Open access peer-reviewed chapter

Genetic Variability for Resistance to Leaf Blight and Diversity among Selected Maize Inbred Lines

By Abera Wende, Hussein Shimelis and Eastonce T. Gwata

Submitted: March 31st 2017Reviewed: August 8th 2017Published: December 20th 2017

DOI: 10.5772/intechopen.70553

Downloaded: 289

Abstract

Maize (Zea mays L.) is an important staple food crop in sub-Saharan Africa (SSA). The productivity of the crop is limited partly by the leaf blight disease caused by Exserohilum turcicum. In breeding for resistance to leaf blight, the germplasm needs to be well-characterized in order to design efficient breeding programs. This study evaluated the (i) genetic variability among maize inbred lines and (ii) diversity of selected medium to late maturity tropical maize inbred lines for hybrid breeding. Plants of 50 maize inbred lines were artificially inoculated in the field during 2011 and 2012. Disease severity and incidence as well as grain yield were measured. A subset of 20 elite maize inbred lines was genotyped using 20 SSR markers. The germplasm showed significant differences in reaction to leaf blight and were classified as either resistant or intermediate or susceptible. Mean disease severity varied from 2.04 to 3.25. Seven inbred lines were identified as potential sources of resistance to leaf blight for the genetic improvement of maize. The genotyping detected 108 alleles and grouped the inbred lines into five clusters consistent with their pedigrees. The genetic grouping in the source population will be useful in the exploitation of tropical maize breeding programs.

Keywords

  • leaf blight
  • inbred line
  • mid-altitude
  • maize
  • pedigree

1. Introduction

Maize (Zea mays L.) is an important staple food crop in sub-Saharan Africa (SSA). It is the third most important cereal crop after wheat and rice [1]. It is used for both livestock feeds and human consumption. In SSA, maize accounts for about 70% of the human food [2]. The demand for maize is expected to increase by >90.0% in SSA by 2020 [3]. However, the productivity of the crop is limited by several abiotic and biotic stresses. Among these abiotic factors, insect pests, such as the stem borers and weevils, cause considerable economic damage on the crop [4, 5]. In addition, fungal diseases such as gray leaf spot (Cercospora zeae-maydis Tehon & Daniels), common leaf rust (Puccinia sorghi Schr.), and turcicum leaf blight (TLB) (Exserohilum turcicum) often pose a serious threat to maize production [6].

In particular, TLB, also known as the northern corn leaf blight, can devastate the crop in high rainfall, humid areas [6, 7]. TLB reduces the seed quality, resulting in diminished germination capacity, low sugar content as well as predisposition to stalk rot [8, 9]. The use of resistant varieties is an inexpensive method for combating TLB [10]. Currently, there are efforts to incorporate durable resistance into maize germplasm particularly in SSA where some commercial varieties as well as elite parental inbred lines are reportedly vulnerable to TLB [11, 12]. For example, in Ethiopia, maize productivity is low (averaging about 2.5 t/ha) in the smallholder production systems partly due to TLB and other stresses. Spurred by the need to enhance maize productivity for farmers, the national maize improvement program in Ethiopia recently embarked on a breeding project aimed at developing leaf blight resistant hybrid varieties that are adapted to the major maize-growing areas of the country which are predominantly in the mid-altitude to subhumid agroecologies [13]. However, hybrid breeding for resistance to leaf blight requires knowledge of the genetic variability of the germplasm in terms of its reaction to TLB as well as its characterization into distinct genetic groups that can be hybridized in order to exploit heterosis.

The variability in the host (maize) plant resistance to the disease occurs in either the qualitative or the quantitative form. The qualitative form of resistance is race specific and is governed by a single or few genes but the quantitative form of resistance is race nonspecific and polygenic [14, 15]. In addition, qualitative resistance can break down due to the emergence of new virulent races of the pathogen through genetic mutation and recombination events [12, 15]. The pathogen E. turcicum exhibits a wide range of variability [16], and new races are capable of overcoming previously resistant varieties [7]. For instance, the resistance conferred by the Htn gene(s) is characterized by chlorotic and necrotic lesions or lesions surrounded by a yellow-to-light-brown margin (without spore formation), which limits the growth and spread of the disease [12, 14]. In contrast, the resistance conferred by Htn gene is expressed as a delay in lesion formation typically showing at the pollination stage [17, 18]. Lesion size, together with area under disease progress curve (AUDPC) as well as disease severity and incidence, are commonly used in evaluating maize genotypes for resistance to TLB [19, 20]. However, phenotypic evaluations in conventional breeding approaches are unable to detect the presence of favorable alleles in the germplasm. Therefore, marker-assisted selection and DNA fingerprinting techniques have been effectively used to increase the efficiency of conventional breeding, particularly the time required for developing new improved varieties in maize [12].

The presence of discrete genetic groups among inbred lines is attributed to increased allelic diversity which is useful in optimizing hybrid vigor. Assigning inbred lines into well-differentiated genetic clusters can reduce the creation and evaluation of many undesirable crosses [21]. Molecular markers assist in characterizing inbred lines and in establishing distinct clusters of genotypes based on genetic diversity, which is useful in maize breeding programs [22, 23]. Molecular markers were applied successfully to allocate maize germplasm into heterotic groups [2426]. In a study which compared different markers for their effectiveness in estimating genetic grouping among maize inbred lines, SSR markers revealed the highest level of polymorphism due to their codominant nature and high number of alleles per locus [27]. Therefore, the study reported in this chapter was designed to evaluate the (i) genetic variability in reaction to TLB among maize inbred lines under field conditions and (ii) diversity of selected medium to late maturity tropical maize inbred lines for hybrid breeding using selected SSR markers.

2. Materials and methods

2.1. Field evaluation

2.1.1. Germplasm and testing location

Fifty inbred lines were used in the study. The lines were adapted to the mid-altitude agroecologies in Ethiopia and were obtained from the national maize research program and the international maize and wheat improvement center (CIMMYT). Inbred line CML-197, which was obtained from CIMMYT, served as susceptible check ( Table 1 ). The field trial was conducted at Bako (37°09′ E; 09°06′ N; 1650 m above sea level). It receives approximately 1200 mm rainfall annually ( Table 2 ) and is representative of the mid-altitude subhumid agroecological region in Ethiopia.

EntryPedigreeOrigin
1CML 202CIMMYT
2CML 442CIMMYT
3CML 312CIMMYT
4CML 464CIMMYT
5Gibe-1-91-1-1-1-1BAKO
6CML 445CIMMYT
7CML 443CIMMYT
8CML 197CIMMYT
9A-7033BAKO
10CML 205/208//202-X-2-1-2-B-B-BBAKO
11CML 395CIMMYT
12F-7215BAKO
13DE-78-Z-126-3-5-5-1-1BAKO
1430H83-7-1-1-1-2-1BAKO
15I100E-1-9-1-1-1-1-1BAKO
16SZYNA99F2-81-4-3-1BAKO
17X1264DW-1-2-1-1-1BAKO
18124-b (113)BAKO
19SC22BAKO
20SC715-121-1-3BAKO

Table 1.

The pedigree and origin of maize inbred lines that were evaluated for diversity using SSR markers.

Month20112012
Rainfall (mm)Temperature (C0)RH (%)Rainfall (mm)Temperature (C0)RH (%)
January15.9020.2058.000.0020.4052.70
February2.0020.9050.904.4021.8047.50
March58.8021.9053.9016.2023.0048.90
April68.1020.4052.4030.7024.0062.50
May222.2021.3058.5092.823.0055.60
June295.0019.9067.50153.3020.2066.90
July224.1019.3069.30138.2019.5076.00
August294.6019.1075.60263.6019.7064.00
September131.3020.0065.90157.5020.1074.40
October53.2020.2059.806.0021.0050.50
November60.1020.0059.8017.1020.3049.70
December0.0019.8054.506.7021.545.70
Total1425.30886.50

Table 2.

Average monthly rainfall, temperature, and relative humidity at Bako during the 2011 and 2012 cropping seasons.

RH = relative humidity.

2.1.2. Field experiments

Inbred lines were evaluated using the lattice design with three replications. Trials were conducted for two consecutive seasons (in 2011 and 2012) during the main rainy season (May to September) in Ethiopia. The seed of each genotype was planted manually in the field in a two-row plot 5.1 m long × 0.75 m at 30.0 cm intra-row spacing. Phosphorus (in the form of diammonium phosphate) was applied once at planting at 100.0 kg/ha. Nitrogen fertilizer (in the form of urea) was applied at 100.0 kg/ha in two splits with 50% at planting and the rest at 37 days after emergence. Standard maize trial management practices were applied throughout each season at the location.

2.1.3. Leaf blight inoculum collection, preparation and inoculation

Isolates of E. turcicum were obtained from diseased maize leaf samples that were collected from fields where the disease is prevalent. The infected leaves were excised into small sections (approx. 1.0 cm2 each) prior to surface sterilization using 2.5% Sodium hypochlorite for about 3 min and subsequently rinsed with sterile distilled water and blot-dried before plating on PDA in petri dishes for incubation at room temperature for 3–4 days. Pure cultures were prepared by subculturing from the isolation plates followed by incubation for 7–10 days in order to obtain sufficient growth. The inoculum was prepared by flooding the cultures with sterile distilled water and scrapping the surface with microscopic slides to dislodge the conidia and then filtered using cheese cloth after which the concentration of the conidia suspension was adjusted to approximately 105 conidia per milliliter using a hemocytometer [28].

Maize plants growing in the field were inoculated at the four to six leaf growth stages during the middle of the main rainy season (mid-July) in Ethiopia. The inoculations were accomplished by spraying (manually, with the aid of an atomizer) the maize plant with the conidia suspension until runoff after which fine mist water was sprayed over the inoculated plants in order to create conducive conditions for disease development. This inoculation procedure was carried out during the evening when there was sufficient moisture in the air.

2.1.4. Data collection and analysis

In each season, the disease was visually assessed in the field 2–3 weeks after inoculation. Ten randomly selected plants were tagged and used for successive disease assessments. Plants were rated at 10-day intervals for percent incidence, lesion length, and lesion width. In order to determine the rate of lesion expansion, 2 lesions out of the 10 plants were measured (and marked for subsequent tracing) at 10-day intervals.

Disease severity was scored using a scale of 1–5 where:

1.0 = very slightly infected, one or two restricted lesions on lower leaves or trace.

2.0 = slight-to-moderate infection on lower leaves, a few scattered lesions on lower leaves.

3.0 = abundant lesions on lower leaves, a few on middle leaves.

4.0 = abundant lesions on lower and middle leaves extending to upper leaves.

5.0 = abundant lesions on all leaves, plant may be prematurely killed by blight.

The AUDPC was determined from the disease severity scores obtained in both seasons. The AUDPC parameter was calculated using Eq. (1) below as described previously [29]:

AUDPC=i=1n1yi+yi+1ti+1ti2E1

where n = number of observations, ti = number of days after planting for the ith disease assessment, and yi = disease severity.

The parameter was used to quantify the epidemic from the beginning to the peak of the disease. The grain yield was calculated using the average shelling percentage of 80% adjusted to 12.5% moisture. Data sets of the quantitative measurements from individual trials were subjected to standard analysis of variance procedures using the GenStat release 14.2 computer software program [30].

2.2. Marker evaluation

2.2.1. Germplasm

Twenty maize inbred lines were used in the study. Eight of these inbred lines were originally developed for the mid-altitude and subhumid agroecologies at CIMMYT, whereas the remainder was developed by the local Ethiopian maize research program and was well adapted to mid-altitude areas. The local inbred lines were developed from three heterotic groups (that are commonly used in the country) namely Kitale synthetic II, Ecuador 573, and Pool 9A.

2.2.2. DNA sampling

DNA was collected from 3- to 4-week-old plants (tagged for identification), using Whatman FTA cards and the modified protocol of FTA paper technology [31]. Ten DNA samples from each of the 20 inbred lines were then bulked (in order to eliminate variation within each entry) and used for the diversity analysis at the INCOTEC-PROTEIOS laboratory in South Africa (Incotec, SA Pty. Ltd., South Africa) utilizing 20 SSR markers. PCR products of all of the 20 primers were fluorescently labeled and separated by capillary electrophoresis on an ABI 3130 automatic sequencer (Applied Biosystems, Johannesburg, South Africa). Analysis was performed using GeneMapper 4.1. The data matrices of the genetic distances were used to create the dendrogram using the unweighted pair group method with arithmetic mean allocated (UPGMA). The polymorphism information content (PIC) was calculated as:

PIC = 1 − ∑fi.

where fi is the frequency of the ith allele [32].

3. Results and discussion

3.1. Disease development and severity

Disease ratings were significantly different among the 50 inbred lines (P < 0.001), and 11 were classified as resistant, 26 as intermediate, whereas the remainder was classified as susceptible ( Tables 3 and 4 ). The resistant inbred lines (e.g., 136-a and 142-1-e) attained lower disease severity scores compared to the susceptible check CML-197 ( Tables 3 and 4 ). No accession was immune to the disease. In addition, there were highly significant (P < 0.001) differences for lesion length among inbred lines in both 2011 and 2012. The inbred lines Pool9A-4-4-1-1-1, SZSYNA-99-F2-803-4-1, and CML 197 showed comparatively larger lesion lengths, whereas the lesion length of CML 202 and CML 312 showed consistently small lesion lengths over the two seasons. Resistance to E. turcicum in maize germplasm was previously associated with a reduction in percent leaf area as well as small lesions [33].

No.Inbred lineDSSReaction typeIncidence (%)Lesion length (cm)AUDPCTSWYield (t ha−1)
1CML 2022.00R46.819.88408.3223.32.22
2CML4422.734I78.4313.40612.5223.32.40
3CML 3122.413I61.5210.35385.0276.73.03
4CML 4642.210I55.6413.82595.0223.33.79
5Gibe-1-91-1-1-1-12.534I71.3214.57408.3321.72.90
6CML 4452.523I65.2014.02571.7213.33.34
7CML 4432.934S69.6113.48595.0211.72.07
8Gibe-1-158-1-1-1-12.496I66.4211.37507.5281.73.43
9A70332.881S68.6315.37641.7273.32.58
10(CML 205/CML208//CML 202)
-X2-1-2-B-B-B
2.696S83.5815.88571.7300.05.60
11CML3952.388I71.0814.07420.0338.34.96
12CML 4442.526I69.1218.28443.3260.02.95
13DE-78-Z-126-3-2-2-1-12.688S67.8914.48536.7280.04.14
1430H83-7-1-1-1-2-12.00R53.1910.90495.8210.03.14
15ILoo’E-1-9-1-1-1-1-12.00R56.6215.62420.0346.74.83
16SZSYNA-99-F2-814-3-12.00R42.4010.77466.7315.02.46
17X1264DW-1-2-1-1-1-12.889S70.5915.00571.7213.31.94
18124-b(113)2.559I59.8015.27606.7365.03.53
19SC222.760S85.7814.72501.7271.73.56
20SC-715-1211-32.466I67.4013.47396.7336.73.45
21DE-105-Z-126-30-1-2-2-12.00R61.2714.55420.0235.02.89
22Gibe-1-20-2-2-1-12.663S69.1218.78501.7301.72.62
23Kuleni-0080-4-2-1-1-1-12.022I61.5216.38449.2326.73.72
24Pool9A-4-4-1-1-12.677S68.6321.35670.8288.34.85
2530H83-5-1-4-2-1-12.486I63.9716.27484.2308.34.27
26Iloo’E-5-5-3-12.639I74.2613.48560.0328.34.41
27SZSYNA-99-F2-2-7-3-1-12.00R57.3511.77478.3206.72.77
28SC-715-154-1-12.206I65.2011.97402.5280.05.89
29BH6609(F2)-10-2-1-2-12.333I61.7611.83402.5300.03.98
30143-5-I2.305I60.2915.48420.0325.06.84
31144-7-b1.90R58.0912.87385.0330.04.45
32(LZ-955459/LZ955357)-B-1-B-B2.369I67.1612.20431.7256.72.98
33139-5-j2.00R53.4313.78385.0258.32.56
3430H83-56-1-1-1-1-12.351I57.3510.22495.8205.03.57
35SZSYNA-99-F2-80-3-4-12.653I73.5320.05525.0293.33.15
36124-b(109)2.901S81.8615.48536.7310.05.54
37F72152.417I63.7314.72455.0393.33.86
38136-a1.80R51.4713.82238.0396.74.41
39DE-78-Z-126-3-2-1-2-12.631I70.8314.85595.0286.73.83
40Gibe-1-186-2-2-12.549I51.9614.88350.0373.32.70
41Pool9A-128-5-1-1-12.718I71.4313.12595.0278.32.45
4230H83-7-1-5-1-1-1-12.00R52.4512.05379.2220.02.60
43SZSYNA-99-F2-3-6-2-12.587I70.8312.33618.3256.72.36
44SC-715-13-2-12.434I61.7612.87420.0248.32.34
45SC-22-430(63)3.033S80.1511.57478.3311.72.48
46Kuleni-C1-101-1-1-13.028S75.4917.07700.0258.32.84
47Iloo’E-1-12-4-1-12.355I51.9610.30443.3276.72.43
48(DRB-F2-60-1-2)-B-1-B-B-B2.791S75.9816.23600.8270.02.66
49142-1-e2.00R62.0115.02595.0323.33.94
50CML 1973.028S88.4818.07525.0271.750
LSD0.426018.5137.504129.9372.641.465
Pr > f************
CV (%)3.317.610.616.215.925
Overall mean2.48665.4914.16493.9284.13.52

Table 3.

Maize leaf blight reactions, grain yield, and thousand seed weight of 50 inbred lines tested during 2011 at Bako research Center in Ethiopia.

DSS = disease severity score (0.00–5.00); R = resistant (1.0–2.00); I = intermediate (2.10–2.50); susceptible (2.51–5.00); and TSW = thousand seed weight.

** = Significant at 0.05 and 0.01 probability levels, respectively.

No.Inbred lineDSSReaction typeIncidence (%)Lesion length (cm)Lesion width (cm)TSWYield (t/ha)
1CML 2022.39R40.6912.001.331732.15
2CML4422.69S72.5513.671.672102.67
3CML 3122.47I64.2212.330.832203.25
4CML 4641.92R52.4513.001.032073.01
5Gibe-1-91-1-1-1-12.53S74.0220.331.502603.76
6CML 4452.42I65.6914.331.172073.36
7CML 4432.97S64.7113.001.001831.93
8Gibe-1-158-1-1-1-12.39I58.8212.001.572702.93
9A70332.81S58.8213.331.332402.41
10(CML 205/CML208//CML 202)
-X2-1-2-B-B-B
2.64S86.7622.331.832375.83
11CML3952.33I70.5921.672.003135.04
12CML 4442.61S65.6923.332.002302.67
13DE-78-Z-126-3-2-2-1-12.67S65.2018.331.502502.6
1430H83-71-1-1-2-11.89R39.7113.331.671872.91
15ILoo’E-1-9-1-1-1-1-12.14I54.4123.671.332934.62
16SZSYNA-99-F2-814-3-11.69R27.9414.001.002572.01
17X1264DW-1-2-1-1-1-12.36I72.5519.331.331831.92
18124-b(113)2.34I45.1016.331.673033.13
19SC222.05I91.1816.672.002303.14
20SC-715-121-1-33.07S70.1016.002.172702.64
21DE-105-Z-126-30-1-2-2-11.57R69.6120.671.832303.42
22Gibe-1-20-2-2-1-12.48I77.4525.001.332873.08
23Kuleni-0080-4-2-1-1-1-14.29I58.3320.331.332833.8
24Pool9A-4-4-1-1-12.42I62.2525.671.672705.35
2530H83-51-4-2-1-12.47I67.1622.002.002604.39
26Iloo’E-5-5-3-12.61S77.9414.331.002603.5
27SZSYNA-99-F2-2-7-3-1-12.22I55.8815.001.501702.88
28SC-715-154-1-12.14I73.5315.671.832175.01
29BH6609(F2)-10-2-1-2-12.33I54.9015.331.532431.74
30143-5-I2.08I51.9618.002.172735.95
31144-7-b1.89R59.3118.001.003333.47
32(LZ-955459/LZ955357)-B-1-B-B2.28I67.6516.671.332002.72
33139-5-j2.03I44.1219.331.072371.8
3430H83-561-1-1-1-12.22I50.9813.000.832032.93
35SZSYNA-99-F2-80-3-4-12.81S76.4727.671.832373.38
36124-b(109)3.03S82.8419.331.332705.59
37F72152.5I62.7521.331.072732.92
38136-a1.75R42.1617.331.333633.62
39DE-78-Z-126-3-2-1-2-12.56S65.2019.001.002373.54
40Gibe-1-186-2-2-12.81S49.0219.331.333602.3
41Pool9A-128-5-1-1-12.67S68.3615.331.672232.87
4230H83-71-5-1-1-1-11.94R50.0016.672.001932.84
43SZSYNA-99-F2-3-6-2-12.28I63.2415.332.002332.36
44SC-715-13-2-12.47I66.6716.331.172102.34
45SC-22-430(63)3.08S89.7114.331.672272.05
46Kuleni-C1-101-1-1-12.81S68.637.671.172373.07
47Iloo’E-1-12-4-1-12.17I33.3310.671.332431.57
48(DRB-F2-60-1-2)-B-1-B-B-B2.72S67.6521.002.002302.48
49142-1-e1.81R49.5116.331.502874.29
50CML 1973.39S98.5319.672.672134.41
LSD0.39619.1599.0130.902471.3
Pr > f***********
CV (%)10.118.832.136.911.924.7
Overall mean2.4362.9317.311.512453.23

Table 4.

Maize leaf blight reactions, grain yield, and thousand seed weight of 50 inbred lines tested during 2012 at Bako research Center in Ethiopia.

DSS = disease severity score (0.00–5.00); R = resistant (1.0–2.00); I = intermediate (2.10–2.50); susceptible (2.51–5.00); and TSW = thousand seed weight.

*; ** = Significant at 0.05 and 0.01 probability levels, respectively.

The significant differences detected among genotypes in this study across the 2 years (cropping seasons) was attributable to a range of factors such as favorable climatic conditions, the inoculation method employed, and proper disease rating. In other studies, the development of NLB was attributed to pathogenic fitness and environmental conditions [34]. In Ethiopia, the disease infection and epidemics in maize occur largely during the main production season particularly in the wet and humid areas. Therefore, breeding for resistance to the disease in such areas is critical.

Disease severity scores in both cropping seasons were significantly different (P < 0.01) ( Tables 3 and 4 ). During the two seasons, the lowest severity scores were observed for the inbred lines CML 202, 144-7-b, and 142-1-e. In contrast, relatively high severity scores were observed for CML 197, Kuleni-C1-101-1-1-1, and SC-22-430(63), suggesting that they were susceptible to the disease. The final severity score and AUDPC values provided sufficient estimation of the reaction of the inbred lines to E. turcicum. The inbred lines that were classified as resistant showed significantly lower AUDPC values than the susceptible ones ( Figure 1 ). Furthermore, susceptible inbred lines tended to show a rapid increase in severity of the disease compared with the resistant lines culminating in higher severity scores toward maturity unlike the resistant ones. The severity of the disease was slightly higher in 2011 than 2012 ( Tables 3 and 4 ). This was likely due to the low rainfall that was received at flowering in 2012, which was not conducive for the development of the disease. Nonetheless, the environmental conditions were generally favorable for leaf blight development during the two testing seasons. Previous studies involving leaf blight showed that the dropper inoculation was efficient and minimized the chances of disease escape from evaluation [9]. In this study, the inoculation technique was easy to employ and reliable. There were clear differences between resistant and susceptible genotypes, and at the flowering stage, the later genotypes exhibited a moderate increase in diseased leaf tissue. In some cases, relatively less susceptible individual genotypes were identifiable. The selection of such less susceptible genotypes can result in the accumulations of minor genes that can elevate the level of field resistance [3537].

Figure 1.

Area under disease progress curve for resistant (red) and susceptible (green) maize inbred lines inoculated with isolates of E. turcicum in the field.

3.2. Genetic polymorphism

The twenty SSR primers identified 108 alleles among the 20 maize inbred lines. Between 1 to 11 alleles were scored across the SSR loci ( Table 5 ). Two loci (Phi 037, Umc1296) each revealed only a single allele. The maximum number of alleles (11) was detected at the Bnlg 2190 locus. The maximum PIC estimated for all loci was 0.8028 with a mean of 0.54 ( Table 5 ). The expected heterozygosity (He) values, as a measure of allelic diversity at a locus, varied from 0.0000 to 0.8395 with an average of 0.5774. These values were well correlated with the number of alleles. Ten SSR loci (Umc1568, Nc003, Umc2214, Umc2038, Phi085, Umc1153, Bnlg238, Phi054, Bnlg2190, and Bnlg240) attained a PIC value >0.6, which indicated their potential to detect differences between the inbred lines.

SSR locusRepeat typesBin numberNumber allelesPIC valueHe
Umc1568TCG1.0260.68330.7250
Bnlg176___1.0340.30920.3378
Bnlg182___1.0360.55100.5888
Phi 037AG1.0810.00000.0000
Bnlg 108___2.0440.42530.4637
Nc003AG2.0660.74290.7778
Umc2214CTT2.180.70750.7350
Bnlg602___3.0460.47010.4900
Umc2038GAC4.0640.63110.6925
Phi085AACGC5.0640.66950.7222
Umc1153TCA5.0980.66830.7036
Bnlg238___680.76890.7922
Umc1296GGT6.0710.00000.0000
PhiI015AAAC8.0870.51120.5938
Umc1367CTG9.0520.49490.5850
Phi054AG10.0360.80280.8255
Umc1677GGC10.0570.30470.3750
Bnlg2190AG10.06110.82240.8395
Bnlg2408.0670.77770.8025
umc2361CCT8.0620.37430.4986

Table 5.

Information about the 20 SSR loci used in this study.

PIC = polymorphic information content and He = heterozygosity.

The genetic diversity of the germplasm is one of the most important factors limiting the number of alleles identified per microsatellite locus during screening. However, other factors such as the number of SSR loci and repeat types as well as the methodologies employed for the detection of polymorphic markers have been reported to influence allelic differences. In this work, the mean number of alleles (5.4) was in agreement with those reported in maize [38]. Similarly, values of number of SSR loci used in this study closely agreed with the findings reported previously [13, 39]. In addition, the mean PIC value determined in the present investigation was in agreement with the findings that were obtained in earlier studies that involving the use of SSR markers on maize inbred lines [40, 41]. The PIC value demonstrates the usefulness of the SSR loci and their potential to detect differences among the inbred lines based on their genetic relationships. The dinucleotide SSR loci (phi054, nc003, bnlg2190) identified the largest mean number of alleles (7.67) and mean PIC (0.79), as compared to tri-, tetra-, and penta-nucleotide repeats in the study, which was in close agreement with previous observations in maize [40, 42].

In this study, automated analysis was used for screening the microsatellites, resolving allelic variation better than using gel electrophoretic analysis for instance. This may be particularly important for SSR loci containing dinucleotide repeats whose amplification products are between 130 and 200 bp, because PCR products differing by two base pairs cannot be resolved with agarose gel electrophoresis [40, 43].

The ability to measure genetic distances between the inbred lines that reflect pedigree relationship ensures a more stringent evaluation of the adequacy of marker profile data; hence, the minimum genetic distance which was revealed between CML-202 and I100E-1-9-1-1-1-1-1 (0.28) was a good indication, confirming the power of SSR markers to distinguish closely related inbred lines. Similar findings were reported for maize inbred lines using SSR markers [4446].

3.3. Cluster analysis

The dendrogram obtained using the UPGMA clustering algorithm based on SSR data matrices grouped the inbred lines into five categories ( Figure 2 ). This information, in combination with the pedigree records and combining ability tests, will be valuable for selecting (or identifying) optimal crosses and assigning inbred lines into heterotic groups. The greatest distance was found between the cluster containing the inbred line CML-202 line and the cluster of the inbred line Gibe-1-91-1-1-1-1. Cluster I consisted of inbred lines that are adapted to mid-altitude as well as some originating from CIMMYT. Most of the mid-altitude inbred lines in this group originated from the heterotic group Kitale Synthetic II and constitute the largest group in the cluster. In Cluster II, CIMMYT inbred lines CML312 and CML395 were grouped along with two local inbred lines, with two subdivisions in the main group. Cluster III contained two major subgroups, one containing CIMMYT inbred lines and the other containing local inbred lines. In terms of pedigree, these inbred lines are closely related and belong to the heterotic group AB, thus supporting the observation of a positive relationship between the pedigree and the SSR marker groupings in this study. In another cluster, two CIMMYT inbred lines (CML-443 and CML-197) were grouped closely, as revealed on the UPGMA dendrogram ( Figure 2 ). These two inbred lines were also grouped in the same heterotic groups A and AB, based on their heterosis. Cluster V consisted of one CIMMYT inbred line and two locally adapted mid-altitude inbred lines. The separation of these elite mid-altitude maize inbred lines into genetically distinct groups may be associated with high heterotic response and increased combining ability useful for hybrid development.

Figure 2.

Dendrogram showing genetic relationship among 20 maize inbred lines tested using 20 SSR markers. The five clusters among the inbred lines are denoted from I to V.

The majority of the inbred lines (60%) that were evaluated in this study were previously developed by the national maize breeding program in Ethiopia. Because of the potential of encountering genetic admixtures or incomplete pedigree records in breeding programs, discrepancies in classification of germplasm may occur when comparing molecular results with classification based on pedigree relatedness. The effects of selection, genetic drift, and mutation may contribute to these discrepancies. The technique of clustering inbred lines can create apparent discrepancies, when one inbred line that is related to two inbred lines from separate clusters is then grouped with the inbred to which it is more closely related [40, 47]. Nonetheless, the SSR markers separated most of the inbred lines into distinguishable clusters, which generally agreed with the existing pedigree records and the findings that were reported previously [27, 42].

4. Conclusions

The inbred lines showed significant differences in reaction to the leaf blight disease and were classified into three categories namely resistant, intermediate, or susceptible. The mean disease severity and upper leaf area infection varied from 2.04 to 3.25 and 3.3% to 100% respectively. Seven inbred lines were identified as potential sources of resistance to leaf blight for the genetic improvement of maize under the mid-altitude agroecology in Ethiopia. The genotyping detected 108 alleles and grouped the inbred lines into five clusters consistent with their pedigrees. The genetic grouping present in the population as determined in this study will be useful in the exploitation of tropical germplasm for hybrid maize breeding programs. The inbred lines that were identified as resistant to leaf blight can be considered as source material for disease resistance under the mid-altitude agroecological conditions in Ethiopia. The genetic grouping of the inbred lines was valuable information for future maize breeding programs. The use of SSR markers was able to provide complimentary information regarding the relatedness of the elite inbred lines that were evaluated. The high PIC value across all loci was strong evidence confirming the potential for SSR markers to discriminate between inbred lines of diverse sources and even between closely related genotypes. A number of loci that were identified with high PIC values indicated their usefulness for diversity analysis of maize inbred lines. The approach used in the study enables clear differentiation between inbred lines and their classification into distinct groups based on genetic distance estimates generated through selected polymorphic SSR primers.

There will be merit in establishing resistance breeding program aimed at developing varieties with increased adult plant resistance to TLB in Ethiopia. Such varieties offer one of the most effective and affordable ways to overcome the problem of leaf diseases of maize in the mid-altitude agroecology in Ethiopia and similar environments in SSA. Therefore, further testing of the resistant germplasm identified in this study across more locations and seasons will also be merited.

© 2017 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abera Wende, Hussein Shimelis and Eastonce T. Gwata (December 20th 2017). Genetic Variability for Resistance to Leaf Blight and Diversity among Selected Maize Inbred Lines, Maize Germplasm - Characterization and Genetic Approaches for Crop Improvement, Mohamed El-Esawi, IntechOpen, DOI: 10.5772/intechopen.70553. Available from:

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