As one of three staple crops, nutritional traits in maize are important for human and animal nutrition. Grain quality-related traits are closely related to grain commercial value. Understanding the genetic basis of quality-related traits in maize would be helpful for breeding high-quality maize varieties. In this study, two association panels (AM122 and AM180) were subjected to genome-wide association analysis of grain quality-related traits, including protein content, oil content, starch content and fibre content. In total, 98 SNPs (P<1×10-4) were identified to be significantly associated with these four grain quality-related traits. By integrating two sets of public transcriptome data, 31 genes located in 200 kb regions flanking the associated SNP showed high expression during kernel development and were differentially expressed in two maize inbred lines, KA225 and KB035, with significantly different quality. These genes might regulate maize grain quality by participating in plant hormone processes, autophagy processes and others. All these results could provide important reference information for breeding high‑quality maize varieties.
Research Article
Genome Wide Association Analysis of Kernel Nutritional Quality in Two Natural Maize Populations
https://doi.org/10.21203/rs.3.rs-2103357/v1
This work is licensed under a CC BY 4.0 License
Journal Publication
published 08 Mar, 2023
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Maize
Grain quality
Genome-wide association analysis
Expression profile
Candidate genes
Maize (Zea mays L.), which originates from the American continent, is one of the most important crops and is also an important source of feed, food, energy and industrial raw materials worldwide. Maize plays an important role in ensuring food security. Due to its nutritional components, an increasing number of researchers have paid attention to the nutritional quality of corn for a healthy diet, including oil, amylose, carotenoid, anthocyanin and other components. These traits have undergone selection to meet the diverse nutritional needs of humans, such as oil, starch and carotenoids (Fang et al., 2020; Karn et al., 2017). To improve the oil content in maize, at the University of Illinois, germplasm resources have been selected to produce 18.47% oil in high oil (HO) varieties and 0.05% oil in low oil (LO) varieties by two-way selection from 1896 (Lucas et al., 2013). Similar work was conducted at China Agriculture University, which increased the oil content from 4.71% to 15.55% in the Beinongda High Oil (BHO) population after 18 cycles of improvement (Song et al., 2004) and improved the oil content from 3.53% to 12.66% in another high oil population with disease resistance and high combining ability (Wang et al., 2009).
As the major component in maize kernels, most enzymes in the fatty acid, starch and protein biosynthesis pathways have been biochemically characterized in various species (Morell et al., 1995; Slabas et al., 1992). Moreover, a series of genes related to kernel nutrition were cloned, and their function in regulating kernel quality has been clarified. For example, the representative protein-related genes opaque1 (o1), floury4 (fl4) and mucronate (Mc) were reported (Wang et al., 2014; Wang et al., 2012). Some genes, such as fatty acid desaturation 2 (fad2), fatty acid desaturase-2 (fad6) and diacylglycerol acyltransferase (DGAT1-2), have been reported to influence oil content in maize (Mikkilineni et al., 2003; Zheng et al., 2008). Additionally, starch content-related genes such as Shrunken1 (Sh1), Shrunken2 (Sh2) and Brittle2 (Bt2) have been identified (Hannah et al., 2012; Jiang et al., 2013). Other key regulatory factors, such as Opaque2 (O2) and ZmABI19, were also found to cooperate in early grain development and grain filling initiation as core transcription factors (Yang et al., 2021; Zhang et al., 2016). However, a role for nonpathway genes in the modulation of kernel quality variation is also important (Wang et al., 2018). Thus, more work at the genetic level is needed to identify the corresponding genes for kernel nutritional improvement.
In recent decades, scientists have dissected the genetic basis of kernel nutritional compositions using reverse genetics, including linkage and association mapping (Cook et al., 2012; Fang et al., 2020; Hu et al., 2021; Li et al., 2013; Wang et al., 2016; Yang et al., 2013; Zheng et al., 2021). For instance, Karn et al. (Karn et al., 2017) conducted joint-linkage quantitative trait locus (QTL) mapping using 961 near isogenic lines (Teosinte ×B73) and identified two starch, three protein, and six oil QTLs, which explained 18, 23, and 45% of the total variation, respectively. Li et al. (Li et al., 2018) used GWAS to identify 27 SNPs related to amylose synthesis in an association panel including 464 maize inbred lines. Fang et al. (Fang et al., 2020) detected 98 single QTLs for 33 oil and carotenoid traits in a BC2F5 population from a cross between Teosinte and Mo17. Hu et al. (Hu et al., 2021) identified 50 unique single QTLs for starch content in a multiparent population containing six recombinant inbred line populations. Additionally, an increasing number of molecular markers closely linked to kernel quality QTLs or located in functional genes have been developed for starch, fatty acid, and carotenoid contents (Chai et al., 2012; Dudley et al., 2004; Fu et al., 2013; Yan et al., 2010) and applied to germplasm improvement (Hao et al., 2014).
In this study, we evaluated four main nutritional quality-related traits by near infrared spectroscopy (NIRS) in two association panels consisting of 205 maize inbred lines and conducted GWAS by integrating 42,542 single nucleotide polymorphism (SNP) markers. Our study aimed to identify associated SNPs for those quality-related traits contributing to nutritional components in a new germplasm pool and to predict the interaction network for the candidate genes identified for different quality-related traits. We anticipate that these results will provide a potential reference for high protein, high starch, high oil or high lysine breeding in maize.
Materials and field experiments
In this study, a total of 205 maize inbred lines were collected from several domestic breeding departments. They were divided into two subsets according to the planting year, and GWASs were conducted separately. One subset included 122 inbred lines (abbreviated AM122) planted in 2019 in Yulin (19YL) of Shaanxi Province, Yinchuan (19YC) in Ningxia Hui Autonomous Region and Urumqi (19UR) in Xinjiang Uygur Autonomous Region. The materials were planted in a completely randomized block design with two rows, two replicates, 60 cm row spacing and 20 cm plant spacing. The other subset included 180 inbred lines (abbreviated as AM180) planted in 2020 in Zhangye (20ZY) in Gansu Province, Yinchuan (20YC) in Ningxia Hui Autonomous Region, Urumqi (20UR) in Xinjiang Uygur Autonomous Region, and Taiyuan (20TY) in Shanxi Province. The accessions were grown in a completely randomized block design with two-row plots without duplication, 60 cm row spacing and 20 cm plant spacing. Other field management followed local agricultural practices.
Phenotypic investigation and statistical analysis
Genotyping and genome-wide association analysis
For genotyping, fresh young leaves of all accessions were collected, and genomic DNA was extracted using a modified CTAB method. All samples were sent for genotype detection at Kenfeng Company (Bei dahuang Kenfeng Seed Industry Co., Ltd., Harbin, China) using the single nucleotide polymorphism (SNP) 6H60K chip platform created by Beijing Academy of Agriculture and Forestry Sciences. Considering that artificial breeding materials were used, SNPs with a minimum allele frequency (MAF) less than 2% and a genotype deletion rate greater than 20% were filtered by PLINK (v1.90 b6.9). Finally, 42,200 high-quality SNPs in AM122 and 42,541 high-quality SNPs in AM180 remained for subsequent association analysis. The mean decay distance of the linkage disequilibrium within the related population was calculated by the PopLDdecay (V3.41) program (Zhang et al., 2019). GWASs for each trait in each environment and with the BLUP value were conducted using the FarmCPU model implemented in the GAPIT package in R software with both K and Q matrices (Liu, et al., 2016).
Prediction of candidate genes
The physical location of the SNPs was identified based on the maize genomic sequence version 3.0 (B73_RefGen_v3; http://www.maizesequence.org), and two expression datasets were used to explore the candidate genes in the 200 kb (LD decay distance) region surrounding the associated SNPs and to perform analyses. The first dataset contained the kernel expression data in B73 (Chen et al., 2014), and the other dataset contained RNA sequences of kernels for two inbred lines, including KA225 and KB035, in six developmental stages, including 21 days after pollination (DAP), 28 DAP, 35 DAP, 42 DAP, 49 DAP, and 56 DAP (Qu et al., 2022). The genes were regarded as differentially expressed genes (DEGs) based on the criteria (log2FC (fold change) > 1 or log2FC < -1, p.adj < 0.05). Then, they were clustered and visualized using R -/packet pheatmap (http://www. r-project.org/). All candidate genes were annotated using public databases, including MaizeGDB, NCBI (https://www.ncbi.nlm.nih.gov/) and UniProt (https://www.Uniport.org/). These candidate genes were then used to screen differentially expressed genes (DEGs) and perform GO analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the OmicShare website (https://www.omicshare.com/). Then, the protein interaction network of proteins encoded by candidate genes was analysed in STRING (https://string-preview.org/), and Cytoscape software (v3.9.0) was used to visualize the protein interaction network.
Statistical analysis of data
Each replicate consisted of kernel samples from independent ears taken from different maize plants. Statistical analyses of the data obtained were performed using IBM SPSS 26.0 and R software. Statistical significance of the phenotypic data was analysed by F test. (*P<0.05, **P<0.01, ***P<0.001). Statistical significance of genotype data was analysed by Student’s t test. (*P<0.05, **P<0.01, ***P<0.001). The error bar in the column chart is the standard deviation. Graphs were created using Adobe Illustrator 2018.
Accession numbers
Gene data from this article can be found in NCBI or MaizeGDB databases under the following accession numbers:
adh1F(GRMZM2G416388), CesA-1(GRMZM2G112336), CesA2(GRMZM2G027723), CesA3
(GRMZM2G039454), CesA-9(GRMZM2G018241),
zmm8(GRMZM2G102161), MADS1
(GRMZM2G171365), MADS76(GRMZM2G070034), MADS5(GRMZM2G003514), ZAG1(GRMZM2G052890),
NRH1(GRMZM2G029845), NRH2(GRMZM2G085960), DAD1(GRMZM5G847530), sqsh1(GRMZM2G029396), sqs1(GRMZM2G108225), gpm532
(GRMZM2G127067), atg8b(GRMZM2G419694), atg8c(GRMZM2G076826), atg8d(GRMZM2G134613),
atg8e(GRMZM2G014975).
To evaluate the quality characterization in AM122 and AM180, four nutrition-related traits were qualified, including protein, oil, starch and fibre, by NIRS. The observed phenotypes of the four grain quality-related traits showed diversity across variants in each experimental environment, and the trends of the two panels tended to be consistent (Fig. 1). All absolute values for skewness and kurtosis for the four traits were less than 1, which meant that they fit a normal distribution (Table S1). The coefficient of variation (CV) showed extensive continuous variation for three of the phenotypes (the CV ranged from 6.86% to 15.44%), except for the starch content (1.50%-2.53%). It is worth noting that the CV of each trait in AM180 was higher than that in AM122, which may be because of the larger population and richer genetic background in AM180 (Table S1). Moreover, the broad-sense heritability (h2) for each trait was greater than 80%, which ranged from 80.74%% to 91.82%, indicating that genetic factors play a major role in determining these traits and could stabilize inheritance. Correlation analysis showed that there was a significant positive correlation between the locations for the same trait, and the two panels showed the same trend (Fig. S1).
Genome-wide association analysis
After deleting low-quality SNPs, 42,200 and 42,541 high-quality SNPs were saved for further research in AM122 and AM180, respectively. According to the Bonferroni adjustment, the significant SNPs were identified using the threshold -log10P=4. Combined with LD analysis, we retained independent associations with the highest significance level at significant sites when R2 was >0.6 between related sites in the LD interval (Fig. 2, Table S2). In total, nine, six, fourteen and sixteen SNPs were significantly correlated with protein content, oil content, starch content and fibre content in AM122, respectively. Eleven, twenty-four, eleven and fifteen SNPs were significantly correlated with the four quality-related traits in AM180 (Fig. 2, Table S2). Six colocalized sites were detected two or more times. For each unique significant SNP, the phenotypic variance explained (PVE) ranged from 0.54%-18.97% with an average PVE of 8.42%. In addition, nine of these loci explained more than 15% of the phenotypic variance, which is usually considered a major effect for the related trait. For seven of the nine major associated SNPs (six associated with starch), the frequency of the favoured allele was larger than that of the less favoured allele, which means that high quality (especially for starch content) was unconsciously selected for during the breeding process (Fig. S4). Furthermore, these colocated or major SNPs can be explored for molecular markers for quality improvement.
Prediction and annotation of the candidate genes
According to the linkage disequilibrium (LD) decay distance (200 kb) calculated by PopLDdecay (Fig. S2), we considered the genes approximately 200 kb from the associated SNPs as candidate genes based on the B73 reference genome in MaizeGDB (B73_RefGen_v3, https://www.maizegdb.org/). Combined with dynamic transcriptomic data for grain development from 0 days after pollination (DAP) to 38 DAP in the maize inbred line B73 (Chen et al., 2014), 287 candidate genes were expressed with RPKM≥1 in the kernel development process, including 75, 74, 68 and 77 genes that may affect the contents of fibre, oil, protein and starch, respectively (Table S3). Furthermore, cluster analysis showed that there were 23, 18, 17, and 9 highly expressed candidate genes for these four quality-related traits (Table S4). Notably, the GRMZM5G818977 gene was identified for both oil content and starch content. Among the 67 candidate genes, 31 were differentially expressed in kernels at later developmental stages of KA225 and KB035, in which there were significant differences in the four quality-related traits (Fig. 3, Table S5). These genes might be responsible for differences in quality-related traits.
Furthermore, GO analysis showed that these genes were enriched in cellular process, metabolic process, single-organism process, multicellular organismal process, biological regulation, developmental process, cellular component organization or biogenesis, response to stimulus, regulation of biological process and other biological processes (Fig. 4a). By KEGG analysis, the 31 candidate genes were annotated into ubiquitin-mediated proteolysis, sesquiterpenoid and triterpenoid biosynthesis and nicotinate and nicotinamide metabolism (Fig. 4b).
Regulation network of the candidate genes
There was an inverse correlation across each of the kernel components; that is, if one substance was high, then the other components were relatively reduced. To understand the regulatory relationship for the four quality-related traits in maize, 31 candidate genes were used to construct an interaction network in String (https://cn.string-db.org/). Of these, 28 genes were obtained from the regulatory networks, interacted with various functional proteins by forming independent or interrelated modules, and participated in the regulation of a variety of biological networks (Fig. 5).
It is worth noting that there were interactions between modules related to the same or different traits. For oil content, three genes, GRMZM2G033787 (M12) annotated as phosphatase, GRMZM2G057910 (M13) annotated as NAD-dependent dihydrogenase and GRMZM2G049588 (M14) annotated as glutamine-dependent NAD+ synthetase, interacted with each other through GRMZM2G402319. Moreover, GRMZM2G033787 (M12) interacted with one fibre candidate gene GRMZM2G043338 (M7) via adh1F, which encodes cystathionine beta synthase domain protein 1 (CBS); the domain protein maintains the balance of redox reactions in cells (Wang, 2015). In addition, M12 also shared several common interacting proteins (including NRH1 and NRH2) with the other fibre candidate gene GRMZM2G179432 (M1), which encodes a Haloacid dehalogenase-like hydrolase (HAD) superfamily protein. Another network in the oil content-related modules included GRMZM2G064993 (M15) and GRMZM2G027862 (M17), which interacted with four cellulose synthase genes (CesA-1, CesA2, CesA3 and CesA-9) participating in sugar metabolism. For starch content, there was an interaction network with fibre content that included the starch-related protein GRMZM2G085849 (M23), which directly interacted with the fibre-related protein GRMZM2G053764 (M2) and indirectly interacted with the fibre-related protein GRMZM2G127632 (M4) via GRMZM2G424582. For fibre content, module M9 (GRMZM2G102161, zmm8) formed an independent network with several members of the MADS family, including MADS1, MADS76, MADS5 and ZAG1, which played an important role in inflorescence and kernel development (Lid et al., 2004; Riechmann et al., 1997). These results may provide a deep understanding of the coordination among the four quality-related traits.
Grain quality is a complex quantitative trait in maize that is controlled by several major genes and a large number of minor genes (Li et al., 2013; Liu, et al., 2016; Zheng et al., 2021). Here, two association populations named AM122 and AM180 were planted in multiple locations in 2019 and 2020, respectively, to dissect the genetic basis of grain quality-related traits. The average quality of AM122 was slightly higher than that of AM180, and the diversity in the phenotypes was lower in AM122 than in AM180. These phenomena resulted from the different numbers and genetic backgrounds of the materials, which caused the difference in the identified SNPs in the two panels. A similar conclusion that different populations and mapping methods will affect the detection of association sites has been found in many other studies (Lid et al., 2004; Wang et al., 2009; Wang et al., 2010; Wassom et al., 2008; Zhao et al., 2018).
By conducting GWAS, 98 significantly associated loci for the four quality-related traits were detected, and there were 287 genes within the 200 kb region surrounding the associated loci. Compared with other genetic analyses for the four quality-related traits, some of the 98 identified SNPs have been reported. For protein-associated SNPs, the SNP Affx-291404884 in bin3.05 was located in the QTL region related to starch by linkage mapping using two RIL populations from two crosses between one high-oil maize inbred and two normal dent maize inbred lines (Yang et al., 2013), and SNP Affx-291429042 in genetic bin6.07 was identified several times using F2:3 and BC2F2 populations constructed by DAN232×N04 (Liu et al., 2008). For starch-related SNPs, the SNP Affx-291408126 was consistent with the starch-related QTL (umc2085-umc2202) mapped in multiple environments (Zhao et al., 2018). Additionally, Affx-291406396 in bin 6.04 was significantly associated with oil content in the region of umc65-nc009 found in previous studies (Wassom et al., 2008). Wang et al. also mapped this region for grain oil (qlOIL2-6-1) and starch content (qSTA2-6-2) (Wang et al., 2010). For fibre-associated SNPs, Affx-291375791 and Affx-291420214 were found many times in the reported QTL (IVNDFD tc, ADL/NDF tc, adf7, ivdmd7-1, ivdmd7-2, ndf7-1, ndf7-2)(Courtial et al., 2014,2013; Li et al., 2017). Additionally, some new SNPs were discovered in this study, such as bin9.05, bin9.06 and bin10.03, and the PVE of these SNPs ranged from 13.00 to 18.97, which can provide information for further studies. Therefore, mapping QTLs in multiple populations can provide more genetic information.
To improve the candidate gene selection efficiency, we combined two sets of dynamic transcriptomic data for grain development. Thirty-one genes remained as candidate genes due to their expression in B73 and were expressed differently in kernels in KA225 and KB035 with different grain qualities. For fibre content, the associated SNP Affx-291428764 with 13.31% PVE, there was one putative gene GRMZM2G009681 encoding cinnamoyl-coA reductase 1 (named ZmCCR1), and its homologous gene in rice restored vessel and xylary fibre integrity and increased biomass by vessel-specific reintroduction of CCR1 in dwarfed ccr1 mutants ((De Meester et al., 2018). Furthermore, two homologous genes in Brassica napus could accumulate lignins, flavonoids and glucosinolates and enhance lodging resistance (De Meester et al., 2018; Yin et al., 2022). According to the predicted interaction network, the ZmCCR1(M11) protein interacted with several proteins encoded by phenylalanine ammonia lyase (PAL), including GRMZM2G170692 (PAL), GRMZM2G074604 (PAL1), GRMZM2G063917 (PAL4) and GRMZM2G081582 (PAL5) (Fig. 5 and Table S6). Additionally, another protein putative gene, GRMZM2G060611, around the SNP Affx-159075163 on chromosome 2 encoded dolichyl-diphosphooligosaccharide protein glycosyltransferase subunit STT3A, which was predicted to interact with DAD1 and participate in the protein glycosylation pathway, which is part of the protein modification pathway (Rini et al., 2022).
Moreover, by integrating the annotation and interaction network for the 31 putative genes, we know that multiple pathways participated in regulating grain quality in maize. For instance, the candidate gene GRMZM2G105787, which is located in the confidence interval of the identified SNP (Affx-291424190), was annotated as a squalene monooxygenase (SE1) involved in the biosynthesis of brassinosteroids (BRs) and was predicted to interact with other BR biosynthesis-related genes (such as sqsh1, sqs1, and gpm532 as shown in Fig. 5) to influence BR biosynthesis (Sun et al., 2021). Another putative gene GRMZM2G102161 (Zmm8, M9) near the fibre-related SNP Affx-291409101 with 18.97% PVE was considered a candidate gene (Table 1), and it can regulate ZAG1, MADS1 and other MADS-box transcription factors, which have been reported to be involved in the metabolism of starch and glucose in the endosperm and to affect the water dehydration rate (Dong et al., 2019; Du et al., 2021; Kim et al., 2022; Qu et al., 2016) (Fig. 5). Furthermore, the candidate GRMZM2G078238 (ZmLAZ1) protein has been reported to play a role as a transmembrane organic solute transporter in various pathways and organs, tissues or organelles at different stages of development (Liu et al., 2020). Here, the ZmLAZ1 protein was predicted to interact with several autophagy-related proteins, including atg8b, atg8c, atg8d and atg8e (Fig. 5), which participate in programmed cell death during endosperm development (Chung et al., 2009). In summary, the mechanism regulating grain quality in maize is complex and needs to be further studied. The information on candidate genes in this study will provide a valuable reference for cloning candidate genes underlying maize quality-related traits.
In this study, two inbred line populations were subjected to genome-wide association analysis of four grain quality-related traits. A total of 98 significantly associated SNP markers were detected. By integrating two sets of public transcriptome data, we identified 31 genes as candidate genes related to the four grain qualities. Furthermore, by annotating and predicting the interaction network, we found that these genes mainly cooperate with various plant hormones and participate in a variety of metabolic pathways to regulate nutrient accumulation, but the putative genes need to be further clarified. This study provides a reference for analysing the molecular regulatory mechanism of corn grain nutrients.
Acknowledgements
All the authors thank those at the Key Laboratory of Biology and Genetic Improvement of Maize in the Arid Area of the Northwest Region who provided help in field experiments and phenotypic data collection.
Funding
This work was supported by the Shaanxi Province Research and Development Project [2021LLRH-07 and 2021ZDLNY01-06] and the Innovation Project of the National Key R&D Program of China (2018YFD0100200).
Author contributions statement
Shutu Xu and Xinhua Zhang conceived the research and designed the experiments. Wenting Wan, Ying Wu, Die Hu, Fan Ye, Hangyu Liang, Xiaopeng Wu, Xinyue Qi and Haiyang Zhou performed the field experiments and phenotype collection. Wenting Wan performed data analysis and wrote the manuscript. Shutu Xu and Jiquan Xue obtained funding and directed the study. All authors have reviewed and approved the manuscript, as submitted to Molecular Breeding.
Data availability
Data can be obtained from the National Agricultural Science Data Center in China (https://www.agridata.cn/). The accession number for the genotype data is A1C6936557C165AA6602839C316D1BCB, and the accession number for the phenotype data is BF5CA18FD7F1AFF75CAFBEDEFE3D4525. The RNA-seq data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE158816.
Ethics approval and consent to participate
Not applicable.
Consent for publication
The authors give consent for the publication.
Conflicts of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Tables are available in Supplementary Files section.