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Article

QTL Analysis Reveals Conserved and Differential Genetic Regulation of Maize Lateral Angles above the Ear

by
Yanbin Zhu
1,2,3,†,
Bo Song
1,2,†,
Yanling Guo
1,2,
Baobao Wang
4,
Changcheng Xu
2,
Hongyu Zhu
2,
Lizhu E
1,
Jinsheng Lai
1,
Weibin Song
1 and
Haiming Zhao
1,*
1
State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing 100193, China
2
National Key Laboratory of Maize Biological Breeding, Key Laboratory of Genetics and Breeding of Main Crops in Northeast Region, Ministry of Agriculture and Rural Affairs, Liaoning Dongya Seed Industry Co., Ltd., Shenyang 110164, China
3
Sanya Institute of Henan University, Sanya 572025, China
4
Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
*
Author to whom correspondence should be addressed.
†
The authors contributed equally to this work.
Plants 2023, 12(3), 680; https://0-doi-org.brum.beds.ac.uk/10.3390/plants12030680
Submission received: 17 December 2022 / Revised: 30 January 2023 / Accepted: 31 January 2023 / Published: 3 February 2023
(This article belongs to the Special Issue Functional Genomics and Molecular Breeding of Crops)
Browse Figures
Versions Notes

Abstract

:
Improving the density tolerance and planting density has great importance for increasing maize production. The key to promoting high density planting is breeding maize with a compact canopy architecture, which is mainly influenced by the angles of the leaves and tassel branches above the ear. It is still unclear whether the leaf angles of different stem nodes and tassel branches are controlled by similar genetic regulatory mechanisms, which limits the ability to breed for density-tolerant maize. Here, we developed a population with 571 double haploid lines derived from inbred lines, PHBA6 and Chang7-2, showing significant differences in canopy architecture. Phenotypic and QTL analyses revealed that the genetic regulation mechanism was largely similar for closely adjacent leaves above the ears. In contrast, the regulation mechanisms specifying the angles of distant leaves and the angles of leaves vs. tassel branches are largely different. The liguless1 gene was identified as a candidate gene for QTLs co-regulating the angles of different leaves and the tassel branch, consistent with its known roles in regulating plant architecture. Our findings can be used to develop strategies for the improvement of leaf and tassel architecture through the introduction of trait-specific or pleiotropic genes, thus benefiting the breeding of maize with increased density tolerance in the future.

1. Introduction

Maize is one of the most important crops, serving as a source of food, feed and industrial materials. Maintaining a sufficient supply of maize is vital for ensuring food security worldwide. Research has shown that during the history of maize breeding, increasing planting density and density tolerance has been the most important technical measure to improve maize yield. For example, Duvick conducted comprehensive studies on the hybrids released in the United States from 1934 to 2004 and found that the yield per plant and hybrid heterosis did not largely increase during the process of increasing the yield per unit area of American corn, while the planting density and density tolerance continued to increase [1,2,3]. Mansfield et al. also found that the increase in corn yield in the United States from 1930 to 2010 was strongly correlated with the planting density and variety density tolerance [4]. These observations demonstrate that increasing planting density and cultivating varieties with density tolerance are important for increasing maize production.
A key measure to increase density tolerance in maize is to breed plants with a compact architecture, which is conducive to increasing ventilation and light transmission and reducing the competition between plants. For example, introgressing the favorable allele of the leaf angle-regulating gene Upright Plant Architecture2 into modern hybrids was shown to significantly enhance maize yields under high-density conditions [5]. It has been reported that the most important requirement for maize ideotype is the compact plant configuration above the ear [6]. The three leaves around the upper-most ear (the first leaf above uppermost ear, the leaf of the uppermost ear and the first leaf below the uppermost ear) are vital to the formation of maize yield. The compact upper ear configuration is vital for ventilation, light transmission, the interception of light energy, and maintenance of effective photosynthetic efficiency. During the process of modern maize breeding, the leaves above the ear became more and more compact [7,8].
The canopy of corn plants is generally composed of tassels and three to nine leaves above the ear [8]. The angles of leaves and tassels are the keys to determining the light transmittance and canopy structure [9,10]. Therefore, it is very important to analyze the genetic basis of regulation of these angles and to shape a compact canopy structure for cultivating density-tolerant maize varieties. However, it is still unclear whether the leaf angles of different nodes and tassel branch angles are regulated differently, which limits our ability to breed density-tolerant plants.
In this study, we investigated the angles of the first (LA1), second (LA2), third (LA3), and flag leaves (FLA) above the top-most ear, and the angle of the first tassel branch (TBA) using a double haploid (DH) population derived from two inbred lines, PHBA6 and Chang7-2, with significant differences in leaf angle and tassel branch angle. QTL analysis of these traits was carried out in the DH population using a high-density linkage map. We found that the genetic regulatory basis was largely similar between adjacent leaves but relatively different between leaves and tassel branches. The liguless1 (LG1) gene was identified as a candidate gene for QTLs controlling leaf and tassel branch angles, and it may play an important role in the regulation of these traits. These results are important for guiding the targeted or universal improvement of specific leaf or tassel configurations in the future.

2. Results

2.1. Phenotyping of a DH Population Derived from PHBA6 × Chang7-2

To study the genetic basis for regulation of lateral angles above the ear, we selected two inbred lines, PHBA6 and Chang7-2, with significant differences in leaf and tassel angles and constructed a DH population composed of 571 lines. Compared with Chang7-2, PHBA6 had a larger leaf and tassel branch angles (Figure 1).
We measured the LA1, LA2, LA3, and FLA above the top-most ear and the TBA of PHBA6, Chang7-2 and 571 DH lines in six environments and calculated the best linear unbiased prediction (BLUP) values (Supplementary Table S1). The mean of LA1, LA2, LA3, FLA and TBA for PHBA6 are 28.6, 29.0, 31.6, 54.7 and 53.9, respectively. The means of LA1, LA2, LA3, FLA and TBA for Chang7-2 are 20.9, 19.4, 21.0, 22.7 and 21.5, respectively. The means of LA1, LA2, LA3, FLA and TBA for the progenies of 571 DH lines are 32.0, 31.0, 32.5, 35.2 and 32.3, respectively. The LA1 of 571 DH lines varied from 19.6 to 46.1 degrees. The LA2 of 571 DH lines varied from 18.2 to 47.3 degrees. The LA3 of 571 DH lines varied from 18.6 to 49.7 degrees. The FLA of 571 DH lines varied from 18.6 to 56.0 degrees. The TBA of 571 DH lines varied from 10.8 to 59.6 degrees. All of these five traits showed transgressive segregation. Among them, the FLA and TBA showed wider variability. We also found that LA1, LA2, and LA3 had higher broad-sense heritabilities (H2 ≥ 93.39%) than TBA (H2 = 84.53%) and FLA (H2 = 70.96%) (Figure 2). Almost all the lateral angle-related traits showed a continuous normal distribution, indicating that these traits are controlled by quantitative trait loci (QTLs), which is similar to the conclusions reported previously [11,12,13].
Correlation analysis of BLUP values for different traits showed a very strong correlation between the angles of adjacent leaves (LA1, LA2, and LA3, r ≥ 0.933). The correlation between angles of leaves at farther distances apart were still significant; however, the correlation gradually decreased with the distance between the position of different leaves. For example, the correlation coefficient between FLA and LA3 was 0.547, whereas that between FLA and LA1 was only 0.463. The TBA and FLA, which are adjacent to each other, were moderately correlated, whereas the correlations of TBA with LA1, LA2, and LA3 were weak (Figure 2). The significant correlation among these traits indicated that there may be some shared genetic regulators of these angles, while the difference in correlation also indicated that some regulators may be unique or may differentially regulate these angles [14].

2.2. High-Density Linkage Map Construction

We next analyzed the genetic basis of lateral angle regulation using the massively parallel 3’ end RNA sequencing (MP3RNA-seq) strategy [15] to genotype the 571 DH lines. After SNP calling and strict filtering (see Materials and Methods), we retained 436 DH lines (135 lines with missing genotype data were excluded from the analysis) and 26,917 non-redundant SNPs for high density linkage map construction. The total length of the genetic map was 833.94 cM, and the average genetic distance and physical distance between the markers were 0.033 cM and 0.082 Mb, respectively (Table 1), indicating the high resolution of our map.
To verify the accuracy and validity of our map, we first performed QTL analysis of cob color with simple genetic structure in the DH population. The difference in cob color between the parents of the DH population was remarkable, so the cob color traits of 311 lines in the DH population were successfully scored. The ratio of red cobs to white cobs was 141:170, which very close to the 1:1 ratio expected if cob color is controlled by a single gene (Chi-square test, p = 0.100) [16]. QTL analysis revealed a very significant QTL locus on chromosome 1 between 47.589118 and 48.956707 Mb (peak at 48.336939 Mb, LOD = 2308.5), which is located within the tandem repeat region containing the known cob color gene Pericarp color1 (P1) [17] (Figure 3). The precise mapping of the P1 gene demonstrated the quality of our map.

2.3. QTL Analysis of Lateral Angles in the DH Population

To clarify the genetic basis for control of lateral angles above the top-most ear, QTL mapping was performed for the five lateral angle traits in the DH population using the high-density linkage map (Table 1). A total of 42 lateral angle-related QTLs were identified (Table 2), with 12, 8, 9, 6, and 7 QTLs identified for LA1, LA2, LA3, FLA, and TBA, respectively. The additive effects of these QTLs varied from 0.82° to 7.57° with an average of 3.03°. For most QTLs, the allele decreasing the angle was contributed by Chang7-2, which indicates that Chang7-2 may be an important donor for the improvement of compact plant architecture above the ear. The phenotypic variation explained by these QTLs ranged from 0.85% to 21.8%, with an average of 6.42%. Most of the lateral angle QTLs (35/42) were minor effect QTLs, which was consistent with the findings from previous genetic analyses of leaf angle [8,11,12,13] and also reflected the complexity of the regulation of the angle traits. The detected QTLs explained 64.44%, 57.15%, 52.57%, 44.33%, and 50.90% of the phenotypic variation for LA1, LA2, LA3, FLA, and TBA, respectively. These results were consistent with the findings from phenotypic analysis that LA1 has the highest heritability and FLA has the lowest (Figure 2).
We next analyzed the distribution of the 42 QTLs and the overlap between them. QTLs were found on all chromosomes except for chromosomes 6 and 7. There were seven overlapping QTLs among LA1, LA2, and LA3 (Table 3), and these QTLs explained a large portion of the phenotypic variation of these three traits (34.21–48.32%), indicating that the mechanisms regulating these different lateral angles are very similar. Interestingly, we found that the overlapping QTLs between adjacent leaf angles (LA1 vs. LA2, LA2 vs. LA3) could explain more phenotypic variation than those between more distant angles (LA1 vs. LA3). There were no overlapping QTLs between the most distant leaf angles (FLA vs. LA3). For FLA vs. LA1 and FLA vs. LA2, there was only one overlapping QTL each, and the proportion of explained phenotypic variation was small (12.11–21.08%). These results implied that the regulation mechanisms for the angles of distant leaves may be largely different. We further investigated the overlapping QTLs between the tassel and leaf angles. The result indicated that there was only one overlapping QTL between LA1, LA2, FLA and TBA, respectively. No overlapping QTL was detected between TBA and LA3. These results indicated that although there may be some similar factors regulating the angle of tassel and leaf, their genetic basis was generally largely different.

2.4. Candidate Gene Analysis

There have been many studies on the genetic basis of maize leaf angle and tassel branch angle, and genes affecting the lateral angles have been reported. When searching our QTL intervals, we found several previously reported functional genes or homologs of these genes that may be candidate genes (Figure 4). The same intervals on chromosome 10 for qLA1_10, qLA2_10, and qLA3_10 contained a YABBY-like transcription factor gene, ZmYAB14 (Zm00001d025944). Previous reports have shown that the YABBY family of transcriptional regulators regulate the angle and architecture of maize leaves [18]. Therefore, ZmYAB14 is a likely candidate gene for these QTLs. qTBA_1a, which is located at the beginning of chromosome 1 contains the important maize brassinosteroid (BR) synthesis gene ZmDWF4 (181 kb from the QTL peak). ZmDWF4 encodes a cytochrome P450, steroid 22-alpha-hydroxylase, that catalyzes C-22 hydroxylation in the BR biosynthesis pathway [19]. BR plays an important role in the regulation of plant lateral angles. Thus, ZmDWF4 may be a candidate gene for qTBA_1a. Finally, the key lateral boundary definition gene LG1, which encodes a square promoter binding transcriptional regulator [20], was located in the QTL confidence intervals of qLA1_ 2a, qLA2_2a, qFLA_2a, and qTBA_2A, very close to the peaks (43, 74, 45, and 573 kb, respectively). This, combined with the known role of LG1 in regulating LA and TBA [21,22], makes LG1 a likely candidate gene for these QTLs controlling LA and TBA.

3. Discussion

3.1. A Large-Scale DH Line Population Improving the Accuracy of QTL Mapping with BLUPs

Previous studies have mapped many QTLs for lateral angles in maize using F2:3 populations, recombinant inbred line (RIL) populations or a few other segregating populations [23,24,25,26,27]. Compared with RIL populations, the DH approach quickly converts heterozygous materials to completely homozygous lines, that greatly reduces the time to build a stable mapping population [28]. There is no dominant effect of genes in the DH lines; thus, the additive and epistatic effects of quantitative trait genes can be studied more accurately. Moreover, the DH populations can be used to eliminate the influence of competition among individual plants and reduce the environmental test error compared with the F2:3 populations and the other early segregating populations. The cost of sequencing continues to decrease along with the development of high-throughput sequencing technology. Meanwhile the system for the rapid creation of large-scale DH lines has been established gradually [29,30,31]. Therefore, DH populations are ideal for mapping QTLs and identifying candidate genes [32,33].
BLUPs could minimize the phenotyping errors and best estimate the genetic effect influenced for a trait. BLUPs, instead of values of individual environments, were widely used in QTL mapping or GWAS study [13,34,35,36]. Thus, analysis by BLUP is more suitable for our purpose, revealing the conserved and differential genetic regulation of maize leaf and tassel branch angles. In fact, we also performed QTL analysis for individual environments. We summarized the QTL results for individual environments as following figures (Figure 5). As shown in the figures, most of the QTLs detected by BLUP were stably detected by individual environments. In this study, we used the BLUP values of 571 DH lines with 26,917 SNPs to identify 42 QTLs for lateral angle traits. The phenotypic variation explained by these QTLs ranged from 0.85% to 21.8%, with an average of 6.42%. The confidence intervals of some of these QTLs were less than 1 Mb in length. This demonstrates the mapping accuracy that can be obtained when using large-scale maize DH line populations.

3.2. Comparison with Previous QTL Studies

We compared the published QTL/genes for lateral angle with those identified in this study. The most prominent region, qLA1_2a, qLA2_2a and qFLA_2a for LA were found to be located nearby the known classic LG1 gene on chromosome 2. The LG1 gene encodes a squamosa promoter binding (SPB) translational regulator, which plays a key role in the process of ligule and auricle formation [20,37]. Tian et al. detected a significant QTL for leaf angle in the 2-Mb region at the nearby LG1 on chromosome 2 in the maize nested association mapping (NAM) population [13]. Ku et al. used a meta-QTL analysis to find an mQTL in the region between umc1165 and bnlg1297, which overlapped with the location of LG1 in F2:3 families of Yu823 and Yu87-1 [12]. Li et al. identified a QTL region (qLA2a) that ranged from 3.09 to 4.30 Mb on chromosome 2, which was located nearby to the interval of the LG1, using three RIL populations derived from crossing Huangzaosi with Huobai, Weifeng322, and Lv28 [25]. Ding et al. and Tang et al. detected qLA2-1 and qLA2.1 in the same region, bin 2.01 on chromosome 2, which overlapped with the location of LG1, in a four-way cross population of D276, D72, A188 and Jiao51 and a RIL population derived from B73 and SICAU1212, separately [23,38]. These QTLs that were detected in different genetic backgrounds and environments shared a high congruence, which supported the candidacy of LG1 for qLA1_2a, qLA2_2a and qFLA_2a. In addition to lacking ligules and auricles, lg1 mutants have significantly smaller leaf and tassel branch angles [21,22], which is consistent with this gene being located in QTL intervals for both LA and TBA (qLA1_2a, qLA2_2a, qFLA_2a, and qTBA_2A). However, the leaves of lg1 mutants is lacking a proper ligular region and displays an upright habit of growth; both PHBA6 and Chang7-2 have normal ligules and auricles. We speculate that functional variants may be located in the regulatory region of LG1. This locus, as the only one identified, may regulate almost all lateral angles (LA1, LA2, FLA, TBA) above the ear. In the future, this locus can be used to improve the ear configuration of breeding materials through molecular breeding. Further exploration of the functional natural variation of this locus would be of great benefit to breeding of density tolerant maize in the future. qLA1_10, qLA2_10, and qLA3_10 were in the same interval on chromosome 10 which contained a YABBY-like transcription factor gene, ZmYAB14. The YABBY family of transcriptional regulators regulate the angle and architecture of maize leaves [18]. This QTL region was closely neighboring the QTLs for lateral angles identified on chromosome 10 in the previous studies [13,25,26,39,40]. There was no known gene co-location with another large-effect QTL qLA2_3a and its overlapping QTL qLA3_3a, suggesting that there was an undiscovered gene controlling maize leaf angle.
qTBA_1a, which is located at the beginning of chromosome 1 contains the important maize brassinosteroid (BR) synthesis gene ZmDWF4 (181 kb from the QTL peak). Ku et al. detected the corresponding QTL region for LA (bin 1.02 of chromosome 1) where the DWAF4 gene was located using two different F2:3 populations. Liu et al. detected the ZmDWF4 gene co-locating with one large-effect QTL, qLA1_2 for LA by using high-density SNP markers and a F2:3 population of H082183 × Lv28. Dzievit et al. identified the ZmDWF4 gene in one prominent genomic bin on chromosome 1 for LA across the F2 and F2:3 generation for the B73 and Mo17 populations. Therefore, ZmDWF4 may be a candidate gene for qTBA_1a. ZmDWF4 and encodes a cytochrome P450, steroid 22-alpha-hydroxylase, that catalyzes C-22 hydroxylation in the BR biosynthesis pathway [19]. BR plays an important role in the regulation of plant lateral angles. Previous studies have revealed the effects of plant phytohormones, especially BR, in regulating leaf angles. Zhao et al. expounded that LC2 may regulate leaf angle through a BR-independent pathway and participate in the feedback control of BR signaling, using a rice leaf inclination2 (lc2, three alleles) mutant [41]. Feng et al. confirmed that SLG, a BAHD acyltransferase-like protein gene is involved in BR homeostasis by positively regulating endogenous BR levels to control leaf angle for planting density in rice [42]. Liu et al. found that TaSPL8 might regulate the lamina joint, the tissue connecting the leaf blade and sheath by binding to the promoter of the brassinosteroid biogenesis gene CYP90D2, and activating its expression [43]. We believe that the causal genes for our angle QTLs may also be involved in BR synthesis/signaling, and this needs to be further studied in the future.

4. Materials and Methods

4.1. Plant Materials and Phenotypic Data Collection

For DH lines production, the F1s, derived by crossing of PHBA6 and Chang7-2, were crossed with a maize haploid inducer CAU3 in 2014 in Sanya (The F1s were used as female parent, and CAU3 as male parent). The putative haploid kernels were selected based on the lack of R1-mediated purple anthocyanin in the scutellum of the haploid embryo [29] and planted at the field nursery during the 2015 summer in Beijing. The putative haploids were further screened using the features of shorter stature and smaller biomass in the field. Approximately 1% of the haploid plants were successfully self-crossed by the natural doubling method.
The DH population used here includes 571 lines, and was derived by crossing PHBA6 and Chang7-2. The DH lines and their parents were phenotyped in six environments (each location in an individual year was considered an environment), namely the Haidian (40.14° N, 116.19° E) district in Beijing in 2019 and 2020, and Shunyi (40.23° N, 116.56° E) district in Beijing in 2021 and the Shenbei (42.03° N, 123.59° E) district in Shenyang in 2019, 2020 and 2021. A randomized complete block design was used with two replications in each environment. Trials were performed with one-row plots: row length of 1 m; five plants per plot; row spacing of 0.5 m for Haidian and Shunyi districts in Beijing (80,000 plants per hectare) and row spacing of 0.6 m in the Shenbei district in Shenyang (66,666 plants per hectare). PHBA6 and Chang7-2 were used as controls, and they were planted in every 20 DH lines, alternately. All the materials were grown under natural field conditions in each environment and field management was in accordance with local practices. LA1, LA2, LA3, and FLA were measured on three plants per plot, while cob color and TBA were measured on three to five plants per plot. LA1, LA2, and LA3 measurements were collected at Haidian, Shunyi, and Shenbei. Cob color, FLA, and TBA were collected at Shunyi and Shenbei only. Cob color was divided into red and white. LA1, LA2, and LA3 were recorded as the angle between the midrib and upper stem of the first, second, third leaves above top-most ear node, respectively. FLA was recorded as the angle between midrib and upper stem of the flag leaf. TBA was recorded as the angle between the first primary branch and the central spike of tassel. BLUP values were calculated for each phenotype across different environments using the lme4 package [17] in R, and these values were used for subsequent analysis. The heritability (H2) estimates were calculated by R software as reported previously [17].

4.2. RNA Isolation, MP3RNA-Seq, and SNP Calling

Leaf tissues from three seedlings per DH line were bulked for total RNA extraction, which was used for construction and sequencing of cDNA libraries using the MP3RNA-seq method [15]. The DH lines for which library construction was unsuccessful, or little sequencing data were obtained (135 lines in total), were excluded from further analysis.
SNP calling was conducted using the software samtools (v0.1.16) and bcftools (v0.1.16). Only uniformly mapped reads and non-duplicated reads were used after filtering [44]. Then, the MP3RNA-seq data for PHBA6 and Chang 7-2 were analyzed for SNP identification (location: refer to the B73 genome of version 4). A total of 35,836 high-quality SNPs were detected between PHBA6 and Chang 7-2 with a read depth ≥ 5. In addition, the SNPs with partial segregation greater than 2/1 (Chi square test, p < 1.0 × 10−7) or a heterozygosity rate greater than 15% were discarded, and the DH lines with a heterozygosity rate greater than 15% were also eliminated. Ultimately, 26,917 SNPs in 571 DH lines were retained for further genetic map construction.

4.3. Genetic Map Construction

Genotype calling and recombination breakpoint determination for each DH were conducted using a sliding window approach [45,46] with minor modifications.
(1) In each 15-SNP window, the DH genotype was defined by the ratio of alleles from PHBA6 and Chang7-2:
The window was called homozygous if > 11/15 of the sites in the window were alleles from either of the parents or heterozygous otherwise. Using the sliding window method, we found that recombination breakpoints were detectable as a region of several heterozygous SNPs that did not exceed more than six continuous windows. Therefore, we set the heterozygous regions spanning less than seven uninterrupted windows as breakpoints and divided them into two at the midpoint. Then adjacent windows with the same genotype were merged together as a block.
(2) Blocks with fewer than five sequenced SNPs or a physical length less than 300 kb were set as missing to avoid calling false double crossover events.
(3) Adjacent windows and successive small blocks with frequently transient genotypes were merged into a larger heterozygous block, and heterozygous blocks with fewer than 15 SNPs or a physical length less than 1 Mb were set as missing to avoid false estimation.

4.4. QTL Analysis

The QTL analyses of lateral angles were conducted using the composite interval mapping (cim) method in the R package R/qtl as reported [46]. The LOD threshold was defined by 1000 permutations at a significance level of p < 0.05. The 1.5 LOD-drop method was used for defining the QTL confidence interval. A linear QTL model was used for evaluation of QTL effect size.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/plants12030680/s1, Supplementary Table S1. The BLUP values and means in each environment for LA1, LA2, LA3, FLA and TBA of the DH lines.

Author Contributions

Y.Z. investigation, data processing and writing; B.S., investigation, data collection; Y.G. and B.W., formal analysis and editing; C.X., H.Z. (Hongyu Zhu) and L.E., funding managed the project, data processing; J.L. and W.S. review and editing; H.Z. (Haiming Zhao) managed the project and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Program of China (2018YFA0901003), the National Natural Science Foundation of China (32272143, 31971959 and 32022065), the Special Project of Agricultural Biological Breeding (2022ZD04006), the Henan Provincial Joint Science and Technology R&D Project (222301420103) and the Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City (2021JJLH0087).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data needed to evaluate the results in the paper are present in the paper or the supplementary materials.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Phenotypic differences between the parental inbred lines PHBA6 and Chang7-2. (A) Comparison of plant morphology and angles of the first leaves (LA1), second leaves (LA2), third leaves (LA3), and flag leaves (FLA) above the upmost ear and the angles of the first tassel branch (TBA) between PHBA6 and Chang7-2. (B) Bar plot of LA1, LA2, LA3, and FLA above the upmost ear and the TBA of PHBA6 (n = 15) and Chang7-2 (n = 15). p-values of two tailed t-test are shown above each plot.
Figure 1. Phenotypic differences between the parental inbred lines PHBA6 and Chang7-2. (A) Comparison of plant morphology and angles of the first leaves (LA1), second leaves (LA2), third leaves (LA3), and flag leaves (FLA) above the upmost ear and the angles of the first tassel branch (TBA) between PHBA6 and Chang7-2. (B) Bar plot of LA1, LA2, LA3, and FLA above the upmost ear and the TBA of PHBA6 (n = 15) and Chang7-2 (n = 15). p-values of two tailed t-test are shown above each plot.
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Figure 2. The distributions of leaf and tassel angles and correlation between traits in the 571 DH lines. (A–E) The phenotypic distributions and heritabilities of LA1 (A), LA2 (B), LA3 (C), FLA (D), and TBA (E) of the 571 DH lines. H2, heritability. (F) Correlation among LA1, LA2, LA3, FLA, and TBA of the 571 DH lines. The correlation coefficients are shown above the diagonal and scatterplots are shown below the datapoints.
Figure 2. The distributions of leaf and tassel angles and correlation between traits in the 571 DH lines. (A–E) The phenotypic distributions and heritabilities of LA1 (A), LA2 (B), LA3 (C), FLA (D), and TBA (E) of the 571 DH lines. H2, heritability. (F) Correlation among LA1, LA2, LA3, FLA, and TBA of the 571 DH lines. The correlation coefficients are shown above the diagonal and scatterplots are shown below the datapoints.
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Plants 12 00680 g003 550
Figure 3. A genetic map of the DH population and QTL mapping of the cob color gene Pericarp1 (P1). (A) Graphical representation of the genotypes of 571 DHs. Red, PHBA6 genotype; blue, Chang7-2 genotype. (B) Image showing the cob colors of PHBA6 (red) and Chang7-2 (white). (C) Results of QTL mapping of cob color in the DH population. The QTL peak corresponding to the P1 gene is indicated. The dotted red line corresponds to the cutoff line.
Figure 3. A genetic map of the DH population and QTL mapping of the cob color gene Pericarp1 (P1). (A) Graphical representation of the genotypes of 571 DHs. Red, PHBA6 genotype; blue, Chang7-2 genotype. (B) Image showing the cob colors of PHBA6 (red) and Chang7-2 (white). (C) Results of QTL mapping of cob color in the DH population. The QTL peak corresponding to the P1 gene is indicated. The dotted red line corresponds to the cutoff line.
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Figure 4. Results of QTL mapping of LA1, LA2, LA3, FLA, and TBA. The y-axis represents -log10 transformed p-values from t-testing; the red dotted line represents the cutoff. The positions of the candidate genes LG1, ZmDWF4, and ZmYAB14 are indicated by vertical bold lines. LA1_BLUP_cim, LA2_BLUP_cim, LA3_BLUP_cim, FLA_BLUP_cim and TBA_BLUP_cim represents the QTL mapping that was conducted with the BLUP values calculated for LA1, LA2, LA3, FLA, and TBA across different environments separately and the composite interval mapping was used mapping each trait.
Figure 4. Results of QTL mapping of LA1, LA2, LA3, FLA, and TBA. The y-axis represents -log10 transformed p-values from t-testing; the red dotted line represents the cutoff. The positions of the candidate genes LG1, ZmDWF4, and ZmYAB14 are indicated by vertical bold lines. LA1_BLUP_cim, LA2_BLUP_cim, LA3_BLUP_cim, FLA_BLUP_cim and TBA_BLUP_cim represents the QTL mapping that was conducted with the BLUP values calculated for LA1, LA2, LA3, FLA, and TBA across different environments separately and the composite interval mapping was used mapping each trait.
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Figure 5. Comparisons between QTLs for lateral angles of BLUPs and individual environments. (A–E) The results of QTL mapping for LA1 (A), LA2 (B), LA3 (C), FLA (D) and TBA (E) of BLUP and individual environments, separately. The y-axis represents -log10 transformed p-values from t-testing; the red dotted line represents the cutoff. LA1_BLUP_cim, LA2_BLUP_cim, LA3_BLUP_cim, FLA_BLUP_cim and TBA_BLUP_cim represents the QTL mapping that was conducted with the BLUP values calculated for LA1, LA2, LA3, FLA, and TBA across different environments separately and the composite interval mapping was used mapping each trait. BJ2019, BJ2020, BJ2021, SY2019, SY2020 and SY2021 represents the environment of the Haidian district in Beijing in 2019 and 2020, and Shunyi district in Beijing in 2021 and the Shenbei district in Shenyang in 2019, 2020 and 2021.
Figure 5. Comparisons between QTLs for lateral angles of BLUPs and individual environments. (A–E) The results of QTL mapping for LA1 (A), LA2 (B), LA3 (C), FLA (D) and TBA (E) of BLUP and individual environments, separately. The y-axis represents -log10 transformed p-values from t-testing; the red dotted line represents the cutoff. LA1_BLUP_cim, LA2_BLUP_cim, LA3_BLUP_cim, FLA_BLUP_cim and TBA_BLUP_cim represents the QTL mapping that was conducted with the BLUP values calculated for LA1, LA2, LA3, FLA, and TBA across different environments separately and the composite interval mapping was used mapping each trait. BJ2019, BJ2020, BJ2021, SY2019, SY2020 and SY2021 represents the environment of the Haidian district in Beijing in 2019 and 2020, and Shunyi district in Beijing in 2021 and the Shenbei district in Shenyang in 2019, 2020 and 2021.
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Table
Table 1. Profile of the linkage map.
Table 1. Profile of the linkage map.
ChrPhysical Distance (Mb)Genetic Distance (cM)
Number aMean bMax cLength dMean bMax c
145290.06815.603114.390.0251.174
232760.07510.891110.280.0344.788
329540.0808.11186.100.0291.782
426610.09317.56874.450.0282.532
537300.0604.08189.080.0241.782
618640.09312.17371.680.0382.660
715000.12113.77985.960.0578.851
826440.0685.86374.290.0281.659
919860.08013.30867.940.0342.030
1017730.0857.15959.770.0332.532
Total269170.08217.568833.940.0338.851
a The number of SNPs. b The average physical (Mb) or genetic (cM) distance between adjacent SNPs. c The maximum physical (Mb) or genetic (cM) distance between adjacent SNPs. d The total genetic distance of the chromosome.
Table
Table 2. Summary of the QTLs for lateral angles identified in this study.
Table 2. Summary of the QTLs for lateral angles identified in this study.
QTLOverlapping QTLsTraitChrPeak MarkerPeak
Position (cM)		Peak
Position (Mb)		LODLeft
Position (cM)		Right
Position (cM)		Left
Position (Mb)		Right
Position (Mb)		Var (%) aAdd b
qLA1_1aqLA2_1a, qLA3_1aLA11M1_116632.79548.3374.8330.01737.88143.58159.4215.101.58
qLA1_1bqLA2_1b, qLA3_1bLA11M1_243561.082198.27215.1359.68663.862197.395201.73811.152.88
qLA1_2aqLA2_2a, qFLA_2a, qTBA_2aLA12M2_2338.0214.27814.325.8309.1813.5504.64312.113.45
qLA1_2bqLA2_2b, qLA3_2bLA12M2_119441.98256.79824.2540.35843.48249.33663.03312.313.85
qLA1_2cqLA3_2cLA12M2_336081.294239.2463.1577.13389.575232.638240.6082.491.14
qLA1_3a LA13M3_103529.584112.4279.7427.16830.50428.624124.1328.57−2.63
qLA1_3bqLA2_3b, qLA3_3bLA13M3_274571.527221.4333.3856.82883.324199.396232.9011.14−0.82
qLA1_8a LA18M8_924.0332.6743.420.32910.5790.3295.5671.201.35
qLA1_8bqLA2_8, qLA3_8LA18M8_240858.637172.5273.3655.85569.779170.455177.7122.961.71
qLA1_9a LA19M9_20416.82312.8913.539.51826.8438.65523.3971.98−0.99
qLA1_9b LA19M9_114036.426122.5244.1733.41640.704116.939136.7561.89−1.28
qLA1_10qLA2_10, qLA3_10LA110M10_112233.420125.5735.9430.77334.804110.756129.6773.55−1.96
qLA2_1aqLA1_1a, qLA3_1aLA21M1_117133.14148.9574.6529.21137.88140.59959.4216.331.82
qLA2_1bqLA1_1b, qLA3_1bLA21M1_241959.108197.05011.0354.25261.888184.642199.7187.802.73
qLA2_2aqLA1_2a, qFLA_2a, qTBA_2aLA22M2_2247.3284.1577.196.4078.6013.7634.55214.184.26
qLA2_2bqLA1_2b, qLA3_2bLA22M2_136643.94292.16814.5741.17445.22750.706103.71712.124.38
qLA2_3aqLA3_3aLA23M3_149935.920160.13011.7734.99936.958157.122162.3709.14−3.20
qLA2_3bqLA1_3b, qLA3_3bLA23M3_276472.450221.9093.3168.29485.290217.463234.0832.16−1.50
qLA2_8qLA1_8b, qLA3_8LA28M8_238957.715171.9124.8655.85566.649170.455176.4342.931.89
qLA2_10qLA1_10, qLA3_10LA210M10_112133.420125.5734.2830.77338.624110.756134.5192.49−1.75
qLA3_1aqLA1_1a, qLA2_1aLA31M1_116632.79548.3373.8129.78738.68642.45762.4665.871.74
qLA3_1bqLA1_1b, qLA2_1bLA31M1_243561.082198.2729.2459.68663.862197.395201.7387.552.77
qLA3_2a LA32M2_1294.5582.8025.910.3855.8301.3343.41911.744.03
qLA3_2bqLA1_2b, qLA2_2bLA32M2_119441.98256.79810.8639.66345.22748.215103.71711.204.06
qLA3_2cqLA1_2cLA32M2_336081.294239.2463.9778.86089.575235.987240.6083.811.83
qLA3_3aqLA2_3aLA33M3_150035.920160.1308.9834.65438.227156.318168.8716.80−2.82
qLA3_3bqLA1_3b, qLA2_3bLA33M3_276472.450221.9093.4366.90684.022215.395233.0551.62−1.43
qLA3_8qLA1_8b, qLA2_8LA38M8_244460.836174.1523.7455.85569.779170.455177.7122.791.90
qLA3_10qLA1_10, qLA2_10LA310M10_119936.421132.1043.4230.77343.697110.756138.7081.36−1.35
qFLA_2aqLA1_2a, qLA2_2a, qTBA_2aFLA2M2_2358.0214.28024.107.3289.1814.1574.64321.085.67
qFLA_2b FLA2M2_211654.368192.0275.1050.75956.454187.006194.2594.352.50
qFLA_4 FLA4M4_258066.584241.6043.7762.65270.067238.274242.8813.112.03
qFLA_5a FLA5M5_305862.087202.9846.0756.66163.813182.116210.0452.653.01
qFLA_5b FLA5M5_374782.921219.98811.5280.14585.962219.017220.8918.75−3.78
qFLA_8 FLA8M8_28114.8809.1915.2612.65919.4387.20712.5154.392.56
qTBA_1a TBA1M1_76422.95830.6133.3818.09130.01719.58643.5810.853.35
qTBA_1b TBA1M1_205249.527160.0889.2648.60754.022117.695182.7058.83−6.86
qTBA_1c TBA1M1_395490.487280.6103.3688.40995.226277.727288.3551.86−3.17
qTBA_2aqLA1_2a, qLA2_2a, qFLA_2aTBA2M2_2499.4114.80812.838.60111.3934.6065.71514.237.57
qTBA_2b TBA2M2_180648.338174.01912.9045.22749.607103.988183.6528.936.48
qTBA_3a TBA3M3_44323.48016.9566.0020.08624.97911.69120.8836.954.91
qTBA_3b TBA3M3_236157.633203.07612.5656.25358.668198.496206.5039.256.70
a Phenotypic variance explained. b Additive effects for detected QTLs.
Table
Table 3. Summary of the overlap between QTLs controlling different angles. Upper right, the number of QTLs overlapping between two traits. Lower left, the percentage of variance explained by overlapping QTLs. The number to the left of the slash is variance explained for the trait in the same row and the number to the right of the slash is for the trait in the same column.
Table 3. Summary of the overlap between QTLs controlling different angles. Upper right, the number of QTLs overlapping between two traits. Lower left, the percentage of variance explained by overlapping QTLs. The number to the left of the slash is variance explained for the trait in the same row and the number to the right of the slash is for the trait in the same column.
LA1LA2LA3FLATBA
LA1NA7711
LA248.02/48.32NA711
LA334.21/38.7037.19/42.97NA00
FLA21.08/12.1121.08/14.180NA1
TBA14.23/12.1114.23/14.18014.23/21.08NA
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Zhu, Y.; Song, B.; Guo, Y.; Wang, B.; Xu, C.; Zhu, H.; E, L.; Lai, J.; Song, W.; Zhao, H. QTL Analysis Reveals Conserved and Differential Genetic Regulation of Maize Lateral Angles above the Ear. Plants 2023, 12, 680. https://0-doi-org.brum.beds.ac.uk/10.3390/plants12030680

AMA Style

Zhu Y, Song B, Guo Y, Wang B, Xu C, Zhu H, E L, Lai J, Song W, Zhao H. QTL Analysis Reveals Conserved and Differential Genetic Regulation of Maize Lateral Angles above the Ear. Plants. 2023; 12(3):680. https://0-doi-org.brum.beds.ac.uk/10.3390/plants12030680

Chicago/Turabian Style

Zhu, Yanbin, Bo Song, Yanling Guo, Baobao Wang, Changcheng Xu, Hongyu Zhu, Lizhu E, Jinsheng Lai, Weibin Song, and Haiming Zhao. 2023. "QTL Analysis Reveals Conserved and Differential Genetic Regulation of Maize Lateral Angles above the Ear" Plants 12, no. 3: 680. https://0-doi-org.brum.beds.ac.uk/10.3390/plants12030680

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