search for




 

Genetic Diversity and Association Analyses of Canadian Maize Inbred Lines with Agronomic Traits and Simple Sequence Repeat Markers
Plant Breeding and Biotechnology 2018;6:159-169
Published online June 1, 2018
© 2018 Korean Society of Breeding Science.

Kyu Jin Sa, Tak Ki Hong, and Ju Kyong Lee*

Department of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Korea
Correspondence to: *Corresponding author: Ju Kyong Lee, jukyonglee@kangwon.ac.kr, Tel: +82-33-250-6415, Fax: +82-33-255-5558
Received March 14, 2018; Revised May 8, 2018; Accepted May 19, 2018.
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

We evaluated genetic diversity and population structure in 32 Canadian maize inbred lines and performed association analysis for five agronomical traits and 50 simple sequence repeat (SSR) markers. Genetic diversity analysis revealed a total of 381 alleles at the 50 SSR loci. The average number of alleles per locus was 7.6. The average genetic diversity and polymorphic information content values were 0.709 and 0.676, respectively. The average major allele frequency was 0.414. Population structure analysis indicated that these maize inbred lines were comprised of four major groups and one admixed group based on a membership probability threshold of 0.80. A general linear model showed 20 marker-trait associations involving 12 SSR markers associated with the four agronomic traits except for leaf length. For these marker-trait associations, phi056, mmc0022, bnlg1621, bnlg1695, phi116, and bnlg1028 were associated with only one trait. The other nc005, bnlg1012, phi065, and umc1982 were associated with two traits. Two SSR markers, mmc0111 and umc1038, were associated with three traits. These results will help in optimizing the choice of parents for crossing combinations, as well as in selecting markers for marker-assisted selection for maize improvement.

Keywords : Maize inbred line, Genetic diversity, Association analysis, Population structure, Marker-trait association
INTRODUCTION

Maize (Zea mays L.) is one of the most important agricultural crops serving food for humans and livestock feed, biofuel in the world. In Korea, waxy or sweet maize is mainly used for edible fresh maize, while normal maize is used for food as seed or powder and livestock feed. Today, maize consumption in Korea is increasing as the population transitions from a traditional diet based on rice to a Western diet based on meat. However, most of the maize consumed in Korea depends on imports except for waxy maize. To reduce maize imports, it is necessary to develop new varieties that are suitable for the environment in Korea and have excellent yield and quality. Korean maize breeding program is mainly carried out using inbred lines derived from domestic landrace (Park et al. 2012). Due to the limitations of breeding materials, genetic background is narrow, and development of elite varieties is limited (Park et al. 2012). Therefore, to improve the diversity of maize genetic resources, it is urgent to collect and introduce maize resources from abroad.

Classification of collected inbred lines from others allows the maize breeders to choose the most suitable and best hybrid combination and may reduce field testing cost and time (Reid et al. 2011). Crosses among inbred lines derived from different genetic background are known to have better-combining ability (Barata and Carena 2006). Therefore, information for the genetic variation among maize inbred lines have a significant impact on the improvement of new varieties because it is useful in planning crosses for the hybrid and development of inbred line, assigning lines to heterotic groups, and protecting the plant variety (Hallauer et al. 1988; Pejic et al. 1998). In traditional breeding, genetic diversity and relationships among inbred lines are usually evaluated based on the morphological and pedigree data, and heterosis. However, there are some limitations that the morphological characteristics often were influenced by environment interactions. Additionally, the pedigree record requires accurate records and is consumed many resources and time for the testing of pedigree. Therefore, to increase the efficiency of hybrid combinations, the assignment of inbred lines and assessment of genetic diversity and population structure among breeding materials is required for maize breeding programs.

Collected inbred lines can be classified by pedigree, quantitative genetic analysis, heterosis and molecular data (Mumm and Dudley 1994; Fan et al. 2003). Out of these, the use of molecular marker-based techniques in genetic studies like the estimation of genetic diversity and population structure has advanced remarkably in recent years. Among diverse molecular markers, such as RFLPs, RAPDs, AFLPs, SNPs, and simple sequence repeats (SSRs), also called microsatellites, are considered to be one of the most useful markers for estimating genetic variation because of their reliability, reproducibility, and discrimination among maize inbred lines (Akagi et al. 1997; Enoki et al. 2002). In plant breeding programs, identifying the genetic basis of agronomic traits is a fundamental scientific problem for crop improvement (Pasam et al. 2012). Recently, association mapping analysis using linkage disequilibrium (LD) is identified to analyze agronomic traits and molecular markers has advantages that reduce the experimental time and costs (Flint-Garcia et al. 2005; Yu and Buckler 2006). Therefore, association analysis is widely used to analyze a variety of crops such as rice (Borba et al. 2010), maize (Xue et al. 2013), and soybeans (Hu et al. 2014).

In this study, 32 maize inbred lines were introduced from Canada to obtain a diversity of breeding materials for the development of new elite maize hybrids. Therefore, our objective was to evaluate the genetic diversity of 32 Canadian maize inbred lines from Eastern Cereal and Oilseed Research Center using SSR markers and to investigate their population structure and clustering patterns. This study also tried to confirm the association between 5 agronomic traits and 50 SSR markers. These results will help to further Korean maize breeding programs.

MATERIALS AND METHODS

Plant materials and phenotypic evaluation

The entry number and pedigree of 32 Canadian maize inbred lines are listed in Table 1. All maize accessions were obtained from Eastern Cereal and Oilseed Research Center of Canada. To assess the morphological variation of the Canadian maize inbred lines, ten individuals of each accession were evaluated using completely randomized design with two replicates and 70 × 25 cm of planting density for 5 agronomic traits at the College of Agriculture and Life Sciences, Kangwon National University, Chuncheon, Gangwon-do in 2017. This study assessed 5 agronomic traits, including stem thickness (ST), plant height (PH), ear height (EH), leaf width (LW), and leaf length (LL) (Table 2). Basic statistics and correlation analysis were performed using Microsoft Office Excel 2010.

DNA extraction and SSR analysis

The genomic DNA was extracted from maize young leaves with the protocol of Dellaporta et al. (1983), with minor modifications. Fifty SSR primers (5 loci per chromosome) were used to evaluate genetic variations and association analysis in 32 Canadian maize inbred lines from Eastern Cereal and Oilseed Research Center in Canada. The SSR primers used in this study were obtained from MaizeGDB ( http://www.maizegdb.org/).

SSR amplifications were conducted in a total volume of 30 μL and consisted of 20 ng genomic DNA, 1× PCR buffer, 0.3 μM forward and reverse primers, 0.2 mM dNTPs, and 1 U Taq DNA polymerase (Biotools, Valie de Tobalina, Madrid, Spain). The PCR cycling conditions were as follows, pre-denaturation at 94°C for 5 minutes, followed by two 1-minute denaturation cycles at 94°C, a 1-minute annealing cycle at 65°C, and one 2-minute extension at 72°C. After the two cycle, the annealing temperature was decreased in 1°C increments every two cycles until a final temperature of 55°C was reached. The last cycle was then repeated 20 times. The final cycle included 10-minute extension at 72°C to ensure full extension.

Electrophoresis and fragment detection

The final reaction product (5 μL) was mixed with 10 μL electrophoresis loading buffer (98% formamide, 0.02% Bromophenol blue, 0.02% Xylene cyanol, and 5 mM NaOH). After denaturation and immediate cooling, 2 μL sample was loaded on a 6% denaturing (7.5 M urea) acrylamide-bisacrylamide gel (19:1) in 1× TBE buffer, and electrophoresed at 1800 V and 60 W for 120 minutes. The separated fragments were visualized using a silver-staining kit (Promega, USA).

Data analysis

PowerMarker 3.25 program (Liu and Muse 2005) was used to calculate the number of alleles, allele frequency, major allele frequency (MAF), gene diversity (GD), and polymorphic information content (PIC). The genetic similarities (GS) were calculated for each pair of accessions using the Dice similarity index (Dice 1945). The similarity matrix was used to construct an Unweighted Pair Group Method with Arithmetic Mean Algorithm (UPGMA) dendrogram with the help of SAHN-clustering from NTSYSpc version 2.1 (Rohlf 1998).

We used model-based program STRUCTURE 2.2 (Pritchard and Wen 2003) to analyze population structure (Q matrix), where the membership coefficient for each individual in each subpopulation was run five times for each cluster (K), ranging from 1 to 10, using the admixture model with a burn-in of 100,000 and a replication of 100,000. Because the estimated log probability of data [LnP(D)] overestimated the number of subgroups, the ad hoc criterion (ΔK) described by Evanno et al. (2005) was used to determine the most probable value of K. The run of the estimated numbers of subgroups showing the maximum likelihood was used to assign maize inbred lines with membership probabilities ≥ 0.80 to subgroups. The maize inbred lines with membership probabilities < 0.80 were assigned to an admixed group (Wang et al. 2008). TASSEL 3.0 (Bradbury et al. 2007) was used to evaluate marker-trait associations using a Q general linear model (GLM). The Q GLM method was performed using a Q-matrix derived from the STRUCTURE program. The number of permutation runs was set to 10,000 to obtain a marker significance value of P ≤ 0.05.

RESULTS

Phenotypic analysis and correlation analysis

Phenotypic variations for five agronomic traits in 32 Canadian maize inbred lines are shown in Table 2. The average of ST value was 1.8 ± 0.2, ranging from 1.2 to 2.4. The PH value ranged from 93.4 to 214.6, with an average of 158.3 ± 28.3. The average EH value was 51.6 ± 16.4, ranging from 4.8 to 83.8. The LW value ranged from 2.8 to 10.2, with an average of 7.9 ± 1.2. The average LL value was 57.4 ± 7.6, ranging from 36.6 to 76.8. We also confirmed correlation coefficients among five agronomic traits in 32 maize inbred lines. All combinations, except of PH and LW, showed significant correlation with the significance level at 0.05. Among them, PH and EH (0.700**), EH and LL (0.676**), and PH and LL (0.621**) showed relatively higher correlation coefficients than the other combinations (Table 2).

Genetic diversity among 32 maize inbred lines

This study used a total of 50 SSR loci to evaluate the genetic diversity among 32 Canadian maize inbred lines (Fig. 1, Table 3). A total of 381 alleles were detected in 32 maize inbred lines. The number of alleles per locus ranged from 3.0 to 15, and the average number of alleles per locus was 7.6 (Table 3). The average MAF was 0.414 with a range of 0.156 to 0.906. Also, the average GD value was 0.709 with a range of 0.176 to 0.910. The average PIC was 0.676 with a range of 0.171 to 0.903 (Table 3). Of the 381 alleles, 142 private alleles (37.3%) were detected in 32 maize accessions. The frequency of rare alleles (frequency < 0.1) was 65.1% (248 of 381 alleles), whereas intermediate (frequency 0.1–0.5) and abundant alleles (frequency > 0.5) comprised 31.8% (121 alleles) and 3.1% (12 alleles) of 381 alleles, respectively (Fig. 1).

Population structure and cluster analysis among 32 Canadian maize accessions

For whole maize inbred lines, the highest ΔK value was confirmed for K = 4 (Fig. 2). Based on a membership threshold of 0.8 (Wang et al. 2008), the maize inbred lines were divided into group I, group II, group III, group IV, and the admixed group. Twelve maize inbred lines (CO417, CO419, CO420, CO421, CO423, CO428, CO438, CO439, CO442, CO444, CO445, and CO450) were assigned to group I. Group II contained two maize inbred lines (CO446, CO451). Group III also contained two maize inbred lines (CO436 and CO437). The six maize inbred lines were assigned to group IV (CO430, CO431, CO432, CO433, CO441, and CO449). The admixed group contained ten maize inbred lines (CO416, CO418, CO425, CO429, CO434, CO435, CO440, CO443, CO447, and CO448) composed of line with membership lower than 0.8 (Fig. 3). A dendrogram of the 32 maize inbred lines using UPGMA is presented in Fig. 4, which shows four clusters with a GS value of about 25%. Group I contained 23 inbred lines (CO416, CO433, CO441, CO449, CO432, CO430, CO431, CO448, CO434, CO435, CO443, CO429, CO418, CO425, CO442, CO421, CO444, CO439, CO417, CO438, CO419, CO436, and CO437). Group II contained six inbred lines (CO420, CO423, CO440, CO446, CO451, and CO447). Group III contained two inbred lines (CO445 and CO450), and Group IV contained only one inbred line (CO428) (Fig. 4).

Association analysis using Q GLM

Association analysis between sets of 50 SSR markers and five phenotypic traits in 32 maize inbred lines were performed by Q GLM. Twenty marker-trait associations involving 12 SSR markers associated with the four agronomic traits except LL trait using Q GLM (Table 4) were detected. Among 20 marker-trait associations, six SSR markers, phi056, mmc0022, bnlg1621, bnlg1695, phi116, and bnlg1028, were associated with only one trait. Meanwhile, four SSR markers, nc005, bnlg1012, phi065, and umc1982, were associated with two traits. Moreover, two SSR markers, mmc0111 and umc1038, were associated with three traits.

DISCUSSION

Evaluation of genetic variation is a prerequisite not only in understanding the genetic diversity and genetic relationship of a species, but also in the development of new cultivars in crops. Particularly, the information on morphological variation in breeding materials is important to develop new inbred lines in maize crop. In this study, five agronomic traits were evaluated in 32 Canadian maize inbred lines. Lodging resistance is an important phenotypic variation in maize breeding program. Phenotypic traits such as SD, PH, EH, and PH/EH ratio are important for determining lodging resistance (Kashiwagi et al. 2008; Cai et al. 2012). The PH is associated with biomass, lodging resistance, grain yield (Teng et al. 2013), and lower EH is most significantly contribute to lodging resistance (Cai et al. 2012). Also, the ratio of PH to EH under 50% is associated with high lodging resistance (Lee et al. 2009). The PH/EH ratio for all inbred lines in our study was under 50% suggesting high lodging resistance.

Information on genetic diversity and genetic relationships for the developed breeding materials is important for breeding programs. In addition, analysis of population structure of breeding materials is essential for association analysis (Flint-Garcia et al. 2005). To estimate genetic diversity and population structure in 32 Canadian maize inbred lines, 50 SSR loci (5 loci per chromosome) covering the whole maize genome were analyzed. This study detected a total of 381 alleles, with an average number of 7.6 alleles per locus in 32 Canadian maize inbred lines, and average GD along with PIC of 0.709 and 0.676, respectively (Table 3). Genotyping 129 maize inbred lines with 105 SSR loci in previous study, including some of the same inbred lines used in this study, they observed genetic diversity with an average of 3.62 alleles per locus with an average PIC of 0.68 (Reid et al. 2011). In another study, Sa et al. (2011) confirmed genetic diversity with an average of 5.38 alleles per locus and an average PIC of 0.587 in 40 normal maize inbred lines using 50 SSR markers. In comparison, the average allele number and PIC value in our study was on higher side. Therefore, the Canadian maize inbred lines with high genetic variability were considered to have high value as breeding material in the future maize breeding program in Korea.

In our study, the population structure of 32 Canadian maize inbred lines was investigated two different methods using a model-based clustering method (STRUCTURE) and distance-based phylogenetic methods (NTSYs). The model-based clustering method revealed the maximum ΔK value for K = 4, based on the method described by Evanno et al. (2005) (Figs. 2 and 3). 32 Canadian maize inbred lines were divided into four groups I, II, III, and IV by the distance-based phylogenetic method (Fig. 4). Although all maize inbred lines were divided into four subgroups by two different methods, the population structure patterns were not clearly distinguished based on their pedigree and heterotic groups. The information for population structure identified in this study should be useful in the selection of potentially successful crosses between these Canadian maize inbred lines.

Determining genes controlling agronomic traits is important for effective corn breeding programs. Association analysis is a powerful tool to detect marker-trait associations using linkage disequilibrium (Mezmouk et al. 2011; Sa et al. 2011). The Q GLM using population structure identified 20 marker-trait associations between 12 SSR markers and four traits in this study (P < 0.05) (Table 4). Some of the SSR primers in our study have been used in another previous QTL mapping studies. In a previous study, mmc0111 and phi065 were a flanking marker for QTL of plant height (Teng et al. 2013). The mmc0022 was associated with EH in this study, was a flanking marker for QTL for leaf greenness, the area of the third leaf (Jompuk et al. 2005). Moreover, nc005 is linked to QTL for lodging resistance (Lima et al. 2006). Another SSR marker, bnlg1621, related to LW in this study, is linked to QTL for traits related to Fusarium ear rot resistance (Abdel-Rahman et al. 2016) and QTL for traits related to gray leaf spot and flowering time (Liu et al. 2016). The bnlg1695 was associated with the PH in this study, was a flanking marker for QTL for phosphorus efficiency and traits related to roots (Chen et al. 2008). The umc1638 is linked to QTL for leaf length (Ku et al. 2012). The locus bnlg1012, associated with EH and SD in our analysis, was also associated with ear row number in other study (Sabadin et al. 2008). The bnlg1012, associated with EH and SD in our analysis, was also associated with ear row number in another study (Sabadin et al. 2008). Finally, bnlg1028 is linked to QTL for carotenoid composition (Kandianis et al. 2013). Thus, some SSR markers were found related to different traits, suggesting potential pleiotropy or tight linkage of genes. In conclusion, this study successfully confirmed the genetic diversity and population structure of 32 Canadian maize inbred lines. Four-cluster population structures by two different methods were found. Also, marker-trait associations were confirmed, which can assist in marker-assisted selection (MAS) for breeding programs. These results will help breeders choose parents for crossing combinations in maize breeding programs in Korea.

ACKNOWLEDGEMENTS

This study was supported by the Golden Seed Project (No. 213009-05-1-WT821, PJ012650012017), Ministry of Agriculture, Food, and Rural Affairs (MAFRA), Ministry of Oceans and Fisheries (MOF), Rural Development Administration (RDA), and Korea Forest Service (KFS), and 2017 Research Grant from Kangwon National University (No. D1001220-01-01).

Figures
Fig. 1. Histograms of allele frequencies for the 381 alleles in 32 Canadian maize inbred lines.
Fig. 2. Rate of change in the log probability of data between true K values (ΔK) described by Evanno et al. (2005).
Fig. 3. Assignment of 32 Canadian maize inbred lines to K = 4 by STRUCTURE program.
Fig. 4. UPGMA dendrogram base on the 50 SSR markers in 32 Canadian maize inbred lines.
Tables

Derivation of 32 Canada maize inbred lines used in this study.

Code No.Entry No.PedigreeHeterotic group
1CO416(A632 × CO125) CO125 (2)E.Flint
2CO417CB3 × CM383P3994
3CO418Ottawa Cold Tolerant Syn COE.Flint
4CO41924-44-1Minn13
5CO420CM423(DOR × A)Iodent
6CO421DEAIodent
7CO423Unknow Commercial HybridE.Butler
8CO425(B87 × CB8) CB8P3994
9CO428OH43 × H99Lanc
10CO429Pioneer 3707Lanc
11CO430Fusarium Resistant SyntheticP3990
12CO431Fusarium Resistant SyntheticIodent
13CO432Fusarium Resistant SyntheticMinn13
14CO433Pride K127Minn13
15CO434CM105 × A632BSSS
16CO435A632 × A634BSSS
17CO436CO275 × CO300P3994
18CO437European SyntheticE.Flint
19CO438CB3 × CL29P3994
20CO439Nebraska BSSSBSSS
21CO440Pride 5 × CO258Minn13
22CO441Jacques 7700 × CO298Lanc
23CO442Iodent/NSSIodent
24CO443B104 × CO272E.Flint
25CO444S1381 × CO328BSSS
26CO445CO386 × W64AHTLanc
27CO446CO341 × CO328BSSS
28CO447CO352 × CO328BSSS/Minn
29CO448CO237 × CO431P3990/Iodent
30CO449CO432 × CO433Minn13
31CO450Eyespot Resistant Synthetic (99ESR)BSSS/Mix
32CO451CO309 × CO328BSSS/Minn

Correlation coefficient, mean and standard deviation for 5 agronomic traits in total 32 Canadian maize inbred lines.

STPHEHLWLL
ST (Stem thickness)0.396*0.523**0.484**0.456**
PH (Plant height)0.700**0.2920.621**
EH (Ear height)0.383*0.676**
LW (Leaf width)0.425*
LL (Leaf length)
Mean (cm)1.8158.351.67.957.4
SD0.228.316.41.27.6
Min1.293.44.82.836.6
Max2.4214.683.810.276.8

* and **stand for significant at the 0.05 and 0.01 probability level, respectively.


Total number of alleles and genetic diversity index for 50 SSR loci in 32 Canadian maize inbred lines.

SSR lociChr.Allele size range (bp)No. of alleleMAFz)GDy)PICx)
bnlg1564195–12080.3750.7850.76
dupssr121115–13870.4380.7070.664
phi0561250–27060.3750.7150.663
phi0941160–20060.6880.5020.478
umc2012170–12080.4060.7050.659
phi109642135–14530.5940.5610.496
umc15512150–16580.4060.760.731
umc1042285–12090.250.8260.805
dupssr21295–13070.2810.7910.761
mmc01112145–22080.4380.7360.706
bnlg1182370–190110.3440.7830.757
mmc00223125–16080.3440.7810.752
mmc02513120–190150.2190.8890.879
umc1639390–10530.8130.320.294
umc1394395–18540.9060.1760.171
umc1086485–10580.4060.7480.716
umc17204120–17550.4380.6930.642
nc0054120–190120.2810.8340.815
phi021485–13560.4380.6930.642
bnlg16214165–215150.2190.8850.875
bnlg16955105–170130.250.8480.832
bnlg565555–130110.5310.6890.673
phi0085100–11540.750.40.395
umc1225590–135100.2190.8610.846
phi0245160–17550.5310.5940.522
bnlg2496105–155110.3750.7890.766
phi1236145–15540.3440.7010.642
umc11786145–16540.8440.2770.262
bnlg1371680–13560.3130.7830.751
nc0136100–12570.2810.8140.789
bnlg657775–11580.50.7070.684
phi1167160–18030.4690.6390.567
umc10667135–16050.5310.6450.6
umc13597220–31590.50.7030.678
umc1671775–8530.5310.5490.452
umc18638120–205120.2190.8710.858
umc16708115–13050.5310.6250.57
umc25948105–11550.4380.6820.627
bnlg11528150–260100.2190.8440.825
umc16658120–24570.50.6990.672
mmc0051990–10050.50.6620.615
phi0659130–235150.1560.910.903
bnlg244970–13080.3440.8110.79
bnlg10129220–13070.2810.8010.773
umc19829210–29030.5310.5960.522
bnlg21010120–15080.3440.7790.749
umc103810105–140120.3130.8280.711
bnlg102810140–22560.3440.770.735
bnlg145110105–18590.1880.8630.848
mmc050110155–19090.1880.8610.846
Total381
Average7.60.4140.7090.676

z)MAF: Major Allele Frequency,

y)GD: Genetic Diversity,

x)PIC: Polymorphic Information Content.


Information on marker-trait associations using a Q general linear model (Q GLM).

SSR markerChr.Phenotypic traitP-valueR2
phi0561SD0.04730.1
mmc01112EH0.00258.9
LW0.00160.5
PH0.02545.5
mmc00223EH0.00767.7
nc0054EH0.0261.1
PH0.03658
bnlg16214LW0.01879.3
bnlg16955PH0.04771.7
phi1167EH0.02921.6
bnlg10129EH0.04338.8
SD0.02741.8
phi0659PH0.00677.5
SD0.04619.1
umc19829PH0.01326
SD0.02422.7
bnlg102810PH0.00843.3
umc103810EH0.01379.5
LW0.00981.2
SD0.03973.7

References
  1. Abdel-Rahman, MM, Bayoumi, SR, and Barakat, MN (2016). Identification of molecular markers linked to Fusarium ear rot genes in maize plants Zea mays L. Biotechnology & Biotechnological Equipment. 30, 692-699.
    CrossRef
  2. Akagi, H, Yokozaki, Y, Inagaki, A, and Fujimura, T (1997). Highly polymorphic microsatellites of rice consist of AT repeats, and a classification of closely related cultivars with these microsatellite loci. Theor Appl Genet. 94, 61-67.
    Pubmed CrossRef
  3. Barata, C, and Carena, MJ (2006). Classification of North Dakota maize inbred lines into heterotic groups based on molecular and testcross data. Euphytica. 151, 339-349.
    CrossRef
  4. Borba, TCO, Brondani, RPV, Breseghello, F, Coelho, ASG, Mendonça, JA, and Rangel, PHN (2010). Association mapping for yield and grain quality traits in rice (Oryza sativa L.). Genet Mol Biol. 33, 515-524.
    CrossRef
  5. Bradbury, PJ, Zhang, Z, Kroon, DE, Casstevens, TM, Ramdoss, Y, and Buckler, ES (2007). TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 23, 2633-2635.
    Pubmed CrossRef
  6. Cai, H, Chu, Q, Gu, R, Yuan, L, Liu, J, and Zhang, X (2012). Identification of QTLs for plant height, ear height and grain yield in maize (Zea mays L.) in response to nitrogen and phosphorus supply. Plant Breed. 131, 502-510.
    CrossRef
  7. Cha, HJ, Choi, YP, Song, IK, Bok, TG, and Lee, HB (2009). Botanical and ear characteristices of the yellow glutinous corn hybrid, Daehakchal Gold 1, at various planting stages. J Agri Sci. 36, 123-127.
  8. Chen, JY, Xu, L, Cai, YL, and Xu, J (2008). QTL mapping of phosphorus efficiency and relative biologic characteristics in maize (Zea mays L.) at two sites. Plant Soil. 313, 251-266.
    CrossRef
  9. Dellaporta, SL, Wood, J, and Hicks, JB (1983). A simple and rapid method for plant DNA preparation. Version II Plant Mol Biol Rep. 1, 19-21.
    CrossRef
  10. Dice, LR (1945). Measures of the amount of ecologic association between species. Ecology. 26, 297-302.
    CrossRef
  11. Enoki, H, Sato, H, and Koinuma, K (2002). SSR analysis of genetic diversity among maize inbred lines adapted to cold regions of Japan. Theor Appl Genet. 104, 1270-1277.
    CrossRef
  12. Evanno, G, Regnaut, S, and Goudet, J (2005). Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 14, 2611-2620.
    Pubmed CrossRef
  13. Fan, XM, Tan, J, Chen, HM, and Yang, JY (2003). Heterotic grouping for tropical and temperate maize inbreds by analyzing combining ability and SSR markers. Maydica. 48, 251-257.
  14. Flint-Garcia, SA, Thuillet, AC, Yu, JM, Pressoir, G, Romero, SM, and Mitchell, SE (2005). Maize association population: a high resolution platform for quantitative trait locus dissection. Plant J. 44, 1054-1064.
    CrossRef
  15. Hallauer, AR, Russell, WA, and Lamkey, KR (1988). Corn breeding. Corn and corn improvement, Sprague, GF, and Dudley, JW, ed. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, pp. 463-564
  16. Hu, Z, Zhang, D, Zhang, G, Kan, G, Hong, D, and Yu, D (2014). Association mapping of yield-related traits and SSR markers in wild soybean (Glycine soja Sieb. and Zucc.). Breed Sci. 63, 441-449.
    Pubmed KoreaMed CrossRef
  17. Jompuk, C, Fracheboud, Y, Stamp, P, and Leipner, J (2005). Mapping of quantitative trait loci associated with chilling tolerance in maize (Zea mays L.) seedlings grown under field conditions. J Exp Bot. 56, 1153-1163.
    Pubmed CrossRef
  18. Kandianis, CB, Stevens, R, Liu, WP, Palacios, N, Montgomery, K, and Pixley, K (2013). Genetic architecture controlling variation in grain carotenoid composition and concentrations in two maize populations. Theor Appl Genet. 126, 2879-2895.
    Pubmed KoreaMed CrossRef
  19. Kashiwagi, T, Togawa, E, Hirotsu, N, and Ishimaru, K (2008). Improvement of lodging resistance with QTLs for stem diameter in rice (Oryza sativa L.). Theor Appl Genet. 117, 749-757.
    Pubmed CrossRef
  20. Ku, LX, Zhang, J, Guo, SL, Liu, HY, Zhao, RF, and Chen, YH (2012). Integrated multiple population analysis of leaf architecture traits in maize (Zea mays L.). J Exp Bot. 63, 261-274.
    CrossRef
  21. Lima, MDA, de Souza, CL, Bento, DAV, de Souza, AP, and Carlini-Garcia, LA (2006). Mapping QTL for grain yield and plant traits in a tropical maize population. Mol Breed. 17, 227-239.
    CrossRef
  22. Liu, K, and Muse, SV (2005). PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics. 21, 2128-2129.
    Pubmed CrossRef
  23. Liu, L, Zhang, YD, Li, HY, Bi, YQ, Yu, LJ, and Fan, XM (2016). QTL mapping for gray leaf spot resistance in a tropical maize population. Plant Dis. 100, 304-312.
    CrossRef
  24. Mezmouk, S, Dubreuil, P, Bosio, M, Décousset, L, Charcosset, A, and Praud, S (2011). Effect of population structure corrections on the results of association mapping tests in complex maize diversity panels. Theor Appl Genet. 122, 1149-1160.
    Pubmed KoreaMed CrossRef
  25. Mumm, RH, and Dudley, JW (1994). A classification of 148 U.S. maize inbreds: I. Cluster analysis based on RFLPs. Crop Sci. 34, 842-851.
    CrossRef
  26. Park, KJ, Lee, JK, Sa, KJ, and Koh, HJ (2012). Genetic analyses for yield components and taste-associated traits in F2:3 population derived from the cross between waxy and sugary maize inbred line. Korean J Breed Sci. 44, 328-337.
  27. Pasam, RK, Sharma, R, Malosetti, M, van Eeuwijk, FA, Haseneyer, G, and Kilian, B (2012). Genome-wide association studies for agronomical traits in a worldwide spring barley collection. BMC Plant Biol. 27, 12-16.
  28. Pejic, I, Ajmone-Marsan, P, Morgante, M, Kozumplick, V, Castiglioni, P, and Taramino, G (1998). Comparative analysis of genetic similarity among maize inbred lines detected by RFLPs, RAPDs, SSR, and AFLPs. Theor Appl Genet. 97, 1248-1255.
    CrossRef
  29. Pritchard, JK, and Wen, W (2003). Documentation for STRUCTURE software: Version 2.http://web.stanford.edu/group/pritchardlab/structure.html
  30. Reid, LM, Xiang, K, Zhu, X, Baum, BR, and Molnar, SJ (2011). Genetic diversity analysis of 119 Canadian maize inbred lines based on pedigree and simple sequence repeat markers. Can J Plant Sci. 91, 651-661.
    CrossRef
  31. Rohlf, FJ (1998). NTSYS- pc: Numerical taxonomy and multivariate analysis system. Version: 2.02. Setauket, New York: Exeter Software
  32. Sa, KJ, Kim, JA, Park, KJ, Park, JY, Goh, BD, and Lee, JK (2011). Analysis of genetic diversity and population structure for core set of waxy and normal maize inbred lines using SSR Markers. Kor J Breed Sci. 43, 362-373.
  33. Sabadin, PK, Souza, CL, Souza, AP, and Garcia, AAF (2008). QTL mapping for yield components in a tropical maize population using microsatellite markers. Hereditas. 145, 194-203.
    CrossRef
  34. Teng, F, Zhai, L, Liu, R, Bai, W, Wang, L, and Huo, D (2013). ZmGA3ox2, a candidate gene for a major QTL, qPH3.1, for plant height in maize. Plant J. 73, 405-416.
    CrossRef
  35. Wang, R, Yu, Y, Zhao, J, Shi, Y, Song, Y, and Wang, T (2008). Population structure and linkage disequilibrium of a mini core set of maize inbred lines in China. Theor Appl Genet. 117, 1141-1153.
    Pubmed CrossRef
  36. Xue, Y, Warburton, ML, Sawkins, M, Zhang, X, Setter, T, and Xu, Y (2013). Genome-wide association analysis for nine agronomic traits in maize under well-watered and water-stressed conditions. Theor Appl Genet. 126, 2587-2596.
    Pubmed CrossRef
  37. Yu, J, and Buckler, ES (2006). Genetic association mapping and genome organization of maize. Curr Opin Biotechnol. 17, 155-160.
    Pubmed CrossRef