Introduction

Maydis leaf blight (MLB) disease or southern corn leaf blight (SCLB), caused by Cochliobolus heterostrophus (Drech.) [(Bipolaris maydis (Nishik. and Miyake) Shoemaker)] is one of the potential threats to global maize production. The MLB on maize distributed across the globe; however, shows higher incidences in regions of high temperature of 20–32°C and high humid weather (> 80%). The pathogen C. heterostrophus possess three physiological races, namely race O, race-T (Hooker 1972; Ullstrup 1972), and race C (Wei 1988). The race-T of a pathogen is more prevalent in the USA. The extensive usage of CMS-T cytoplasm based maize lines to develop commercial maize hybrids in the USA resulted in a major epidemic during 1970 (Ullstrup 1972). Race C, more prevalent in China, is pathogenic to maize inbred lines with CMS-C cytoplasm (Wei 1988). Similarly, the race ‘O’ predominantly prevalent in the southern Atlantic coast of the USA, India, Africa and Western Europe (Misra 1979; Balint-Kurti et al. 2007), which can infect all types of susceptible maize cultivars irrespective of the cytoplasm (Hooker et al. 1970; Smith 1975) and can reduce the grain yield up to 41% (Sharma and Rai 2000).

Controlling the disease through protective fungicides is not so effective when there is high disease pressure coupled with the predominance of susceptible cultivars in the regions (Poole and Arnaudin 2014). At this juncture, the exploitation of genetic host resistance could be the best and sustainable means of MLB disease management. However, only a few reports are available on the genetics and gene(s) of MLB resistance in maize for race-O. Moreover, to date, none of the maize cultivars have been reported a complete immune against MLB, nevertheless few inbred lines were reported conferring a significant degree of resistance against MLB (Sharma and Rai 2005; Manjunatha et al. 2019). The nonavailability of immune maize inbred lines and genes conferring complete immunity have enforced the breeders to exploit the polygenic and quantitative resistance to MLB.

The choice of an effective breeding procedure for the improvement of quantitative disease resistance depends on the inheritance, genetic effects, nature and magnitude of gene action governing a resistance mechanism. Several reports are available on the inheritance pattern of MLB resistance in maize, namely monogenic recessive (Faluyi and Olorode 1984; Zaitlin et al. 1993; Chang and Peterson 1995; Cai et al. 2003), digenic linked recessive (Craig and Fajemisin 1969), digenic independent recessive (Thompson and Bergquist 1984), multi-genic partial dominant and quantitative (Pate and Harvey 1954; Kumar et al. 2016) and dominant R gene (Rai et al. 2010) inheritance. Recently, quantitative trait loci (QTL) mapping showed a significant contribution of bin location 3.04 and 9.04 in assigning resistance to MLB (Carson et al. 2004; Balint-Kurti and Carson 2006; Balint-Kurti et al. 2006; Kump et al. 2011). Further, there are a few reports available on gene action and genetic effects associated with MLB (Thompson and Bergquist 1984; Burnette and White 1985; Sharma et al. 2003). However, hardly there are any multi-location studies on dissection of genetic effects for MLB resistance in subtropical maize inbred lines.

Among the various biometrical techniques, generation mean analysis is one of the powerful methods for detecting the gene action (Hayman 1958) for quantitatively inherited traits, including the disease resistance (Shashikumar et al. 2010; Akbar et al. 2018). The genetic analysis provides the information on main genetic effects (additive and dominance) and digenic interactions (additive × additive [i], additive × dominance [j] and dominance × dominance [l]) responsible for the inheritance of a trait. These estimates of the genetic effects give insight about formulating the breeding strategy for handling the breeding material in the segregating generation. Further, the knowledge on the nature and magnitude of the gene action of MLB resistance could help in designing the breeding programme for the development of MLB resistant subtropical maize cultivars. Therefore, the present investigation was designed to understand the inheritance, nature and magnitude of the gene effects associated with MLB resistance in subtropical maize inbred lines.

Materials and methods

Development of population

Genetic material consisted of six generations (P1, P2, F1, F2, BC1P1 and BC1P2). Two genotypes CM119 and SC-7-2-1-2-6-1 (plant germplasm registration no. INGR 07025) were selected based on contrasting disease reaction to MLB disease. The F1 was derived by crossing the inbred lines CM119 × SC-7-2-1-2-6-1 during the rainy season (kharif), 2014 at New Delhi. The seeds of F1s were raised during rabi season 2014–2015 at Winter Nursery Centre, ICAR-Indian Institute of Maize Research, Hyderabad. The F1s were self-pollinated to produce F2 seeds, while BC1P1 and BC1P2 seeds were developed by backcrossing with parents CM119 and SC-7-2-1-2-6-1, respectively.

Evaluation of genetic material

The six populations, namely P1, P2, F1, F2, BC1P1 and BC1P2 generations were evaluated for their spectrum of resistance against MLB disease under artificial epiphytotic conditions at (i) Research Farm, ICAR-Indian Agricultural Research Institute, New Delhi (216 m above mean sea level (AMSL); 28°36′50″N, 77°12′32″E), (ii) Research Field, Chaudhary Charan Singh Haryana Agricultural University-Regional Research Station, Uchani, Karnal (256 m AMSL; 29°73′N, 76°98′E) and (iii) Research Field, CSK HPKV Hill Agriculture Research and Extension Centre, Dhaulakuan, Himachal Pradesh (468 m AMSL; 30°46′N, 77°48′E) during the rainy season (kharif), 2016. All the agronomic operations and management practices were followed as per the standard procedure (Prakash et al. 2019). However, no fungicides were used to ensure absolute disease expression. Each replicate consisted of an average of 20 plants from each parent (P1 and P2), 23 plants of F1, 258 plants of F2, 108 plants of BC1P1 and 107 plants of BC1P2.

Inoculum preparation and mass multiplication

The virulent isolates of C. heterostrophus (Delhi, Karnal and Dhaulakuan) were procured from the respective test locations. The purification and mass multiplication of isolates were done at Cereal Laboratory of Plant Pathology Division, IARI, New Delhi. The culture was multiplied on potato dextrose agar (PDA) medium in the BOD incubator at 28 ± 2°C. The multiplied isolates were used to inoculate the susceptible maize genotype CM119 separately to ensure the purity of isolate. Subsequently, the hyphal tips of C. heterostrophus were placed on PDA. Mass multiplication of C. heterostrophus pure culture was undertaken on sorghum seeds (Payak and Sharma 1983).

Pathogen inoculation and recording of disease reaction

The pathogen isolates from the respective locations were used for pathogen inoculation in the test locations. In each test location, the pathogen inoculation was carried out twice with an interval of 10 days between first and second inoculation to ensure proper disease establishment. The first inoculation was done by placing the optimum quantity of the inoculum into the central whorl of 25 days old maize plants (Payak and Sharma 1983). The second inoculation was undertaken on 35 days old maize plants to create high disease pressure. The disease reaction was measured 20–25 days after the second inoculation with standard 1–5 scale; where 0.0–1.0 was for highly resistant, 1.1–2.0 for resistant, 2.1–3.0 for moderately resistant, 3.1–4.0 for moderately susceptible and 4.1–5.0 for susceptible (Payak and Sharma 1983) (table 1).

Table 1 The disease scale employed to quantify the disease incidence in various generations (Payak and sharma 1983).
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Biometrical analysis

The scaling test (Hayman and Mather 1955) and joint-scaling test (Cavalli 1952) were performed to test the adequacy of the additive–dominance model. The four scales were calculated using the formulae: \( A = 2\overline{{BC_{1} P_{1} }} - \bar{P}_{1} - \bar{F}_{1} \); \( B = 2\overline{{BC_{1} P_{2} }} - \bar{P}_{2} - \bar{F}_{1} \); \( C = 4\bar{F}_{2} - 2\bar{F}_{1} - \bar{P}_{1} - \bar{P}_{2} \) and \( D = 2\bar{F}_{2} - \overline{{BC_{1} P_{1} }} - \overline{{BC_{1} P_{2} }} \). The significance of scales was tested as per Jinks and Jones (1958). Wherever the additive dominance model was inadequate, the six-parameter model of Hayman (1958) was employed to separate the genetic variance into main effects. The genetic effect components encompass mean effect parameter [m], additive [d], dominance [h], additive x additive [i], additive x dominance [j] and dominance x dominance [l] types of gene action. The generation variance analysis was performed as per Mather and Jinks (1982):

$$ {\text{Y}} = {\text{m}} +\upalpha\left[ {\text{a}} \right] +\upbeta\left[ {\text{d}} \right] +\upalpha^{2} \left[ {\text{i}} \right] + 2\upalpha \upbeta \left[ {\text{j}} \right] +\upbeta^{2} \;\;\left[ {\text{l}} \right], $$

where Y is the generation mean of target trait, m is the mean of all possible homozygous lines which can be derived from a target cross, α and β are the coefficients of genetic parameters, [a] is the additive gene effects, [d] is the dominance gene effects, [i] is the additive x additive gene effects, [j] is the additive x dominance gene effects, and [l] is the dominance x dominance gene effects.

The variance components were calculated to decipher the genetic parameters using following formulae: additive variance (D) = 4VF2 − 2 (VBC1P1 + VBC1P2); dominance variance (H) = 4VF2 – 1/2VD – VE and environmental variance (E) = 1/3 (VP1 + VP2 + VF1); where F1 is first filial generation, F2 is second filial generation, BC1P1 and BC1P2 are first backcross generations with parents P1 and P2, respectively. The narrow sense-heritability was computed from variance components as per Warner (1952) and Mather and Jinks (1982) in F2 and F∞ generations, respectively and genetic advance as per Falconer and Mackay (1996) with following formulae.

$$ h^{2} \left( {F_{2} } \right) = \frac{0.5D}{{\left( {0.5D + E} \right)}}, $$
$$ h^{2} \left( {F_{\infty } } \right) = \frac{D}{{\left( {D + E} \right)}}, $$

The Castle–Wright equation (Castle 1921; Wright 1952) was employed to estimate the minimum number of resistance loci contributing to MLB resistance:

$$ n = \frac{{\left( {m_1 - m_2} \right)^{2} }}{{8\left( {VF_{2} - VF_{1} } \right)}}, $$

Where n is minimum number of genomic regions associated with MLB resistance, m1 and m2 are the means of parent P1 and P2, respectively, VF1 and VF2 are the variances of F1 and F2 generations, respectively.

Results

Generation means and variance

The means with standard error, range and variance for parents, F1, F2 and backcross generations (BC1P1 and BC1P2) across locations are presented in table 2. Inbred line SC-7-2-1-2-6-1(P2) showed a resistant (score 1.88–2.0) and CM-119 (P1) was moderately susceptible (score 3.91–3.98) across the testing locations and found significantly different from each other for MLB resistance (figure 1). The disease scale of F1’s was significantly higher (moderately resistant) than resistant parent SC-7-2-1-2-6-1 (P2) in all the test locations. Further, the mean performance of F1 for disease tolerance has significantly differed from the parents (P1 and P2) in all the locations except Dhaulakuan, where the mean performance of F1 was on par with the P2. Similarly, F1 showed numerically and significantly better resistance than F2 across the test locations. Interestingly, the mean resistance response of F1 was on par with BC1P1 and BC1P2 populations and mean of BC1P1 and BC1P2 were found nonsignificant among the test locations. Further, across the locations, F1, F2, BC1P1 and BC1P2 showed moderately resistant score (2.1–3.0).

Table 2 Mean, range and variances for MLB disease reaction across the generations and locations.
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Figure 1
figure 1

The response of parental maize inbred lines to MLB disease reaction: (a) Inbred CM-119 (P1) showing moderately susceptible disease reaction (10 days after the second inoculation) with abundant lesions on lower and middle leaves and extending to upper leaves. (b) Inbred line SC-7-2-1-2-6-1(P2) showing a resistance reaction (10 days after the second inoculation) with light infection characterized by moderate number of lesions on lower leaves.

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The parental generations showed the least variance across the locations, whereas F2 generation recorded the maximum variance among the generations owing to segregation of resistance/susceptible loci. The range of variation across the location for F2 (2.50 ± 0.00) was quite high as compared to backcross derived generations (BC1P1: 2.17 ± 0.17; BC1P2: 2.00 ± 0.00) and first filial generation (1.50 ± 0.00).

Adequacy of the additive–dominance model

The Mather’s scaling tests were employed to predict the adequacy of the additive–dominance model of MLB resistance. All the four scales, i.e. A, B, C and D were significantly different from zero across the test locations except scale C at Karnal (table 3). The result of the scaling test suggested the inadequacy of the additive–dominance model for explaining MLB disease resistance and represents the presence of nonallelic interactions associated with MLB resistance. The joint scaling test also revealed the presence of intergenic interaction and suggested that a simple additive–dominance model was insufficient to explain MLB disease resistance.

Table 3 The estimated value of scales A, B, C and D for MLB disease reaction for all the locations.
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Genetic effects

The various genetic effects associated with MLB resistance are presented in table 4. In the present investigation, the six-parameter model of Jinks and Jones (1958) was employed due to the presence of epistatic interactions. The epistatic effects were found more than the main effects at all locations. Among the epistatic interactions, dominance × dominance [l] was found positive and significant across the locations. In contrast, additive × additive [i] and additive × dominance [j] gene interactions were negative and significant across the locations. Additionally, among all the three types of interactions, dominance × dominance [l] was the most prevalent over additive × additive [i] and additive × dominance [j] interactions. Further, dominance gene effect [h] and dominance × dominance [l] possessed opposite sign across the test locations indicating the presence of duplicate gene interaction. Similarly, joint scaling test revealed the predominance of dominance effects [h] over the additive effects [d] (table 4).

Table 4 Estimates of genetic effects and parameters of MLB resistance across the locations using different genetic models.
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Generation variance components and genetic parameters

The generation variance components and genetic parameters are tabulated in table 4. The analysis revealed the predominance of dominance variance (H = 1.00 to 1.04) over additive (D = 0.43 to 0.61) and environmental (E = 0.11 to 0.14) variances. The narrow-sense heritability estimates ranged from 0.59 to 0.72 in F2, and 0.74 to 0.84 in F∞ generations. The estimation of the minimum number of MLB resistance loci revealed the presence of two to three loci across the test locations. H/D is a good indicator of dominance and ranged from 1.70 (Karnal) to 2.34 (Dhaulakuan).

The inheritance pattern of MLB resistance

The inheritance pattern of MLB was observed by testing goodness of fit for the segregation ratio of F2 generation using the chi-square test in all the test locations (table 5). Visual scoring categorized 258 plants into resistant and susceptible based on disease reaction at New Delhi (resistant, 234; susceptible, 24), Dhaulakuan (resistant, 245; susceptible, 13) and Karnal (resistant, 238; susceptible, 20). The proportion of resistant and susceptible plants was equivalent to 9.75:1, 18.85:1 and 11.9:1 at New Delhi, Dhaulakuan and Karnal, respectively which was tested against a 3:1 ratio. The significant chi-square values for a 3:1 ratio revealed that the segregation pattern does not follow monogenic inheritance. Similarly, the observed values were tested against digenic (15:1) and trigenic (63:1) inheritance. The nonsignificant and significant chi-square values were observed for 15:1 and 63:1 ratios, respectively. Therefore, the nonsignificant chi-square values for 15:1 ratio showed that the observed and expected frequencies were comparable to the segregation pattern of 15:1. Hence, the segregation pattern of MLB resistance followed a digenic inheritance pattern.

Table 5 Segregation ratio of F2 for MLB resistance in various test locations.
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However, when these generalized groups were further divided, it was observed that the highly resistant and moderately resistant plants among resistant plants were found at New Delhi, Dhaulakuan and Karnal in the ratio of 0:46, 0:57 and 0:56, respectively. Further, among susceptible plants, susceptible and highly susceptible plants were found in the ratio of 24:0, 13:0 and 20:0 at New Delhi, Dhaulakuan and Karnal, respectively. Subgroups categorization revealed that not even a single plant exited in the extreme classes, i.e., highly resistant and highly susceptible. The proportion of plants was observed in the ratio of 0:1.91:7.83:1:0; 0:4.38:14.46:1:0 and 0:2.8:9.1:1:0 at New Delhi, Dhaulakuan and Karnal, respectively which was tested against 1:4:6:4:1 ratio of digenic inheritance. The significant chi-square values reflected that the data were not best to fit into 1:4:6:4:1 ratio. Further, testing of observed values against 11:5 ratio, which is the combined form of 1:4:6:4:1 (1+4+6=11; 4+1=T5) also revealed the significant chi-square values. Therefore, in conclusion, two genes confer resistance against maydis leaf blight disease, however other ratios, namely 3:1, 63:1, 11:5 and 1:4:6:4:1 showed the significant chi-square values which reflected that these were not the best fitting ratios (table 5).

Discussion

Maize serves as an important source of food and nutrition in developed and developing countries (Mallikarjuna et al. 2014; Agrawal et al. 2018). MLB disease, caused by the fungus C. heterostrophus, is one of the potential threats to corn production. The development of resistant cultivars is the most sustainable and cost-effective approach in the management of diseases in crops which necessitates the knowledge on the genetics of disease resistance. The biometrical approach generation mean analysis provides useful genetic information for designing of breeding strategies to take advantage of gene interactions, and effects that exist in the succession breeding generations. The F1 generation showed a moderate level of MLB resistance (2.15–2.18), although the resistance level is lower than the resistant parent, P2 (table 2). The lower mean performance of F1 generations (2.15–2.28) over mid parental values (2.91–2.95) and nonsignificant differences among the backcross and F1 generations indicating the importance of dominance gene action on MLB resistance, in the direction of the better parent (P2). Therefore, the dominance nature of MLB resistance would be suitable for the exploitation of heterosis for MLB resistance in commercial maize hybrids production. The nonsegregating parental (P1 and P2) and first filial (F1) generations showed minimal phenotypic variation, with narrow range of disease scale (1.00–1.50) as compared to a broader range of variances (1.50–2.50) in segregating generations (F2, BC1P1 and BC1P2) (table 1). The higher narrow range of variation in F1 and parents indicates the minimal influence of environment on MLB inheritance across the three test locations. The larger phenotypic variance associated with segregating generations is associated with segregation of loci and modifiers associated with MLB resistance and susceptibility.

Further, the larger variation in segregating generations was following the theoretical components of F2 (VF2 = VA + VD + VE) and backcross (VBC = ½VA + VD + VE) generation variances (Sullenberger et al. 2018). The genetic variance components revealed the predominance of additive (0.43–0.61) and dominance variance (1.00–1.04) over the environmental variances (0.11–0.15) across the test locations. The preponderance of genetic variances over the environment resulted in the higher narrow-sense heritability (h2) of 0.59 to 0.72 in F2 and 0.74 to 0.82 in F∞ generations. Therefore, the directional phenotypic selection for the MLB resistance could be employed to exploit the moderately high h2 of MLB resistance.

Genetic analysis of target traits to determine the genetic effects is essential for initiating the planned and scientific breeding programmes. Determination of individual gene effects for quantitative characters is not easy; on the other hand, it could be summed up as cumulative effects of all the genes (Wright 1952). Generation mean analysis allows partitioning of total variability into additive and nonadditive components, which is helpful in selecting the mating design, defining time of selection and breeding methodology. The inadequacy of the additive–dominance model in the study enforced to opt six-parameter model. The joint scaling test showed significant and negative additive effects [d] across the locations, and the six-parameter model showed nonsignificant additive effects in Dhaulakuan and Karnal, although small significant negative additive effect were found in New Delhi. On the other hand, the dominance effects [h] were significant and negative across the test locations in both joint scaling test and six-parameter model analysis. The negative additive and dominance effects of MLB resistance provide an opportunity to enhance the MLB resistance through hybrid breeding. Further, the dispersal of resistance alleles in parents and genetic recombination during the crossing process could be the main reason for negative dominance effects (Lyimo et al. 2011). Additionally, the significant and negative values of additive × additive [i] and additive × dominance [j] suggest that the genes fixed in CM119 and SC-7-2-1-2-6-1 are acting against each other to enhance the MLB disease resistance. Among the interaction components, the negative and significant values of fixable additive × additive [i] and nonfixable additive × dominance [j] gene effects across the locations could be exploited to enhance the MLB resistance through line development and hybrid breeding, respectively. In the duplicate epistasis interaction, the internal cancellation of oppositely signed dominance and dominance × dominance gene effects could reduce heterosis for MLB resistance. Further, the presence of duplicate epistasis indicated the likelihood of getting transgressive segregates for MLB resistance in the succeeding generations (Sharma et al. 2017). Additionally, values of H/D ratio across the locations indicate the predominance of dominance over additive gene action.

The inheritance pattern of disease reaction is crucial to decide the mating design and breeding strategy for target trait improvement. The generation mean and segregation ratio analysis results showed that two genes govern the inheritance of MLB resistance with duplicate epistasis. Further, the digenic inheritance was supported by minimum number of gene governing MLB resistance by Castle–Wright equation (table 4). Similarly, the digenic inheritance of MLB resistance was observed in temperate maize (Craig and Fajemisin 1969; Thompson and Bergquist 1984). Further, genomics studies identified two to three major bin location for MLB (Balint-Kurti et al. 2006, 2007; Belcher et al. 2012). On the other hand, a few studies showed polygenic inheritance with partial dominance of MLB resistance in maize (Kump et al. 2011; Kumar et al. 2016). Additionally, our results from generation mean analysis showed the presence of significant interactions and epistasis. Therefore, the inheritance of MLB resistance could be associated with a few major genes coupled with minor QTLs. Further, the nature of resistance inheritance could be population-specific and specific population demands, appropriate breeding methods for the improvement of MLB resistance in maize (Hettiarachchi et al. 2009).

In conclusion, presently, there are hardly any subtropical maize inbred lines showing complete immunity for MLB across the environments. Therefore, maize inbred line SC-7-2-1-2-6-1 could be effectively used as a source of MLB resistance in subtropical maize breeding. Further, SC-7-2-1-2-6-1 inbred may be exploited as a source to generate the maize transgressive segregates by crossing with other inbred lines showing the variable level of MLB resistance and agronomically superior phenotype (Sharma et al. 2003; Kumar et al. 2016). Additionally, despite quantitative nature and partial dominance, the digenic inheritance of MLB resistance in subtropical maize could be exploited for the rapid conversion of maize inbred lines using stringent phenotypic selection.