ABSTRACT
Northern leaf blight (NLB) is a major foliar disease caused by fungus Exserohilum turcicum that leads to limited production of cereals in the Sub-Saharan Africa. Maize is normally susceptible to NLB from the seedling stage to maturity making it expensive in the management and control. The disease lowers production of maize up to 80%, threatening food security in the region. However, to achieve increased food production, improved agricultural technologies should be adopted, whereby research institutions and breeders have continued assessing the breeding values and using advanced technologies for phenotyping diseases. Currently, new technologies have been incorporated where digital imagery tools are used for detecting foliar diseases in the field earlier enough before the severity is high. To curb this major problem of foliar diseases in maize quantitative trait loci (QTL) mapping is recommended and adopted to assist as an effective and efficient tool in breeding to generate resistant host plants. QTL mapping enhances in identification and evaluation of potential sources of resistance followed by introgression of favorable alleles into susceptible variety. This study was implemented to; i) compare the visual scoring method of phenotyping foliar diseases with the digital imagery methodology under a high disease pressure area. ii) Identifying the genomic regions associated with resistance to Northern leaf blight disease through quantitative trait loci (QTL) mapping. One hundred and ninety-two double haploid (DH) lines obtained from International maize and wheat improvement Center (CIMMYT) were test crossed to 2 single cross parents (CML539 x Laposta Seq F64) and (CML 312 x Laposta Seq F64). An alpha lattice design with two replications was used to evaluate the 192DH hybrids with three commercial local checks across two locations in Kenya under high disease pressure area condition during 2016-2017 growing seasons. Each plot measured 4m long spaced at 0.75m between rows and 0.25m between hills. Data was collected on days to anthesis, grain yield, plant and ear aspect, number of ears, plant and ear height and northern corn leaf blight where the disease severity was scored using a CIMMYT scoring scale of 1-5 where 1-there are no infections, the plant is fully clean, 2- light infection with moderate number of lesions on the lower leaves, 3-moderate infection with abundant lesions on the lower leaves and a few lesions on the middle leaves, 4- heavy infection with lesion abundant on all leaves, 5- very heavy infection with lesions on all leaves. At flowering stage, image analysis was conducted using a Nikon camera where images of the maize plot were taken; scanners were also used where maize leaves from every plot were scanned to obtain a clear view of the damaged lesions. All data collected was analyzed using Meta-R software to obtain the analysis of variance. It was concluded from the studies that digital imagery analysis led to more efficient and effective breeding since it gives accurate and precise information on the field data and also it consumes less time. To identify genomic loci associated with NLB resistance, double haploid (DH) lines from two bi-parental mapping populations were genotyped and marker trait association analysis carried out. Genome-Wide Association Study (GWAS) revealed a major quantitative trait locus (QTL) on chromosome 5 and chromosome 7 that were significantly associated with NLB resistance. This study provides important insights into the genetic architecture underlying resistance to NLB, and identified a useful set of polymorphic single nucleotide polymorphism (SNPs) to be used in breeding for NLB resistance.
TABLE OF CONTENTS
DECLARATION I
DEDICATION II
ACKNOWLEDGEMENT III
TABLE OF CONTENTS IV
LIST OF TABLES VIII
LIST OF FIGURES IX
ACRONYMS X
ABSTRACT XI
CHAPTER ONE
INTRODUCTION
1.1 Background information 1
1.2 Problem statement 2
1.3 Justification 3
1.4 Objectives 3
1.4.1 Main objective 3
1.4.2 Specific objectives 4
1.4.3 Hypotheses 4
CHAPTER TWO
LITERATURE REVIEW
2.1 Maize taxonomy, botany, growth and development 5
2.2 Production of maize in the world 6
2.2.1 Maize Production in the Sub-Saharan Africa 7
2.3 Maize production constraints 8
2.3.1 Northern Leaf Blight 8
2.3.2 Source of resistance 13
2.3.3 Plant immune system 14
2.3.4 Genetics of Northern leaf blight resistance 15
2.3.5 Breeding for Northern Leaf Blight resistance 16
2.4 Plant phenotyping 18
2.5 Quantitative trait loci mapping 20
2.5.1 Genome – wide association mapping 21
2.5.2 Application of Molecular markers for Northern leaf blight 23
CHAPTER THREE
COMPARISON OF IMAGE ANALYSIS AND VISUAL SCORING FOR PHENOTYPING REACTION OF MAIZE TO NORTHERN LEAF BLIGHT
3.1 Abstract 29
3.2 Introduction 30
3.3 Material and methods 32
3.3.1 Description of parental inbred lines used in the study 32
3.3.2 Site description 33
3.3.3 Activities 33
3.3.4 Data collection 34
3.3.5 Statistical Analysis 35
3.4 Results 37
3.4.1 Analysis of Variance 37
3.4.2 Phenotypic correlations 41
3.5 Discussion and conclusion 42
CHAPTER FOUR
MAPPING GENOMIC REGION ASSOCIATED WITH RESISTANCE TO NORTHERN LEAF BLIGHT DISEASE IN TROPICAL GERMPLASM
4.1 Abstract 45
4.2 Introduction 46
4.3 Material and methods 47
4.3.1 Plant materials and field design 47
4.3.2 Evaluation of Resistance for Northern leaf blight 47
4.3.3 Statistical data Analysis 48
4.3.4 Genotypic data analyses 48
4.3.5 Linkage analysis and inclusive composite interval mapping 49
4.3.6 Joint linkage association mapping (JLAM) 49
4.3.7 Genome –wide association analysis 50
4.4 Results 50
4.4.1 Phenotypic variation and heritability 50
4.4.2 QTL mapping for NLB Resistance 52
4.4.3 Joint linkage association mapping (JLAM) analysis 54
4.5 Discussion and Conclusion 59
4.5.1 QTL mapping of NLB resistance 59
CHAPTER FIVE
5.1 GENERAL DISCUSSION
5.2 CONCLUSION AND RECOMMENDATION 63
REFERENCES 64
LIST OF TABLES
Table 3. 1 Origin, genetics and agronomic character of plant materials 32
Table 3. 2 NLB disease scoring 34
Table 3. 3: Means, genotypic variance components (σ²G), error variance (σ²e), narrow sense heritability (h²), least significant difference, and coefficient of variance, of CML312/LaPSF64 DH population evaluated for NCLB disease, agronomic and image traits across environments 39
Table 3. 4: Means, genotypic variance components (σ²G), error variance (σ²e), narrow sense heritability (h²), least significant difference, and coefficient of variance, of LaPSF64XCML 539 DH population evaluated for NCLB disease, agronomic and image traits across environments 40
Table 3. 5: Phenotypic correlation among different traits in a combined association measured for 192 DH lines 41
Table 4. 1: Origin, genetics and agronomic characteristics of germplasm 47
Table 4. 2: Combined Means, components of variance for disease severity, agronomic traits, image traits 51
Table 4. 3: Detection of QTL associated with resistance to NCLB, their physical positions and genetic effects of the QTL in two DH populations CML539xLaPOSTA F64 and LAPOSTAF64xCML 312 53
Table 4. 4: Analysis of traits associated markers, allele substitution (α), effects and total phenotypic variance (R²) of the joint linkage association mapping in Double haploid population based on three different biometric models 55
LIST OF FIGURES
Figure 1: Disease cycle of Northern leaf blight (obtained from www.pioneer.com) 9
Figure 2: Maize plant showing Northern Leaf Blight lesions (picture taken by Arnet) 11
Figure 3: GBS libraries construction (picture taken from (Elshire, Glaubitz, Sun, Poland, Kawamoto, & Buckler, 2011) Elshire et al., 2011) 28
ACRONYMS
ANOVA - Analysis of Variance
CV - Coefficient of Variation
CIMMYT - International Maize and Wheat Improvement Center
CML - CIMMYT maize inbred line
DH - Doubled haploids
KALRO - Kenya Agricultural and Livestock Research Organization
LSD - Least Significance Difference
SAS - Statistical Analysis Software
GWAS - Genome wide association studies
LD - Linkage disequilibrium
MAS - Marker Assisted Selection
MLN - Maize Lethal Necrosis
MSV - Maize Streak Virus
TLB - Turcicum leaf blight
QTL - Quantitative Trait Loci
SNP - Single Nucleotide Polymorphism
SSA - Sub Saharan Africa
CHAPTER ONE
INTRODUCTION
1.1 Background information
Maize (Zea mays) originated from Balsas River basin of southwestern Mexico about 9000 years ago (Matsuoka, et al., 2002) Since then maize has spread geographically and economically becoming one of the most important food crops adapted globally (CIMMYT, 1999- 2000). It’s also the second largest crop adapted in the world after rice. Maize can be grown over a range of agro ecological zones defined by the total rainfall received, elevation, maturity period and the length of the growing season (FAO Statistics, 2000). Maize is grown from 50°N to 40°S and a sea level of up to 4000m altitude in areas with 250 mm to 5000mm of rainfall per year (Doswell et al., 1996). The optimum temperature for maize growth and development is 18°C to 32°C with temperature of 35°C. It has a growing cycle ranging from 3 months to 13 months (CIMMYT, 1999- 2000). However, the continuous diversification and high demand for maize production has led to the need for genetic improvement of various agricultural and economical important traits.
In sub-Saharan countries, maize has accounted for 22 to 25 percent of starchy staple consumption from 1980, representing the largest single source of calories, followed closely by cassava. It also ranks the first among rice and wheat due to its diverse uses and relatively lower price. Maize is used directly for human consumption since it has great nutritional value as it contains 10% protein, 73% starch, 8.5% fibre, 4% oil, 3.0% sugar and 1.7% ash (Ranum et al., 2014). It also contains 1.2 to 5.7 % edible oil; this oil is widely used for cooking and for manufacturing hydrogenated oil. The oil has the quality of reducing cholesterol in the human blood like sunflower oil.
White maize which is in two types mainly dented and flint is associated with different food products (FAOSTAT, 1997), the dent maize is soft and floury therefore it’s used for porridges, while flint maize has a hard, vitreous endosperm used primarily for gruel or couscous maize flour which is easily stored after drying or milling it. In some parts of Sub- Saharan Africa such as Malawi, flint maize has been preferred to dent because of smaller losses incurred in traditional storage and processing practices (VIB, 2017).
Maize is a multi-purpose food crop which may be consumed fresh as green roasted cobs, boiled separately or mixed with legumes and other foods. In industries, maize is used for processing foods such as corn meal, sweetener and starch, recently there has been interest in using maize for production of fermentation products such as ethanol which is a substitute for petroleum based fuels; the combs and stalks are used to provide domestic fuel especially in rural areas. Maize stalks, leaves and remains from the cobs are used to feed animals directly or making silage which is very nutritious particularly to dairy cattle thus enhancing high milk production. Processed feeds such as bran are given to poultry and pigs (VIB, 2017).
1.2 Problem statement
Cereals are the most important sources of food in the world whereby millions of consumers in both developing and developed countries rely on as their preferable staple food. Production of cereals globally is facing serious challenges since the current production rates cannot provide enough food to meet the rising demand of the world’s population by 2050, thus affecting the global food security (Conway and Barbier, 2013). Constraints that mainly affect crop production negatively are abiotic stresses, biotic stress and socio-economic factors which include poor soils, low-yielding varieties, inadequate access to farm inputs like fertilizers and improved seeds (VIB, 2017). Recent study showed that Sub-Saharan Africa crop losses and low yields are highly attributed to biotic stresses such as foliar diseases, weeds and insect pests (Bekeko, 2013).
1.3 Justification
Few decades ago, screening of crop diseases to identify resistant germplasm has always depended on traditional ways which are confounded with high error rate due to biasness. The use of high throughput digital imagery tools is currently replacing the traditional phenotyping methods since in most crops growing regions digital imagery tools have been introduced to enhance proper phenotyping of field crops on various traits such as disease severity, insect attack and nutrient levels. Additionally, these digital imagery technologies provide new opportunities to plant researchers to study a wider range of physiological and developmental plant processes with greater efficiency. Digital imagery tools such as unmanned aerial vehicle (UAV) have therefore been proposed for use in this study to collect disease data on fields to test its efficacy in obtaining precise and accurate information (Xu, et al., 2020). Breeding for resistance requires quantifying and genotyping of the plant population to identify the genetic bases in the traits (Goggin, Argelia, & Christopher, 2015).
1.4 Objectives
1.4.1 Main objective
To improve disease monitoring, in maize fields through use of high-throughput tools for high precision data and mapping of Quantitative trait loci (QTL) related to Northern leaf blight.
1.4.2 Specific objectives
1) To compare high-throughput phenotyping with the visual crop evaluation of Northern leaf blight diseases among Kenyan and International maize germplasm.
2) To identify the genomic region associated with resistance to Northern leaf blight disease through genome- wide association selection (GWAS) in tropical germplasm.
1.4.3 Hypotheses
1) Visual crop evaluation of foliar diseases may lead to biased or inaccurate results unlike high-throughput phenotyping platforms.
2) There are sources of resistance to NLB among Kenyan and international maize germplasm.
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