quality SNPs with an MAF ≥0.05, the number of SNPs per chromosome ranged from 2287 SNPs on chromosome 10 to 4494 SNPs on chromosome 1. The average distance between neighbouring markers on different chromosomes varied from 59.4 to 69.8 kb, with an average of approximately 65.9 kb. The proportion of the reduction of SNPs with MAF < 0.05 was greater on chromosomes 1, 6 and 7 than on the other chromosomes.

Table 1 Chromosomal distribution and proportion of polymorphic markers used for computing genetic diversity and relationships (46,046 SNPs) and for population structure, LD decay and association analyses (31,983 SNPs)
Full size table

Genetic diversity

A total of 46,046 SNPs were used to estimate the MAF and PIC for AM126 and each group. The MAF and PIC distribution of all SNPs are provided in Fig. 1. Among the 46,046 SNPs, 28.08% showed an MAF of less than 0.05, and 12.43% exhibited a PIC of less than 0.05 in AM126. The average MAF for AM126 was 0.164, varying from 0.010 to 0.500, and the average PIC was 0.198, varying from 0.020 to 0.398 (Table 2). The Shaan B group showed a higher average MAF (0.166) and PIC (0.200) than the Shaan A group, which displayed an average MAF of 0.134 and PIC of 0.157. Furthermore, 32 inbred lines (the same sample size as for Shaan A) were selected randomly from the Shaan B group 10 times to eliminate the effect of sample size. The results confirmed that the Shaan B group exhibited higher genetic diversity than the Shaan A group, with an average MAF of 0.161 (0.154–0.178) and PIC of 0.191 (0.180–0.220).

Fig. 1
figure 1

Distribution of MAF and PIC in AM126, the Shaan A group and the Shaan B group. MAF distribution (a) and PIC distribution (b)

Full size image
Table 2 MAF and PIC of different groups determined using 46,046 SNPs
Full size table

The chromosomal distribution of unique polymorphic sites is provided for further comparison of the genetic diversity between the Shaan A and Shaan B groups (Fig. 2). Among the 46,046 SNPs, 11,284 SNPs (24.5%) were unique to the Shaan B group and were widely distributed on all chromosomes, whereas the Shaan A group contained 2644 unique SNPs, which only accounted for 5.7% of the total. The number and distribution of unique SNPs on chromosomes showed obvious differences between the Shaan A and Shaan B groups. The analysis indicated that Shaan B had a broader genetic basis than Shaan A group.

Fig. 2
figure 2

Distribution of polymorphic loci across the genome in the Shaan A and Shaan B groups. The distribution of unique SNPs in the Shaan A and Shaan B groups is labelled in red and yellow, respectively, and blue represents the SNPs common to the Shaan A and Shaan B groups

Full size image

Relative kinship and population structure

To elucidate the relationships among the inbred lines, all 46,046 SNPs were used to compute kinship coefficients. The pairwise relative kinship coefficients in AM126 ranged from 0.00 to 1.03. A total of 61.82% of the relative kinship values were equal to 0, and 36.62% of the relative kinship values varied from 0.05 to 0.5. Only 0.15% of the relative kinship values exceeded 0.7. The remaining 1.38% of the paired relative kinship values ranged from 0.5 to 0.7 (Fig. 3a, Additional file 4). The kinship heatmap is shown in Additional file 5. Low relative kinship was observed for AM126, which is consistent with known pedigrees.

Fig. 3
figure 3

Distribution of the pairwise kinship and population structure in AM126. The proportion of pairwise kinship coefficients ranging from 0 to 1 is shown (a). Plot of the cross validation error value in the range of K = 1 to 15 (b). Population structure based on k = 6 (c)

Full size image

The 31,983 high-quality SNPs were used to estimate ancestry in Admixture, based on the maximum-likelihood approach [37]. The cross validation error value for K ranging from 1 to 15 was computed to infer the population structure of AM126 (Fig. 3b). The lowest cross validation error value was observed when K = 6, suggesting that AM126 could be divided into six subgroups (Subs 1, 2, 3, 4, 5, and 6) (Fig. 3c, Additional file 6). In addition, comparison with previous breeding experience and pedigree backgrounds also indicated that K = 6 was a logical number for the subpopulation. PH6WC, belonging to the Reid group, was included in Sub 2, which was composed of 4 inbreds selected from the Shaan B group and 24 inbreds selected from the Shaan A group. Therefore, Sub 2 is also referred to as the Reid subgroup. Sub 1 contained 33 inbreds from the Shaan B group and 3 inbreds from the Shaan A group. Sub 3 consisted of 9 inbreds from the Shaan B group, which was a much smaller number than in the other subpopulations. Twenty-four Shaan B group inbred lines were clustered into Sub 4. Twelve Shaan B inbred lines and 2 Shaan A inbred lines were grouped into Sub 6. Additionally, 3 Shaan A inbred lines and 12 Shaan B inbred lines that showed a lower probability were assigned to Sub 5, which was also referred to as the mix subgroup. The resulting population structure of AM126 can be used for further analysis and shows that the Shaan A group presents less ancestral diversity than the Shaan B group.

Genome-wide association study

The grain yield was counted and was found to follow a normal distribution at each location (Additional file 7). In Yangling and Yulin, the average yields were 195.11 and 442.26 kg/mu, varying from 81.59 to 338.36 and 282.45 to 687.58 kg/mu, respectively (Table 3), and the coefficient of variation (CV) were 25.00% and 17.39%, respectively. The grain yields in Yangling and Yulin were significantly positively related at the p = 0.01 level, with a Pearson correlation coefficient of 0.519. Additionally, in this panel, the grain yield displayed a high heritability of over 83.33%. These results suggested that the grain yield was highly variable in this population.

Table 3 Descriptive statistics, correlation coefficient between the two environments and broad-sense heritability of the yield traits
Full size table

GWAS of the grain yield of AM126 was performed separately for the two locations (Yangling and Yulin) using 31,983 high-quality SNPs. As shown in the quantile-quantile plots of grain yield (Fig. 4b, d), fewer false positives were found after application of the MLM with a population structure and relationship (Q + K) model, and we used these results to annotate associated genes and identify candidate genes. A total of 51 lead SNPs, corresponding to 33 loci, were significantly associated (P < 1 × 10− 3) with yield. Only one of these loci was the same in the two environments. The Manhattan plots showed that the SNPs associated with yield were spread across the genome in Yangling and were distributed mainly on chromosome 4 in Yulin (Fig. 4a, c). When the R2 value was less than 0.01, the LD decay distance was about 150 kb in AM126 (Additional file 8). Therefore, we identified candidate genes in a 300 kb region around the positions of significantly associated SNPs and discovered that two intervals in the samples from the two environments were mostly overlapping.

Fig. 4
figure 4

Results of the GWAS of grain yield in AM126. Manhattan plot (a) and quantile-quantile plot (b) in Yangling. Manhattan plot (c) and quantile-quantile plot (d) in Yulin. The red line in the Manhattan plots corresponds to the Bonferroni-adjusted threshold (P < 1 × 10− 3)

Full size image

According to the gene and protein annotations from Maize Sequence (http://ensembl.gramene.org/Zea_mays/Info/Index), MaizeGDB (http://www.maizegdb.org) and InterPro (http://www.ebi.ac.uk/interpro), genes that may be associated with yield were identified and were illustrated in Table 4. Among the genes with functional annotations, Zm00001d027610, which encodes a vegetative storage protein and is located within the overlapping interval on chromosome 1 (8,876,216-9,176,216 bp), appeared to be a candidate gene that may be associated with grain yield (Fig. 5a, c). In another region with overlapping interval on chromosome 7 (5,969,535-6,348,940 bp), we identified the gene Zm00001d018819, encoding viviparous-14, which is involved in the abscisic acid (ABA) biosynthesis pathway (Fig. 5b, d). In addition, 28 candidate genes were identified based on significant SNPs associated with grain yield in a single environment. Zm00001d025617 encodes general regulatory factor (GF) 2, which belongs to the 14–3-3 family (IPR000308), on chromosome 10. Zm00001d053298, also known as GBP14, encodes the GLABROUS1 enhancer-binding protein (GeBP) transcription factor, which belongs to the GeBP family (IPR007592) and is located on chromosome 4. However, we did not find genes of known function at the loci with the lowest p values; these genes may not be directly involved in the relevant pathway, or the SNP loci may be linked with nearby genes.

Table 4 Markers and genes significantly associated with yield in the two environments
Full size table
Fig. 5
figure 5

Region plot of four SNPs associated with grain yield, which are located within 2.5 Mb on both sides of the lead SNP. Zm00001d027610 was identified based on the lead SNP, which was associated with grain yield in Yangling (a) and Yulin (c). Zm00001d018819 was identified based on the lead SNP, which was related to grain yield in Yangling (b) and Yulin (d)

Full size image

Discussion

Many researchers have analysed complex quantitative traits using association panels collected from hundreds of inbreds from all over the world, especially in maize [38, 39]. These inbreds usually originate from different breeding programmes around the world. However, the materials employed in present study were derived from the same breeding project, and they had been cultivated under high-stress conditions for nearly 10 years. Therefore, to analyse genetic diversity and kinship within this germplasm, the inbreds must be used more accurately in breeding programmes. Using the AM126 panel to perform association mapping will provide new insight into combining molecular genetics and conventional breeding.

Maize has abundant genetic diversity, and abundant genetic diversity of maize germplasm greatly benefits crop breeding [40]. Artificial selection for favourable alleles has gradually reduced genetic diversity in maize and increased the abundance of some favourable low-frequency alleles [41]. This selection will help us to identify new genes. In this study, the PIC and MAF of the entire panel were 0.198 and 0.164, respectively. These values are significantly lower than those reported in other studies of diversity in maize inbred lines [20], including the PIC and MAF of 0.29 and 0.33, respectively, obtained in a previous study using 2846 SNPs across 32 inbred lines selected from the Shaan A and Shaan B groups [42]. However, the average MAF was relatively higher than that of 538 maize inbred lines (CMLs) determined using 955,120 SNPs with MAF ≥0.01 [43]. These differences were mainly caused by the choice of maize germplasm and SNP filtration criteria. In general, compared to ordinary association mapping, the genetic diversity of the breeding population was lower. Additionally, GBS can produce a very high miss rate and a large number of SNPs with a very low frequency [44]. Low-frequency SNPs may facilitate the identification of complex traits that rely on low-frequency and rare variants [45]. In addition, the inbreds from the Shaan B group were found to be more diverse than those from the Shaan A group in this study. These results showed that inbreds from the Shaan A group might have experienced stricter selective conditions than those from the Shaan B group during inbred selection under the same environment.

Population structure is the foundation of hybrid breeding and a key factor in association mapping [36]. According to long-term breeding experience, maize inbreds have been divided into several heterotic groups. However, it remains unclear how many heterotic groups of maize exist; indeed, researchers have not reached a consensus in this regard. For maize in the USA, the classic view is that maize should be separated into two heterotic groups—stiff stalk (SS) and non-stiff stalk (NSS) [46]. Following this breeding strategy and grouping, inbred lines were divided into two divergent heterotic groups according to the different requirements of the parents of hybrids. In Europe, the flint and dent heterotic groups have been widely developed to produce superior hybrids [47]. However, in China, opinions have differed due to the unclear relationships among Chinese maize inbreds. Zhang et al. suggested that 269 inbred lines could be assigned to six heterotic groups based on different methods [48]. Research using 84 parental lines showed that heterotic groups originating from Lancaster, Reid, Tang SPT, Zi330 and E28 in the early 1990s had become Reid, Tem-tropic I, Zi330, Tang SPT and Lancaster, respectively, in the early twenty-first century [49]. Many studies have divided widely used inbred lines into three or more than heterotic groups [50,51,52], though some reports have indicated that the inbreds found in China can be assigned to two divergence groups. For example, 362 important inbreds from Southwest China were divided into two heterotic groups: temperate and tropical [53]. Additionally, 155 inbred lines were separated into two groups, with seven subgroups, using 82 SSRs [54], and 367 diverse inbreds lines were divided into two heterotic groups, composed of six subgroups [55]. In this study, two divergent heterotic groups, Shaan A and Shaan B, were cultivated through long-term artificial selection based on breeding experience in China and abroad. The population could be divided into 6 subgroups. The Shaan A and Shaan B groups exhibit a clear population structure that can serve as a reference for hybrid breeding and reduce bias in association analysis. The results suggested that the breeding strategy of two divergent heterotic groups has been successful in this population.

The main objective in maize breeding programmes is to increase grain yield [56]. GWAS is an effective tool for identifying genes and dissecting the genetic architecture related to traits, which furthers genetic improvement in crops [18]. In this study, 51 significant SNPs associated with yield were detected across two environments. By searching for candidate genes 150 kb up- and downstream of significant SNPs, 30 genes that were associated with grain yield were identified. Among these genes, Zm00001d027610 and Zm00001d018819 were identified in both environments. Zm00001d027610 encodes a vegetative storage protein. Previous studies have shown that vegetative storage proteins play an important role in the mobilization of amino acids and defence against biotic and abiotic stresses in Arabidopsis thaliana and soybean [57, 58]. Another gene (Zm00001d018819) identified in both environments encodes the viviparous-14 protein, which is involved in the biosynthesis of the ABA pathway in maize according to its functional annotation. ABA plays a key role in diverse growth processes [59]. Therefore, viviparous-14 may be involved in determining grain yield by regulating the biosynthesis of ABA. Moreover, the GBP14 gene (Zm00001d053298), discovered in Yulin, was annotated as a GeBP transcription factor belonging to the DUF573 family, which includes GeBP and GeBP-like proteins as well as storekeeper and storekeeper-like (STKL) transcription factors (http://pfam.xfam.org/family/PF04504.13). In Arabidopsis, GeBP and GeBP-like proteins play roles in cytokine hormone pathway regulation [60]. The STK protein may regulate patatin expression in potato tubers [61], and an STKL factor participates in the glucose (Glc) signalling pathway in Arabidopsis [62]. Therefore, the candidate gene can likely increase grain yield. Finally, Zm00001d025617, identified in Yangling, encodes grf 2, belonging to the 14–3-3 protein family; this family includes zmgf14–4 and zmgf14–6, which exhibit prominent expression during maize kernel development [63]. The levels of 14–3-3 proteins significantly decrease under salt stress in maize to improve maize adaption [64]. The GF14 protein may play an important role in response to biotic and abiotic stresses in Arabidopsis [65]. Hence, we infer that Zm00001d025617 and these genes belonging to the 14–3-3 protein family have similar functions in maize. More work needs to be conducted to verify the functions of these candidate genes.

The genetic improvement of maize yield not only improves yield potential, but also increases stress tolerance [66]. Grain yield is a complex trait and is highly influenced by environmental variation [67]. The experimental locations in this study were Yangling and Yulin, which are both irrigated areas, but one exhibits a sub-humid monsoon climate and the other a semiarid monsoon climate. The different climate and soil conditions resulted in different grain yields, which ranged from 195.11 (Yangling site) to 442.26 kg/mu (Yulin site). Compared to the candidate genes identified in Yulin, the inbreds cultivated in Yangling displayed more stress-related genes in their genome. For example, Zm00001d034563, which encodes dirigent protein (DIR) 4, belongs to the plant DIR family, which has been reported to respond to environmental stress [68]. Additionally, Zm00001d029679 encodes an ethylene-responsive transcription factor (ERF). AtERF53 increases drought tolerance by facilitating stress-responsive gene expression in Arabidopsis [69]. A short GC-box motif has been identified in the ERF genes, which are essential for the response to ethylene, influencing plant growth and development [70]. Zm00001d033834 encodes a GRAS transcription factor. Relevant studies have shown that GRAS family protein play roles in gibberellin (GA) signaling, which not only regulate pant growth and development, but also respond to abiotic stress [71, 72]. These studies indicate that the genes identified in the present study could be involved in responses to biotic and abiotic stresses. The differences in phenotype and genotype observed at the two locations clearly revealed an interaction between the genotype and environment (G × E), suggesting that the population in Yangling suffered worse growth conditions, which is consistent with the uneven rainfall distribution and continuous high temperatures recorded in Yangling. However, some genes that are significantly associated with drought tolerance, such as ZmNAC111, ZmVPP1 and ZmDREB2A, were not identified in this population [73,74,75]; it is possible that these genes were purified through long-term selection in the population.

Conclusions

In the present study, the genetic diversity, population structure, relationships and association maps related to the grain yield of 126 inbred lines (AM126) selected from the Shaan A and Shaan B groups cultivated at Northwest A&F University were assessed using high-quality SNPs. The MAF and PIC of AM126 were 0.164 and 0.198, respectively. The Shaan B group exhibited a higher MAF and PIC and more unique SNPs than did the Shaan A group. Therefore, the Shaan B group has a broader genetic basis than the Shaan A group. AM126 was divided into 6 subgroups according to genotype analysis and breeding experience, and the inbreds display low relative kinship. GWAS was performed on this population to identify regions associated with grain yield using a population structure and kinship (Q + K) model. A total of 30 candidate genes related to grain yield were identified, among which two were identified in both environments and the others in a single environment. The largest proportion of the genes identified in this study were associated with abiotic stress, which is consistent with the climate conditions. These results illustrate the genetic characteristics of the breeding population and provide favourable alleles related to maize yield, and they can serve as a guideline for hybrid breeding and genetic improvement in the future.

Abbreviations

ABA:

Abscisic acid

AM126:

126 inbred lines

CV:

Coefficient of variation

GA:

Gibberellin

GAPIT:

Genomic Association and Prediction Integrated Tool-R package

GBS:

Genotyping-by-sequencing

GWAS:

Genome-wide association study

MAF:

Minor allele frequency

PIC:

Polymorphism information content

SD:

Standard deviation

References

  1. Zhang C, Zhou Z, Yong H, Zhang X, Hao Z, Zhang F, Li M, Zhang D, Li X, Wang Z. Analysis of the genetic architecture of maize ear and grain morphological traits by combined linkage and association mapping. Theor Appl Genet. 2017;130(5):1011–29.

    Article  PubMed  CAS  Google Scholar 

  2. Buckler ES, Stevens NM. Maize Origins, Domestication, and Selection. 2005.

    Google Scholar 

  3. Romay MC, Millard MJ, Glaubitz JC, Peiffer JA, Swarts KL, Casstevens TM, Elshire RJ, Acharya CB, Mitchell SE, Flintgarcia SA. Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol. 2013;14(6):R55.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. FAO. Global agriculture towards 2050. Brieing paper for FAO high-level expert forum on “How to feed the world 2050,” Rome. 21–13 Oct. 2009. Available at http://www.fao.org/wsfs/world-summit/en (veriied 6 Dec. 2010). Food and Agriculture Organization of the United Nations, Rome 2009.

  5. Yan J, Warburton M, Crouch J. Association mapping for enhancing maize ( L.) genetic improvement. Crop Sci. 2011;51(2):433.

    Article  Google Scholar 

  6. Tilman D, Cassman KG, Matson PA. Agricultural sustainability and intensive production practices. Nature. 2002;418:671–8.

    Article  PubMed  CAS  Google Scholar 

  7. Chen F, Fang Z, Gao Q, Youliang Y, Jia L, Yuan L, Mi G, Zhang F. Evaluation of the yield and nitrogen use efficiency of the dominant maize hybrids grown in north and Northeast China. Sci China Life Sci. 2013;56(6):552.

    Article  PubMed  Google Scholar 

  8. Sabadin PK, Júnior CLS, Souza AP, Garcia AAF. QTL mapping for yield components in a tropical maize population using microsatellite markers. Hereditas. 2008;145(4):194–203.

    Article  Google Scholar 

  9. Lu M, Xie C, Li X, Hao Z, Li M, Weng J, Zhang D, Bai L, Zhang S. Mapping of quantitative trait loci for kernel row number in maize across seven environments. Mol Breed. 2010;28(2):143–52.

    Article  Google Scholar 

  10. Li H, Peng Z, Yang X, Wang W, Fu J, Wang J, Han Y, Chai Y, Guo T, Yang N. Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet. 2013;45(1):43–50.

    Article  PubMed  CAS  Google Scholar 

  11. Zuo W, Chao Q, Zhang N, Ye J, Tan G, Li B, Xing Y, Zhang B, Liu H, Fengler KA. A maize wall-associated kinase confers quantitative resistance to head smut. Nat Genet. 2015;47(2):151.

    Article  PubMed  CAS  Google Scholar 

  12. Chen W, Gao Y, Xie W, Gong L, Lu K, Wang W, Li Y, Liu X, Zhang H, Dong H. Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet. 2014;46(2):714.

    Article  PubMed  CAS  Google Scholar 

  13. Thornsberry JM, Goodman MM, Doebley J, Kresovich S, Nielsen D, Buckler ES. Dwarf8 polymorphisms associate with variation in flowering time. Nat Genet. 2001;28(3):286–9.

    Article  PubMed  CAS  Google Scholar 

  14. Wen Z, Zhao T, Zheng Y, Liu S, Wang C, Wang F, Gai J. Association analysis of agronomic and quality traits with SSR markers in glycine max and glycine soja in China:II. Exploration of elite alleles. Acta Agron Sin. 2008;34(8):1339–49. (in Chinese)

    Article  CAS  Google Scholar 

  15. Fan H, Wen Z, Wang C, Wang F, Xing G, Zhao T, Gai J. Association analysis between agronomic-processing traits and SSR markers and genetic dissection of specific accessions in Chinese wild soybean population. Acta Agron Sin. 2013;39(5):775–88. (in Chinese)

    Article  CAS  Google Scholar 

  16. Sukumaran S, Dreisigacker S, Lopes M, Chavez P, Reynolds MP. 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(2):353–63.

    Article  PubMed  CAS  Google Scholar 

  17. Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet. 2010;42(11):961.

    Article  PubMed  CAS  Google Scholar 

  18. Atwell S, Huang YS, Vilhjálmsson BJ, Willems G, Horton M, Li Y, Meng D, Platt A, Tarone AM, Hu TT. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature. 2010;465(7298):627.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Liu L, Du Y, Shen X, Li M, Sun W, Huang J, Liu Z, Tao Y, Zheng Y, Yan J. KRN4 controls quantitative variation in maize kernel row number. PLoS Genet. 2015;11(11):e1005670.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Lu Y, Yan J, Guimarães CT, Taba S, Hao Z, Gao S, Chen S, Li J, Zhang S, Vivek BS. Molecular characterization of global maize breeding germplasm based on genome-wide single nucleotide polymorphisms. Theor Appl Genet. 2009;120(1):93–115.

    Article  PubMed  CAS  Google Scholar 

  21. Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, Charcosset A, Clarke JD, Graner EM, Hansen M, Joets J. A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. Plos One. 2011;6(12):e28334.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Unterseer S, Bauer E, Haberer G, Seidel M, Knaak C, Ouzunova M, Meitinger T, Strom TM, Fries R, Pausch H. A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array. BMC Genomics. 2014;15(1):823.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011;6(5):e19379.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Ott A, Liu S, Schnable JC, Yeh C, Wang KS, Schnable PS. tGBS® genotyping-by-sequencing enables reliable genotyping of heterozygous loci. Nucleic Acids Res. 2017;45(21):e178.

  25. Suwarno WB, Pixley KV. Genome-wide association analysis reveals new targets for carotenoid biofortification in maize. Theor Appl Genet. 2015;128(5):851–64.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Brekke BH. Agronomic and phenotypic responses to 75 years of recurrent selection for yield in the Iowa stiff stalk synthetic maize population. Dissertations & Theses - Gradworks. 2010.

  27. Knapp SJ. Confidence intervals for heritability for two-factor mating design single environment linear models. Theor Appl Genet. 1986;72(5):587–91.

    Article  PubMed  CAS  Google Scholar 

  28. Murray MG, Thompson WF. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res. 1980;8(19):4321–5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Schnable PS, Liu S, Wu W. Genotyping by next-generation sequencing. US, WO 2013106737 A1[P]. 2013.

  30. Ewing B, Green P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 1998;8:186–94.

    Article  PubMed  CAS  Google Scholar 

  31. Ewing B, Hillier M, Wendl C, Green P. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 1998;8:175–85.

    Article  PubMed  CAS  Google Scholar 

  32. Wu TD, Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics. 2010;26:873–81.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23(19):2633–5.

    Article  PubMed  CAS  Google Scholar 

  34. Liu K, Muse SV. PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics. 2005;21(9):2128–9.

    Article  PubMed  CAS  Google Scholar 

  35. Tang Y, Liu X, Wang J, Li M, Wang Q, Tian F, Su Z, Pan Y, Liu D, Lipka AE. GAPIT version 2: an enhanced integrated tool for genomic association and prediction. Plant Genome. 2016;9(2):1-9.

  36. Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2006;38(2):203–8.

    Article  PubMed  CAS  Google Scholar 

  37. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19(9):1655–64.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Deng M, Li D, Luo J, Xiao Y, Liu H, Pan Q, Zhang X, Jin M, Zhao M, Yan J. The genetic architecture of amino acids dissection by association and linkage analysis in maize. Plant Biotechnol J. 2017;15(10):1250.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Cui Z, Luo J, Qi C, Ruan Y, Li J, Zhang A, Yang X, He Y. Genome-wide association study (GWAS) reveals the genetic architecture of four husk traits in maize. BMC Genomics. 2016;17(1):946.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Liu D, Wang J, Wang X, Yang X, Sun J, Chen W. Genetic diversity and elite gene introgression reveal the japonica rice breeding in northern China. J Integr Agric. 2015;14(5):811–22.

    Article  CAS  Google Scholar 

  41. Vigouroux Y, Mitchell S, Matsuoka Y, Hamblin M, Kresovich S, Smith JSC, Jaqueth J, Smith OS, Doebley J. An analysis of genetic diversity across the maize genome using microsatellites. Genetics. 2005;169(3):1617–30.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Wang W, Xu S, Gao J, Zhang X, Guo D, Li X, Xue J. Analysis of genetic diversity of maize inbred lines based on SNP markers. J Maize Sci. 2015;2:41–5. (in Chinese)

    CAS  Google Scholar 

  43. Wu Y, San Vicente F, Huang K, Dhliwayo T, Costich DE, Semagn K, Sudha N, Olsen M, Prasanna BM, Zhang X, et al. Molecular characterization of CIMMYT maize inbred lines with genotyping-by-sequencing SNPs. Theor Appl Genet. 2016;129(4):753–65.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Brouard JS, Boyle B, Ibeagha-Awemu EM, Bissonnette N. Low-depth genotyping-by-sequencing (GBS) in a bovine population: strategies to maximize the selection of high quality genotypes and the accuracy of imputation. BMC Genet. 2017;18(1):32.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Ibeagha-Awemu EM, Peters SO, Akwanji KA, Imumorin IG, Zhao X. High density genome wide genotyping-by-sequencing and association identifies common and low frequency SNPs, and novel candidate genes influencing cow milk traits. Sci Rep. 2016;6:31109.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Duvick DN, Smith JSC, Cooper M. Long-term selection on a commercial hybrid maize breeding program. Plant Breed Rev. 2004;24:109–51.

  47. Boppenmaier J, Melchinger AE, Seitz G, Geiger H, Herrmann R. Genetic diversity for RFLPs in European maize inbreds. III. Performance of crosses within versus between heterotic groups for grain traits. Plant Breed. 1993;113:219-26.

  48. Zhang R, Xu G, Li J, Yan J, Li H, Yang X. Patterns of genomic variation in Chinese maize inbred lines and implications for genetic improvement. Theor Appl Genet. 2018;131(6):1–15.

    CAS  Google Scholar 

  49. Teng W, Cao Q, Chen Y, Liu X, Men S, Jing X, Li J. Analysis of maize heterotic groups and patterns during past decade in China. Agric Sci Chin. 2004;3(7):481–9. (in Chinese)

    Google Scholar 

  50. Wang R, Yu Y, Zhao J, Shi Y, Song Y, Wang T, Li Y. Population structure and linkage disequilibrium of a mini core set of maize inbred lines in China. Theor Appl Genet. 2008;117(7):1141–53.

    Article  PubMed  CAS  Google Scholar 

  51. Xie C, Zhang S, Li M, Li X, Hao Z, Bai L, Zhang D, Liang Y. Inferring genome ancestry and estimating molecular relatedness among 187 Chinese maize inbred lines. J Genet Genom. 2007;34(8):738–48.

    Article  Google Scholar 

  52. Yang X, Gao S, Xu S, Zhang Z, Prasanna BM, Li L, Li J, Yan J. Characterization of a global germplasm collection and its potential utilization for analysis of complex quantitative traits in maize. Mol Breed. 2011;28(4):511–26.

    Article  Google Scholar 

  53. 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 Genomics. 2016;17:697.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Yang X, Yan J, Shah T, Warburton ML, Li Q, Lin L, Gao Y, Chai Y, Fu Z, Yi Z. Genetic analysis and characterization of a new maize association mapping panel for quantitative trait loci dissection. Theor Appl Genet. 2010;121(3):417–31.

    Article  PubMed  Google Scholar 

  55. Wu X, Li Y, Shi Y, Song Y, Wang T, Huang Y, Li Y. Fine genetic characterization of elite maize germplasm using high-throughput SNP genotyping. Theor Appl Genet. 2014;127(3):621–31.

    Article  PubMed  Google Scholar 

  56. Iqbal M, Khan K, Sher H, Al-Yemeni MN. Genotypic and phenotypic relationship between physiological and grain yield related traits in four maize (Zea mays L.) crosses of subtropical climate. Sci Res Essays. 2011;6(13):2864-72.

  57. Mason HS, Mullet JE. Expression of two soybean vegetative storage protein genes during development and in response to water deficit, wounding, and jasmonic acid. Plant Cell. 1990;2(6):569–79.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Liu Y, Ahn J, Datta S, Salzman R, Moon J, Huyghues-Despointes B, Pittendrigh B, Murdock LL, Koiwa H, Zhu-Salzman K. Arabidopsis vegetative storage protein is an anti-insect acid phosphatase. Plant Physiol. 2005;139(3):1545.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Phillips J, Artsaenko O, Fiedler U, Horstmann C, Mock HP, Müntz K, Conrad U. Seed-specific immunomodulation of abscisic acid activity induces a developmental switch. EMBO J. 1997;16(15):4489–96.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Chevalier F, Perazza D, Laporte F, Le HG, Hornitschek P, Bonneville JM, Herzog M, Vachon G. GeBP and GeBP-like proteins are noncanonical leucine-zipper transcription factors that regulate cytokinin response in Arabidopsis. Plant Physiol. 2008;146(3):1142.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Zourelidou M, Torres-Zabala MD, Smith C, Bevan MW. Storekeeper defines a new class of plant-specific DNA-binding proteins and is a putative regulator of patatin expression. Plant J. 2002;30(4):489–97.

    Article  PubMed  CAS  Google Scholar 

  62. Chung MS, Lee S, Min JH, Huang P, Ju HW, Kim CS. Regulation of Arabidopsis thaliana plasma membrane glucose-responsive regulator (AtPGR) expression by a. Thaliana storekeeper-like transcription factor, AtSTKL, modulates glucose response in Arabidopsis. Plant Physiol. 2016;104:155.

    CAS  Google Scholar 

  63. Yao D, Liu X, Yin Y, Han S, Yang L, Yang L, Hao D. Affinity chromatography revealed insights into unique functionality of two 14-3-3 protein species in developing maize kernels. J Proteome. 2015;114:274.

    Article  CAS  Google Scholar 

  64. Cui D, Wu D, Liu J, Li D, Xu C, Li S, Li P, Zhang H, Liu X, Jiang C. Proteomic analysis of seedling roots of two maize inbred lines that differ significantly in the salt stress response. PLoS One. 2015;10(2):e0116697.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Rooney MF, Ferl RJ. Sequences of three Arabidopsis general regulatory factor genes encoding GF14 (14–3-3) proteins. Plant Physiol. 1995;107(1):283–4.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Tollenaar M, Lee EA. Yield potential, yield stability and stress tolerance in maize. Field Crops Res. 2002;75(2–3):161–9.

    Article  Google Scholar 

  67. Moreau L, Charcosset A, Gallais A. Use of trial clustering to study QTL x environment effects for grain yield and related traits in maize. Theor Appl Genet. 2004;110(1):92–105.

    Article  PubMed  CAS  Google Scholar 

  68. Paniagua C, Bilkova A, Jackson P, Dabravolski S, Riber W, Didi V, Houser J, Gigli-Bisceglia N, Wimmerova M, Budinska E. Dirigent proteins in plants: modulating cell wall metabolism during abiotic and biotic stress exposure. J Exp Bot. 2017;68(13):3287-301

  69. Cheng M, Hsieh EJ, Chen J, Chen H, Lin T. Arabidopsis RGLG2, functioning as a RING E3 ligase, interacts with AtERF53 and negatively regulates the plant drought stress response. Plant Physiol. 2012;158(1):363–75.

    Article  PubMed  CAS  Google Scholar 

  70. Fujimoto SY, Ohta M, Usui A, Shinshi H, Ohmetakagi M. Arabidopsis ethylene-responsive element binding factors act as transcriptional activators or repressors of GCC box–mediated gene expression. Plant Cell. 2000;12(3):393.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Pysh LD, Wysocka-Diller JW, Camilleri C, Bouchez D, Benfey PN. The GRAS gene family in Arabidopsis: sequence characterization and basic expression analysis of the SCARECROW-LIKE genes. Plant J. 2010;18(1):111–9.

    Article  Google Scholar 

  72. Colebrook EH, Thomas SG, Phillips AL, Hedden P. The role of gibberellin signalling in plant responses to abiotic stress. J Exp Bot. 2014;217(1):67–75.

    Article  CAS  Google Scholar 

  73. Mao H, Wang H, Liu S, Li Z, Yang X, Yan J, Li J, Tran LSP, Feng Q. A transposable element in a NAC gene is associated with drought tolerance in maize seedlings. Nat Commun. 2015;6(8326):8326.

    Article  PubMed  CAS  Google Scholar 

  74. Wang X, Wang H, Liu S, Ferjani A, Li J, Yan J, Yang X, Qin F. Genetic variation in ZmVPP1 contributes to drought tolerance in maize seedlings. Nat Genet. 2016;48(10):1233.

    Article  PubMed  CAS  Google Scholar 

  75. Qin F, Kakimoto M, Sakuma Y, Maruyama K, Osakabe Y, Tran LS, Shinozaki K, Yamaguchi-Shinozaki K. Regulation and functional analysis of ZmDREB2A in response to drought and heat stresses in Zea mays L. Plant J. 2007;50(1):54–69.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

We thank the project supported by the Innovation Project of Science and Technology of Shaanxi Province (2017ZDXCL-NY-02-04).

Funding

This study was funded by the Innovation Project of Science and Technology of Shaanxi Province (2017ZDXCL-NY-02-04). The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

Author information

Authors and Affiliations

  1. Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China

    Ting Li, Jianzhou Qu, Yahui Wang, Liguo Chang, Kunhui He, Dongwei Guo, Xinghua Zhang, Shutu Xu & Jiquan Xue

  2. Maize Engineering Technology Research Centre of Shaanxi Province, Yangling, China

    Ting Li, Jianzhou Qu, Yahui Wang, Liguo Chang, Kunhui He, Dongwei Guo, Xinghua Zhang, Shutu Xu & Jiquan Xue

Authors
  1. Ting Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianzhou Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yahui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liguo Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kunhui He
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dongwei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xinghua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Shutu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jiquan Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JX and SX conceived and designed the experiments. JQ, YW, LC and KH contributed to the collection of phenotypic data and DNA extraction, TL and JQ analyzed the data. XZ, DG and JX contributed to modify the manuscripts. TL and SX wrote the paper. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Shutu Xu or Jiquan Xue.

Ethics declarations

Ethics approval and consent to participate

All the plant materials used in this research were provided by Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, College of Agronomy, Northwest A&F University, China. The field experiments were conducted under local legislation and permissions.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional files

Additional file 1:

Table S1. Numbers, groups and names of the AM126 lines. (XLSX 14 kb)

Additional file 2:

Table S2. The grain yield data of the AM126 lines. (XLSX 17 kb)

Additional file 3:

The hapmap file of SNP genotyping data of the AM126 lines. (TXT 13628 kb)

Additional file 4:

Table S3. Pairwise kinship coefficients between AM126 lines calculated with 46,046 SNPs. (XLSX 57 kb)

Additional file 5:

Figure S1. Heatmap of AM126 based on the kinship matrix computed using 46,046 SNPs. (PDF 135 kb)

Additional file 6:

Table S4. Co-ancestral membership of AM126 when K = 6. (XLSX 57 kb)

Additional file 7:

Figure S2. Frequency map of grain yield measured in the samples from Yangling (a) and Yulin (b). (PDF 220 kb)

Additional file 8:

Figure S3. Picture illustrating LD decay distance in AM126. (PDF 96 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, T., Qu, J., Wang, Y. et al. Genetic characterization of inbred lines from Shaan A and B groups for identifying loci associated with maize grain yield. BMC Genet 19, 63 (2018). https://doi.org/10.1186/s12863-018-0669-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12863-018-0669-9

Keywords

  • Maize
  • Genetic diversity
  • Grain yield
  • Genome-wide association study

BMC Genomic Data

ISSN: 2730-6844

Contact us