International Journal of Genomics

International Journal of Genomics / 2021 / Article

Research Article | Open Access

Volume 2021 |Article ID 9979146 | https://doi.org/10.1155/2021/9979146

Arthur Pfunye, Rwafa Rwafa, Stanford Mabasa, Edmore Gasura, "Genome-Wide Association Studies for Striga asiatica Resistance in Tropical Maize", International Journal of Genomics, vol. 2021, Article ID 9979146, 8 pages, 2021. https://doi.org/10.1155/2021/9979146

Genome-Wide Association Studies for Striga asiatica Resistance in Tropical Maize

Academic Editor: Paulo M. Pinto
Received21 Mar 2021
Revised28 May 2021
Accepted30 May 2021
Published21 Jun 2021

Abstract

Striga asiatica L. is a parasitic weed in cereal crops including maize leading to tremendous yield losses up to 100% under severe infestation. The available S. asiatica control methods include cultural control options such as uprooting and burning the Striga plants before they flower, field sanitation, crop rotation, intercropping, organic matter usage, improved fallows, and application of herbicides. Resource limitation among smallholder farmers renders almost all of the control methods impossible. Development and use of Striga resistant genotypes are seen as the most feasible management option. Marker identification formulates tools that are faster, cheaper, and easier to utilise in breeding for S. asiatica resistance which has low heritability. The objective of this study was to identify single nucleotide polymorphism (SNP) markers for Striga resistance using the genome-wide association study (GWAS). Genotyping by sequencing was done on tropical maize inbred lines followed by their evaluation for Striga resistance. Analysis of variance showed significant () variation among evaluated genotypes for Striga resistance traits such as germination distance, germination percentage, haustoria root attachments, total Striga plants emerged, total biomass, and growth rate. There were also significant differences () for cobs, leaves, stems, and roots weight. The broad sense heritability was fairly high (up to 61%) for most traits. The means for derived traits on stress tolerance indices were subjected to a -test, and significant differences () were found for leaves, stem, roots, shoots, and total biomass. The Manhattan plots from GWAS showed the presence of three SNP markers on chromosome numbers 5, 6, and 7 for total Striga plants emerged. The identified markers for resistance to S. asiatica should be validated and utilised to breed for Striga resistance in tropical maize.

1. Introduction

Information about the genetic variation present within and between various populations and their structure can play a valuable role in the efficient utilisation of plants [1]. During the last three decades, the world has witnessed a rapid increase in the knowledge about genome sequences [2] and the physiological and molecular roles of various plant genes, which has revolutionized the applications of molecular genetics in plant breeding programs. Genetic markers are important developments in the field of plant breeding [3]. Genetic markers are closely related with the target gene, and they act as signs or flags of the gene they are associated with [4]. Genetic markers are broadly grouped into two categories, classical and molecular. Classical markers include morphological, cytoplasmic, and biochemical markers [5]. Classical markers are less preferred by breeders because they are less polymorphic and are highly influenced by the environment as well as influenced by the plant growth stage [4]. Molecular markers such as single-nucleotide polymorphism (SNP) markers are the most ideal to use in marker-facilitated breeding because they are codominant, uniformly distributed throughout the genome, highly reproducible, and highly polymorphic [6]. The genome-wide association study (GWAS) is a modern technique used to detect connotation between genetic variants and traits in samples from populations mainly based on SNP markers [7]. This recently discovered technique has led to advancement in the arena of genotyping and sequencing; therefore, it is a powerful technique to study the genetics of natural variation and traits of interest [7]. The trait of attentiveness in this study is Striga asiatica resistance in tropical maize.

Striga spp. is a parasitic weed in cereal crops including maize leading to tremendous yield losses especially in Africa. In east Africa, there is Striga hermonthica while Striga asiatica is found in southern Africa. Striga resistance is defined as the plant’s ability to produce satisfactory grain yield under severe Striga infestation, while at the same time carrying fewer flowering Striga above the ground [8]. Generally, crops may exhibit the ability to resist Striga at any of the growing stages from germination, attachment, subterranean growth, emergence, flowering, and seed production [9]. There are many mechanisms by which crops develop resistance to Striga including production of low germination stimulators, growth retardants, and tolerance [10]. Low germination stimulators are based on the low efficiency of the maize in stimulating Striga to germinate and a lessened extensive root system of the maize genotype. Growth retardants do not stop Striga from germinating and attaching to the roots but rather retard its overall growth, delay emergency, and reduce its vigor. Tolerance is the ability of the host plant to produce high grain yield yet supporting high number of total Striga plants emerged [11]. Marker-assisted breeding can give rise to better solution to current problems caused by S. asiatica in maize production in sub-Saharan Africa.

The major problem for maize production in Africa is very low yields that are stagnated at around 1.2 tons/hectare/year [12]. Poor agronomic practises, drought stress, pests, and diseases are among other reasons that have been causing low maize yields. However, the ongoing encounter between maize and S. asiatica has the furthermost economic bearing [13]. Striga is a parasitic weed that attacks maize, resulting in retarded plant growth as well as stunted and withered crops, and reduced grain yield [14]. In severe cases, this leads to 100% yield losses and desertion of fields. The availability of dormant seeds in the seed bank makes Striga persistent season after season [15]. The available Striga control methods include cultural control methods such as uprooting and burning the problematic weed before flowering, field sanitation, crop rotation, intercropping, organic matter usage, improved fallows, and application of herbicides [15]. Resource limitation among smallholder farmers renders almost all of the control methods impossible. Development and use of Striga resistant genotypes are seen as the most feasible management option for Striga-related problems [10]. However, little on breeding for Striga resistance has been done [11]. A number of efforts were made in east Africa where Striga hermonthica is a problem while limited efforts exist in southern Africa where Striga asiatica is prevalent [10].

The development of resistance has proven to be strenuous due to its low heritability [16, 17]. Marker identification will formulate tools that are faster, cheaper, and easier to utilise in effectively tackling the problem of breeding for Striga resistance [13, 18, 19]. There is a research gap on the use of SNP markers to improve maize genotypes to resist Striga infestation [20]. Breeding for Striga resistance is the most efficient, cost effective, and environmentally friendly approach to reduce yield loss [10]. There is need to identify SNP markers in maize that are associated with resistance to Striga using genome-wide association study (GWAS) because SNP markers are abundantly available in the maize genome, codominant, evenly distributed throughout the genome, and highly reproducible and polymorphic. However, to date, only reports exist for Striga hermonthica found in east Africa and absent for Striga asiatica found in southern Africa [13, 18, 21, 22]. Identification of SNP markers will solidify convectional breeding methods and bring rejuvenation to maize production in resource poor African farmers by reducing yield losses due to Striga infestation [13]. There is need to develop a breeding tool that can be used to breed new varieties that are resistant to Striga asiatica based on SNP markers. The aim of this study was to identify SNP markers for Striga resistance using the genome-wide association study (GWAS).

2. Materials and Methods

2.1. Planting Materials

A total of 222 maize inbred lines comprising of 192 inbred lines from International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe, and 30 inbred lines from International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria, were used in the study (Table 1). The 30 maize inbred lines were bred for resistance to S. hermonthica using genes from Zea diploperennis, a wild relative of maize but were not yet tested for resistance to Striga asiatica in southern Africa.


Entry numberGenotype names

2TZISTR1116
3TZISTR1172
4TZSTR170
5IITATZISTR1132
6TZISTR1119
7TZISTR1175
8TZSTR179
9IITATZISTR1137
10TZISTR1133
11TZISTR1178
12TZSTR182
13IITATZISTR1138
14TZISTR1134
155057
16TZSTR184
17IITATZISTR1156
18TZISTR1154
199540
21TZSTR186
22IITATZISTR1157
23TZISTR1160
26IITATZISTR1158
27TZISTR1162
29TZSTR189
30IITATZISTR1159
31TZISTR1166
33IITATZSTR194

2.2. Genomic DNA Isolation and Genotyping

Seeds were shipped to the Biosciences for East and Central Africa (BeCA) at the International Livestock Research Institute (ILRI) (BeCA-Hub-ILRI) Nairobi, Kenya. The seeds of the 222 inbred lines were germinated, and the DNA was extracted from fresh tissues that were one week old using the modified CTAB method. The DNA was checked for quality using the agarose gel and quantity using a spectrophotometer. Genotyping was done using the genotyping by sequencing (GBS) platform according to the protocol of the Integrated Genotyping Support Services (IGSS) [23] at BeCA-Hub-ILRI, Nairobi, Kenya.

2.3. Laboratory Screening Experiment

The agar gel assay was done for Striga germination percentage (GP) and Striga germination distance (GD). The CIMMYT germplasm was not screened for resistance given that the technique is very difficult to implement in the laboratory and the greenhouse for a large number of genotypes. Furthermore, preliminary evaluations had shown less variability for Striga resistance parameters in the CIMMYT germplasm. This may suggest that some of the germplasm sources were from regions without this parasite and lack of coevolution of the host, and the parasite did not allow development of resistance.

Striga asiatica seeds collected from different farmer’s fields in Rushinga communal area at the end of 2015/2016 rainfall season were preconditioned by placing them on a glass fibre paper and wetted with distilled water in a 9 cm diameter Petri dish that were sealed using cello tape and wrapped in a black polythene paper to exclude light and were kept in the glass house for 28 days. The S. asiatica seeds were surface sterilised by immersing them in 1% sodium hypochlorite for 30 minutes before setting up the assay. The assay setup was done following the standard procedures [11], but with slight modifications as follows. A total of 150 μl of preconditioned S. asiatica seeds were pipetted into eight (9 cm diameter) Petri dishes using a 50 μl micropipette. A total of 90 ml of autoclaved water agar was poured into the Petri dishes containing Striga seeds. The agar was poured just before it became cool enough to solidify.

A seed to represent each maize genotype pregerminated by soaking in water for 48 hours was submerged in the solidifying agar at the edge of the dish with radicle pointing across the Petri dish. Each genotype had three replicates. The Petri dishes were then incubated for 48 hours, at 27°C. After 48 hours, the Petri dishes were examined using a dissecting microscope at ×100 magnification to observe germination of the Striga seeds. Striga seed observed to be germinating at the closest distance from the maize was recorded and used as the index for Striga germination. A Petri dish lid graduated with 32 () squares was used to count the number of germinating Striga seeds out of the seeds under focus in one square and expressed as a percentage.

Furthest germination distance was measured and recorded. The number of germinated seeds as a percentage of the total number of seeds visible in a given quadrant under a light microscope was also recoded per each genotype.

2.4. Greenhouse Experiment

One hundred and eighty-six asbestos pots measuring 15 cm diameter and 20 cm height were filled with fine sand soil up to three-quarter full. In 50% of the pots, the top seven centimetres of dry soil was thoroughly mixed with 0.05 g of S. asiatica seeds (approximately 12,250 seeds). Mixing of S. asiatica seeds with soil was done by shaking the soil with S. asiatica seeds in a polythene bag. Planting of maize was done on the 16th of December 2018. One seed was planted in moist soil seven centimetres deep per pot for all the maize genotypes both in the infested and uninfested pots. The top seven centimetres was mixed with 2 g/pot of compound D. The maize emerged on the 23th of December 2018.

The pots were watered daily using a watering can fitted with a fine rose to keep the pots moist. Other weeds were removed from the pots by hand pulling to allow interactions between maize and Striga only. Ammonium nitrate (34.5% N) was applied in the splits of 0.8 g at four weeks after planting (WAP), 0.8 g at six WAP, and 0.4 g at 10 WAP to achieve an application rate of 30 kg N/ha.

Data was collected on a number of traits. The number of total emerged Striga asiatica plants per pots was recorded at weekly interval from nine weeks of planting to harvesting. The total number of S. asiatica haustoria root attachments was counted at maturity. Maize roots were washed by gently immersing the root ball in a large bucket of water, and decaying old attachments were counted and weighed as well. Maize plant height was measured from the base of the stem up to the ligule of the last fully expanded leaf at weekly intervals from three WAP to 10 WAP. Above-ground biomass included stem, leaf, and cob biomass. After cutting the maize plant at the base of the stem using secateurs, the leaves and the cobs were removed from the stem and dried in khaki envelops in an oven at 80°C for 72 hours and then weighed using a digital balance. Thoroughly washed maize roots were put in a khaki envelop paper and oven dried at 80°C for 72 hours and then weighed using a digital balance. The root-to-shoot ratio was calculated as weight of oven-dried roots divided by weight of dried total above-ground biomass. The stress tolerance index was calculated as the difference between uninfested and infested observations divided by the observation under uninfested conditions [24].

2.5. Data Analysis

A total number of 45,000 SNP markers were sampled in this study. The RStudio software was then used for cluster analysis to depict possible groups using Gower’s distance and neighbor joining algorithm [25]. Not all of the 222 materials were evaluated for Striga resistance. Analysis of variance (ANOVA) for different maize parameters recorded was done using GenStat software 18th edition. Mean separation was done using the 5% Fischer’s protected least significant difference (LSD). Although there were significant differences among materials used, there were no significant variation among the CIMMYT materials sampled and tested, but variation existed among the IITA materials (Table 2). In this regard, only the phenotypic traits of the IITA materials are presented. Genome-wide association studies were done between phenotypic and genotypic data using the Genome Association and Prediction Integrated Tool (GAPIT) package in RStudio [25].


Source of variationDFGermination distanceGermination percentage

Replications332.31628.9
Inbred lines29258.43679.1
Residual9049.49268.1
IVC52.24102.75
EVC49.49268.1
VP101.73370.85
BSH51.3527.71
GM17.5940.08
GCV41.0925.29
PCV57.3448.05

DF: degrees of freedom, IVC: inbred line variance component, EVC: error variance component, VP: phenotypic variance, BSH: broad sense heritability, GM: grand mean, GCV: genotypic coefficient of variation, PCV: phenotypic coefficient of variation. means significant at .

3. Results

There were highly significant differences () among genotypes for germination percentage and germination distance (Table 2), with some genotypes having fairly low mean values (Table 3). There were significant differences () among inbred lines for total Striga plants emerged and haustoria root attachment (Table 4).


Inbred lineGermination distanceInbred lineGermination percentage

181.67a110a
273a270a
204a320.33a
324.67a200.67a
318.33a181.67a
349.33a235.33a
113a165.33a
2316.67ab135.67a
1119.67ab16.33a
1621.33ab26.67a
1931.67ab256.67a
532.33ab286.67a
2233.33ab57.33a
2435.33ab317.67a
235.67ab217.67a
1036.33ab348.33a
1337.67ab49a
2842ab339a
743.67ab1511a
1454ab1911.33a
455ab2211.67a
955.33ab1412a
360ab712.33a
1560.67ab2414.33a
3365.67ab2918a
2975.67ab1021.33a
2190.67ab923.33a
25123.67ab2623.67a
26150ab324a
17169.33b1726.33a
Mean46.32Mean10.12
5% LSD75.785% LSD15.18

LSD: least significant difference.

Source of variationDFTotal Striga plants emergedHaustoria root attachment

Replications2145.885332
Inbred lines29169.145195
Residual5886.292150
IVC20.71761.25
EVC86.292150
VP1072911.25
BSH19.3626.15
GM10.1246.32
GCV44.9659.56
PCV102.2116.48

DF: degrees of freedom, IVC: inbred line variance component, EVC: error variance component, VP: phenotypic variance, BSH: broad sense heritability, GM: grand mean, GCV: genotypic coefficient of variation, PCV: phenotypic coefficient of variation. and means significant at and , respectively.

An unbalanced combined ANOVA including inbred line, number of replicates, and environment factor as either infested or uninfested showed highly significant () differences for cob, leaf, roots, shoots, stem, total biomass, and growth rate (Table 5). The growth rate and total biomass were significantly () affected by conditions (infested against uninfested) and inbred line. Lower heritability values were observed for stem, and fairly high heritability values were observed for cob, leaf, root, shoots, total biomass, and growth rate.


SOVDFCobLeafRootsShootsStemTotal biomassGrowth rate

Rep2277.927.82741.9802.245.9686712.09
Rep. block27178.122.54579.6524.773.87311626.65
Striga12809.170.2522,381.58757.51036.771411414.53
Inbred29375.631.29543862.874.71497543.21
Inbred x Striga2917418.65427.2460.475.29221014.47
Residual87186.818.83242.3466.745.81218116.71
Total175229.721.77501.1588.765.04276024.48
V error186.818.83242.3466.745.81218116.71
Var GE0061.6309.839.670
Var G31.472.0850.1266.024.82465.674.42
VP218.2720.91354.05532.7260.452656.3321.13
Mean23.4311.0319.249.914.49119.135.3
GCV23.9413.0636.8716.2815.1518.125.95
PCV63.0641.459846.2553.6643.2713.02
Heritability0.500.400.410.460.280.560.61

SOV: source of variation, DF: degrees of freedom, V error: variance for error, VGE: variance for genotype × environment interaction, VG: variance for genotype, VP: phenotypic variance, GCV: genetic coefficient of variation, PCV: phenotypic coefficient of variation. , , and mean significant at 0.05, 0.01, and 0.001probability levels, respectively.

Data was sorted for each trait from minimum value to maximum value and was divided at the centre into two groups, A and B. A -test analysis for the unpaired groups was done, and significant differences () were found for leaves, roots, shoots, and total biomass (Table 6).


GRTICobTILeafTIRootTIShootTIStemTITbioTI

Group A mean18.1515.7319.59.0718.9112.2722
Group A size131181411117
Variance for group A104.880629.993.617.84.6
Standard error of group A2.832.690.861.462.911.270.81
Group B mean17.8218.3628.925.7516.6926.2730
Group B size1711101613158
Variance for group B99.9100.212.12686.223.38.5
Standard error of group B2.423.011.11.272.571.241
Mean differences0.33-2.63-9.4-166792.21-13.99-8
Standard error of the difference3.724.041.451.933.871.821.34
-value0.09-0.65-6.45-8.640.57-7.68-5.94
Degrees of freedom28201628222413
-probability0.930.522<0.001<0.0010.573<0.001<0.001

GRTI: growth rate tolerance index, CobTI: cob biomass tolerance index, LeafTI: leaf biomass tolerance index, ShootTI: shoot biomass tolerance index, StemTI: stem biomass tolerance index, TbioTI: total biomass tolerance index.

Interestingly significant () positive correlations were found for some trait such as leaf biomass tolerance index and stem biomass tolerance index, and shoot biomass tolerance index and root biomass tolerance index (Table 7). Correlation was 100% for height and growth rate because growth rate was calculated using plant height.


GRTI1

HTI1.001
LTI0.230.181
RSRTI-0.41-0.12-0.361
RTI-0.18-0.060.560.21
ShTI0.410.400.75-0.430.271
StTI0.220.20.61-0.30.340.831
TBTI0.260.320.81-0.270.600.910.791
GRTIHTILTIRSRTIRTIShTIStTITBTI

GRTI: growth rate tolerance index, HTI: height tolerance index, LTI: leaf biomass tolerance index, RSRTI: root-to-shoot biomass ratio tolerance index, RTI: root biomass tolerance index, ShTI: shoot biomass tolerance index, StTI: stem biomass tolerance index, TBTI: total biomass tolerance index.

4. Discussion

The existence of genetic variation in Striga resistance parameters among maize materials indicated the potential to use this variation in the identification of markers associated with Striga resistance. Indeed, markers at chromosomes 5, 6, and 7 for total Striga plants emerged were identified, and these could be validated and used in future breeding programs (Figure 1). The three SNP markers found for total Striga plants emerged suggest the feasibility of making selections effectively at genomic level. The ability of Striga to emerge shows the compatibility of the host and parasite. In resistant cases of incompatibility, the number of Striga plants that emerge would be greatly reduced or can be absent at all [10, 13]. Information on quantitative traits loci for Striga resistance is very scarce [18, 21, 26]. Adewale et al. [21] identified a set of 24 SNP markers for various traits including Striga damage rating in maize inbred lines from east Africa while working on Striga hermonthica. Markers for Striga counts and Striga damage were found on chromosome 1, 3, 7, 8, 9, and 10. The number of emerged Striga plants should be related to the extent of the associated damage.

Interestingly, in this study, markers for total Striga plants emerged were found on chromosome 7 just like markers for Striga damage rating which have been also found by Adewale et al. [21] to be located on several chromosomes including number 7. Working on Striga hermonthica, Gowda et al. [22] also found SNP markers for emerged Striga plants and Striga counts at chromosomes 5 and 7, respectively. These findings are in line with the observations from this study that also found the markers total Striga plants emerged at chromosomes 5, 6, and 7 for Striga asiatica, a strain that is found in southern Africa.

At the moment, there are no maize varieties specifically bred for Striga asiatica resistance in southern Africa [11]. However, various resistance mechanisms to Striga asiatica have been reported in maize inbred lines of tropical origin [10]. These resistance mechanisms could be exploited in breeding better varieties; however, the heritability for Striga resistance is low [16, 17]. When heritability is low, the use of molecular markers is usually effective [22]. The SNP markers identified in this study for the total number of Striga plants emerged could thus go a long way if they are validated and utilised in breeding programs.

Agriculture has faced tremendous challenges in crop production in the past decades due to many abiotic and biotic factors such as parasitic weeds. In the future, it is predicted that there will be a further reduction in crop production due to predicted changes in temperature and rainfall patterns. Breeding for resistance to environmental, weeds, insects, and disease stress in plants is the most appropriate strategy in plants especially targeting yield increment among resource poor farmers. Striga asiatica plague is one of the major yield reduction factors in Africa among resource poor farmers [13]. Efforts must be made in the region to develop maize varieties that are resistant to such parasitic weeds.

The development of new varieties using the convectional breeding approach is usually labour-intensive and slow [22]. New breeding methodologies have been proposed with the advent of molecular markers. However, for those molecular markers to be developed, phenotypic screening is also required for different resistance mechanisms [10]. Modern phenotypic screening tools that incorporate remote sensing could help reduce the human error in experiments. However, these tools are yet to be developed for Striga resistance breeding thus necessitating again the need for molecular screening.

Phenotypic screening is subject to many challenges. The plant reacts by exhibiting phenotypic plasticity when the genotype is grown under various environmental conditions, and this plasticity is particularly big under extreme stress conditions such as Striga infestation. Phenotyping in general is labour-intensive, time-consuming, and costly and requires destruction of plants at fixed times or at particular phenological stages. A further source of variation can be introduced by human error, and for Striga breeding, incorporation of modern phenomics is encouraged.

Most traits had positive correlations. Correlations indicated that some traits can be used for phenotypic selection for grain yield under Striga stress. There are many reports in which indirect selection was done via other traits in breeding [27, 28]. The use of molecular markers would not replace phenotypic screening in breeding. There is therefore a need to integrate the various screening tools ranging from phenomics to genomics.

5. Conclusion

Three SNP markers have been identified on chromosomes 5, 6, and 7 for total Striga plants emerged. These makers can be validated and used in marker-assisted breeding or in genomic prediction for Striga asiatica resistance.

Data Availability

The phenotypic and genotypic data used to support the findings of this study are included within the supplementary information files (available here).

Conflicts of Interest

The authors do not have conflict of interests.

Acknowledgments

This work was supported by the BecA-ILRI Hub through the Africa Biosciences Challenge Fund (ABCF) program. The ABCF Program is funded by the Australian Department for Foreign Affairs and Trade (DFAT) through the BecA-CSIRO partnership; the Syngenta Foundation for Sustainable Agriculture (SFSA); the Bill & Melinda Gates Foundation (BMGF); the UK Department for International Development (DFID) and the Swedish International Development Cooperation Agency (Sida).

Supplementary Materials

Table S1: the phenotypic data file contains the raw data collected on various Striga resistance traits on 30 maize inbred lines from IITA. Table S2: the genotypic data file contains the HapMap genotypic information for the 30 maize inbred lines from IITA. The two data files can be used to run genome-wide association studies (GWAS), and the GAPIT package in the R software was used by the authors. (Supplementary Materials)

References

  1. S. Stoeckel, S. Arnaud-Haond, and S. A. Krueger-Hadfield, “The combined effect of haplodiplonty and partial clonality on genotypic and genetic diversity in a finite mutating population,” Journal of Heredity, vol. 112, no. 1, pp. 78–91, 2021. View at: Publisher Site | Google Scholar
  2. Y. Feng, S. Zou, H. Chen, Y. Yu, and Z. Ruan, “BacWGSTdb 2.0: a one-stop repository for bacterial whole-genome sequence typing and source tracking,” Nucleic Acids Research, vol. 49, no. D1, pp. D644–D650, 2021. View at: Publisher Site | Google Scholar
  3. I. Diouf and L. Pascual, “Multiparental population in crops: methods of development and dissection of genetic traits,” in Crop Breeding, Humana, New York, NY, 2021. View at: Google Scholar
  4. B. C. Collard, M. Z. Z. Jahufer, J. B. Brouwer, and E. C. K. Pang, “An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts,” Euphytica, vol. 142, no. 1-2, pp. 169–196, 2005. View at: Publisher Site | Google Scholar
  5. M. A. Nadeem, M. A. Nawaz, M. Q. Shahid et al., “DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing,” Biotechnology & Biotechnological Equipment, vol. 32, no. 2, pp. 261–285, 2018. View at: Publisher Site | Google Scholar
  6. N. N. Nguyen, M. Kim, J. K. Jung et al., “Genome-wide SNP discovery and core marker sets for assessment of genetic variations in cultivated pumpkin (Cucurbita spp.),” Horticulture Research, vol. 7, no. 1, pp. 1–10, 2020. View at: Publisher Site | Google Scholar
  7. Y. Liu, F. Wang, A. Zhang et al., “Development and validation of functional markers (tetra-primer ARMS and KASP) for the bacterial blight resistance gene xa5 in rice,” Australasian Plant Pathology, vol. 50, no. 3, pp. 323–327, 2021. View at: Publisher Site | Google Scholar
  8. M. Chitagu, J. T. Rugare, and S. Mabasa, “Screening maize (Zea mays) genotypes for tolerance to witchweed (Striga asiatica L. Kuntze) infection,” Journal of Agricultural Science, vol. 6, no. 2, p. 160, 2014. View at: Publisher Site | Google Scholar
  9. A. T. Shayanowako, M. Laing, H. Shimelis, and L. Mwadzingeni, “Resistance breeding and biocontrol of Striga asiatica (L.) Kuntze in maize: a review,” Acta Agriculturae Scandinavica, Section B—Soil & Plant Science, vol. 68, no. 2, pp. 110–120, 2018. View at: Publisher Site | Google Scholar
  10. E. Gasura, P. Setimela, S. Mabasa, R. Rwafa, S. Kageler, and C. Nyakurwa, “Response of IITA maize inbred lines bred for Striga hermonthica resistance to Striga asiatica and associated resistance mechanisms in southern Africa,” Euphytica, vol. 215, no. 10, pp. 1–15, 2019. View at: Publisher Site | Google Scholar
  11. C. S. Nyakurwa, E. Gasura, P. S. Setimela, S. Mabasa, J. T. Rugare, and S. Mutsvanga, “Reaction of new quality protein maize genotypes to Striga asiatica,” Crop Science, vol. 58, no. 3, pp. 1201–1218, 2018. View at: Publisher Site | Google Scholar
  12. J. E. Cairns, K. Sonder, P. H. Zaidi et al., “Maize production in a changing climate: impacts, adaptation, and mitigation strategies,” Advances in Agronomy, vol. 114, pp. 1–58, 2012. View at: Publisher Site | Google Scholar
  13. B. Badu-Apraku, S. Adewale, A. Paterne, M. Gedil, and R. Asiedu, “Identification of QTLs controlling resistance/tolerance to Striga hermonthica in an extra-early maturing yellow maize population,” Agronomy, vol. 10, no. 8, p. 1168, 2020. View at: Publisher Site | Google Scholar
  14. S. Runo, S. Macharia, A. Alakonya, J. Machuka, N. Sinha, and J. Scholes, “Striga parasitizes transgenic hairy roots of Zea mays and provides a tool for studying plant-plant interactions,” Plant Methods, vol. 8, no. 1, p. 20, 2012. View at: Publisher Site | Google Scholar
  15. G. Ejeta and L. G. Butler, “Host-parasite interactions throughout the Striga life cycle, and their contributions to Striga resistance,” African Crop Science Journal, vol. 1, no. 2, 2011. View at: Publisher Site | Google Scholar
  16. L. Akanvou and E. V. Doku, “Heritability of traits associated with Striga [Striga hermonthica (Del.) Benth.] resistance in an open-pollinated maize population,” African Crop Science Journal, vol. 6, no. 2, pp. 129–135, 1998. View at: Publisher Site | Google Scholar
  17. B. Badu-Apraku, B. E. Ifie, A. O. Talabi, E. Obeng-Bio, and R. Asiedu, “Genetic variances and heritabilities of traits of an early yellow maize population after cycles of improvement for Striga resistance and drought tolerance,” Crop Science, vol. 58, no. 6, pp. 2261–2273, 2018. View at: Publisher Site | Google Scholar
  18. B. Badu-Apraku, S. Adewale, A. A. Paterne, M. Gedil, J. Toyinbo, and R. Asiedu, “Identification of QTLs for grain yield and other traits in tropical maize under Striga infestation,” PloS One, vol. 15, no. 9, article e0239205, 2020. View at: Publisher Site | Google Scholar
  19. M. Gedil and A. Menkir, “An integrated molecular and conventional breeding scheme for enhancing genetic gain in maize in Africa,” Frontiers in Plant Science, vol. 10, p. 1430, 2019. View at: Publisher Site | Google Scholar
  20. W. A. Mengesha, A. Menkir, N. Unakchukwu et al., “Genetic diversity of tropical maize inbred lines combining resistance to Striga hermonthica with drought tolerance using SNP markers,” Plant Breeding, vol. 136, no. 3, pp. 338–343, 2017. View at: Publisher Site | Google Scholar
  21. S. A. Adewale, B. Badu-Apraku, R. O. Akinwale, A. A. Paterne, M. Gedil, and A. L. Garcia-Oliveira, “Genome-wide association study of Striga resistance in early maturing white tropical maize inbred lines,” BMC Plant Biology, vol. 20, pp. 1–16, 2020. View at: Google Scholar
  22. M. Gowda, D. Makumbi, B. Das et al., “Genetic dissection of Striga hermonthica (Del.) Benth. Resistance via genome-wide association and genomic prediction in tropical maize germplasm,” Theoretical and Applied Genetics, vol. 134, no. 3, pp. 941–958, 2021. View at: Publisher Site | Google Scholar
  23. X. Delannay, G. McLaren, and J. M. Ribaut, “Fostering molecular breeding in developing countries,” Molecular Breeding, vol. 29, no. 4, pp. 857–873, 2012. View at: Publisher Site | Google Scholar
  24. A. Oswald and J. K. Ransom, “Response of maize varieties to Striga infestation,” Crop Protection, vol. 23, no. 2, pp. 89–94, 2004. View at: Publisher Site | Google Scholar
  25. RStudio Team, RStudio: Integrated Development for R, RStudio, Inc., Boston, MA URL, 2015, http://www.rstudio.com/.
  26. I. O. Amusan, P. J. Rich, A. Menkir, T. Housley, and G. Ejeta, “Resistance to Striga hermonthica in a maize inbred line derived from Zea diploperennis,” New Phytologist, vol. 178, no. 1, pp. 157–166, 2008. View at: Publisher Site | Google Scholar
  27. J. Derera, P. Tongoona, B. S. Vivek, N. Van Rij, and M. D. Laing, “Gene action determining Phaeosphaeria leaf spot disease resistance in experimental maize hybrids,” South African Journal of Plant and Soil, vol. 24, no. 3, pp. 138–143, 2007. View at: Publisher Site | Google Scholar
  28. E. Gasura, P. S. Setimela, A. Tarekegne et al., “Variability of grain-filling traits in early maturing CIMMYT tropical maize inbred lines,” Crop Science, vol. 54, no. 2, pp. 530–536, 2014. View at: Publisher Site | Google Scholar

Copyright © 2021 Arthur Pfunye et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Related articles

No related content is available yet for this article.
 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views598
Downloads617
Citations

Related articles

No related content is available yet for this article.

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.