Genome-wide association studies in tropical maize germplasm reveal novel and known genomic regions for resistance to Northern corn leaf blight

Rashid, Zerka; Sofi, Mehrajuddin; Harlapur, S. I.; Kachapur, R. M.; Dar, Z. A.; Singh, Pradeep Kumar; Zaidi, P.H.; Vivek, B.; Nair, Sudha

doi:10.1038/s41598-020-78928-5

Cited by 37 publications

(22 citation statements)

References 86 publications

(105 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The BTx623 × SC155 QTL on chromosome one colocalized with the ortholog of a translation initiation factor IF‐2 (Poland et al., 2011). The BTx623 × SC155 QTL on chromosome two overlapped with six previously identified candidate genes, including genes with a leucine‐rich repeat domain and a basic‐leucine zipper domain (Poland et al., 2011; Rashid et al., 2020), a gene encoding a BTB/POZ (Poland et al., 2011), a gene with an EXPERA domain (Ding et al., 2015), a SANT‐associated gene (Rashid et al., 2020), and a glutathione S‐transferase encoding gene (Wisser et al., 2011). There was no overlap with QTL for anthracnose or target leaf spot reported in previous studies using the BTx623 × SC155 mapping population (Kimball et al., 2019; Patil et al., 2017).…”

Section: Resultsmentioning

confidence: 82%

Identification of quantitative trait loci for sorghum leaf blight resistance

et al. 2022

View full text Add to dashboard Cite

Sorghum leaf blight and northern corn leaf blight, both caused by Exserohilum turcicum {(Pass.) K. J. Leonard and Suggs [syn. Setosphaeria turcica (Luttr.) K. J. Leonard and Suggs.]}, are major diseases of sorghum [Sorghum bicolor (L.) Moench] and maize (Zea mays L.), respectively. Examining the genetic architecture of resistance in sorghum will lead to a better understanding of the relationship between resistance in sorghum and maize, which can ultimately enhance management options in both crops. In 2018 and 2019, we evaluated two sorghum recombinant inbred line (RIL) populations for resistance to E. turcicum. The BTx623 × IS3620C and BTx623 × SC155 populations consisted of 235 and 81 RILs, respectively. Resistance in both populations was moderately to highly heritable. We identified a total of six quantitative trait loci (QTL) across the two populations. Three QTL with smallto moderate-effect sizes were identified in the BTx623 × IS3620C population. Three QTL, including a large-effect QTL on chromosome three that explained 24% of the variation, were identified in the BTx623 × SC155 population. We compared the identified QTL with the position of northern corn leaf blight candidate genes and found eight candidate resistance gene orthologs that colocalize with the sorghum leaf blight QTL. There were also several nucleotide-binding leucine-rich repeat encoding genes within the candidate intervals. Understanding host resistance in multiple species furthers our understanding of the Exserohilum turcicum patho-system.

show abstract

Section: Resultsmentioning

confidence: 82%

Identification of quantitative trait loci for sorghum leaf blight resistance

et al. 2022

View full text Add to dashboard Cite

show abstract

“…A GWAS was conducted using a mapping panel that included tropical/subtropical inbred lines from CIMMYT breeding programs in Asia, Mexico, Kenya, Zimbabwe, and Colombia that are also acclimatized to the Asian tropics. The CAAM panel was previously used to study traits like resistance to sorghum downy mildew (Rashid et al, 2018), northern leaf corn blight (Rashid et al, 2020), and root traits under drought conditions (Zaidi et al, 2016) in Asian environments. Phenotypic evaluation of CAAM panel for charcoal rot at Hyderabad and Ludhiana, revealed that the panel was skewed toward susceptibility, possibly because both these locations had ideal environment for pathogen infection and spread, and the artificial inoculation using the toothpick method reduced the chances of escapes.…”

Section: Discussionmentioning

confidence: 99%

“…of non-missing markers, where IBS1 and IBS2 are the states in which the two inbred lines share one or two alleles at a marker (Bishop and Williamson, 1990). Linkage disequilibrium (LD) was estimated on adjacent pairwise r 2 -values between adjacent SNPs among the SNPs from the GBS data and physical distances between those SNPs as described in Rashid et al (2020). Manhattan and quantilequantile plots were created using the association results.…”

Section: Gwas and Haplotype Regressionmentioning

confidence: 99%

Identification and Validation of Genomic Regions Associated With Charcoal Rot Resistance in Tropical Maize by Genome-Wide Association and Linkage Mapping

et al. 2021

Self Cite

View full text Add to dashboard Cite

Charcoal rot is a post-flowering stalk rot (PFSR) disease of maize caused by the fungal pathogen, Macrophomina phaseolina. It is a serious concern for smallholder maize cultivation, due to significant yield loss and plant lodging at harvest, and this disease is expected to surge with climate change effects like drought and high soil temperature. For identification and validation of genomic variants associated with charcoal rot resistance, a genome-wide association study (GWAS) was conducted on CIMMYT Asia association mapping panel comprising 396 tropical-adapted lines, especially to Asian environments. The panel was phenotyped for disease severity across two locations with high disease prevalence in India. A subset of 296,497 high-quality SNPs filtered from genotyping by sequencing was correcting for population structure and kinship matrices for single locus mixed linear model (MLM) of GWAS analysis. A total of 19 SNPs were identified to be associated with charcoal rot resistance with P-value ranging from 5.88 × 10−06 to 4.80 × 10−05. Haplotype regression analysis identified 21 significant haplotypes for the trait with Bonferroni corrected P ≤ 0.05. For validating the associated variants and identifying novel QTLs, QTL mapping was conducted using two F2:3 populations. Two QTLs with overlapping physical intervals, qMSR6 and qFMSR6 on chromosome 6, identified from two different mapping populations and contributed by two different resistant parents, were co-located with the SNPs and haplotypes identified at 103.51 Mb on chromosome 6. Similarly, several SNPs/haplotypes identified on chromosomes 3, 6 and 8 were also found to be physically co-located within QTL intervals detected in one of the two mapping populations. The study also noted that several SNPs/haplotypes for resistance to charcoal rot were located within physical intervals of previously reported QTLs for Gibberella stalk rot resistance, which opens up a new possibility for common disease resistance mechanisms for multiple stalk rots.

show abstract

“…Right now, GWAS have been widely used to detect the genomic regions correlated with the complex traits in human, animals, and plants. In plants, GWAS have been detected many genes associated to the complex traits, such as in Arabidopsis, rice, maize, sorghum, wheat, barley, and many other plants [2,[9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Phenotypic data and genome-wide genotypes are required in GWAS.…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association Study of Complex Traits in Maize Detects Genomic Regions and Genes for Increasing Grain Yield and Grain Quality

Dwiningsih

2022

ASSET

View full text Add to dashboard Cite

This review describes the current status of genome-wide association study (GWAS) of the major crops in maize (Zea mays L.) concentrate on performing association mapping as a novel method in associating genetic and complex traits, current strategy in analyzing of phenotype and genotype data to identify population structure and linkage disequilibrium. GWAS has an important role in food security because this method identified many crucial genomic regions of important traits in the most commercialize crops of the world, such as maize. These complex traits including yield, grain quality, biofortification, biotic and abiotic resistance. GWAS has many advantages correlated with reducing genotyping cost and research time, increasing mapping resolution and larger allele number. Meanwhile, GWAS has two main limitations related to population size and the number of markers. There are many software packages for data analysis in GWAS. The most commonly software that was used in GWAS especially in this crop is TASSEL because frequently updated. Recently, many research papers concentrated on GWAS in maize. GWAS analysis accelerated identification of genetic regions, candidate genes within these genomic regions and their metabolomic analysis correlated to the complex traits in maize for increasing grain yield and grain quality to fulfill the market demands.

show abstract

Genome-wide association studies in tropical maize germplasm reveal novel and known genomic regions for resistance to Northern corn leaf blight

Cited by 37 publications

References 86 publications

Identification of quantitative trait loci for sorghum leaf blight resistance

Identification of quantitative trait loci for sorghum leaf blight resistance

Identification and Validation of Genomic Regions Associated With Charcoal Rot Resistance in Tropical Maize by Genome-Wide Association and Linkage Mapping

Genome-Wide Association Study of Complex Traits in Maize Detects Genomic Regions and Genes for Increasing Grain Yield and Grain Quality

Contact Info

Product

Resources

About