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Association Study for Drought Tolerance of Flint Maize Inbred Lines Using SSR Markers
Plant Breed. Biotech. 2022;10:257-271
Published online December 1, 2022
© 2022 Korean Society of Breeding Science.

Kyu Jin Sa1, Hyeon Park1,2, Zhenyu Fu1, So Jung Jang1,2, Ju-Kyong Lee1,2*

1Department of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Korea
2Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Korea
Corresponding author: Ju-Kyong Lee, jukyonglee@kangwon.ac.kr, Tel: +82-33-250-6415, Fax: +82-33-259-5558
Received October 30, 2022; Revised November 9, 2022; Accepted November 10, 2022.
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Drought tolerance is derived from complex quantitative traits that are associated with different shoot and root morphological characters. This study assessed the genetic and phenotypic variation of 12 maize inbred lines and performed association analysis of 11 drought-related traits using 360 simple sequence repeats (SSRs), detecting 1,604 alleles, with an average of 4.4 alleles per locus. The average values of gene diversity (GD) and polymorphism information content (PIC) were 0.648 and 0.598, respectively. In principal component analysis (PCA), shoot fresh weight (SFW), shoot dry weight (SDW), stem weight (SW), leaf weight (LW), root fresh weight (RFW), root dry weight (RDW), and leaf area (LA) traits contributed greatly to the PCA. Association analysis was performed using a general linear model with a Q-matrix (Q GLM) and a mixed linear model with Q and K-matrices (Q + K MLM). Twelve SSR markers for drought tolerance trait were detected by Q GLM, and all maize inbred lines were clearly divided into two groups in accordance with their drought tolerance. Duplicated significant marker-trait associations (SMTAs) between Q GLM and Q + K MLM identified eight marker-trait associations involving four SSR markers that were associated with the traits of SW, SFW, RFW, and RDW with a significant level of P < 0.05. The umc1175 and umc2092 were associated with SW and SFW; umc1503 was associated with RFW, SFW, and SW; and umc2341 was associated with RDW. The detection of loci associated with drought-related traits in this study may provide better opportunities to improve maize drought tolerance by marker-assisted selection (MAS).
Keywords : Drought tolerance, Association analysis, Genetic diversity, Marker-trait association, SSR marker
INTRODUCTION

Maize is one of the most important agricultural and economic crops, and it is one of the major sources of human food and livestock feed. Among cereal crops, global production is highest for maize, followed by wheat and rice, while maize ranked second after wheat in terms of harvested area (FAOSTAT 2020). With the global expan-sion of maize-harvested areas, world maize production and yields have been increasing. World production, area harvested, and yield for maize recorded 1162.4 million tons, 202.0 million ha, and 5.8 t/ha, respectively, in 2020 (FAOSTAT 2020). Maize can be divided into several types based on the starch composition of the kernel’s endosperm, such as normal (including dent and flint), waxy, pop, and sweet. Especially, normal maize is widely cultivated and mainly used as food and feed in the world. As the world population is increasing, the scientific community must use all available ways to help farmers meet the ever-increasing demand for food, forage, and other resources. Drought is the primary abiotic stress affecting crop production and harvested areas worldwide because of water limitations. Moreover, maize is more sensitive to drought stress than other crops, such as winter wheat (Webber et al. 2018). Drought stress in maize, especially during the vegetative growth stage, can lead to a decreased growth rate, the extension of the vegetative growth stage, and depth of roots formation in soil (Ao et al. 2020). The seedling stage of maize is especially sensitive to environmental stress such as drought. Although this stage requires less water than the later vegetative and reproductive stages, drought stress will have a greater effect at the early growth stages compared with flowering and anthesis-silking interval (Maiti et al. 1996; Cao and Wj 2004; Bell 2017). It was estimated that there is a 15-20% decrease in maize production yearly because of drought stress and that these losses were expected to increase further (Chen et al. 2012). In consi-deration of continuous climate change and more frequent occurrences of drought, genetic improvements for maize have focused on enhancing drought tolerance (Campos et al. 2004; Lopes et al. 2011). Therefore, the use of drought- tolerant maize inbred lines and cultivars is one of the best strategies for reducing water deficiency in the crop (Tani et al. 2019). However, the degree of tolerance for drought stress varies for growth stages and conditions, variety or accession, and agroecological region (Toscano et al. 2019).

Drought tolerance is derived from complex quantitative traits that are associated with the different shoot and root morphological characters (Yadav and Sharma 2016). The traditional breeding method depends on the phenotypic selection in the field, which is time-consuming and laborious for accurate evaluation and development of a new maize cultivar (Duvick et al. 2004). Such a phenotypic evaluation and selection for breeding programs may be also inaccurate because of environmental factors. However, polymerase chain reaction (PCR)-based molecular markers are not influenced by environmental factors and can be used to detect more accurately genetic diversity and population structure among breeding materials and to predict hybrid performance and heterosis (Kashiani et al. 2012; Solomon et al. 2012). Marker-assisted selection (MAS) using molecular markers allows breeders to select target phenotypes based on genotypes and environment interactions for genetic improvement and selection of crops (Zhang et al. 2011). To utilize MAS, it is necessary to identify the molecular markers and genetic regions associated with target traits (Yu et al. 2005). Therefore, in order to perform efficient MAS studies in many crops, high-density QTL mapping populations with target trait characteristics are required. However, since it takes a lot of time and effort to foster such a QTL mapping population, breeding materials and research methods that can replace it are required. Recently, association mapping methods have been usefully used to prove association markers between markers and traits. In particularly, association mapping analysis enables the identification of marker-trait associ-ations (MTAs) for MAS that have various advantages such as reduction of experimental time and cost compared with quantitative trait loci (QTL) mapping (Flint-Garcia et al. 2005; Yu and Buckler 2006). In addition, association mapping offers three advantages, increased mapping resolution, reduced research time, and greater allele num-bers (Yu and Buckler 2006). Therefore, association mapping has continued to gain favorability in genetic research because of advances in high throughput genomic technolo-gies and superior alleles (Thhornsberry et al. 2001). Recently, molecular markers have been widely utilized for QTL mapping and association studies and MAS for crop breeding and genetic research (Mohan et al. 1997). In particularly, SSR markers provide valuable information about genetic diversity, genetic relationships, and popula-tion structure in crop germplasms because of being highly polymorphic, generally codominant, reproducible, and broadly distributed throughout the plant genome (Powell et al. 1996; Park et al. 2009). These SSR markers has been successfully applied for crop characterization and studies of genetic diversity and desired gene association analysis (Kalivas et al. 2011).

Molecular markers associated with drought tolerance in maize will provide insights for selecting inbred lines and cultivars, which will help in maize breeding programs for enhancing yield and productivity as well as drought tolerance. Thus, this study performed an association analy-sis of 360 SSR markers and 11 traits associated with drought tolerance among 12 drought-tolerant and suscep-tible flint inbred lines, which were selected from our previous study by using morphological characters (Adhikari et al. 2019). The objective of our study is not only to research the genetic diversity and population structure of flint maize inbred lines by SSR molecular markers, but it is also to confirm molecular markers related to drought-tolerant traits using association analysis. The results of this study are expected to provide useful information for future maize breeding programs for drought-tolerant lines.

MATERIALS AND METHODS

Plant materials and morphological analysis

The 12 flint maize inbred lines used in this study were divided into two groups, drought-tolerant and susceptible groups, which were selected from our previous study using ten morphological traits (Adhikari et al. 2019) (Table 1). Among these inbred lines, six inbred lines (FLD1, FLD13, FLD16, FLD18, FLD29, FLD31) were drought-tolerant inbred lines, and the other six inbred lines (FLD12, FLD23, FLD24, FLD33, FLD35, FLD37) were susceptible to drought. For drought-tolerance traits, these 12 inbred lines were scored as susceptible (1) or tolerant (2) based on our previous study. For association analysis, morphological traits from Adhikari et al. (2019) were used to generate the difference condition. Previous study was performed that plants of thirty eight inbred lines were grown in two different conditions as a control condition (well-watered condition) and drought-stressed condition. The control and drought-stressed plants were measured for plant growth attributes, such as plant height (PH), leaf area (LA), leaf weight (LW), stem weight (SW), shoot fresh weight (SFW), root fresh weight (RFW), root length (RL), total chlorophyll content (TCC), shoot dry weight (SDW), root dry weight (RDW) (Adhikari et al. 2019, Table 2).

Table 1 . List of maize inbred lines used for the study.

Entry No.Drought tolerance*Accession nameSourcePedigree
FLD01S00hf1Eongdan1492009-2-2-4-4-2-1-1
FLD13Shc2NK487HF47
FLD16Shc6UnknownHF51
FLD18SHF1Unknown-
FLD29S07S8004IP14499IP144-1-T4-1-1-1-1-1-1
FLD31S07S80111P16199IP161-1-T1-1-1-1-2-2-1
FLD12Thc5Ho-5HF46
FLD23TKS118Unknown-
FLD24TSIM6Maysin collection-
FLD33T06S8001ISU pop T-C 8644-27/ISU POP 597IG5004-2-1-1-2-1-1-3-1
FLD35T06S8013ISU INB. 1368/(B87/B73-12)B#5060-2-1-1-T2-1-1-1-1
FLD37T06S8030EV43-SR/9B-55159-4-2-3-T2-1-1-1-1

*T: drought Tolerant, S: drought Susceptible lines.


Table 2 . Means and standard deviations of eleven traits for drought-tolerant and susceptible groups.

Tolerance**PH (cm)LA (cm2)LW (g)SW (g)SFW (g)RFW (g)RL (cm)TCCSDW (g)RDW (g)
FLD1S‒8.3‒34.0‒1.2‒2.6‒3.8‒2.1‒2.6‒2.6‒0.7‒0.3
FLD13S‒5.8‒16.8‒0.4‒1.6‒2.0‒0.6‒16.9‒6.5‒0.5‒0.2
FLD16S‒9.5‒13.8‒0.9‒1.2‒2.2‒1.1‒1.4‒6.1‒0.6‒0.1
FLD18S‒11.4‒32.4‒1.5‒3.5‒5.1‒1.6‒3.8‒4.0‒0.8‒0.1
FLD29S‒15.7‒30.9‒0.8‒0.5‒1.3‒0.2‒7.4‒7.7‒0.2‒0.1
FLD31S‒15.8‒10.1‒1.0‒0.5‒1.5‒0.5‒8.1‒4.4‒0.4‒0.1
Mean*‒11.1±4.1‒23.0±10.6‒1.0±0.4‒1.7±1.2‒2.6±1.5‒1.0±0.7‒6.7±5.7‒5.2±1.9‒0.5±0.2‒0.2±0.1
FLD12T‒6.4‒1.8‒0.2‒0.7‒0.9‒0.7‒5.9‒7.0‒0.20
FLD23T‒3.8‒0.3‒0.4‒0.2‒0.6‒0.2‒1.6‒8.3‒0.20
FLD24T‒3.6‒16.7‒0.5‒0.6‒1.1‒0.5‒1.4‒8.6‒0.20
FLD33T‒7.2‒17.0‒0.2‒0.1‒0.3‒0.4‒5.8‒3.70‒0.1
FLD35T‒8.9‒3.5‒0.5‒0.9‒1.4‒0.3‒0.8‒5.1‒0.1‒0.1
FLD37T‒1.5‒6.6‒0.1‒0.1‒0.2‒0.4‒0.9‒3.5‒0.10
Mean*‒5.2±2.7‒7.6±7.4‒0.3±0.2‒0.4±0.4‒0.7±0.5‒0.4±0.2‒2.7±2.4‒6.0±2.2‒0.1±0.10.0±0.0

**S: susceptible, T: tolerant.

*Average values for each group are expressed as mean ± standard deviation.

PH: plant height, LA: leaf area, LW: leaf weight, SW: stem weight, SFW: shoot fresh weight, RFW: root fresh weight, RL: root length, TCC: total chlorophyll content, SDW: shoot dry weight, RDW: root dry weight.



DNA extraction and SSR amplification

Genomic DNA in young leaves was obtained using the Dellaporta et al. (1983) method with minor modifications. A total of 360 SSR markers, distributed across the ten maize chromosomes (average 36 loci per chromosome), were used for the analysis of genetic variation, population structure, and the association between markers and traits in the 12 flint maize inbred lines (Table 2). Information on SSR markers, such as chromosome location and sequences of forward and reverse primer, were derived from MaizeGDB (http://www.maizegdb.org/).

An SSRs amplification test was carried out using an EX Taq PCR kit (Takara, Ohtsu, Japan). For PCR of the SSRs loci, a total volume of 20 mL of the product conducted 20 ng of genomic DNA, 1 × EX Taq buffer, 0.5 mM of forward and reverse primers, 0.2 mM dNTP mixture, and 1 unit of EX Taq Polymerase. The PCR protocol proceeded as follows: the first step was initial denaturation at 94℃ for 5 minute ; and the second step was denaturation at 94℃ for 1 minute, annealing at 65℃ for 1 minute, and extension at 72℃ for 2 minutes. After the second step, the temperature for the annealing stage was decreased by increments of 1℃ following every annealing stage until a final annealing temperature of 55℃. The second step was then repeated 36 times. After completing the two steps, a final third step was carried out for 5 minutes at 72℃ for an extension.

For the PCR products, DNA electrophoresis analysis was performed with a mini vertical electrophoresis system (MGV-202-33, CBS Scientific Company, San Diego, USA). Three mL of the PCR product was mixed with 3 mL of formamide loading dye (98% formamide, 0.02% BPH, 0.02% xylene C, and 5 mM NaOH). Two mL of the sample was loaded onto a 6% acrylamide‐bisacrylamide gel (19:1) in 0.5X TBE buffer and electrophoresed at 250 V for 40-60 minutes. The separated DNA fragments were then visua-lized using ethidium bromide (EtBr).

Data and statistical analyses

The number of alleles, gene diversity (GD), polymorphic information content (PIC), and major allele frequency (MAF) for drought-tolerant and susceptible inbred lines were identified using PowerMarker software (Liu and Muse 2005). Genetic similarities (GS) between each pair of lines were calculated with the Dice similarity index (Dice 1945). The similarity matrix was then used to construct a dendrogram based on an unweighted pair group method with arithmetic mean (UPGMA), with the help of SAHN- Clustering from NTSYS-pc (Rohlf 1998). Moreover, a principal component analysis (PCA) was performed to estimate relationships for phenotypic variance among maize inbred lines using the NTSYSpc software package (Rohlf 1998).

Population structure among the 12 drought-tolerant and susceptible inbred lines was confirmed by model-based program STRUCTURE software (Pritchard and Wen 2003). This software was executed five times for each simulation subgroup (K value) from 1 to 10 with a burn-in of 100,000 and a run length of 100,000 in an admixture model. The delta K value based on the degree of change for log probability by Evanno et al. (2005) was calculated with STRUCTURE HARVESTER (http://taylor0.biology.ucla. edu/structHarvester/). The subgroup was assigned by using the run result with maximum likelihood among five runs of estimated numbers, with lines with membership proba-bilities of ≥ 0.80 assigned to subgroups, while lines with less than 0.80 were assigned to an admixed group (Stich et al. 2005). Association analysis was performed using TASSEL 3.0 (Bradbury et al. 2007), which was used to confirm marker-trait associations using a mixed linear model (Q + K MLM). The Q + K MLM method was performed by combining the population structure (Q) matrix derived from the STRUCTURE and the kinship (K) matrix derived from the TASSEL at P < 0.05 (Pritchard and Wen 2003; Bradbury et al. 2007). Furthermore, basic statistical analysis was performed using applications in Microsoft Office Excel 2016. Student’s t-test at P < 0.05 and 0.01 were used for the estimation of the statistical difference between the six tolerant and six susceptible lines. Moreover, the correlation for 11 phenotypic traits was calculated. Both analyses used IBM SPSS Statistics version 21 (IBM Corp., Armonk, N.Y., USA).  

RESULTS

Phenotypic analysis and statistical analysis

Phenotypic variation of ten agronomic traits between control (well-watered) and drought conditions in tolerant and susceptible maize inbred groups are summarized in Table 2. The average PH decrease of susceptible lines in drought conditions was ‒11.1 ± 4.1 cm, ranging from ‒5.8 (FLD13) to ‒15.8 (FLD31) cm. On the other hand, the average PH decrease of tolerant lines was ‒5.2 ± 2.7 cm, ranging from ‒1.5 (FLD37) to ‒8.9 (FLD35) cm. The average LA decrease of the susceptible group in drought conditions compared with the well-watered condition was ‒23.0 ± 10.6 cm2, ranging from ‒10.1 (FLD31) to ‒34.0 (FLD1) cm2, while the average value for the tolerant group ranged from ‒0.3 (FLD23) to ‒17.0 (FLD33) cm2, with an average of ‒7.6 ± 7.4 cm2. In the case of LW, the average value of susceptible lines was ‒1.0 ± 0.4, with a range from ‒0.4 (FLD13) to ‒1.5 (FLD18) g. However, drought- tolerant lines had an average value of ‒0.3±0.2, with a range from ‒0.1 (FLD37) to ‒0.5 (FLD24, 35) g. The average SW decrease of susceptible lines in drought conditions was ‒1.7 ± 1.2 g, with a range of ‒0.5 (FLD29, 31) ∼ ‒3.5 (FLD18) g. The average value for SW in the tolerant group ranged from ‒0.1 (FLD33, 37) to ‒0.9 (FLD35) with an average of ‒0.4 ± 0.4. For the SFW trait, the average value of the susceptible group was ‒2.6 ± 1.5, with a range of ‒1.3 (FLD29) ∼ ‒5.1 (FLD18) g, while the tolerant group showed an average value of ‒0.7 ± 0.5, with a range from ‒0.2 (FLD37) to ‒1.4 (FLD35) g. The average RFW decrease of the tolerant group under drought conditions was ‒0.4 ± 0.2 g, ranging from ‒0.2 (FLD23) to ‒0.7 (FLD12) g. Meanwhile, the average RFW decrease of the susceptible group was ‒1.0 ± 0.7 g, ranging from ‒0.2 (FLD29) to ‒2.1 (FLD1) g. The average value for RL in the susceptible and tolerant groups showed ‒6.7 ± 5.7 and ‒2.7 ± 2.4 cm, respectively. Moreover, the RL trait of the susceptible lines ranged from ‒1.4 (FLD16) to ‒16.9 (FLD13) cm, but that of the tolerant lines ranged from ‒0.8 (FLD35) to ‒5.9 (FLD12) cm. The average TCC decrease of the susceptible lines in drought conditions was ‒5.2 ± 1.9, with a range of ‒2.6 (FLD1) ∼ ‒7.7 (FLD29). The average value for TCC in the tolerant group ranged from ‒3.5 (FLD37) to ‒8.6 (FLD24) with an average of ‒6.0 ± 2.2. The average SDW decrease of the susceptible group was ‒0.5 ± 0.2 g, ranging from ‒0.2 (FLD29) to ‒0.8 (FLD18) g, while the average value for the tolerant group ranged from 0.0 (FLD33) to ‒2.0 (FLD12, 23, 24) g, with an average of ‒0.1 ± 0.1 g. The average RDW decrease of the susceptible lines in drought conditions was ‒0.2 ± 0.1 g, with a range of ‒0.1 (FLD16, 18, 29, 31) ∼ ‒0.3 (FLD1) g. The RDW in all tolerant lines except FLD33 and FLD 35 (‒0.1) showed no change in drought conditions with an average of 0.0 ± 0.0 (Table 2, Fig. 1).

Figure 1. Bar graph of ten drought-related traits between tolerant (black) and susceptible (gray) groups. * and ** show the significant differences by t-test at the 0.05 and 0.01 probability level, respectively. PH: plant height, LA: leaf area, LW: leaf weight, SW: stem weight, SFW: shoot fresh weight, RFW: root fresh weight, RL: root length, TCC: total chloro-phyll content, SDW: shoot dry weight, RDW: root dry weight.

Significant differences in phenotypic variation between the tolerant and susceptible maize inbred groups were evaluated by t-test (Fig. 1). The results showed a statisti-cally significant difference in PH, LA, LW, SFW, SDW, and RDW between the tolerant and susceptible maize inbred groups at P < 0.05. Correlation analysis was per-formed to confirm genetic relationships among 11 agrono-mic traits in the 12 flint inbred lines (Table 3). Among all 55 combinations, 16 combinations showed comparatively higher positive or negative coefficients, namely SW and SFW (0.981**), SFW and SDW (0.917**), SW and SDW (0.885**), LW and SFW (0.888**), SW and RFW (0.862**), SFW and RFW (0.856**), LW and SDW (0.839**), RFW and SDW (0.845**), Tolerance and SDW (0.812**), LW and SW (0.782**), Tolerance and LW (0.775**), Tolerance and RDW (0.677**), LW and RFW (0.712**) at P < 0.01 (Table 3).

Table 3 . Correlation analysis among 11 drought-related traits of 12 flint maize inbred lines.

TraitsPHLALWSWSFWRFWRLTCCSDWRDW
Tolerance0.680*0.677*0.775**0.603*0.686*0.5290.447‒0.2110.812**0.677**
PH0.4450.652*0.2350.3750.1250.252‒0.1290.3230.343
LA0.701*0.663*0.703*0.623*0.165‒0.2960.592*0.637*
LW0.782**0.888**0.712**‒0.058‒0.2960.839**0.517
SW0.981**0.862**0.079‒0.3730.885**0.598*
SFW0.856**0.034‒0.3650.917**0.593*
RFW‒0.124‒0.5230.845**0.634*
RL0.0960.1470.370
TCC‒0.281‒0.503
SDW0.594*

* and ** show the significant differences at the 0.05 and 0.01 probability levels, respectively.

PH: plant height, LA: leaf area, LW: leaf weight, SW: stem weight, SFW: shoot fresh weight, RFW: root fresh weight, RL: root length, TCC: total chlorophyll content, SDW: shoot dry weight, RDW: root dry weight.



Moreover, the morphological data were used to perform PCA analysis. The results showed that the first and second principal components accounted for 59.6% and 13.7% of the total variance, respectively (Table 4). The SFW, SDW, SW, LW, RFW, RDW, and LA traits contributed in a positive direction on PC1, and RL contributed in a positive direction on PC2. Based on PC1, all maize inbred lines except FLD29 were clearly separated into two maize inbred groups by their drought tolerance (Fig. 2).

Table 4 . Eigen vector and cumulative variance of the first and second principal components.

TraitsEigen vector
PC1PC2
Shoot fresh weight (SFW)0.964‒0.086
Shoot dry weight (SDW)0.9430.033
Stem weight (SW)0.932‒0.133
Leaf weight (LW)0.8940.040
Root fresh weight (RFW)0.888‒0.364
Root dry weight (RDW)0.7920.145
Leaf area (LA)0.7880.185
Plant height (PH)0.4350.593
Root length (RL)0.1290.829
Total chlorophyll content (TCC)‒0.4800.339
Cumulative variance (%)59.613.7

Figure 2. Projection of the 12 flint maize inbred lines in the first and second principal components.

Genetic diversity among 12 flint inbred lines related to drought tolerance

A total of 360 SSR loci were used to evaluate a genetic diversity index, including GD, PIC, and MAF, among the 12 flint inbred lines (Table 5). The 360 SSR loci appeared in a total of 1,604 alleles in the 12 flint inbred lines. The number of alleles per locus ranged from 2 to 11, and the average number of alleles per locus was 4.4 (Table 5, Supplementary Table 1). The average GD was 0.648, with a range of 0.153- 0.903. The average PIC value was 0.598, with a range of 0.141-0.895. The average MAF was 0.466, with a range of 0.167-0.917 (Table 5). To clearly understand genetic diversity and variation in the six tolerant and six susceptable inbred lines under drought condition, this study verified the allele numbers, GD, PIC, and MAF in the six drought-tolerant and six drought- susceptible inbred lines. Those values for the 360 SSR loci in the tolerant and susceptible maize inbred groups are shown in Table 6. The total number of alleles was 1,241 and 1,174 with an average of 3.4 and 3.3 in each group of the six flint inbred lines, respectively. Furthermore, the averages of the GD, PIC and MAF values were 0.609, 0.551, and 0.494, respectively, in the six drought-tolerant inbred lines. Meanwhile, these values for the six drought-susceptible inbred lines were 0.581, 0.521, and 0.521, respectively (Table 6, Supplementary Table 1).

Table 5 . Total number of alleles and genetic diversity index for 360 SSR loci in the twelve-flint maize inbred lines.

ChromosomeNo. of MKTotal allelesMean of allelesGDPICMAF
Chr.1321424.40.6510.6020.466
Chr.2371704.60.6600.6080.453
Chr.3351494.30.6390.5900.474
Chr.4492364.80.6620.6170.454
Chr.5301284.30.6520.5960.453
Chr.6301344.50.6450.5940.467
Chr.7482134.40.6420.5920.469
Chr.8401774.40.6400.5910.473
Chr.9311424.60.6630.6160.457
Chr.10281134.00.6300.5730.491
Total3601,604----
Mean36.04.4-0.6480.5980.466
Min-2-0.1530.1410.167
Max-11-0.9030.8950.917

GD: gene diversity, PIC: polymorphic information content, MAF: major allele frequency.


Table 6 . Comparison of total number of alleles and genetic diversity index between tolerant and susceptible groups.

ParameterTolerant inbred lines (n = 6)Susceptible inbred lines (n = 6)
No. of alleles1,2411,174
Mean3.43.3
Gene Diversity0.6090.581
Min0.0000.000
Max0.8330.833
PIC0.5510.521
Min0.0000.000
Max0.8100.810
MAF0.4940.521
Min0.1670.167
Max1.0001.000

GD: gene diversity, PIC: polymorphic information content, MAF: major allele frequency.



Population structure analysis in flint maize inbred lines

To confirm the genetic structure and relationships among the 12 flint inbred lines related to drought tolerance, this study used a model-based STRUCTURE program to sub-divide into appropriate subgroups. Because it was difficult to separate subgroups using five replicate sets ranging from 1 to 10 from the LnP(D) of the data, this study applied the ad hoc measure ΔK (Evanno et al. 2005). Although the highest ΔK value was revealed for K = 2 in all 12 flint inbred lines using the 360 SSR loci, all inbred lines were not clearly separated on the basis of drought tolerance (Fig. 3). Based on membership probability for over 0.8, all inbred lines were divided into three groups: Group I, Group II, admixed group. Group I consisted of one drought- susceptible line, FLD16, whereas there were nine inbred lines composed of five drought-tolerant lines (FLD 23, 24, 33, 35, 37) and four drought-susceptible lines (FLD13, 18, 29, 31) in Group II. The FLD12 (tolerant) and FLD1 (susceptable) inbred lines were contained in admixed group (Fig. 3). Moreover, a distance-based dendrogram from the UPGMA analysis was constructed using the 360 SSR loci (Fig. 4). All flint inbred lines were classified into two maize inbred groups at a genetic similarity of 0.281. Group I consisted of five inbred lines, composed of two drought-tolerant lines (FLD12, 23) and three drought- susceptible lines (FLD1, 16, 18); while Group II consisted of seven inbred lines, composed of four drought-tolerant lines (FLD 24, 33, 35, 37) and three drought-susceptible lines (FLD13, 29, 31) (Fig. 4).

Figure 3. Population structure pattern in 12 maize inbred lines based on 360 SSR markers.
Figure 4. UPGMA dendrogram of the 12 flint inbred lines based on 360 SSR markers.

Association analysis using Q GLM and Q + K MLM

Association analysis between a total of 360 SSR markers and 11 phenotypic traits in the 12 flint maize inbred lines was performed by Q GLM and Q + K MLM. This study detected 205 marker-trait associations involving 120 SSR markers associated with the 11 agronomic traits using Q GLM at P < 0.05 (Supplementary Table 2). When we used Q + K MLM, four SSR markers, umc1175, umc1503, umc2092, and umc2503, were associated with SW, SFW, RFW, and RDW traits at a significance level of P < 0.05 (Table 7). Among these MTAs, umc1175 was associated with two traits, SFW and SW, on chromosome 4. Mean-while, umc1503 was associated with three traits, RFW, SFW, and SW, on chromosome 4. Moreover, umc2092 was associated with two traits, SFW and SW, on chromosome 7. SSR marker umc2503 was associated with only one trait, RDW, on chromosome 8 (Table 7).  

Table 7 . Information on overlapping SMTA markers bet-ween Q GLM and Q + K MLM.

SSR markerChr.Phenotypic traitsQ GLMQ+K MLM
umc11754SFW0.0060.040
SW0.0060.042
umc15034RFW0.0000.048
SFW0.0000.048
SW0.0000.048
umc20927SFW0.0060.040
SW0.0060.042
umc25038RDW0.0020.030

DISCUSSION

Drought is a major limiting factor for maize plant growth, development, and productivity (Djemel et al. 2018). In our previous study, we selected six drought- tolerant and six susceptible maize inbred lines by using drought tolerance indices, namely PH, LA, LW, SW, SFW, RFW, RL, TCC, SDW, and RDW (Table 1, Adhikari et al. 2019). Drought stress influences diverse morpho-physiolo-gical characteristics including plant biomass, root length, and shoot length (Jaleel et al. 2008). In this study, we compared the average value for ten traits between drought- tolerant and susceptible groups (Table 2). The results showed that there was a statistically significant difference in PH, LA, LW, SFW, SDW, and RDW between the tolerant and susceptible groups by t-test at P < 0.05, although there was no statistical significance between the groups for some traits, SW, RFW, and RL (Fig. 1). This result is supported by correlation analysis, which showed a high correlation coefficient between drought tolerance with LW, SDW, and RDW at P < 0.01 and with PH, LA, and SFW at P < 0.05 (Table 3).

Correlation analysis helps to confirm the interrelationship between traits related to plant growth and enables recog-nition of traits that can be used for selecting drought tolerant maize inbred lines at the early growth stage (Akinwale et al. 2018). Plants under drought condition was more invest to root than shoot, as revealed by the increased ratio root to shoot in this condition (Boudiar et al. 2020). This study also obtained similar results with the ratio of root to shoot for the drought susceptible group being 0.328 in normal condition and 0.377 in drought condition and that of the tolerant group being 0.384 in well-watered condition and 0.400 in water deficient condition (data not shown). The ratio of root to shoot in drought condition was increased than normal condition in both of susceptible and tolerant groups.

Root dry weight has the potential to be an important trait for selection against water stress (Mehdi et al. 2001). This study also confirmed the association between Tolerance and RDW (Table 3). In this study, PCA was performed to evaluate differentiation among the drought-tolerant and susceptible maize inbred lines and to select informative traits for drought tolerance (Table 4, Fig. 2). The results showed that all maize inbred lines, except FLD29, were clearly divided into two groups based on PC1. The SFW, SDW, SW, LW, RFW, RDW, LA, and RL traits greatly contributed to the positive direction of PC1 and PC2. Thus, these agronomic traits may be considered useful for selection and discrimination among maize inbred lines for drought tolerance in breeding programs.

Information about genetic diversity and relationships and the population structure of breeding materials are useful for the development of new varieties or elite inbred lines in plant breeding programs. In this study, 360 SSR loci (SSR loci per chromosome ranged from 28 for Ch.10 to 49 for Ch. 4) covering the whole maize genome were used to detect genetic variation in 12 flint maize inbred lines related to drought tolerance (Table 5, Supplement Table 1). This study compared the values of a genetic diversity index between the six drought tolerant and six susceptible maize inbred lines. Consequently, the tolerant group showed relatively higher genetic variation than the susceptible group (Table 6).

The population structure using the 360 SSR markers in this study was investigated using a model-based clustering method (STRUCTURE) and distance-based phylogenetic methods (NTSYS). In a model-based clustering pattern based on a probability threshold > 0.8, all inbred lines could be divided into two distinct Groups I and II, and an Admixed group. Most of the maize inbred lines (FLD23, 24, 33, 35, 37 of drought tolerant lines and FLD13, 18, 29, 31 of drought susceptible lines) were designated by Group II. One drought-tolerant inbred line, FLD16, is the only member of Group I. The remaining two inbred lines, FLD12 of tolerant and FLD1 of susceptible, belong to the Admixed group (Fig. 3). A UPGMA dendrogram based on genetic distance was divided into two main groups, and 2-3 subgroups were observed in each main group (Fig. 3). Although two different methods based on model and distance were used, there was no clear separation pattern based on drought tolerance using the 360 SSR markers, and cluster analysis based on genetic distance yielded more information on the genetic diversity of all inbred lines than the model-based method. Moreover, three inbred lines, FLD1, 12, and 16, which were contained in Group I and the Admixed group, were clustered into Group I-1 in the distance-based dendrogram (Fig. 4). Although there is pedigree data for nine inbred lines, three inbred lines, FLD16, 18, and 23, are unknown (Table 1). The population structure information will enhance understanding of the structural organization of the unknown lines for pedigree and source information. Furthermore, this genetic diversity, genetic relationships, and population structure information of the 12 flint maize inbred lines is expected to help in optimizing the selection of cross combinations in the development of new maize cultivars.

Recently, association analysis is more powerful than traditional QTL mapping, because it is effective in detec-ting molecular markers related to targeted morphological traits, such as drought tolerance (Liu and Qin 2021). In our study, 360 SSR loci (average 36 SSRs per chromosome) were used and distributed across the ten maize chromo-somes. However, false positives (Type-I error) are a major problem in association analysis and lead to invalid associations because of population structure (Q) and unequal relatedness (K) (Zhang et al. 2010). To prevent false positives, we used two different methods for asso-ciation analysis, a general linear model based on a Q-matrix (Q GLM) and a mixed linear model based on a Q and K matrix (Q + K MLM) (Tables 7, Supplementary Table 2). Population structure analysis using the Q GLM model identified 205 marker-trait associations, but only eight associations were found using the Q+K MLM model, based on population structure and kinship. In general, the Q + K MLM method detects relatively fewer MTAs (Yu et al. 2006; Kwon et al. 2012). Moreover, this result indicated that the Q + K MLM method is better for decreasing the false positive rate in association analysis. Among marker- trait associations by Q GLM, 12 SSR markers (umc2400, umc2378, umc1872, bnlg2046, umc1969, bnlg1126, umc2334, phi022, umc1088, umc1707, bnlg1117, and umc1716) were detected for the drought tolerance trait. We performed distance-based UPGMA analysis again with the selected 12 SSR markers for verification. The result showed that all maize inbred lines clearly divided into two maize inbred groups in accordance with their drought tolerance at a genetic similarity of 0.123, although there was no clear pattern using the 360 SSR markers (Fig. 5). This result indicates that this set of SSR markers can be useful for selecting drought tolerance in future maize breeding programs. The eight overlapping MTAs between Q GLM and Q + K MLM were associated with the only shoot and root-related traits, excluding PH, TCC, and leaf-related traits (Table 7). In particular, umc1175, umc1503, and umc2092 on chromosomes 4 and 7 were simultaneously associated with the SFW and SW traits. Moreover, two SSR markers, umc1503, and umc2503 on chromosomes 4 and 8, were associated with root-related traits RFW and RDW. These results were supported by higher correlation coefficients being detected between SFW and SW (0.981**), SW and RFW (0.862**), and SFW and RFW (0.856**) than the other combinations.

Figure 5. UPGMA dendrogram of the 12 flint inbred lines based on 12 SSR markers by selecting Q GLM.

Some SSR markers in this study have been detected by other association analysis or QTL mapping studies, although the same SSR markers were not exactly consistent with the same traits in this study. For example, a previous report of QTL mapping by Benke et al. (2014) found that umc2092 was associated with shoot water content, but it was also associated with shoot and stem-related traits SFW and SW in this study. The umc1175 and umc1503 were tightly linked to the akh1 (aspartate kinase-homoserine dehydro-genase1, bin 4.05) and ubi2 (ubiquitin2, 4.09) genes, res-pectively, on chromosome 4 (http://www.maizeGDB.org). Finally, umc2503 has tightly linked to the rgp2 (ras-related protein) gene on chromosome 8 (http://www.maizeGDB.org).

The results of this drought tolerance study for maize provide useful information for understanding the change of leaf, shoot, and root-related traits of 12 tolerant and susceptible flint maize inbred lines in drought conditions, and the SSR markers related to these traits will provide useful information for MAS in maize breeding programs. Also, the identification of the loci associated with drought tolerance in this study may provide better opportunities for maize breeders to enhance maize drought tolerance by MAS.

Supplemental Materials
pbb-10-4-257-supple.zip
ACKNOWLEDGEMENTS

This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF- 2021R1A6A1A03044242), and the Golden Seed Project (No. 213009-05-1-WT821, PJ012650012017), Ministry of Agriculture, Food, and Rural Affairs (MAFRA), Ministry of Oceans and Fisheries (MOF), Korea Forest Service (KFS), Republic of Korea.

References
  1. Adhikari B, Sa KJ, Lee JK. 2019. Drought tolerance screening of maize inbred lines at an early growth stage. Plant Breed Biotechnol 7(4): 326-339.
    CrossRef
  2. Akinwale RO, Awosanmi FE, Ogunniyi OO, Fadoju AO. 2018. Determinants of drought tolerance at seedling stage in early and extra-early maize hybrids. Maydica 62(1): 9.
  3. Ao S, Russelle MP, Varga T, Feyereisen GW, Coulter JA. 2020. Drought tolerance in maize is influenced by timing of drought stress initiation. Crop Sci 60(3): 1591-1606.
    CrossRef
  4. Bell J. 2017. Corn growth stages and development; Texas A&M AgriLife Extension and Research Agronomist, Amarillo: Lubbock, TX, USA.
  5. Benke A, Urbany C, Marsian J, Shi R, von Wirén N, Stich B. 2014. The genetic basis of natural variation for iron homeostasis in the maize IBM population. BMC Plant Biol 14: 12.
    Pubmed KoreaMed CrossRef
  6. Boudiar R, Casas AM, Gioia T, Fiorani F, Nagel KA, Igartua E. 2020. Effects of low water availability on root plac-ement and shoot development in landraces and modern barley cultivars. Agronomy 10: 134.
    CrossRef
  7. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. 2007. TASSEL: software for association map-ping of complex traits in diverse samples. Bioinformatics 23: 2633-2635.
    Pubmed CrossRef
  8. Campos H, Cooper M, Habben JE, Edmeades GO, Schussler JR. 2004. Improving drought tolerance in maize: a view from industry. Field Crops Res 90: 19-34.
    CrossRef
  9. Cao LZX, Wj BXP. 2004. Discuss on evaluating method to drought-resistance of maize in seedling stage. J Maize Sci 12: 73-75.
  10. Chen JP, Xu WW, Velten J, Xin ZG, Stout J. 2012. Characterization of maize inbred lines for drought and heat tolerance. J Soil Water Conserv 67: 354-364.
    CrossRef
  11. Dellaporta SL, Wood J, Hicks JB. 1983. A simple and rapid method for plant DNA preparation, version II. Plant Mol Biol Report 1: 19-21.
    CrossRef
  12. Dice LR. 1945. Measures of the amount of ecologic association between species. Ecology 26:297-302.
    CrossRef
  13. Djemel A, Álvarez-Iglesias L, Pedrol N, López-Malvar A, Ordás A, Revilla P. 2018. Identification of drought tolerant populations at multi-stage growth phases in temperate maize germplasm. Euphytica 214: 138.
    CrossRef
  14. Duvick DN, Smith JSC, Cooper RM. 2004. Long-term selection in a commercial hybrid maize breeding program. Plant Breed Rev 24: 109-151.
    CrossRef
  15. Evanno G, Regnaut S, Goudet J. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14: 2611-2620.
    Pubmed CrossRef
  16. Flint-Garcia SA, Thuillet AC, Yu JM, Pressoir G, Romero SM, Mitchell SE, et al. 2005. Maize association population: a high-resolution platform for quantitative trait locus dissection. Plant J 44: 1054-1064.
    Pubmed CrossRef
  17. Jaleel CA, Manivannan P, Lakshmanan GMA, Gomathinayagam M, Panneerselvam R. 2008. Alterations in morphological parameters and photosynthetic pigment responses of Catharanthus roseus under soil water deficits. Colloids Surf B Biointerfaces 61(2): 298-303.
    Pubmed CrossRef
  18. Kalivas A, Xanthopoulos F, Kehagia O, Tsaftaris AS. 2011. Agronomic characterization, genetic diversity and asso-ciation analysis of cotton cultivars using simple sequence repeat molecular markers. Genet Mol Res 10: 208-217.
    Pubmed CrossRef
  19. Kashiani P, Saleh G, Panandam JM, Abdullah NAP, Selamat A. 2012. Molecular characterization of tropical sweet corn inbred lines using microsatellite markers. Maydica 57: 154-163.
  20. Kwon SJ, Brown AF, Hu J, McGee R, Watt C, Kisha T, et al. 2012. Genetic diversity, population structure and genome- wide marker-trait association analysis emphasizing seed nutrients of the USDA pea (Pisum sativum L.) core collection. Genes Genomics 34: 305-320.
    CrossRef
  21. Liu K, Muse SV. 2005. PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21: 2128-2129.
    Pubmed CrossRef
  22. Liu S, Qin F. 2021. Genetic dissection of maize drought tolerance for trait improvement. Mol Breeding 41: 8.
    CrossRef
  23. Lopes MS, Araus JL, van Heerden PD, Foyer CH. 2011. Enhancing drought tolerance in C4 crops. J Exp Bot 62: 3135-3153.
    Pubmed CrossRef
  24. Maiti RK, Maiti LE, Maiti S, Maiti AM, Maiti M, Maiti H. 1996. Genotypic variability in maize cultivars (Zea mays L.) for resistance to drought and salinity at the seedling stage. J Plant Physiol 148: 741-744.
    CrossRef
  25. Mehdi SS, Ahmad N, Ahsan M. 2001. Evaluation of S1 maize (Zea mays L.) families at seedling stage under drought conditions. Online J Biol Sci 1: 4-6.
    CrossRef
  26. Mohan M, Nair S, Bhagwat A, Krishna TG, Yano M, Bhatia CR, et al. 1997. Genome mapping, molecular markers and marker-assisted selection in crop plants. Mol Breed 3: 87-103.
    CrossRef
  27. Park YJ, Lee JK, Kim NS. 2009. Simple sequence repeat polymorphisms (SSRPs) for evaluation of molecular diversity and germplasm classification of minor crops. Molecules 14: 4546-4569.
    Pubmed KoreaMed CrossRef
  28. Powell W, Morgante M, Andre C, Hanafey M, Vogel J, Tingey S, et al. 1996. The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol Breed 2: 225-238.
    CrossRef
  29. Pritchard JK, Wen W. 2003. Documentation for STRUCTURE software: Version 2.
  30. Rohlf FJ. 1998. NTSYS-pc: Numerical taxonomy and mul-tivariate analysis system. Version: 2.02. Exeter Software, Setauket, New York.
  31. Solomon KF, Zeppa A, Mulugeta SD. 2012. Combining ability, genetic diversity and heterosis in relation to F1 performance of tropically adapted shrunken (sh2) sweet corn lines. Plant Breed 131: 430-436.
    CrossRef
  32. Stich B, Melchinger AE, Frisch M, Maurer HP, Hecknberger M, Reif JC. 2005. Linkage disequilibrium in European elite maize germplasm investigated with SSRs. Theor Appl Genet 111: 723-730.
    Pubmed CrossRef
  33. Tani E, Chronopoulou E, Labrou N, Sarri E, Goufa Μ, Vaharidi X, et al. 2019. Growth, physiological, bio-chemical, and transcriptional responses to drought stress in seedlings of Medicago sativa L., Medicago arborea L. and Their hybrid (Alborea). Agronomy 9(1): 38.
    CrossRef
  34. Thornsberry JM, Goodman MM, Doebley J, Kresovich S, Nielsen D, Buckler ES. 2001. Dwarf8 polymorphisms associate with variation in flowering time. Nat. Genet 28: 286- 289.
    Pubmed CrossRef
  35. Toscano S, Ferrante A, Roman, D. 2019. Response of medi-terranean ornamental plants to drought stress. Horticulturae 5(1): 6.
    CrossRef
  36. Webber H, Ewert F, Olesen JE, Müller C, Fronzek S, Ruane AC, et al. 2018. Diverging importance of drought stress for maize and winter wheat in Europe. Nat Commun 9: 4249.
    Pubmed KoreaMed CrossRef
  37. Yadav S, Sharma KD. 2016. Molecular and morphophysio-logical analysis of drought stress in plants. Plant growth. Intech Open, Upper Saddle River. 149-173.
    CrossRef
  38. Yu J, Arbelbide M, Bernardo R. 2005. Power of in silico QTL mapping from phenotypic, pedigree, and marker data in a hybrid breeding program. Theor Appl Genet 110: 1061- 1067.
    Pubmed CrossRef
  39. Yu J, Buckler ES. 2006. Genetic association mapping and genome organization of maize. Curr Opin Biotechnol 17: 155-160.
    Pubmed CrossRef
  40. Yu J, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, et al. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38: 203-208.
    Pubmed CrossRef
  41. Zhang Y, Li Y, Wang Y, Peng B, Liu C, Liu Z, et al. 2011. Correlations and QTL detection in maize family per se and testcross progenies for plant height and ear height. Plant Breed 130: 617-624.
    CrossRef
  42. Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, et al. 2010. Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42: 355-360.
    Pubmed KoreaMed CrossRef


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