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Genes & Genomics

, Volume 41, Issue 6, pp 667–678 | Cite as

Construction of genetic linkage map and identification of QTLs related to agronomic traits in DH population of maize (Zea mays L.) using SSR markers

  • Jae-Keun Choi
  • Kyu Jin Sa
  • Dae Hyun Park
  • Su Eun Lim
  • Si-Hwan Ryu
  • Jong Yeol Park
  • Ki Jin Park
  • Hae-Ik Rhee
  • Mijeong Lee
  • Ju Kyong LeeEmail author
Research Article
  • 121 Downloads

Abstract

Background

In this study, we used phenotypic and genetic analysis to investigate Double haploid (DH) lines derived from normal corn parents (HF1 and 11S6169). DH technology offers an array of advantages in maize genetics and breeding as follows: first, it significantly shortens the breeding cycle by development of completely homozygous lines in two or three generations; and second, it simplifies logistics, including requiring less time, labor, and financial resources for developing new DH lines compared with the conventional RIL population development process.

Objectives

In our study, we constructed a maize genetic linkage map using SSR markers and a DH population derived from a cross of normal corn (HF1) and normal corn (11S6169).

Methods

The DH population used in this study was developed by the following methods: we crossed normal corn (HF1) and normal corn (11S6169), which are parent lines of a normal corn cultivar, in 2014; and the next year, the F1 hybrids were crossed with a tropicalized haploid inducer line (TAIL), which is homozygous for the dominant marker gene R1-nj (Nanda and Chase in Crop Sci 6:213–215, 1966), and we harvested seeds of the haploid lines.

Results

A total of 200 SSR markers were assigned to 10 linkage groups that spanned 1145.4 cM with an average genetic distance between markers of 5.7 cM. 68 SSR markers showed Mendelian segregation ratios in the DH population at a 5% significance threshold. A total of 15 quantitative trait loci (QTLs) for plant height (PH), ear height (EH), ear height ratio (ER), leaf length (LL), ear length (EL), set ear length (SEL), set ear ratio (SER), ear width (EW), 100 kernel weight (100 KW), and cob color (CC) were found in the 121 lines in the DH population.

Conclusion

The results of this study may help to improve the detection and characterization of agronomic traits and provide great opportunities for maize breeders and researchers using a DH population in maize breeding programs.

Keywords

Maize Genetic map DH population QTLs Agronomic trait SSR marker 

Introduction

Maize (Zea mays L.) is one of the most economically important crops in the world as food, forage, industrial, and energy materials. Improvement of grain yield and yield component traits has become most important in maize breeding programs because of rapid population growth and decreases in the area of cultivated land (Li et al. 2011a). Although grain yield and yield component traits are the most important traits in maize breeding programs, these are very complex quantitative traits that are polygenic with small effects, have low inheritance, and are easily influenced by the environment (Li et al. 2011a). Grain yield related traits are affected by different correlations of some yield components (Yang et al. 2012). For these reasons, breeders and farmers have had difficulty in improving the grain yield of maize and developing new cultivars. Thus, there is a need for more phenotypic and genetic information about grain yield and yield component traits.

In conventional breeding, which is laborious and time-consuming, it is necessary to have many field tests and accurate evaluation in the field in order to improve the important traits for maize yield and quality (Duvick et al. 2004). However, marker-assisted selection (MAS) enables breeders to select desired phenotypes based on genotypes directly and easily (Zhang et al. 2011b). The confirmation of quantitative trait loci (QTLs) is most important for marker assisted selection (Yu et al. 2005). The construction of genetic maps is a first step for application of molecular markers and enables the location of genes or QTLs to be found (Semagn et al. 2006). The associations between agronomic traits and genetic markers, which are confirmed by genetic and QTL mapping, are very helpful for improving yield and yield related traits of crops (Young 1995; Tanksley et al. 1996; Lombard and Delourme 2001). In order to construct a linkage map, it is necessary to develop an appropriate mapping population. Among the different mapping populations, such as RILs (recombinant inbred lines), F2, NILs (near isogenic lines), and double haploid (DH) population, in general, F2 and RIL populations have been used for genetic analysis and QTL mapping in many crop plants (Mclntyre et al. 2010; Liu et al. 2010; Lu et al. 2011). In particular, an RIL population is permanent or immortal without genetic change because this population is homozygous (Semagn et al. 2006). Recently, doubled haploid technology now offers an array of advantages in maize genetics and breeding as follows: first, it significantly shortens the breeding cycle by development of completely homozygous lines in two or three generations, instead of the conventional inbred line development process, which takes at least 6–8 generations to derive lines with ~ 99% homozygosity; and second, it simplifies logistics, including requiring less time, labor, and financial resources for developing new DH lines compared with the conventional RIL population development process (Röber et al. 2005; Forster and Thomas 2005; Geiger 2009; Geiger and Gordillo 2009; Chang and Coe 2009).

In maize breeding programs, improvement of grain yield traits is very difficult because grain yield is related to the environment and also there are many traits that are related to grain yield. For example, plant height (PH) and ear height (EH) are factors related to plant density and lodging resistance (Zhang et al. 2011b). Especially, PH is closely associated with forage and grain yield, canopy photosynthesis, and harvest index (Beavis et al. 1991; Lübberstedt et al. 1998; Troyer and Larkins 1985). In addition, plants with a lower EH are more resistant to lodging among plants with a similar PH (Cai et al. 2012). PH generally has a strong correlation with EH in maize (Landi et al. 1998). Also the ear height ratio (ER) is related with plant lodging resistance and, in general, plants with an ER under 50% have higher lodging resistance (Ryu et al. 2001). Moreover, high-yield maize breeding has become possible by improving plant architecture (Duvick 2005; Li et al. 2011b; Wang et al. 2011). Leaf length is a major trait for plant architecture that affects the ability of light capture for photosynthesis, thereby affecting final grain yield (Wang et al. 2017).

With the development of molecular markers, many studies have reported genetic analysis of grain yield and its component traits, and QTLs have been detected on all ten chromosomes in maize (Landi et al. 1998; Cai et al. 2012; Choe and Rocheford 2012; Cui et al. 2017). Among DNA-based molecular markers, simple sequence repeats (SSR) have been extensively used to construct linkage maps in mapping populations of F2, BC2, NILs, or RILs (Sabadin et al. 2008; Yang et al. 2012; Park et al. 2014; Sa et al. 2015; Zhao et al. 2018). In maize, SSRs are considered one of the most suitable markers for constructing genetic linkage maps and assessing QTLs because of their high level of allelic variation and co-dominant nature (Akagi et al. 1997; Collard et al. 2005; Park et al. 2009; Sa et al. 2015).

Therefore, the objectives of our present study were to: (1) construct a genetic linkage map using SSR markers in the 121 DH lines of a DH population derived from a cross between normal corn (HF1) and normal corn (11S6169), and (2) identify QTLs associated with traits related to grain yield and yield component traits by employing a genetic map of the DH population.

Materials and methods

Plant material and phenotypic evaluation

The DH population used in this study was developed by the following methods: we crossed normal corn (HF1) and normal corn (11S6169), which are parent lines of a normal corn cultivar, in 2014; and the next year, the F1 hybrids were crossed with a tropicalized haploid inducer line (TAIL), which is homozygous for the dominant marker gene R1-nj (Nanda and Chase 1966), and we harvested seeds of the haploid lines. In 2016, DH lines of a DH population were obtained from the haploid plants induced by colchicine treatment. The ten plants each of 121 DH lines were evaluated using completely random design with two replicates and 70 × 25 cm of planting density for 16 agronomic traits at the Maize Experiment Station in 2017. Standard agronomic practices were applied from sowing to harvest (May–October). We assessed 16 agronomic traits, namely days to anthesis (DA), days to silking (DS), anthesis-silking interval (ASI), plant height (PH), ear height (EH), ear height ratio (ER), stem diameter (SD), leaf length (LL), leaf width (LW), ear length (EL), set ear length (SEL), set ear ratio (SER), ear width (EW), ear row number (ERN), 100 kernel weight (100 KW), and cob color (CC) (Table 1). Basic statistics were performed using Microsoft Office Excel 2013. Correlation analysis was calculated using IBM SPSS Statistics 21.
Table 1

The 16 agronomic traits measured in this study of the parents and DH populations

Trait

Investigated generation

Unit

Remark

Days of anthesis (DT)

Parents and DH plants

days

Days from sowing to tasseling

Days of silking (DS)

Parents and DH plants

days

Days from sowing to silking

Anthesis-silking interval (ASI)

Parents and DH plants

days

Interval between anthesis and silking

Plant height (PH)

Parents and DH plants

cm

Distance from soil level to base of tassel

Ear height (EH)

Parents and DH plants

cm

Distance from soil level to base of main ear

Ear height ratio (ER)

Parents and DH plants

%

(Ear height/plant height) × 100

Stem diameter (SD)

Parents and DH plants

mm

Diameter of stem

Leaf length (LL)

Parents and DH plants

cm

Length of leaf

Leaf width (LW)

Parents and DH plants

cm

Width of leaf

Ear length (EL)

Parents and DH plants

cm

Length of ear

Setted ear length (SEL)

Parents and DH plants

cm

Length of setted ear

Setted ear ratio (SER)

Parents and DH plants

%

(Setted ear length/ear length) × 100

Ear width (EW)

Parents and DH plants

cm

Width of ear

Ear row number (ERN)

Parents and DH plants

row

Number of ear row

100 kernel weight (100KW)

Parents and DH plants

g

Weight of 100 kernel

Cob color (CC)

Parents and DH plants

color

Color of cob (1: white, 2: light purple, 3: purple)

DNA extraction and SSR analyses

Genomic DNA was extracted using young leaves from the female and male parents and the DH mapping population was generated by the method described by Dellaporta et al. (1983), with minor modifications. Polymerase chain reactions were performed using SSR markers in a total volume of 30 μL containing 20 ng genomic DNA, 1 × PCR buffer, 0.3 μM of forward and reverse primers, 0.2 mM dNTPs, and 1 unit of Taq polymerase (Biotools). The amplification profile consisted of an initial denaturation at 94 °C for 5 min, followed by 2 cycles of denaturing at 94 °C for 1 min, annealing at 65 °C for 1 min, and extension at 72 °C for 2 min. After the second cycle, the annealing temperature was decreased by 1 °C in every second cycle to 55 °C. The last cycle was repeated 20 times. When the cycles were completed, the extension cycle was extended for 10 min at 72 °C.

The amplified PCR products were added to an equal volume of stop solution (98% deionized formamide, 2 mM EDTA, 0.05% bromophenol blue, 0.05% xylene cyanol) and heated at 95 °C for 5 min. A 3 μL aliquot of each reaction mixture was analyzed with help of 6% denaturing polyacrylamide gel electrophoresis stained with silver.

Linkage mapping and QTL analysis

To construct the linkage map, linkage analysis was performed using Mapmaker 3.0 (Lander et al. 1987). A framework map was selected for each mapping population to facilitate chromosome assignment and map comparisons. Linkage groups were created with logarithm of the odds (LOD) scores of 7.0. All map distances were calculated according to the Kosambi mapping function (Kosambi 1944). Chi square analysis was conducted at a significance threshold of 5% to detect any deviations from the expected Mendelian segregation ratio of 1:1 (A:B).

QTL mapping for 16 agronomic traits related to grain yield and quality was performed on the 121 DH lines of the DH population using a mixed linear model approach and QTL Mapper 1.6 (Wang et al. 1999).

Results

Phenotypic evaluation and correlation analysis for 16 agronomic traits in DH population

The results of phenotypic variation for 16 agronomic traits were evaluated for DH plant families and their parental lines at 2017 (Fig. 1; Table 2). Among all traits that were evaluated in this study, some traits such as PH, EH, ER, SD, LL, and LW showed statistically significant differences between the two parental lines by t test (P < 0.05). Also there was a difference in one qualitative trait, CC, between the parental lines. However, the remaining traits, DA, DS, ASI, EL, SEL, SER, EW, ERN, and 100 KW, showed similar values between the two parental lines. Among the traits with significant differences, the parent HF1 (P1) had longer or higher PH, EH, ER, SD, and LW than the other parent 11S6169 (P2); while LL of HF1 (P1) was shorter than that of 11S6169 (P2). In the case of CC, the parent HF1 (P1) had a purple cob, while 11S6169 (P2) had a white cob (Fig. 1; Table 2).
Fig. 1

Frequency distribution of average value for 16 agronomic traits of parent and DH populations

Table 2

Correlation coefficients and the 16 agronomic characters of parents, DH populations

  

DA

DS

ASI

PH

EH

ER

SD

LL

LW

EL

SEL

SER

EW

ERN

100KW

CC

DA

  

0.932**

0.145

− 0.357**

− 0.292**

− 0.115

− 0.404**

− 0.122

− 0.371**

− 0.287**

− 0.301**

− 0.165

0.025

− 0.166

0.124

0.011

DS

   

0.460**

− 0.424**

− 0.339**

− 0.124

− 0.475**

− 0.159

− 0.471**

− 0.393**

− 0.388**

− 0.154

− 0.072

− 0.239**

0.060

− 0.021

ASI

    

− 0.228*

− 0.168

− 0.050

− 0.283**

− 0.115

− 0.383**

− 0.321**

− 0.267**

0.004

− 0.204*

− 0.215*

− 0.198*

− 0.052

PH

     

0.810**

0.266**

0.499**

0.473**

0.501**

0.341**

0.324**

0.101

0.247**

0.186*

0.154

0.119

EH

      

0.777**

0.274**

0.453**

0.431**

0.296**

0.298**

0.137

0.206*

0.233*

0.032

0.082

ER

       

− 0.053

0.252**

0.203*

0.148

0.168

0.124

0.095

0.206*

− 0.098

0.005

SD

        

0.331**

0.544**

0.445**

0.424**

0.114

0.262**

0.192*

0.209*

0.088

LL

         

0.325**

0.356**

0.347**

0.101

0.251**

0.074

0.293**

− 0.047

LW

          

0.486**

0.460**

0.141

0.170

0.184*

0.083

− 0.061

EL

           

0.855**

0.045

0.366**

0.389**

0.100

− 0.077

SEL

            

0.550**

0.320**

0.387**

0.041

0.025

SER

             

0.037

0.136

− 0.076

0.167

EW

              

0.736**

0.426**

0.206*

ERN

               

− 0.047

0.091

100KW

                

0.163

 

P1 (HF1)

80

84

4

223.1

102.6

46.0

16.0

64.6

9.6

11.4

11.1

97.0

37.5

11.6

29.9

3

 

P2 (11S6169)

79

83

4

211.4

81.9

38.7

12.3

75.8

7.5

11.0

9.9

90.2

36.3

12.0

28.8

1

DH

Mean

75.0

80.3

5.3

206.4

86.8

41.9

15.0

80.1

9.3

12.7

10.5

82.7

36.5

11.8

28.3

 
 

SD

4.1

4.7

1.6

25.5

16.5

5.0

2.5

8.3

1.1

2.3

2.3

9.8

4.4

1.9

4.9

 
 

Min

68

72

0

145.0

46.5

27.5

4.3

58.8

6.0

5.8

4.5

52.8

21.2

4.0

15.8

 
 

Max

91

100

9

282.0

133.8

55.5

24.7

100.0

12.0

19.5

16.5

100.0

45.7

16.3

45.6

 

**Significance at P < 0.01

*Significance at P < 0.05

In addition, the average number of days of DA and DS for the DH plant families were less than those of HF1 and 11S6169. The average number of days of ASI for the DH plant families was more than that for both parental lines. The PH for the DH plant families was shorter than that for both parental lines. The average of EH and ER for the DH population was a mid-value of both parents. In the case of SD, the average value of the DH plant families was similar to that of the parental line HF1. The average LL of the DH plant families was longer than that for both parental lines. The average LW for the DH plant families was longer than that of 11S6169, but similar to that of HF1. The average EL for the DH plant families was longer than that of HF1 and 11S6169. The averages of SEL and EW for the DH plant families were mid-values of those of HF1 and 11S6169. In addition, the average value of SER for the DH plan plant families was lower than that of HF1 and 11S6169. The ERN for the DH plant families was similar to that of both parental lines. The average 100 KW for the DH plant families was less than that of HF1 and 11S6169 (Fig. 1; Table 2). The CC trait was distributed as 51 white, 9 light purple, and 61 purple in the 121 DH plant families (Fig. 1).

Correlation coefficients among the 16 agronomic traits are shown in Table 2. Among all the 120 correlation combinations, 67 combinations showed statistically significant correlation at P < 0.05 (Table 2). Moreover, 46 combinations were positive correlations and 21 combinations were negative correlations. DA showed the highest correlation with DS (0.932**); and then EL via SEL (0.855**), PH via EH (0.810**), EH via ER (0.777**), and EW via ERN (0.736**) had relatively higher coefficients than the other trait combinations (Table 2).

Linkage map construction and Chi square analysis

To construct a linkage map, DNA polymorphisms between the parental lines HF1 and 11S6169 were surveyed with 500 SSR primer pairs. A total of 223 SSR markers showed polymorphisms between the parents and were used for construction of a linkage map. Three-point linkage at LOD 7.0 analyses confirmed that a total of 200 SSR markers were grouped into 10 linkage groups with each of the 10 maize chromosomes (ch.) represented (Fig. 2; Table 3). The remaining 23 SSR markers were excluded from further mapping analysis because these markers were not linked to any group at LOD 7.0. The size of the framework map spanned 1,145.4 cM across all 10 linkage groups. The average distance between pairs of markers among all linkage groups was 5.7 cM. The number of loci was 20 markers in each linkage group. Genetic distance per linkage group ranged from 73.1 cM (ch. 6) to 223.5 cM (ch. 4). Average distance per chromosome ranged from 3.7 cM (ch. 5 and 6) to 11.2 cM (ch. 4) (Fig. 2; Table 3).
Fig. 2

Chromosomal location of quantitative trait loci (QTLs) for 10 agronomic traits. Map distances (on the left) are given in cM (Kosambi function). The segregation distorted markers are highlighted by black circles and white circles, which denote HF1 and 11S6169 preferentially distorted markers, respectively

Table 3

The number of marker loci per chromosome and the chromosome length (cM) in the DH mapping population

Molecular marker

Number of loci in each chromosome

Total loci

1

2

3

4

5

6

7

8

9

10

SSR

20

20

20

20

20

20

20

20

20

20

200

Length (cM)

122.2

76.8

127.9

223.5

74.6

73.1

79.4

121.4

167.3

79.2

1145.4

Avg. loci interval

6.1

3.8

6.4

11.2

3.7

3.7

4.0

6.1

8.4

4.0

5.7

Chi square tests showed that 132 SSR markers exhibited segregation in a 1:1 Mendelian ratio, and the other 68 SSR markers deviated from the expected ratio of 1:1 at a 5% significance threshold. Among the 68 segregation-distorted markers, 30 markers were skewed towards the P1 parent, and these were located on chromosomes 2, 5, 7, and 9. Another 38 markers were skewed towards the P2 parent and were located on chromosomes 1, 3, 4, 6, 8, 9, and 10 (Fig. 2). Loci skewed towards the P1 and P2 parents are highlighted in Fig. 2 with black and white circles, respectively. In general, the chromosomal locations of almost all the SSR markers are in agreement with the locations shown by data at MaizeGDB (http://www.maizeGDB.org).

QTL mapping for agronomic traits

QTL mapping analysis was performed with a total of 16 agronomic traits. From the results, a total of 15 QTLs detected in 10 traits were mapped on maize chromosomes 1, 2, 3, 4, 5, 7, and 10 (Fig. 2; Table 4). Among the total 15 QTLs, two QTLs were associated with PH, one with EH, one with ER, two with LL, two with EL, two with SEL, two with SER, one with EW, one with 100 KW, and one with CC (Fig. 2; Table 4). The two QTLs related to PH (qPH4 and qPH10) were detected on chromosomes 4 and 10 and accounted for 13.78% and 9.70%, respectively, of the phenotypic variance. The one QTL related to EH (qEH10) was identified on chromosome 10 and explained 18.09% of the phenotypic variance. The QTL related to ER (qER5) was identified on chromosome 5 and related with 8.55% of the phenotypic variance. The two QTLs on chromosomes 2 and 7 (qLL2 and qLL7) were associated with LL and accounted for 9.33% and 9.16%, respectively, of the phenotypic variance. The two QTLs on chromosomes 2 and 5 (qEL2 and qEL5) were associated with EL and accounted for 12.48% and 16.76%, respectively, of the phenotypic variance. The two QTLs on chromosomes 7 and 10 (qSEL7 and qSEL10) were associated with SEL and explained 14.36% and 8.68%, respectively, of the phenotypic variance. The two QTLs on chromosomes 2 and 4 (qSER2 and qSER4) were associated with SER and associated with 9.55% and 10.70%, respectively, of the phenotypic variance. The one QTL related to EW on chromosome 3 (qEW3) accounted for 38.08% of the phenotypic variance. The QTL related to 100 KW on chromosome 7 (q100KW7) was associated with 10.58% of the phenotypic variance. The one QTL related to CC on chromosome 1 (qCC1) accounted for 84.93% of the phenotypic variance (Fig. 2; Table 4).
Table 4

Detection of QTL for agronomic traits in the DH populations

Trait

Chr

QTL

Interval

LOD

A

R2

Plant Height

4

qPH4

phi295450–umc1702

2.84

9.96

13.78

10

qPH10

bnlg1451–mmc0501

2.43

8.36

9.70

Ear height

10

qEH10

mmc0501–umc2163

5.06

6.28

18.09

Ear height ratio

5

qER5

umc1048–umc1447

2.46

1.41

8.55

Leaf length

2

qLL2

umc1635–umc1581

2.96

− 2.56

9.33

7

qLL7

bnlg1094–mmc0411

2.52

− 2.54

9.16

Ear length

2

qEL2

bnlg1606–umc1551

3.65

0.76

12.48

5

qEL5

umc1162–umc2301

6.00

0.89

16.76

Setted ear length

7

qSEL7

bnlg155–umc1112

4.26

− 0.82

14.36

10

qSEL10

umc1911–umc1697

2.94

0.64

8.68

Setted ear ratio

2

qSER2

umc1961–umc1845

2.62

− 2.87

9.57

4

qSER4

bnlg1444–umc2365

3.29

− 3.04

10.70

Ear width

3

qEW3

bnlg1601–umc1167

3.29

22.43

38.08

100 kernel weight

7

q100KW7

umc1978–umc1095

2.95

1.57

10.58

Cob color

1

qCC1

umc2124–bnlg1811

45.40

0.86

84.93

Discussion

In this study, we used phenotypic and genetic analysis to investigate DH lines derived from normal corn parents (HF1 and 11S6169). DH technology offers an array of advantages in maize genetics and breeding. The salient steps in DH development are: (1) crossing the source population (usually a hybrid generated using desired lines) as female parent with pollen of the haploid inducer; (2) identification of haploid kernels using anthocyanin color marker; (3) germination of haploid seeds; (4) safe application of colchicine or any other effective chromosome doubling agent to the haploid seedlings; (5) proper agronomic management of D0 seedlings and derivation of D1 (DH) seed by self-pollinating D0 plants; and (6) further selection and utilization of DH lines in breeding programs (Röber et al. 2005; Forster and Thomas 2005; Geiger 2009; Geiger and Gordillo 2009; Prasanna et al. 2012). Therefore, DH technology in maize breeding, based on in vivo haploid induction, is recognized worldwide as an important means of enhancing breeding efficiency. One advantage of DH technology is that the breeding cycle is significantly shortened by the development of completely homozygous lines in two or three generations, and another advantage is that product development is accelerated by the allowance of rapid pyramiding of favorable alleles for polygenic traits influencing maize productivity.

High yield is the most important and essential factor in a maize breeding program, and a field test and genetic analysis are needed to investigate yield related traits. In our study, the average values of the DH plant families for DA, DS, PH, EH, ER, SD, LW, SEL, EW, and ERN showed a middle value between the values of P1 and P2. For the traits ASI, LL, and EL, the values obtained for the DH plant families were higher than those of both parental lines, while those of the two traits SER and 100 KW were lower than those of the parental lines (Table 2). In addition, we detected 67 significant correlation pairs among 16 agronomic traits in a correlation analysis (P < 0.05). Among these significant pairs, most pairs with flowering traits (DA, DS, ASI) showed negative correlations with all other traits except for SER, EW, 100 KW, and CC. Moreover, DA and DS showed the highest correlation in this study. Flowering related traits are important traits for maize breeding programs because these traits have been strongly correlated with grain yield and ear related traits (Almeida et al. 2013; Park et al. 2014). Moreover, combinations for EL with SEL, PH with EH, EH with ER, and EW with ERN showed higher correlations than the other combinations (Table 2). These high correlations have also been shown in many previous studies (Cai et al. 2012; Choe and Rocheford 2012; Sa et al. 2015; Zhao et al. 2018).

In this study, we successfully constructed a linkage map comprising 200 SSR markers from a DH population derived from a cross between HF1 and 11S6169. In addition, almost all the SSR markers in the DH linkage map were well distributed throughout the 10 maize chromosomes; their positions are in agreement with the published mapping in the Maize Genetics and Genomic Database, MaizeGDB (http://www.maizeGDB.org) (Fig. 2; Table 3). The resulting framework map for QTL mapping was used to identify genetic regions associated with 16 agronomic traits related to grain yield and yield component in 2017. Among the 200 SSR markers mapped in our linkage map, we confirmed a Mendelian segregation ratio (1:1) by Chi square tests. Although 132 SSR markers (66%) showed the Mendelian segregation ratio, 68 SSR loci (34%) showed distorted segregation in all chromosomes of the DH population. Such deviation from the expected segregation ratio has been reported in previous research including for maize and other crops (Lee et al. 2006; Liu et al. 2008; Sa et al. 2012). In previous studies, about 13.9 and 14.6% of SSR markers used for mapping showed distorted segregation in the F2 and RIL populations, respectively (Park et al. 2014; Sa et al. 2012). When compared with different mapping populations, our DH population had a higher percentage of segregation distortion markers than the F2 and RIL populations in maize. These results are supported by the results of Yamagishi et al. (2010) in rice. The markers showing segregation distortion for HF1 (P1) were mainly clustered in chromosomes 2, 7, and 9; whereas those distorted for 11S6169 (P2) were mostly clustered in chromosomes 3, 6, and 8. These chromosome regions of skewed markers for HF1 or 11S6169 may be considered as regions of distorted segregation in the maize genome. The chromosomal regions of distorted segregation in this study were a little different from those in previous maize studies (Gardiner et al. 1993; Wang et al. 2012). Gardiner et al. (1993) detected distorted chromosomal regions on chromosomes 1, 2, 3, and 5 using the F2 population. In addition, Wang et al. (2012) confirmed chromosomal regions with segregation distortion in most chromosomes using two F2 and BC mapping populations. These results indicate that segregation distortions from expected Mendelian ratios may be influenced by the mapping population.

Although 16 agronomic traits were used for QTL mapping in this study, a total of 15 QTLs for 10 traits were detected in the DH population derived from HF1 and 11S6169. In detail, the 15 QTLs were detected in PH, EH, ER, LL, EL, SEL, SER, EW, 100 KW, and CC and also located on seven maize chromosomes, 1, 2, 3, 4, 5, 7 and 10, and not on chromosomes 6, 8, and 9 (Fig. 2; Table 4). In a previous study, Collard et al. (2005) reported that a major QTL is defined as one contributing to more than 10% of phenotypic variation. In addition, Alvi and Tuberosa, (2005) defined a major QTL as having a relatively large amount of the proportion of the phenotypic variation being explained by the QTL with R2 > 15%. Among all the QTLs detected in this study, nine QTLs, namely q100KW7 (10.58%), qSER4 (10.70%), qEL2 (12.48%), qPH4 (13.78%), qSEL7 (14.36%), qEL5 (16.76%), qEH10 (18.09%), qEW3 (38.08%), and qCC1 (84.93%) accounted for more than 10% of phenotypic variation and meet the criteria of Collard et al. (2005). Furthermore, four of these QTLs, namely qEL5, qEH10, qEW3, and qCC1, accounted for over 15% of phenotypic variation. Therefore, these nine QTLs in our study may be useful as major QTLs in maize breeding programs.

Our study also compared the QTLs with the same overlapped flanking markers from previous studies and showed that four flanking markers, umc1635, mmc0411, umc1697, and umc2163, were overlapped with those of a previous report by Cai et al. (2012). Among these overlapped markers, two markers, umc1635 and mmc0411, were associated with LL traits in this study on chromosomes 2 and 7, respectively. However, these markers were confirmed for plant height/ear height ratio and kernel number per ear (umc1635) and plant height, kernel number per ear, and 100 kernel weight (mmc0411) in the previous study of Cai et al. (2012). The remaining overlapping flanking markers, umc1697 and umc2163, were mapped on chromosome 10 related to SEL and EH, respectively, in this study; however, in the report of Cai et al. (2012) umc1697 and umc2163 were associated with traits for plant height. In addition, three flanking markers, umc1112, umc2163, and bnlg1094, which were detected in this study were also detected in a previous report by Choe and Rocheford (2012). Although these markers were QTLs for SEL, EH, and LL in this study, in the report of Choe and Rocheford (2012) the QTLs were for cob weight (umc1112), kernel length and weight (umc2163), and lower germinal, kernel weight (bnlg1094). Three markers, bnlg1606, umc1551, and mmc0411, were only related to traits for leaf width in a report by Liu et al. (2017), but these flanking markers were associated with two different traits, EL and LL, in this study. The QTLs for EL, EH, and CC in this study shared the same flanking markers, umc1162, umc2163, and bnlg1811, with those in a previous report by Yang et al. (2012) related to 100 grain weight and grain oil content. Lu et al. (2006) found QTLs for kernel number per row and kernel weight per plant using umc1635, mmc0411, and umc2365, which were associated with QTLs for LL and SER in this study. In a previous study of Zhang et al. (2011b), two QTLs for plant height were detected on chromosomes 2 and 7 with flanking markers umc1845 and bnlg1094. Our study also contained the same flanking markers, but they were associated with different traits, SER (umc1845) and LL (bnlg1094). The flanking markers umc1112 and umc1095 were mapped on QTLs for SEL and 100 KW, respectively, in this study; however, these markers were identified with traits for resistance to Gibberella ear rot in a report by Ali et al. (2005). Even though bnlg1444 on chromosome 4 and umc1447 on chromosome 5 were mapped on QTLs for circularity of kernel and kernel length and perimeter length in Jiang et al. (2015), these markers were confirmed to QTLs for SER and ER in this study. In another report of Liu et al. (2014), bnlg1811 and umc1911 were associated with only one trait, 20-kernel thickness; however, these two flanking markers were associated with CC and SEL, respectively, in our study. Furthermore, umc1162 and umc2163, flanking markers for QTLs related to plant height and top height in a study of Wei et al. (2009), were identified in QTLs related to EL and EH in our study.

Some flanking markers in this study confirmed those of diverse previous studies but for different traits. For example, bnlg1451 for a QTL of PH in this study was confirmed by three different studies for common rust disease resistance, plant height, grain yield, and number of kernels per plant (Danson et al. 2008; Frascaroli et al. 2007; Zhao et al. 2018). One marker, umc2163, was detected on a QTL for EH, but in other studies it was associated with ear length (Li et al. 2009), 100 grain weight and ear diameter (Li et al. 2011a), and days to pollen shed (Wang et al. 2010). Flanking marker bnlg1811 for CC in this study was associated with two different traits for leaf and stem arsenic concentration (Ding et al. 2011a) and grain filling rate (Liu et al. 2011). Also the maize P locus is known to be involved in the synthesis of a red flavonoid pigment in cobs in bin 1.03 based on QTL mapping (Lechelt et al. 1989; Meyer et al. 2007). The QTL for CC in this study appears to be closely related with the P locus, but the location of bnlg1811 detected in this study is bin 1.04. The mmc0501 was only mapped to one trait for PH in this study, but it was associated with many QTLs for diverse traits such as root length, anthesis-silking interval, leaf number below the top ear, and total leaf number in previous studies (Qiu et al. 2007; Zhang et al. 2011a). In this study and a previous study (Ding et al. 2011b), umc1702 was associated with PH and test weight, respectively. According to a previous study of Yang et al. (2005), umc1702 is very closely linked to the Rpi1 gene that confers dominant resistance to stalk rot in maize. Three different flanking markers, umc1635, mmc0411, and bnlg1094, associated with LL in this study were detected in three different traits in previous studies, namely three ear-leaves area (Cui et al. 2017), internode number (Tang et al. 2007), and number of rows on the ear (Sabadin et al. 2008). Two flanking markers of qEL2, bnlg1606 and umc1551, were confirmed in the studies of Zhang et al. (2006) (plant height and ear height) and Guo et al. (2013) (oil content of maize kernel). The bnlg155 for SEL was also related with a QTL for grain yield in a previous study of Almeida et al. (2013). Two markers of qEW3, bnlg1601 and umc1167, were associated with the trait for EW in this study; however, bnlg1601 was detected in a QTL for in vivo haploid induction rate (Prigge et al. 2012) and umc1167 was confirmed for traits for upper leaf number and plant height (Salvi et al. 2011).

Although several flanking markers with the potential for MAS were detected in this study that had the same target traits compared with previous reports, most markers were associated with different traits. These results indicate that it is not always simple and easy to compare QTLs and flanking markers obtained from other studies because of differences in the genetic design, genetic maps, markers used, and statistical methods (Sabadin et al. 2008). Another possibility is that some QTLs or genes have pleiotropic effects on multiple traits (Li et al. 2011a). The co-localization of QTLs for agronomic traits may indicate pleiotropy and/or tight linkage, even though there is no co-localization of the QTLs. Some QTLs were not perfectly co-localized, but they shared a common flanking marker. In qPH10 and qEH10, the two QTLs shared mmc0501. This tight linkage was supported by high correlation of PH and EH; thus, these markers for closely linked QTLs may be considered as useful markers for MAS for agronomic traits.

Conclusions

The results of this study may help to improve the detection and characterization of agronomic traits and provide good opportunities for maize breeders and researchers using a DH population in maize breeding programs, and also the detection of loci associated with grain yield and yield component traits may provide further opportunities for maize breeders and researchers to improve maize cultivars by MAS.

Notes

Acknowledgements

This study was supported by the Cooperative Research Program for Agriculture Science and Technology Development (Project title #PJ013157012018, Project #PJ013308012018), Rural Development Administration, Republic of Korea, 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), Rural Development Administration (RDA), and Korea Forest Service (KFS), Republic of Korea.

Compliance with ethical standards

Conflict of interest

Jae-Keun Choi declares that he has no conflict of interest. Kyu Jin Sa declares that he has no conflict of interest. Dae Hyun Park declares that he has no conflict of interest. Su Eun Lim declares that she has no conflict of interest. Si-Hwan Ryu declares that he has no conflict of interest. Jong Yeol Park declares that he has no conflict of interest. Ki Jin Park declares that he has no conflict of interest. Hae-Ik Rhee declares that he has no conflict of interest. Mijeong Lee declares that she has no conflict of interest. Ju Kyong Lee declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human subjects or animals performed by any of the above authors.

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Copyright information

© The Genetics Society of Korea 2019

Authors and Affiliations

  1. 1.Gangwon-do Agricultural Research and Extension ServicesMaize Research InstituteHongcheonKorea
  2. 2.Department of Applied Plant Sciences, College of Agriculture and Life SciencesKangwon National UniversityChuncheonKorea
  3. 3.Department of Medical BiotechnologyKangwon National UniversityChuncheonKorea
  4. 4.Department of Anatomy Cell BiologyKangwon National University School of MedicineChuncheonKorea

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