Keywords

1 Introduction

Maize (Zea mays L.) is an important annual cereal crop of family Poaceae, having wider adaptability under varied environmental conditions, and popularly used for human food, animal feed and industrial usage (Shiferaw et al. 2011). It is originated in the highlands of Mexico (~8700 years ago) and known as ‘queen of cereals’, owing to its highest genetic yield potential (Piperno et al. 2009). Maize is grown on ~193.25 million hectare (mha) with a production of 1116.19 million tonnes (mt) with a productivity of 5.78 tha−1 (USDA 2020). The area under maize spans from the latitude 58° N to the latitude 40° S, and it is harvested every month of the year in one region or the other. Akin to any crop species, maize production is severely plagued by several abiotic and biotic stresses (Gong et al. 2014). Biotic stress includes stresses caused by virus, bacteria, fungi and nematodes, parasites and insect pests. Crops are regularly exposed to biotic stresses, which cause changes in metabolism and damages at various levels resulting in faltered productivity (Gimenez et al. 2018). The diseases are major culprits of biotic stress-induced damages in maize followed by insect pests and parasitic weeds mainly Striga hermonthica (Keno et al. 2018).

The outbreaks of diseases in maize are one of the well-documented crop epidemics in the world’s history. For instance, the outbreak of southern corn leaf blight (SCLB), caused by Bipolaris maydis race T, was the first reported disease outbreak in maize during 1970–1971. This was mainly due to the indiscriminate use of T-type male sterile cytoplasm (cms – Texas) which occupied 85% of corn fields in the United States (Ullstrup 1972). With around 50–100% yield reduction, the economic loss due to this disease reached as high as one billion dollars. An outbreak due to northern corn leaf blight (NCLB) was observed in North China due to the extensive cultivation of a high-yielding, but susceptible, variety ‘Xian Yu335’ (Pu 2013). Similarly, a severe epidemic of maize lethal necrosis (MLN), a viral disease, was documented in Kenya in September 2011, which disseminated to other African countries. The yield loss due to this disease was estimated up to 126,000 metric tons which translated into $52 million in Kenya in 2012 (Mahuku et al. 2015; Terefe and Gudero 2019). Tar spot complex (TSC) caused by three fungal pathogens is another major disease in Latin American countries and has the reported potential of inciting up to 51% yield loss in maize. TSC first reported in 1904 in Mexico later spread to other countries (Mottaleb et al. 2019). Recently, FAW (fall armyworm) is causing devastation of maize crop across the countries (Prasanna et al. 2022). In India, it is first reported from Bangalore Rural and Chikkaballapur districts during May–June 2018 (Ganiger et al. 2018) and South Karnataka during the first fortnight of July 2018 (ICAR-NBAIR pest alert, 2018), the pest infected 40–70% of the crop, quickly spreading to the rest of the country (Tippannavar et al. 2019). Thus, disease and pests pose a serious threat to sustainable production of maize across the globe.

Breeding for stress-tolerant genotypes is an important and economically viable strategy to combat various biotic stresses. This can significantly reduce the dependence on chemical control for the management of diseases and pests and enhance the export value of the produce while ensuring the consumer safety. The information on the genetic control of the target trait is an important prerequisite of any breeding programme (Zunjare et al. 2015a; Muthusamy et al. 2016), which guides the breeders in choosing the most adequate breeding strategy. With the advent of molecular markers and statistical models, mapping of quantitative trait loci (QTLs) or genomic regions encoding a particular trait has picked up pace. The principle of ‘linkage mapping’ has been widely employed for mapping QTLs in crop plants through developing biparental segregating population for trait of interest (Collard et al. 2005). However, association mapping or linkage disequilibrium (LD) mapping is based on statistical associations of genetic markers with phenotypes in natural populations (Huang and Han 2013). With the rapid advancements in genomics, decreasing genotyping cost and available genome sequences, genome-wide LD mapping has become popular and powerful approach to dissect genetic architecture of complex traits (Huang and Han 2013). Both mapping techniques have their own pros and cons; for instance, the power of QTL detection is higher in case of biparental mapping study while resolution of QTL is higher for LD-based mapping. Several studies have been reported for identification of QTLs and the underpinning candidate genes for various biotic stresses in maize. An attempt was made to review these reports in a systematic manner. The chapter was organized into different sections starting with a brief description of important pests and diseases of maize followed by defence mechanisms of biotic stress resistance, QTL mapping studies and attempts to improve maize genotypes using QTLs or genes.

2 Biotic Stresses: Types, Major Symptoms and Losses Caused

The most prevalent maize diseases are leaf blight, ear rot, maize rough dwarf disease, sugarcane mosaic disease, maize streak virus disease, maize dwarf mosaic disease, maize lethal necrosis virus disease and high plain disease, while the most prevalent insect pests are borers, fall armyworm, shoot fly and the storage weevils (Table 1). The detail of above biotic stresses are briefly described here under.

Table 1 List of the important biotic stresses prevalent in maize
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2.1 Major Diseases

2.1.1 Fungal Diseases

  1. 1.

    Turcicum leaf blight: Turcicum leaf blight (TLB) or northern corn leaf blight (NCLB) is the most important foliar disease in northern hills, northeastern hills and peninsular part of India. The fungus Setosphaeria turcica (anamorph Exserohilum turcicum) is the causal pathogen with characteristic symptom of ‘cigar-shaped’ tan or greyish lesions on leaf surface (Chen et al. 2016a). It is prevalent in majority of maize-cultivating countries of the world having cool climate (temperatures 20–25 °C), 90–100% relative humidity and low luminosity (Wu et al. 2014). Under favourable condition of infection with no treatment, it has potential to cause yield loss up to 70%.

  2. 2.

    Gibberella ear rot (GER): It is caused by Fusarium graminearum fungus which also causes rot diseases in the stalks and roots of maize. The main symptoms include reddish grain formation at the ear tip initially. GER reduces the yield and quality of kernels and triggers the accumulation of mycotoxins (vomitoxin and zearalenone) which cause serious health problems in humans and animals (Brauner et al. 2017).

  3. 3.

    Diplodia ear rot (DER): It is caused by pathogen Stenocarpella maydis and main symptoms are bleached husks, whitish fungal growth on grains and rotted ears. Early infections of DER lead to complete ear rotting, while late infections may result in partial rotting of ears. It results into loss of nutritive value of kernels and deposition of fungus causing mycotoxins (Baer et al. 2021).

  4. 4.

    Aspergillus ear rot (AER): It is caused by fungus called Aspergillus that produces aflatoxin which is carcinogen and can potentially be dangerous to livestock (Woloshuk and Wise 2011). Aflatoxin contamination of maize is a major problem in the southern parts of the United States (Mitchell et al. 2016). Symptoms of AER include green to yellowish fungal growth on and between grains near the ear tip mostly (Woloshuk and Wise 2011).

  5. 5.

    Fusarium ear rot (FER): This maize disease is prevalent worldwide affecting grain yield and quality that is caused by fungus Fusarium verticillioides (Ding et al. 2008). Disease incidence is usually 10–20% in China, but in favourable conditions, it can reach up to 50% (Wen et al. 2021). The disease also poses a serious health hazard due to the accumulation of mycotoxin called fumonisin. Typical symptoms include scattered individual kernels with whitish pink to lavender Fusarium growth. Fungus-affected grains may have a ‘starburst’ pattern of white streaks.

2.1.2 Viral Diseases

  1. 1.

    Maize rough dwarf virus disease (MRDV): It is a damaging viral disease with symptoms like internode shortening, malformed tassels and significant delays in vegetative growth (Wang et al. 2019). It is primarily caused by three viruses, namely, maize rough dwarf virus (MRDV), Mal de Rio Cuarto virus (MRCV) and rice black-streaked dwarf virus (RBSDV). MRDV and MRCV are prevalent in Europe and in South America, respectively, while RBSDV is prevalent in China. The small brown plant hopper Laodelphax striatellus is the carrier of RBSDV virus (Wang et al. 2003).

  2. 2.

    Sugarcane mosaic virus disease (SCMV): It is one of the serious pathogens causing severe yield losses in both sugarcane and maize. SCMV was first detected in sugarcane in 1919 and in maize in 1963, both in the United States (Signoret 2008). High incidence of SCMV was also reported in maize in China and Argentina (Perera et al. 2009; Xu et al. 2008). The mosaic pattern with contrasting shades of green to yellow chlorotic areas typically appears on SCMV incidence. The complete plant may become stunted on early infection of SCMV. The disease is spread by several aphid species.

  3. 3.

    Maize streak virus disease (MSV): It is endemic to sub-Saharan Africa, and yield losses due to this virus are reportedly as high as 100% with an annual economic loss of US$120 M and US$480 M (Martin and Shepherd 2009). Hence, it is considered as the biggest threat to the food security of sub-Saharan Africa (Shepherd et al. 2010). Among several strains of MSV, MSV-A causes economically tangible disease (Martin et al. 2001). The virus is mainly transmitted by leafhopper Cicadulina spp. frequently found in the late sown maize fields or with susceptible varieties (Muimba-Kankolongo 2018). The virus mainly damages the plants younger than six weeks old. The top and bottom surfaces of leaves have yellowish and light green streaks on younger plants while mature plants show whitish, yellow and light green streaks running parallel to the leaf veins. The infected plants can be severely stunted if the crop is attacked during the 4–5 leaf stages. Abnormal bunching of flowers and shoots and reddish pigmentation may also be observed in later stages.

  4. 4.

    Maize dwarf mosaic virus disease (MDMV): This disease is caused by MDMV belonging to the genus Potyvirus and is the most common disease of monocotyledonous plants (Kannan et al. 2018). The yield loss due to MDMV disease can be up to 70% resulting from disturbed photosynthesis and increased respiration (Mikel et al. 1981). The stunted, bunchy infected plants with short internodes have a stippled mottle or mosaic of light and dark green on the youngest leaves. The more yellowing will appear as plants mature and temperatures rise. The virus is also transmitted by various aphid species (over 20).

  5. 5.

    Maize lethal necrosis (MLN) disease: This is due to maize chlorotic mottle virus (MCMV) which also belongs to Potyviridae. The disease is transmitted by maize thrips (Frankliniella williamsi). As mentioned earlier, this virus caused an outbreak in southern rift valley of Kenya during 2011 and later spread to other regions of Africa (Redinbaugh and Stewart 2018). MLN disease causes long yellow stripes on leaves which are wider than those caused by maize streak virus disease. As the disease progresses, the maize leaves turn yellow and dry out from the edges towards the midrib. MLN disease can also cause dwarfing and premature aging of the plants, and ultimately the plants die. Late infection prevents tassel formation and produces poorly filled cobs.

  6. 6.

    High plains disease (HPD): This disease is caused by wheat mosaic virus (WMoV) and noted first time in the 1990s on maize in Idaho and later in Utah in 1994 (Jensen et al. 1996). This is one of the extremely difficult diseases to control since it is reported to be seed borne (Jensen et al. 1996). The symptoms include weakened root systems, stunted growth and yellowing of the leaves, sometimes with yellow streaks and flecks. Reddish-purple discolourations or wide yellow bands are often seen on mature leaves. The bands turn tan or pale brown as the tissue dies. HPD in sweet corn is transmitted by insect called wheat curl mite (Aceria tosichella).

2.2 Major Insect Pests

  1. 1.

    Stem borer (SB): SB (Chilo partellus) is a major insect pest and infests maize during the kharif season all over India causing a yield loss of about 26–80% in different agro-climatic regions. Typical symptoms involve a ‘dead heart’ due to the withering of central shoot. The larvae later mine and feed on internal tissues. When cut open, tunnel can be observed inside the stem wherever larvae have traversed. This is visible externally as bored holes on the stem near the nodes (Ong’amo et al. 2016). The younger larvae crawl and feed on tender folded leaves causing typical ‘shot hole’ symptom.

  2. 2.

    Pink borer (PB): PB (Sesamia inferens) affects maize crop with yield loss in the range of 25.7–78.9%. PB larvae feed inside the leaf sheath in groups on the epidermal layer, preferably on first three leaf sheaths. The larvae later enter the plant at the base by making a hole and damage the inner portion of the stem. The younger plants show dead hearts while the old plants become weak and are prone to lodging due to heavy winds (Baladhiya et al. 2018).

  3. 3.

    Mediterranean corn borer (MCB): MCB (Sesamia nonagrioides Lefebvre) or corn stalk borer is the most damaging pest of maize in Southern European countries. They can incite a yield loss of up to 80% (Zanakis et al. 2009). In the European Mediterranean area, MCB coexists with ECB, and this duo has the potential of causing a higher damage to the plants (Velasco et al. 2007). Larvae feed inside the stems led to ‘dead heart’ symptoms as terminal leaves die.

  4. 4.

    Fall armyworm (FAW): FAW (Spodoptera frugiperda) is one of the most destructive pests of maize across the world. As referred before, this pest was under high alert during 2018 with outbreak starting from Karnataka state to other parts of India. Initially, the symptoms appear as elongated papery windows across the leaves caused by first and second instar FAW larvae which feed by scrapping on leaf surface. Once the larva enters third instar, its feeding causes ragged-edged round to oblong holes on leaves. The fifth instar starts feeding voraciously, consuming larger areas of leaves, while the sixth instar larva extensively defoliates the leaves. In reproductive stage of the maize crop, tassel and corn ears are the vulnerable parts. Tassel damage is most common, which does not lead to economic damage, but boring into corn ears directly affects the yield. Sweet corn ear is more prone to FAW damage, which render the ears unmarketable (Ganiger et al. 2018; Tippannavar et al. 2019).

  5. 5.

    Shoot fly (SF): SF (Atherigona spp.) is a serious pest in spring maize crop sown in February–March of the year in northwestern plain zone of India and reported to cause up to 60% plant loss. The SF affects the maize plants at the seedling stage where maggots feed on young growing plants resulting in drying of the seedlings or ‘dead heart’ (Jindal 2013).

  6. 6.

    Weevils: Post harvest grain loss due to weevil insects belonging to genus Sitophilus is a major biotic stress concern in maize. Sitophilus zeamais (maize weevil) is found in Latin America, Europe and Africa; and Sitophilus granaries (granary weevil) is prevalent in temperate climate, while Sitophilus oryzae (rice weevil) is supposedly originated in Indian sub-continent (Zunjare 2012). Larvae of weevils feed within the grain kernels and adults emerge making holes on the grain. Hence, eggs, larvae and pupae are not visible on the grain. Only adults can be found wandering over the surface of grain (Hossain et al. 2007; Zunjare et al. 2014, 2016). Nearly, 12–20% grain loss is quite common and may reach up to 80% under favourable conditions of infestation (Zunjare et al. 2015a, b, c).

In addition to above insect pests, there are other minor pests of maize such as cob borer (Helicoverpa armigera Hubner), tobacco caterpillar (Spodoptera litura Fabricius), flower chafer beetle (Chiloloba acuta Wiedemann), Oxycetonia versicolor (Fabricius), armyworm (Mythimna separata Walker), cut worm (Agrotis ipsilon Rott.), grasshopper (Hieroglyphus nigrorepletus Bol.), aphid (Rhopalosiphum maidis Fitch), leafhoppers (Pyrilla perpusilla Walker) and Angoumois grain moth (Sitotroga cerealella Olivier).

3 Defence Mechanisms in Maize Against Pathogens and Insects

Plants, being sessile in nature, have to either maintain harmony with these insect pests and diseases or fight against them for their survival since they cannot run away from the predators like animals. Maize plants recognize several kinds of elicitors released by insects. Oral secretion and ovipositional fluids of the herbivorous insects are the principal sources of these elicitors. For instance, oral secretions from Mythimna separata caterpillar contain >10 different kinds of fatty acid – amino acid conjugates, the most abundant being hydroxylated FAC volicitin (Qi et al. 2016). M. separata secretions trigger the enhanced production of jasmonic acid and its derivative JA-Ile (JA-isoleucine conjugate). In addition, maize plants can also detect other insect-derived elicitors, such as caeliferin and inceptin (Schmelz et al. 2011). Maize land races release some volatile compounds in response to oviposition by stem borer Chilo partellus, which attracts both egg and larval parasitosis (Tamiru et al. 2011).

3.1 Phytoalexins

After the recognition of elicitors, the plants put forth various defence mechanisms – physical and chemical to contain the damage being caused by the insect. Secondary metabolites constitute major class of chemical defence elicited in the plants, popularly referred to as ‘phytoalexins’ (Smith 1996). There are two types of secondary metabolites in plants – volatile and nonvolatile. The main objective of releasing volatile secondary metabolites is to attract natural enemies of invading pest, which is termed as indirect defence response. For instance, maize plants release indole, a volatile aromatic compound for protection against S. littoralis as direct defence (Veyrat et al. 2016). Another compound, methyl salicylate (MeSA), also acts as a strong deterrent against leafhoppers (Cicadulina storeyi) in maize (Oluwafemi et al. 2011). Both of these compounds can prime the plants against the attack of respective pests. The production of a terpene compound, (E)-β-caryophyllene, confers resistance to Diabrotica virgifera by attracting an entomopathogenic nematode Heterorhabditis megidis (Rasmann et al. 2005). This seems to be effective against stem borer as well (C. partellus) by attracting its natural enemy, an egg parasitoid C. sesamiae (Tamiru et al. 2011). The attack of S. littoralis was reported to incite the localized production of 1,3-benzoxazin-4-ones, phospholipids, N-hydroxycinnamoyl tyramines, azelaic acid and tryptophan in maize seedlings (Marti et al. 2013). Benzoxazinoids (BXs) are a group of well-characterized compounds which play a significant role in maize defence against herbivorous insects (Handrick et al. 2016). BXs are demonstrated to be toxic to European corn borer (O. nubilalis) and Asian corn borer (O. furnacalis). For instance, DIMBOA (2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one) is popularly reported to be effective against these corn borers; however, the underlying mechanism still needs to be elucidated (Glauser et al. 2011). Lectins are glycoproteins which function in defending the plants against a range of pests. These are jasmonate inducible and were reported in maize apart from other monocots such as rice, barley, wheat and rye (Jiang et al. 2006). In addition, oxylipins such as 9-oxylipin and 10-oxo-11-phytoenoic acid are strongly induced in maize silks upon infection by corn earworm (Helicoverpa zea) (Christensen et al. 2014).

3.2 Phytoanticipins

These are constitutively produced defence-related compounds which are inactive in native state but are activated and recruited once there is an insect attack. Thus, these compounds are produced in anticipation of an insect attack, hence termed ‘phytoanticipins’. For example, maize silks constitutively produce maysin which is a C-glycosyl flavone. However, the quinones which are derived from it are toxic to corn earworm (Waiss Jr et al. 1979). Sometimes, the dormant compound and its activator are localized in different organelles within the cell. Upon tissue disruption by the invading insect, they are brought together which leads to the production of active compound. Benzoxazinoids are stored in vacuoles as glucosides. Due to the tissue disruption, these glucosides are hydrolysed by plastid localized glucosidases leading to the production of toxic aglycones (Frey et al. 2009).

Several of the above mechanisms are also active against pathogens in maize. Phytoalexins such as kauralexins and zealexins are also induced against pathogens (Schmelz et al. 2011; Huffaker et al. 2011), and benzoxazinoids are produced mainly against fungal pathogens (Ahmad et al. 2011). In addition, maize utilizes certain physical barriers to ward off or curtail the spread of the pathogen. Increased accumulation of suberin offers barrier against Fusarium graminearum (Santiago et al. 2007) and Aspergillus flavus (Spangler 2008). Deposition of callose in the cell wall and local hypersensitivity reaction are the other physical barriers deployed by maize (Morris et al. 1998). Ribosome-inactivating proteins and PR (pathogenesis-related) proteins are also synthesized in response to pathogen attack (Moeller and Tiffin 2005). Thus, the mechanisms of maize tolerance to pathogens are poorly understood relative to insect pests and need further study.

4 QTL Analysis for Biotic Stresses

Host plant resistance in maize for the biotic stress is of two categories: (i) qualitative resistance based on single gene resistance (R genes) and (ii) quantitative resistance based on multi-gene resistance. Most of the genetic resistance exploited by maize breeders display quantitative inheritance. This may be because maize is relatively more genetically diverse than other cereals owing to its outcrossing (Buckler et al. 2001). Maize breeders, therefore, can access more diversity within adapted germplasm and identify major and minor QTLs to achieve effective levels of biotic stress resistance (Balint-Kurti and Johal 2009). Many of the QTLs governing biotic stress resistance in maize are minor with small additive effects which work in tandem. For instance, both NCLB and SCLB of maize show quantitative inheritance with the preponderance of both additive and non-additive gene actions (Ranganatha et al. 2017; Jakhar et al. 2021). Hence, reciprocal recurrent selection (RRS) is the suitable method for improving resistance to these diseases. We present here the details of QTLs/genomic regions identified so far for various diseases and insect pests based on analysis of both biparental mapping populations and association mapping panels (Table 2).

Table 2 List of QTLs identified for different biotic stresses in maize
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4.1 Turcicum Leaf Blight/Northern Corn Leaf Blight

Initially, Welz and Geiger (2000) re-analysed the data from previous authors (before 2000) and reported 23 QTLs, many being major QTLs with R2 values as high as 25.6%. Nearly, 29 QTLs were identified using a large nested association mapping (NAM) population (5000 inbred lines) using 1.6 million SNPs, and most of the underlying candidate genes included protein kinases with plausible role in plant defence response (Poland et al. 2011). Van Inghelandt et al. (2012) reported four SNPs which showed significant association with NCLB resistance. The NCLB resistance corrected for flowering time each with phenotypic variability explained (PVE) of 0.36–14.29% in a set of 4149 maize inbred lines representing Europe and North America. Two stable QTLs were identified of which qNCLB5.04 explained about 19% and 20% of phenotypic variation in the experimental years 2012 and 2013, respectively, and it was associated with both NCLB score and lesion width and hence proposed as valuable candidate for marker-assisted selection (MAS) (Chen et al. 2016a). Tian et al. (2018) reported six QTLs in P178 × G41-based backcross inbred lines (BILs) with PVE range of 4–23%, including two stable QTLs on chromosomes 2 and 8. Another major QTL, qNCLB7.02 with a PVE of 10.11–5.29% was reported by Wang et al. (2018). Three QTLs were identified for NCLB resistance, among which qNCLB-8-2 contributed maximum PVE of 16.34% followed 10.24% by qNCBL-5 (Ranganatha et al. 2021). Recently, GWAS (genome-wide association study) using three association panels revealed nearly 22 SNPs and 17 significant haplotypes which co-localized with previously reported genes and QTLs for NCLB resistance in maize (Rashid et al. 2020). In an intercontinental trial with a set of 742 F1-derived doubled haploid (DH) lines along with test crosses, a total of 17 QTLs were identified accounting for a variance of 3.57–30.98%, of which two QTLs, q4 and q5, were found stable across locations and seasons evaluated in Brazil and Europe (Galiano-Carneiro et al. 2021a).

4.2 Gibberella Ear Rot (GER)

One major QTL (28.9% PVE) was identified for GER on chromosome 3 (Ali et al. 2005). Around 17 QTLs were identified for GER in three mapping populations derived using three different donors (Cheng351, Dan598 and JiV203), of which qRger7.1, from the resistant parent, Cheng351, was a major QTL with 20.16–41.84% PVE (Wen et al. 2020). One stable QTL, q1, was also identified for GER with 10.17–21.84% PVE by Galiano-Carneiro et al. (2021b).

4.3 Diplodia Ear Rot (DER)

Tembo et al. (2014) identified the QTL ‘Sm_4,1’ for resistance to S. maydis, causing DER on chromosome 4. Also, they have identified a QTL with pleiotropic effect on chromosome 1 which is 22 cM from umc1269 marker. In another study, about eleven QTLs were identified for DER using multi-parent mapping populations and four different models of which one QTL on chromosome 5 showed maximum PVE of 7.18% (Baer et al. 2021).

4.4 Fusarium Ear Rot (FER)

FER is considered a complex trait controlled by many minor QTLs, with moderate heritability and high environmental influence posing significant hurdles for breeding-FER-resistant varieties. Genomic selection is the viable option for improving resistance against this disease. The first report on QTLs for resistance to FER was given by Perez-Brito et al. (2001) who found nine and seven QTLs in two F2 populations with a PVE of 30–44%. In a subsequent study, two separate populations were utilized for mapping FER resistance and resistance to fumonisins. Seven QTLs were found with cumulative PVE of 47% for FER resistance and nine QTLs explained 67% of the variation for mean fumonisin concentration (Robertson-Hoyt et al. 2006). Two stable QTLs on chromosome 3 were reported by Ding et al. (2008) coupled with a major QTL with 13–22% PVE on the same chromosome. Four QTLs were identified on chromosomes 3, 4, 5 and 6, of which the one on chromosome 4 was a major QTL with PVE of 10.2% (Li et al. 2011). Another major QTL was reported on chromosome 4 (with PVE of 17.95%), which was later validated in near isogenic line (NIL) background (Chen et al. 2012). Seven QTLs and the associated markers were reported for FER resistance in terms of grain yield per main ear and test weight (Abdel-Rahman et al. 2016). Combined GWAS (using 818 tropical maize inbred lines) and QTL analysis (using four biparental mapping populations) revealed eight co-localized QTLs on chromosomes 2, 3, 4, 5, 9 and 10, with 38 putative candidate genes related to disease resistance (Chen et al. 2016b). In addition, for 8 QTLs, 43 candidate genes (in vicinity of 57 SNPs) were mapped for F. verticillioides resistance. Among these genes, GRMZM2G0081223, AC213654.3_FG004 and GRMZM2G099255 were common across both mapping strategies and can be considered as valid candidate genes for FER resistance (Ju et al. 2017). This study has also validated the markers reported by previous authors. In another study, 15 and 17 QTLs were identified for FER resistance and fumonisin B1 (FB1) mycotoxin, respectively, and 24 candidate genes were validated by coupling with transcriptomic data (Maschietto et al. 2017). In contrast, a SNP-based GWAS study identified several minor QTLs governing FER resistance. Through mixed linear model (MLM), 19 marker trait associations (MTAs) were identified with an average PVE of 1.60% (Liu et al. 2021). Around 20 QTLs were identified for FER resistance among which qRfer1, qRfer10, and qRfer17 were found to be major QTLs explaining phenotypic variation as high as 26.58 to 43.36%, 11.76 to 18.02%, and 12.02 to 32 21.81%, respectively (Wen et al., 2021). A combined QTL analysis for both FER and GER resistance unveiled four QTLs reflecting that LP4637 (donor) provides dual tolerance to both the ear rot genus (Giomi et al. 2016). Thirty-nine MTAs were identified for resistance to fumonisin accumulation in maize kernels separately which assembled into 17 QTLs (Samayoa et al. 2019). A multi-parent advanced generation intercross (MAGIC) population derived from eight founder parents was analysed for resistance to FER, and 13 putative QTLs were identified with minor affects. Nevertheless, there are distinct regions, 210–220 Mb on chromosome 3 and 166–173 Mb on chromosome 7 which harbour QTLs for FER and fumonisin content resistance in maize (Butron et al. 2019). NBS-LRR receptors and transcription factors involved in redox reaction and peroxidase activity seem to play a pivotal role in FER resistance (de Jong et al. 2018). This was discovered through DArT Seq-based GWAS study using 242 maize inbred lines. Two MTAs were found in the genes governing programmed cell death when 267 inbred lines were analysed using 47,445 SNPs (Zila et al. 2013). No disease resistance-related genes were identified in a GWAS study by Zila et al. (2014) despite a large panel of 1687 inbred lines iterating the complex nature of FER tolerance. A nested association mapping (NAM) population-based mapping (four populations with common parent) unveiled 110 QTLs for FER and fumonisin resistance (Morales et al. 2019). Four defence-related genes, a gibberellin 2-oxidase4, a glucosyltransferase, a Ras-related protein RHN1 and a phosphoribosylanthranilate transferase (PAT), are found to be putative candidate genes for FER in 183 field maize and popcorn inbred lines (Coan et al. 2018).

4.5 Stalk Rot

Two QTLs, qRfg1 (major) and qRfg2 (minor), were mapped from the resistant inbred 1145 (Yang et al. 2010; Zhang et al. 2012a). The ZmCCT gene was found to be the causal gene at qRfg1 (Wang et al. 2017). The resistance in maize plants is governed by insertion or deletion activities of CACTA-like transposon in the promoter of ZmCCT which causes histone modification and DNA methylation. The transposon insertion silences the gene and renders the plants susceptible to stalk rot. ZmAuxRP1 is the candidate gene of qRfg2. Upon infection, the expression of this gene is downregulated resulting in the arrest of root growth, albeit increasing the resistance to stalk rot (Ye et al. 2019). ZmAuxRP1 is reported to enhance the resistance of FER as well. Resistance of Anthracnose stalk rot (ASR) is governed by Rcg1, a major QTL which explained ~50% variation (Jung et al. 1994), and the underpinning candidate gene is an NB-LRR governing resistance gene (Frey et al. 2011). Diplodia stalk rot seems to be under the control of both additive and non-additive gene actions, and hence, RRS is the best breeding method for tolerance improvement (Carson and Hooker 1981; Badu-Apraku et al. 1987).

4.6 Maize Rough Dwarf Disease (MRDD)

Two QTLs were reported for disease susceptibility index (DSI) in Mo17 × BLS14-derived F2:3 population. These two QTLs jointly governed 36.2% of PVE (Di Renzo et al. 2004). A major QTL, qMrdd8, was identified on chromosome 8 for MRDD resistance with a PVE of 24.6–37.3% across the environments (Shi et al. 2012). This was later fine-mapped to a region of 347 kb and coupled with RNA-Seq two candidate genes CG1 and CG2 that were identified (Liu et al. 2016a). A recessive major QTL, qMrdd1, was found on chromosome 8 using GWAS which was subsequently validated and fine-mapped to a 1.2 Mb region (Tao et al. 2013a). The underlying candidate gene was later found to be Rab GDP dissociation inhibitor alpha (RabGDIα) which is responsible for host susceptibility (Liu et al. 2020). Another major QTL qMRD8 was identified on chromosome 8 with a PVE of 12.0–28.9% in addition to other QTLs, qMRD2, qMRD6, qMRD7 and qMRD10 (Luan et al. 2012). A single dominant locus was identified on chromosome 8 using SLAF-Seq-based bulked segregant analysis (BSA), and two SSR markers 6F29R29 and 6F34R34 were reported to be linked to this QTL. This region harbours around 32 candidate genes with defence-related functions (Li et al. 2018). Recently, a partially dominant resistance QTL, qMrdd2, for MRDD (with 20.4% PVE) was identified on chromosome 2 in recombinant inbred line (RIL) population derived from 80,007 (resistant) × 80,044 (sensitive) (Zhang et al. 2021).

4.7 Sugarcane Mosaic Virus (SCMV) Disease

Initial report on QTLs for resistance to SCMV came from Xia et al. (1999) who identified five QTLs located on chromosomes 1, 3, 5, 6 and 10, with two stable and prominent QTLs on chromosomes 3 and 6. Later, these QTLs were validated in a different mapping population (F3 from F6 × FAP0259A) and named as Scm1 (chromosome 6) and Scm2 (chromosome 3), the resistant alleles of Scm1 matched with those reported by Xia et al. (1999), whereas the alleles of Scm2 were different. However, later by saturating these two regions with a greater number of microsatellite markers, Yuan et al. (2003) demonstrated that Scm1 locus houses two QTLs. Similarly, two QTLs, Scm1 and Scm2, were reported for SMV resistance on chromosomes 6 and 3, respectively (Xu et al. 1999). Three QTLs were identified for SMV disease, two QTLs (Scm2a, PVE of 13.34%, and Scm2b, PVE of 41.85%) clustered together on chromosome 3, whereas third QTL was found on chromosome 6, Scm1 with a PVE of 7.66% (De Souza et al. 2008). Thus, regions on chromosomes 3 and 6 seem to harbour genes for SCMV disease resistance in maize which was further confirmed by a meta-QTL analysis (Lü et al. 2008).

4.8 Grey Leaf Spot (GLS)

Resistance to GLS is under the control of multitude of genes with cumulative additive effects and a significant G × E interaction (Lyimo et al. 2011). An advanced generation intercross RIL population derived from the cross Mo17 × B73 was utilized to identify five QTLs, and drawing parallels with previously reported QTLs, two hotspots were identified (Balint-Kurti et al. 2008a). Two stable and major QTLs, qRgls1 and qRgls2 on chromosomes 8 and 5, were consistently detected across locations. Furthermore, qRgls1 was fine-mapped to an interval of 1.4 Mb (Zhang et al. 2012). QTLs identified for flowering time and GLS resistance were found to overlap reflecting the relation between these two traits in YML32 × Ye478-based F2:3 population. A major QTL, qRgls.yaas-8-llqFt.yaas-8 with PVE of ~18% and 16.2%, was identified for GLS disease score and flowering time, respectively (Liu et al. 2016b). Three major QTLs, qGLS1.04, qGLS2.09 and qGLS4.05 with PVE of >10%, were reported using a NAM population. Later, qGLS1.04 was fine-mapped to two intervals of 6.5 Mb and 5.2 Mb, and the underpinning candidate gene was also identified, that is, putative flavin monooxygenase gene (Benson et al. 2015). Four stable QTLs qRgls.CH-4, qRgls.CH-1, qRgls.CH-2 and qRgls.CH-6 were in F2:3 population (08-641 and 446 as parents), of which qRgls.CH-6 was novel (He et al. 2018). Through linkage mapping, 22 QTLs were identified with qGLS7–105 on chromosome 7 being the major QTL (PVE of 28.2%), and 14 QTLs were found through GWAS with PVE of 6–8% individually (Kibe et al. 2020). A major QTL, qRgls1.06, explaining 55% of the total variance was identified through BSA-Seq for resistance to GLS which was later fine-mapped to 2.38 Mb region (Sun et al. 2021).

4.9 Southern Corn Leaf Blight (SCLB)

Three major QTLs on chromosomes 1, 2 and 3 were identified for SLLB resistance in a RIL population derived from Mo17 × B73 (Carson et al. 2004). Similarly, two major QTLs accounting for 80% of phenotypic variance were identified on chromosomes 3 and 9 using a RIL population of the cross NC300 × B104 (Balint-Kurti et al. 2006). Later, same group identified four stable QTLs in a different mapping population, B73 × Mo17, two on chromosome 3 (within same interval) and one each on chromosomes 1 and 8 (Balint-Kurti et al. 2007). The QTL found on chromosome 3 almost matched in its location with the one identified previously by Carson et al. (2004). This QTL was further fine-mapped to a 0.5 cM interval using two sets of populations, NIL F2:3 one derived from the initial cross Mo17 × B73 and a RIL F2:3 from two RILs of B73 × Mo17, and the candidate genes were elucidated (Kump et al. 2010). Three QTLs were identified using two more mapping populations developed from H99 × B73 and B73 × B52. Furthermore, a cursory comparison with previously reported QTLs revealed two QTL hotspots on chromosomes 3 and 6 (Balint-Kurti et al. 2008b). Nine QTLs each were identified in two sets of mapping populations – B73 rhm1 × NC250A and NC250A × B73 – one developed specifically for SCLB and Ki14 × B73 developed for exploring multiple disease resistance (Zwonitzer et al. 2009, 2010). Similarly, in NAM populations, ~35 QTLs with small additive effects were identified for SCLB resistance along with several underlying defence-related genes such as LRR receptor kinase, AP2 transcription factors, etc. (Kump et al. 2011; Bian et al. 2014). In another study, a large NAM population (5000 recombinant inbred lines from 25 parents with B73 as a common parent) was used to map 48 QTLs for SCLB resistance. Further, among the candidate genes identified, three genes encoding AN1-like zinc finger domain containing protein, LRR protein and BCL-2-associated athanogene 3 protein were separately validated (Li et al. 2018). Four putative QTLs on chromosomes 3, 8 and 9 were found to confer resistance to SLB with a cumulative PVE of 54% (Kaur et al. 2019).

In a QTL analysis for multiple disease resistance, a single RIL population derived from Ki14 × B73, nine, eight and six QTLs were identified for SCLB, GLS and NCLB resistance, respectively, with many QTLs overlapping across diseases (Zwonitzer et al. 2010). Combined QTL mapping for three SCLB, NCLB and GLS using four donors (NC304, NC344, Ki3, NC262) and two recurrent parents (Oh7B, H100) revealed QTLs overlapping for two or more diseases – two QTLs for SCLB and NCLB, seven QTLs for SCLB and GLS and two QTLs for NCLB and GLS and six for all the three diseases (Lopez-Zuniga et al. 2019). Around 44 QTLs identified for resistance against NCLB, SCLB and GLS in previous studies were validated using 12 F2:3 populations, of which 16 QTLs were confirmed and can serve as valuable candidates for MAS (Martins et al. 2019).

5 QTLs for Insect Resistance

5.1 Mediterranean Corn Borer (MCB)

The resistance to MCB is assessed through length of stem tunnels made by MCB larvae. Grain yield under infestations also seems to be equally important trait for screening for MCB tolerance. In an initial report, three QTLs were reported, one for kernel damage and two for stalk tunnelling. Two of these QTLs co-localized with previously reported QTLs for European corn borer (Ordas et al. 2009). In a similar study, three more QTLs were identified for MCB resistance, and underlying five candidate genes involved in cell wall biosynthesis were proposed. Thus, cell wall characteristics primarily decide feeding behaviour of borers (Ordas et al. 2010). Six QTLs were found for various MCB-resistant traits such as tunnel length (1), stalk lodging (1) and ear resistance (4), with PVE of 19.8%, 11.5% and 25–63%, respectively. QTLs governing yield under infestation seem to control MCB tolerance (Samayoa et al. 2014). However, in another study by the same group, no genetic correlation was obtained between tunnel length and grain yield reflecting that this association is background specific. Samayoa et al. (2015a) identified eight QTLs for MCB resistance, most of them being major QTLs including one QTL that can reduce tunnel length up to 8 cm (Samayoa et al. 2015a). A GWAS study using a panel of 302 inbreds revealed 25 MTAs for MCB resistance with PVE of 6–9%. The genes with or close to these SNPs are mostly defence related (Samayoa et al. 2015b). Five QTLs were reported for MCB resistance in terms of tunnel length (3), kernel resistance (1) and stalk damage (1), of which one QTL (in the bin 10.02–10.03) displayed major effect (PVE – 13% for stalk damage and 10% for tunnel length) (Jimenez-Galindo et al. 2017). Interestingly allelic variants of markers linked to these QTLs provided yield advantage as in previous study.

5.2 European Corn Borer (ECB)

Like MCB, resistance to ECB is a quantitative trait with tunnel length being the suitable proxy for mapping studies. Genomic regions responsible for resistance from donor inbred line B52 were found on chromosomes 1, 2, 3 and 4 in a first study (Onukogu et al. 1978). Later, eight QTLs were reported for tunnel length which showed cumulative PVE of 38%, among which two were major QTLs with PVE >13% (Schon et al. 1993). Likewise, six QTLs for tunnel length and five for silk damage rating were reported with a total genotype variance of 50%, and only one QTL was common between these two traits (Bohn et al. 2000). Using RILs derived from the same cross (B73 × B52) as that of Schon et al. (1993), nine QTLs were identified for ECB resistance with a cumulative PV of 59%. However, only one QTL coincided with the previous study (Cardinal et al. 2001). Jampatong et al. (2002) reported nine QTLs (on chromosomes 1, 2, 4, 5, 6 and 8) for first-generation ECB and seven (on chromosomes 2, 5, 6, 8 and 9) for second-generation ECB. However, majority of QTLs were inconsistent across environments. Using De811 as a donor, seven QTLs were detected for ECB resistance. In comparison with the population derived from B73 × B52, only one QTL could match reflecting different genes contributing resistance to ECB (Krakowsky et al. 2002). Two sets of populations were utilized in another study – F2:3 and a test cross population. Four and eight QTLs were found for stalk damage rating (SDR) and tunnel length using F2:3 of the original cross, while six QTLs were identified using test cross progenies. However, of these, only three QTLs for SDR matched between two populations (Papst et al. 2004).

5.3 Southwestern Corn Borer (SWCB)

Resistance to SWCB is a polygenic trait with primarily additive gene action and is assessed phenotypically by observing leaf damage area. Using two mapping populations, CML131 × CML67 and Ki3 × CML139, nine QTLs (chromosomes 1, 5, 7, 8 and 9) were identified in former and five QTLs (chromosomes 1, 6, 8 and 9) in latter population, and no common QTLs were observed between two populations (Groh et al. 1998). In the same year, seven QTLs were reported using a F2 population derived from the cross, Ki3 × CML139, which accounted for a cumulative PV of 30% (Khairallah et al. 1998). Brooks et al. (2005) reported around eight QTLs and two interactions for SWCB resistance using a population generated from the cross of Mp704 and Mo17. Furthermore, mir family of genes and glossy15 locus located on chromosomes 6 and 9, respectively, were proposed as candidate genes for SWCB resistance. In continuation of this work, addition of 151 more SSR markers resulted into 29 QTLs (through CIM) explaining up to 29% of PVE (Womack et al. 2018). Four more QTLs were unveiled for SWCB resistance using Mp708 as a donor parent. The QTLs found on chromosomes 1, 5, 7 and 9 corroborated with those identified using Mp704 in previous study (Brooks et al. 2007).

5.4 Fall Armyworm (FAW)

The resistance to FAW seems to show close association with resistance to SWCB facilitating simultaneous improvement of both these traits. As a testament to this, QTL studies have coupled analysis of both insect pests, and many of the QTLs coincided for these two pests. Brooks et al. (2005) found seven QTLs governing resistance to FAW, of which two QTLs coincided with those identified for SWCB. Similarly, in 2007, the same group reported seven QTLs using a different donor parent. Of these, the QTLs found on chromosomes 1, 5, 7 and 9 overlapped with the QTLs identified for SWCB resistance (Brooks et al. 2007). By enriching the linkage map of Brooks et al. (2005) with additional SSR and SNP markers, Womack et al. (2018) reported 24 and 36 QTLs for FAW resistance through CIM and MIM, respectively.

Thus, many of the genomic regions governing different herbivorous insect pests coincide, indicating few genetic entities confer broad-spectrum resistance against a variety of herbivorous pests in maize and in turn suggesting the apparent possibility of simultaneous improvement of resistance to these pests. This was further attested by a GWAS study using 341 maize genotypes where single candidate gene was responsible for multiple QTNs for pest resistance (Badji et al. 2020). Additionally, a meta-QTL analysis of these QTLs revealed that majority of the regions governing resistance to herbivorous insects in maize harbour QTLs for cell wall constituents such as members of hydroxycinnamate group as well as fibre components indicating their vital role in resistance (Badji et al. 2018).

5.5 Maize Weevil (MW) 

There are limited reports on identifying the genomic regions governing MW resistance. In a first report by Garcia-Lara et al. (2009), five QTLs were found for grain damage, six QTLs for grain weight loss, seven for MW susceptibility index and three for number of adult progenies explaining 28%, 22%, 27% and 10% of PVE, respectively. Almost half of these QTLs showed significant G × E interaction. Later, using the same population, Garcia-Lara et al. (2010) reported 17 QTLs for 11 biochemical parameters with PVE ranging from 25% to 47%. Similarly, 15 QTLs were identified for MW resistance in a RIL population derived from P84 × Kilima with a PVE of 14–51%. Individually, six QTLs were found for grain weight loss, four QTLs for flour production and five for adult progeny (Castro-Álvarez et al. 2015). Badji et al. (2020) recently conducted GWAS using 341 tropical maize lines to study the genetic control of resistance to multiple insect pests like MW, SB and FAW. They identified revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits across the maize genome. Sixteen QTNs were closely associated with multiple traits and six were associated with resistance to both FAW and MW, discovering the pleiotropic genetic control.

6 Qualitative Resistance: R Genes

Qualitative resistance is generally correlated with hypersensitive response at infection site and is also exploited in maize (Steffenson 1992). Examples of Ht genes for resistance to NCLB (Welz and Geiger 2000) and the Rp genes for resistance to common rust (Ramakrishna et al. 2002) have been used in maize breeding. Hm1 was the first reported disease resistance gene conferring resistance to maize leaf blight and ear mould caused by Cochliobolus carbonum (race 1). This gene encodes an NADPH-dependent HC-toxin reductase which neutralizes HC-toxin responsible for the disease (Johal and Briggs 1992). The locus rp1 on chromosome 10 harbours around 14 race-specific resistance genes for common rust in maize caused by P. sorghi (Hulbert 1997). Among these genes, Rp1-D was characterized and is a NB-LRR resistance gene (Collins et al. 1999). However, races virulent to this gene emerged later. At least 18 race-specific genes have been identified for resistance to southern rust in maize, which is more notorious than common rust (Zhu et al. 2021). ZmWAK is the underpinning gene for the major QTL, qHSR1, governing resistance to head smut caused by Sphacelotheca reiliana on chromosome 2 (Zuo et al. 2015). ZmWAK codes for a wall-associated receptor-like protein kinase which primarily functions to restrict the spread of the soil-borne pathogen from root to aboveground parts. A closely related gene, ZmWAK-RLK1, was found to be a candidate gene in Htn1 locus mapped for NCLB resistance (Hurni et al. 2015). ZmREM6.3 is a candidate gene of another NCLB resistance QTL qNLB1.02B73 (Jamann et al. 2016), whereas ZmREM1.3 overexpression in maize plants gave resistance to southern rust (Wang et al. 2019). Another NB-LRR gene, Rcg1, which governs resistance to ASR was characterized using transposon tagging (Frey et al. 2011). ZmCCoAOMT2 was proposed as candidate gene for GLS resistance which encodes caffeoyl-CoA O-methyltransferase involved in lignin production (Yang et al. 2017). This gene was also found to be responsible for resistance to SCLB. In addition, rhm1, another locus conferring SCLB resistance, contains only one causal gene encoding lysine histidine transporter 1 (Zhao et al. 2012).

ZmFBL41 was identified as gene responsible for quantitative resistance against banded leaf and sheath blight in maize. A mutant with two amino acid substitutions in ZmFBL41 prevented its interaction with ZmCAD thwarting the degradation of the latter and resulting in lignin accumulation and resistance (Li et al. 2019). As mentioned before, ZmCCT and ZmAuxRP1 are the causal genes of qRfg1, a major QTL, and qRfg2, a minor QTL governing resistance to stalk rot, respectively. The expression of ZmCCT is regulated by CACTA-like transposon. The insertion of transposon silences the gene with minimum or no response to pathogen attack (Wang et al. 2017). ZmAuxRP1 enhances the synthesis of auxin indole-3-acetic acid (IAA) while repressing benzoxazinoid defence compounds (BXs). It also provides resistance to FER (Ye et al. 2019). Mutants (knockdown) of LOX3 (lipoxygenase) gene displayed reduced ear rot symptoms with decreased conidia formation of Fusarium verticillioides and the subsequent production of mycotoxin fumonisin B1 (Gao et al. 2007, 2009). Another gene, ZmLOX12, however, supressed the disease production (Christensen et al. 2015).

Similarly, h-type thioredoxin encoding gene ZmTrxh is the causal gene of Scmv1, a major locus for SCMV resistance mapped on chromosome 6 (Tao et al. 2013b). ZmABP1 is the causal gene for Scmv2, another SCMV resistance locus which encodes an auxin-binding protein acting during the later stages of viral infection (Leng et al. 2017). ZmGDIα gene is associated with a major QTL, qMrdd1, for MRDD resistance, which encodes a Rab GDP dissociation inhibitor alpha (RabGDIα) (Liu et al. 2020). The recessive allele of the gene ZmGDIα-hel generated by the insertion of helitron transposon into intron 10 renders an alternative splice variant which reduces disease severity by ~30%. Pythium stalk rot in maize is caused by Pythium aphanidermatum and Pythium inflatum. Two dominant genes, RpiQI319-1 and RpiQI319-2, reportedly confer resistance to Pythium stalk rot (Song et al. 2015). Thus, several candidate genes underpinning resistance to biotic stresses, particularly diseases, in maize have been elucidated and characterized, and many more need to be discovered and characterized in the future.

7 Utilization of QTLs Identified in MAS Programmes

MAS provides valuable alternative for conventional breeding for accelerated development of improved crop varieties with host plant resistance. MAS significantly reduces the time through efficient selection, irrespective of stage. Marker-assisted backcross breeding (MABB) can be utilized to correct a specific defect of otherwise popular variety using linked or gene-based marker (Zunjare et al. 2018; Xu et al. 2020). This is particularly relevant in maize biotic stress breeding because of extensive QTL and GWAS reports available in literature. Further many of these QTLs were fine-mapped and validated making them suitable inputs for MAS. However, only few introgression studies exist for biotic stress resistance genes in maize compared to rice and wheat. Zhao et al. (2012) introgressed head smut resistance QTL, qHSR1 (ZmWAK gene), through MABC into ten diverse inbred lines that exhibited improved head smut resistance and yield (Zuo et al. 2015). The introgression of ZmWAK into a Chinese maize line Tongsipingtou led to the development of head smut-resistant variety, Jidan558. Marker-assisted gene pyramiding of two genes, Scmv1 and Scmv2, into the background of maize line, F7, resulted into a completely tolerant line (nearly isogenic line) against sugarcane mosaic virus (Xing et al. 2006). Yang et al. (2017) also developed a multiple disease-resistant line (against SCLB and GLS) by introgression of qMdr9.02. Three putative QTLs for southwestern corn borer were simultaneously mapped and transferred from CML67 into recurrent parent CML204 (Willcox et al. 2002). A major QTL for MRDD, qMrdd8, was introgressed from the donor X178 into the background of seven recipient parents, Huangzao4, Chang7-2, Ye478, Zheng58, Zhonghuang68, B73 and Ji846 using four foreground markers (Xu et al. 2020). MLND resistance QTLs were transferred from KS23-6 into nine locally adapted inbreds using KSAP assays (Awata et al. 2021). Except these, to the best of our knowledge, there were no attempts to introgress genes/QTLs for biotic stress resistance in maize despite the availability of many well-characterized and cloned genes.

8 Conclusion

Diseases and pests are the important stresses encountered by maize crop which often result in complete yield loss. Available literature suggests report of numerous QTLs for various biotic stresses. Many of them have been fine-mapped, and the causal candidate genes have been identified and characterized. Methodical deployment of these genes and QTLs into the susceptible inbreds is highly essential to evolve biotic stress-resilient maize hybrids. Relative to diseases, mapping studies for insect pests received lesser attention, though they are more devastating than diseases. Lack of easy-to-adopt screening protocols might be the reason for this. However, the silver lining here is that resistance to herbivorous insects seems to be controlled by few genomic hot spots, thus facilitating multiple insect resistance by the transfer of a few QTLs. With the advent of high-throughput genotyping techniques, MAS should be exploited to a maximum extent to fast track the development of biotic stress-tolerant maize varieties.