Genome-Wide Analyses and Prediction of Resistance to MLN in Large Tropical Maize Germplasm

Exploiting genomic tools for genetic dissection and improving the resistance to Fusarium stalk rot in tropical maize

KEY MESSAGE

A stable genomic region conferring FSR resistance at ~250 Mb on chromosome 1 was identified by GWAS. Genomic prediction has the potential to improve FSR resistance. Fusarium stalk rot (FSR) is a global destructive disease in maize; the efficiency of phenotypic selection for improving FSR resistance was low. Novel genomic tools of genome-wide association study (GWAS) and genomic prediction (GP) provide an opportunity for genetic dissection and improving FSR resistance. In this study, GWAS and GP analyses were performed on 562 tropical maize inbred lines consisting of two populations. In total, 15 SNPs significantly associated with FSR resistance were identified across two populations and the combinedPOP consisting of all 562 inbred lines, with the P-values ranging from 1.99 × 10-7 to 8.27 × 10-13, and the phenotypic variance explained (PVE) values ranging from 0.94 to 8.30%. The genetic effects of the 15 favorable alleles ranged from -4.29 to -14.21% of the FSR severity. One stable genomic region at ~ 250 Mb on chromosome 1 was detected across all populations, and the PVE values of the SNPs detected in this region ranged from 2.16 to 5.18%. Prediction accuracies of FSR severity estimated with the genome-wide SNPs were moderate and ranged from 0.29 to 0.51. By incorporating genotype-by-environment interaction, prediction accuracies were improved between 0.36 and 0.55 in different breeding scenarios. Considering both the genome coverage and the threshold of the P-value of SNPs to select a subset of molecular markers further improved the prediction accuracies. These findings extend the knowledge of exploiting genomic tools for genetic dissection and improving FSR resistance in tropical maize.

Advancements and Prospects of Genome-Wide Association Studies (GWAS) in Maize

Genome-wide association studies (GWAS) have emerged as a powerful tool for unraveling intricate genotype-phenotype association across various species. Maize (Zea mays L.), renowned for its extensive genetic diversity and rapid linkage disequilibrium (LD), stands as an exemplary candidate for GWAS. In maize, GWAS has made significant advancements by pinpointing numerous genetic loci and potential genes associated with complex traits, including responses to both abiotic and biotic stress. These discoveries hold the promise of enhancing adaptability and yield through effective breeding strategies. Nevertheless, the impact of environmental stress on crop growth and yield is evident in various agronomic traits. Therefore, understanding the complex genetic basis of these traits becomes paramount. This review delves into current and future prospectives aimed at yield, quality, and environmental stress resilience in maize and also addresses the challenges encountered during genomic selection and molecular breeding, all facilitated by the utilization of GWAS. Furthermore, the integration of omics, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics has enriched our understanding of intricate traits in maize, thereby enhancing environmental stress tolerance and boosting maize production. Collectively, these insights not only advance our understanding of the genetic mechanism regulating complex traits but also propel the utilization of marker-assisted selection in maize molecular breeding programs, where GWAS plays a pivotal role. Therefore, GWAS provides robust support for delving into the genetic mechanism underlying complex traits in maize and enhancing breeding strategies.

Association Mapping and Expression Analysis of the Genes Involved in the Wood Formation of Poplar

Xylogenesis is a complex and sequential biosynthetic process controlled by polygenes. Deciphering the genetic architecture of this complex quantitative trait could provide valuable information for increasing wood biomass and improving its properties. Here, we performed genomic resequencing of 64 24-year-old trees (64 hybrids of section Aigeiros and their parents) grown in the same field and conducted full-sib family-based association analyses of two growth and six woody traits using GEMMA as a choice of association model selection. We identified 1342 significantly associated single nucleotide polymorphisms (SNPs), 673 located in the region upstream and downstream of 565 protein-encoding genes. The transcriptional regulation network of secondary cell wall (SCW) biosynthesis was further constructed based on the published data of poplar miRNA, transcriptome, and degradome. These provided a certain scientific basis for the in-depth understanding of the mechanism of poplar timber formation and the molecular-assisted breeding in the future.

Genome-Wide Association Study Discovers Novel Germplasm Resources and Genetic Loci with Resistance to Gibberella Ear Rot Caused by Fusarium graminearum

Gibberella ear rot (GER) in maize caused by Fusarium graminearum is one of the most devastating maize diseases reducing grain yield and quality worldwide. The genetic bases of maize GER resistance remain largely unknown. Using artificial inoculation across multiple environments, the GER severity of an association panel consisting of 316 diverse inbred lines was observed with wide phenotypic variation. In the association panel, a genome-wide association study using a general linear model identified 69 single-nucleotide polymorphisms (SNPs) significantly associated with GER resistance at the threshold of 2.04 × 10-5, and the average phenotypic variation explained (PVE) of these SNPs was 5.09%. We also conducted a genome-wide association study analysis using a mixed linear model at a threshold of 1.0 × 10-4, and 16 significantly associated SNPs with an average PVE of 4.73% were detected. A combined general linear model and mixed linear model method obtained 10 co-localized significantly associated SNPs linked to GER resistance, including the most significant SNP (PZE-105079915) with the greatest PVE value, 9.07%, at bin 5.05 following 10 candidate genes. These findings are significant for the exploration of the complicated genetic variations in maize GER resistance. The regions and genes identified herein provide a list of candidate targets for further investigation, in addition to the elite germplasm resources that can be used for breeding GER resistance in maize.

Fighting Death for Living: Recent Advances in Molecular and Genetic Mechanisms Underlying Maize Lethal Necrosis Disease Resistance

Maize Lethal Necrosis (MLN) disease, caused by a synergistic co-infection of maize chlorotic mottle virus (MCMV) and any member of the Potyviridae family, was first reported in EasternAfrica (EA) a decade ago. It is one of the most devastating threats to maize production in these regions since it can lead up to 100% crop loss. Conventional counter-measures have yielded some success; however, they are becoming less effective in controlling MLN. In EA, the focus has been on the screening and identification of resistant germplasm, dissecting genetic and the molecular basis of the disease resistance, as well as employing modern breeding technologies to develop novel varieties with improved resistance. CIMMYT and scientists from NARS partner organizations have made tremendous progresses in the screening and identification of the MLN-resistant germplasm. Quantitative trait loci mapping and genome-wide association studies using diverse, yet large, populations and lines were conducted. These remarkable efforts have yielded notable outcomes, such as the successful identification of elite resistant donor lines KS23-5 and KS23-6 and their use in breeding, as well as the identification of multiple MLN-tolerance promising loci clustering on Chr 3 and Chr 6. Furthermore, with marker-assisted selection and genomic selection, the above-identified germplasms and loci have been incorporated into elite maize lines in a maize breeding program, thus generating novel varieties with improved MLN resistance levels. However, the underlying molecular mechanisms for MLN resistance require further elucidation. Due to third generation sequencing technologies as well functional genomics tools such as genome-editing and DH technology, it is expected that the breeding time for MLN resistance in farmer-preferred maize varieties in EA will be efficient and shortened.

Maize Lethal Necrosis disease: review of molecular and genetic resistance mechanisms, socio-economic impacts, and mitigation strategies in sub-Saharan Africa

BACKGROUND

Maize lethal necrosis (MLN) disease is a significant constraint for maize producers in sub-Saharan Africa (SSA). The disease decimates the maize crop, in some cases, causing total crop failure with far-reaching impacts on regional food security.

RESULTS

In this review, we analyze the impacts of MLN in Africa, finding that resource-poor farmers and consumers are the most vulnerable populations. We examine the molecular mechanism of MLN virus transmission, role of vectors and host plant resistance identifying a range of potential opportunities for genetic and phytosanitary interventions to control MLN. We discuss the likely exacerbating effects of climate change on the MLN menace and describe a sobering example of negative genetic association between tolerance to heat/drought and susceptibility to viral infection. We also review role of microRNAs in host plant response to MLN causing viruses as well as heat/drought stress that can be carefully engineered to develop resistant varieties using novel molecular techniques.

CONCLUSIONS

With the dual drivers of increased crop loss due to MLN and increased demand of maize for food, the development and deployment of simple and safe technologies, like resistant cultivars developed through accelerated breeding or emerging gene editing technologies, will have substantial positive impact on livelihoods in the region. We have summarized the available genetic resources and identified a few large-effect QTLs that can be further exploited to accelerate conversion of existing farmer-preferred varieties into resistant cultivars.

Identification of Genomic Regions Associated with Agronomic and Disease Resistance Traits in a Large Set of Multiple DH Populations

Breeding maize lines with the improved level of desired agronomic traits under optimum and drought conditions as well as increased levels of resistance to several diseases such as maize lethal necrosis (MLN) is one of the most sustainable approaches for the sub-Saharan African region. In this study, 879 doubled haploid (DH) lines derived from 26 biparental populations were evaluated under artificial inoculation of MLN, as well as under well-watered (WW) and water-stressed (WS) conditions for grain yield and other agronomic traits. All DH lines were used for analyses of genotypic variability, association studies, and genomic predictions for the grain yield and other yield-related traits. Genome-wide association study (GWAS) using a mixed linear FarmCPU model identified SNPs associated with the studied traits i.e., about seven and eight SNPs for the grain yield; 16 and 12 for anthesis date; seven and eight for anthesis silking interval; 14 and 5 for both ear and plant height; and 15 and 5 for moisture under both WW and WS environments, respectively. Similarly, about 13 and 11 SNPs associated with gray leaf spot and turcicum leaf blight were identified. Eleven SNPs associated with senescence under WS management that had depicted drought-stress-tolerant QTLs were identified. Under MLN artificial inoculation, a total of 12 and 10 SNPs associated with MLN disease severity and AUDPC traits, respectively, were identified. Genomic prediction under WW, WS, and MLN disease artificial inoculation revealed moderate-to-high prediction accuracy. The findings of this study provide useful information on understanding the genetic basis for the MLN resistance, grain yield, and other agronomic traits under MLN artificial inoculation, WW, and WS conditions. Therefore, the obtained information can be used for further validation and developing functional molecular markers for marker-assisted selection and for implementing genomic prediction to develop superior elite lines.

Association analysis for resistance to Striga hermonthica in diverse tropical maize inbred lines

Striga hermonthica is a widespread, destructive parasitic plant that causes substantial yield loss to maize productivity in sub-Saharan Africa. Under severe Striga infestation, yield losses can range from 60 to 100% resulting in abandonment of farmers’ lands. Diverse methods have been proposed for Striga management; however, host plant resistance is considered the most effective and affordable to small-scale famers. Thus, conducting a genome-wide association study to identify quantitative trait nucleotides controlling S. hermonthica resistance and mining of relevant candidate genes will expedite the improvement of Striga resistance breeding through marker-assisted breeding. For this study, 150 diverse maize inbred lines were evaluated under Striga infested and non-infested conditions for two years and genotyped using the genotyping-by-sequencing platform. Heritability estimates of Striga damage ratings, emerged Striga plants and grain yield, hereafter referred to as Striga resistance-related traits, were high under Striga infested condition. The mixed linear model (MLM) identified thirty SNPs associated with the three Striga resistance-related traits based on the multi-locus approaches (mrMLM, FASTmrMLM, FASTmrEMMA and pLARmEB). These SNPs explained up to 14% of the total phenotypic variation. Under non-infested condition, four SNPs were associated with grain yield, and these SNPs explained up to 17% of the total phenotypic variation. Gene annotation of significant SNPs identified candidate genes (Leucine-rich repeats, putative disease resistance protein and VQ proteins) with functions related to plant growth, development, and defense mechanisms. The marker-effect prediction was able to identify alleles responsible for predicting high yield and low Striga damage rating in the breeding panel. This study provides valuable insight for marker validation and deployment for Striga resistance breeding in maize.

Discovery and Validation of a Recessively Inherited Major-Effect QTL Conferring Resistance to Maize Lethal Necrosis (MLN) Disease

Maize lethal necrosis (MLN) is a viral disease with a devastating effect on maize production. Developing and deploying improved varieties with resistance to the disease is important to effectively control MLN; however, little is known about the causal genes and molecular mechanism(s) underlying MLN resistance. Screening thousands of maize inbred lines revealed KS23-5 and KS23-6 as two of the most promising donors of MLN resistance alleles. KS23-5 and KS23-6 lines were earlier developed at the University of Hawaii, United States, on the basis of a source population constituted using germplasm from Kasetsart University, Thailand. Both linkage mapping and association mapping approaches were used to discover and validate genomic regions associated with MLN resistance. Selective genotyping of resistant and susceptible individuals within large F₂ populations coupled with genome-wide association study identified a major-effect QTL (qMLN06_157) on chromosome 6 for MLN disease severity score and area under the disease progress curve values in all three F₂ populations involving one of the KS23 lines as a parent. The major-effect QTL (qMLN06_157) is recessively inherited and explained 55%-70% of the phenotypic variation with an approximately 6 Mb confidence interval. Linkage mapping in three F₃ populations and three F₂ populations involving KS23-5 or KS23-6 as one of the parents confirmed the presence of this major-effect QTL on chromosome 6, demonstrating the efficacy of the KS23 allele at qMLN06.157 in varying populations. This QTL could not be identified in population that was not derived using KS23 lines. Validation of this QTL in six F₂ populations with 20 SNPs closely linked with qMLN06.157 was further confirmed its consistent expression across populations and its recessive nature of inheritance. On the basis of the consistent and effective resistance afforded by the KS23 allele at qMLN06.157, the QTL can be used in both marker-assisted forward breeding and marker-assisted backcrossing schemes to improve MLN resistance of breeding populations and key lines for eastern Africa.

Deciphering the Genetic Architecture of Plant Virus Resistance by GWAS, State of the Art and Potential Advances

Growing virus resistant varieties is a highly effective means to avoid yield loss due to infection by many types of virus. The challenge is to be able to detect resistance donors within plant species diversity and then quickly introduce alleles conferring resistance into elite genetic backgrounds. Until now, mainly monogenic forms of resistance with major effects have been introduced in crops. Polygenic resistance is harder to map and introduce in susceptible genetic backgrounds, but it is likely more durable. Genome wide association studies (GWAS) offer an opportunity to accelerate mapping of both monogenic and polygenic resistance, but have seldom been implemented and described in the plant-virus interaction context. Yet, all of the 48 plant-virus GWAS published so far have successfully mapped QTLs involved in plant virus resistance. In this review, we analyzed general and specific GWAS issues regarding plant virus resistance. We have identified and described several key steps throughout the GWAS pipeline, from diversity panel assembly to GWAS result analyses. Based on the 48 published articles, we analyzed the impact of each key step on the GWAS power and showcase several GWAS methods tailored to all types of viruses.

Genome-wide association study of cyanogenic glycosides, proline, sugars, and pigments in Eucalyptus cladocalyx after 18 consecutive dry summers

Natural variation of cyanogenic glycosides, soluble sugars, proline, and nondestructive optical sensing of pigments (chlorophyll, flavonols, and anthocyanins) was examined in ex situ natural populations of Eucalyptus cladocalyx F. Muell. grown under dry environmental conditions in the southern Atacama Desert, Chile. After 18 consecutive dry seasons, considerable plant-to-plant phenotypic variation for all the traits was observed in the field. For example, leaf hydrogen cyanide (HCN) concentrations varied from 0 (two acyanogenic individuals) to 1.54 mg cyanide g^-1 DW. Subsequent genome-wide association study revealed associations with several genes with a known function in plants. HCN content was associated robustly with genes encoding Cytochrome P450 proteins, and with genes involved in the detoxification mechanism of HCN in cells (β-cyanoalanine synthase and cyanoalanine nitrilase). Another important finding was that sugars, proline, and pigment content were linked to genes involved in transport, biosynthesis, and/or catabolism. Estimates of genomic heritability (based on haplotypes) ranged between 0.46 and 0.84 (HCN and proline content, respectively). Proline and soluble sugars had the highest predictive ability of genomic prediction models (PA = 0.65 and PA = 0.71, respectively). PA values for HCN content and flavonols were relatively moderate, with estimates ranging from 0.44 to 0.50. These findings provide new understanding on the genetic architecture of cyanogenic capacity, and other key complex traits in cyanogenic E. cladocalyx.

Genome-wide association mapping in maize: status and prospects

Genome-wide association study (GWAS) provides a robust and potent tool to retrieve complex phenotypic traits back to their underlying genetics. Maize is an excellent crop for performing GWAS due to diverse genetic variability, rapid decay of linkage disequilibrium, availability of distinct sub-populations and abundant SNP information. The application of GWAS in maize has resulted in successful identification of thousands of genomic regions associated with many abiotic and biotic stresses. Many agronomic and quality traits of maize are severely affected by such stresses and, significantly affecting its growth and productivity. To improve productivity of maize crop in countries like India which contribute only 2% to the world's total production in 2019-2020, it is essential to understand genetic complexity of underlying traits. Various DNA markers and trait associations have been revealed using conventional linkage mapping methods. However, it has achieved limited success in improving polygenic complex traits due to lower resolution of trait mapping. The present review explores the prospects of GWAS in improving yield, quality and stress tolerance in maize besides, strengths and challenges of using GWAS for molecular breeding and genomic selection. The information gathered will facilitate elucidation of genetic mechanisms of complex traits and improve efficiency of marker-assisted selection in maize breeding.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02799-4.

Genetic dissection of maize disease resistance and its applications in molecular breeding

Disease resistance is essential for reliable maize production. In a long-term tug-of-war between maize and its pathogenic microbes, naturally occurring resistance genes gradually accumulate and play a key role in protecting maize from various destructive diseases. Recently, significant progress has been made in deciphering the genetic basis of disease resistance in maize. Enhancing disease resistance can now be explored at the molecular level, from marker-assisted selection to genomic selection, transgenesis technique, and genome editing. In view of the continuing accumulation of cloned resistance genes and in-depth understanding of their resistance mechanisms, coupled with rapid progress of biotechnology, it is expected that the large-scale commercial application of molecular breeding of resistant maize varieties will soon become a reality.

Genetic dissection of Striga hermonthica (Del.) Benth. resistance via genome-wide association and genomic prediction in tropical maize germplasm

KEY MESSAGE

Genome-wide association revealed that resistance to Striga hermonthica is influenced by multiple genomic regions with moderate effects. It is possible to increase genetic gains from selection for Striga resistance using genomic prediction. Striga hermonthica (Del.) Benth., commonly known as the purple witchweed or giant witchweed, is a serious problem for maize-dependent smallholder farmers in sub-Saharan Africa. Breeding for Striga resistance in maize is complicated due to limited genetic variation, complexity of resistance and challenges with phenotyping. This study was conducted to (i) evaluate a set of diverse tropical maize lines for their responses to Striga under artificial infestation in three environments in Kenya; (ii) detect quantitative trait loci associated with Striga resistance through genome-wide association study (GWAS); and (iii) evaluate the effectiveness of genomic prediction (GP) of Striga-related traits. An association mapping panel of 380 inbred lines was evaluated in three environments under artificial Striga infestation in replicated trials and genotyped with 278,810 single-nucleotide polymorphism (SNP) markers. Genotypic and genotype x environment variations were significant for measured traits associated with Striga resistance. Heritability estimates were moderate (0.42) to high (0.92) for measured traits. GWAS revealed 57 SNPs significantly associated with Striga resistance indicator traits and grain yield (GY) under artificial Striga infestation with low to moderate effect. A set of 32 candidate genes physically near the significant SNPs with roles in plant defense against biotic stresses were identified. GP with different cross-validations revealed that prediction of performance of lines in new environments is better than prediction of performance of new lines for all traits. Predictions across environments revealed high accuracy for all the traits, while inclusion of GWAS-detected SNPs led to slight increase in the accuracy. The item-based collaborative filtering approach that incorporates related traits evaluated in different environments to predict GY and Striga-related traits outperformed GP for Striga resistance indicator traits. The results demonstrated the polygenic nature of resistance to S. hermonthica, and that implementation of GP in Striga resistance breeding could potentially aid in increasing genetic gain for this important trait.

Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils

Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.

Genetic Dissection of Resistance to Gray Leaf Spot by Combining Genome-Wide Association, Linkage Mapping, and Genomic Prediction in Tropical Maize Germplasm

Gray leaf spot (GLS) is one of the major maize foliar diseases in sub-Saharan Africa. Resistance to GLS is controlled by multiple genes with additive effect and is influenced by both genotype and environment. The objectives of the study were to dissect the genetic architecture of GLS resistance through linkage mapping and genome-wide association study (GWAS) and assessing the potential of genomic prediction (GP). We used both biparental populations and an association mapping panel of 410 diverse tropical/subtropical inbred lines that were genotyped using genotype by sequencing. Phenotypic evaluation in two to four environments revealed significant genotypic variation and moderate to high heritability estimates ranging from 0.43 to 0.69. GLS was negatively and significantly correlated with grain yield, anthesis date, and plant height. Linkage mapping in five populations revealed 22 quantitative trait loci (QTLs) for GLS resistance. A QTL on chromosome 7 (qGLS7-105) is a major-effect QTL that explained 28.2% of phenotypic variance. Together, all the detected QTLs explained 10.50, 49.70, 23.67, 18.05, and 28.71% of phenotypic variance in doubled haploid (DH) populations 1, 2, 3, and F3 populations 4 and 5, respectively. Joint linkage association mapping across three DH populations detected 14 QTLs that individually explained 0.10-15.7% of phenotypic variance. GWAS revealed 10 significantly (p < 9.5 × 10-6) associated SNPs distributed on chromosomes 1, 2, 6, 7, and 8, which individually explained 6-8% of phenotypic variance. A set of nine candidate genes co-located or in physical proximity to the significant SNPs with roles in plant defense against pathogens were identified. GP revealed low to moderate prediction correlations of 0.39, 0.37, 0.56, 0.30, 0.29, and 0.38 for within IMAS association panel, DH pop1, DH pop2, DH pop3, F3 pop4, and F3 po5, respectively, and accuracy was increased substantially to 0.84 for prediction across three DH populations. When the diversity panel was used as training set to predict the accuracy of GLS resistance in biparental population, there was 20-50% reduction compared to prediction within populations. Overall, the study revealed that resistance to GLS is quantitative in nature and is controlled by many loci with a few major and many minor effects. The SNPs/QTLs identified by GWAS and linkage mapping can be potential targets in improving GLS resistance in breeding programs, while GP further consolidates the development of high GLS-resistant lines by incorporating most of the major- and minor-effect genes.

Combination of Linkage Mapping, GWAS, and GP to Dissect the Genetic Basis of Common Rust Resistance in Tropical Maize Germplasm

Common rust (CR) caused by Puccina sorghi is one of the destructive fungal foliar diseases of maize and has been reported to cause moderate to high yield losses. Providing CR resistant germplasm has the potential to increase yields. To dissect the genetic architecture of CR resistance in maize, association mapping, in conjunction with linkage mapping, joint linkage association mapping (JLAM), and genomic prediction (GP) was conducted on an association-mapping panel and five F₃ biparental populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Analysis of variance for the biparental populations and the association panel showed significant genotypic and genotype x environment (GXE) interaction variances except for GXE of Pop4. Heritability (h²) estimates were moderate with 0.37-0.45 for the individual F₃ populations, 0.45 across five populations and 0.65 for the association panel. Genome-wide association study (GWAS) analyses revealed 14 significant marker-trait associations which individually explained 6-10% of the total phenotypic variances. Individual population-based linkage analysis revealed 26 QTLs associated with CR resistance and together explained 14-40% of the total phenotypic variances. Linkage mapping revealed seven QTLs in pop1, nine QTL in pop2, four QTL in pop3, five QTL in pop4, and one QTL in pop5, distributed on all chromosomes except chromosome 10. JLAM for the 921 F3 families from five populations detected 18 QTLs distributed in all chromosomes except on chromosome 8. These QTLs individually explained 0.3 to 3.1% and together explained 45% of the total phenotypic variance. Among the 18 QTL detected through JLAM, six QTLs, qCR1-78, qCR1-227, qCR3-172, qCR3-186, qCR4-171, and qCR7-137 were also detected in linkage mapping. GP within population revealed low to moderate correlations with a range from 0.19 to 0.51. Prediction correlation was high with r = 0.78 for combined analysis of the five F₃ populations. Prediction of biparental populations by using association panel as training set reveals positive correlations ranging from 0.05 to 0.22, which encourages to develop an independent but related population as a training set which can be used to predict diverse but related populations. The findings of this study provide valuable information on understanding the genetic basis of CR resistance and the obtained information can be used for developing functional molecular markers for marker-assisted selection and for implementing GP to improve CR resistance in tropical maize.

QTL Mapping and Prediction of Haploid Male Fertility Traits in Maize (Zea mays L.)

Chromosome doubling of maize haploids is a bottleneck in the large-scale application of doubled haploid (DH) technology. Spontaneous chromosome doubling (SCD) of haploid has been taken as an important method in the production of DH lines and low haploid male fertility (HMF) is a main limiting factor for the use of SCD. To study its genetic basis, haploids of 119 DH lines derived from a cross between inbred lines Qi319 and Chang7-2 were used to map the quantitative trait locus (QTL) contributing to HMF. Three traits including anther emergence rate (AER), anther emergence score (AES) and pollen production score (PPS) of the haploid population were evaluated at two locations. The heritability of the three traits ranged from 0.70 to 0.81. The QTL contributing to AER, AES and PPS were identified on the chromosomes 1, 2, 3, 4, 5, 7, 9 and 10. Five major QTL, qAER5-1, qAER5-2, qAES3, qPPS1 and qPPS5, were found and each could explain more than 15% of the phenotypic variance at least in one environment. Two major QTL, qPPS1 and qPPS5, and two minor QTL, qAES2 and qAER3, were repeatedly detected at both locations. To increase the application efficiency of HMF in breeding programs, genomic prediction for the three traits were carried out with ridge regression best linear unbiased prediction (rrBLUP) and rrBLUP adding QTL effects (rrBLUP-QTL). The prediction accuracies of rrBLUP-QTL were significantly higher than that by rrBLUP for three traits (p < 0.001), which indirectly indicates these QTL were effective. The prediction accuracies for PPS were 0.604 (rrBLUP) and 0.703 (rrBLUP-QTL) across both locations, which were higher than that of AER and AES. Overall, this study provides important information to understand the genetic architecture of SCD of maize haploids.

Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization

Several species of herbivores feed on maize in field and storage setups, making the development of multiple insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to fall armyworm (FAW), whilst bulked grains were subjected to a maize weevil (MW) bioassay and genotyped with Diversity Array Technology's single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance levels of 0.05 and 0.01, respectively, and located within or close to multiple insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple traits, of which, six were associated with resistance to both FAW and MW, suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10-30 kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of the five of the six combined resistance QTNs, thus reinforcing the pleiotropy hypothesis. In addition, through in silico co-functional network inferences, an additional 107 network-based CGs (NbCGs), biologically connected to the 64 GbCGs, and differentially expressed under biotic or abiotic stress, were revealed within MIRGRs. The provided multiple insect resistance physical map should contribute to the development of combined insect resistance in maize.

Maize lethal necrosis (MLN): Efforts toward containing the spread and impact of a devastating transboundary disease in sub-Saharan Africa

Maize lethal necrosis (MLN), a complex viral disease, emerged as a serious threat to maize production and the livelihoods of smallholders in eastern Africa since 2011, primarily due to the introduction of maize chlorotic mottle virus (MCMV). The International Maize and Wheat Improvement Center (CIMMYT), in close partnership with national and international partners, implemented a multi-disciplinary and multi-institutional strategy to curb the spread of MLN in sub-Saharan Africa, and mitigate the impact of the disease. The strategy revolved around a) intensive germplasm screening and fast-tracked development and deployment of MLN-tolerant/resistant maize hybrids in Africa-adapted genetic backgrounds; b) optimizing the diagnostic protocols for MLN-causing viruses, especially MCMV, and capacity building of relevant public and private sector institutions on MLN diagnostics and management; c) MLN monitoring and surveillance across sub-Saharan Africa in collaboration with national plant protection organizations (NPPOs); d) partnership with the private seed sector for production and exchange of MLN pathogen-free commercial maize seed; and e) awareness creation among relevant stakeholders about MLN management, including engagement with policy makers. The review concludes by highlighting the need to keep continuous vigil against MLN-causing viruses, and preventing any further spread of the disease to the major maize-growing countries that have not yet reported MLN in sub-Saharan Africa.

Hybrid Breeding for MLN Resistance: Heterosis, Combining Ability, and Hybrid Prediction

Prior knowledge on heterosis and quantitative genetic parameters on maize lethal necrosis (MLN) can help the breeders to develop numerous resistant or tolerant hybrids with optimum resources. Our objectives were to (1) estimate the quantitative genetic parameters for MLN disease severity, (2) investigate the efficiency of the prediction of hybrid performance based on parental per se and general combining ability (GCA) effects, and (3) examine the potential of hybrid prediction for MLN resistance or tolerance based on markers. Fifty elite maize inbred lines were selected based on their response to MLN under artificial inoculation. Crosses were made in a half diallel mating design to produce 307 F1 hybrids. All hybrids were evaluated in MLN quarantine facility in Naivasha, Kenya for two seasons under artificial inoculation. All 50 inbreds were genotyped with genotyping-by-sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability was moderate to high. We observed that hybrids were superior to the mean performance of the parents for disease severity (−14.57%) and area under disease progress curve (AUDPC) (14.9%). Correlations were significant and moderate between line per se and GCA; and mean of parental value with hybrid performance for both disease severity and AUDPC value. Very low and negative correlation was observed between parental lines marker based genetic distance and heterosis. Nevertheless, the correlation of GCA effects was very high with hybrid performance which can suggests as a good predictor of MLN resistance. Genomic prediction of hybrid performance for MLN is high for both traits. We therefore conclude that there is potential for prediction of hybrid performance for MLN. Overall, the estimated quantitative genetic parameters suggest that through targeted approach, it is possible to develop outstanding lines and hybrids for MLN resistance.

Mapping-by-Sequencing via MutMap Identifies a Mutation in ZmCLE7 Underlying Fasciation in a Newly Developed EMS Mutant Population in an Elite Tropical Maize Inbred

Induced point mutations are important genetic resources for their ability to create hypo- and hypermorphic alleles that are useful for understanding gene functions and breeding. However, such mutant populations have only been developed for a few temperate maize varieties, mainly B73 and W22, yet no tropical maize inbred lines have been mutagenized and made available to the public to date. We developed a novel Ethyl Methanesulfonate (EMS) induced mutation resource in maize comprising 2050 independent M2 mutant families in the elite tropical maize inbred ML10. By phenotypic screening, we showed that this population is of comparable quality with other mutagenized populations in maize. To illustrate the usefulness of this population for gene discovery, we performed rapid mapping-by-sequencing to clone a fasciated-ear mutant and identify a causal promoter deletion in ZmCLE7 (CLE7). Our mapping procedure does not require crossing to an unrelated parent, thus is suitable for mapping subtle traits and ones affected by heterosis. This first EMS population in tropical maize is expected to be very useful for the maize research community. Also, the EMS mutagenesis and rapid mapping-by-sequencing pipeline described here illustrate the power of performing forward genetics in diverse maize germplasms of choice, which can lead to novel gene discovery due to divergent genetic backgrounds.