Dataset Information

Association mapping of quantitative resistance to charcoal root rot in mulberry germplasm.

ABSTRACT: Outbreaks of root rot disease in the productive South Indian sericulture belt have threatened the sustainability of the industry. Macrophomina phaseolina (Tassi) Goid. causing charcoal rot is the predominant pathogen to which all productive mulberry cultivars are susceptible. The present study was undertaken to identify molecular markers associated with charcoal rot resistance in mulberry. A mapping panel comprising 214 diverse entries from the Indian germplasm collection was assessed for charcoal rot resistance by artificial inoculation. Resistance to the pathogen was observed in 20 entries, and 51 were found to be moderately resistant. A total of 773 alleles generated across 105 SSR loci and 20,384 AFLP markers generated using 32 EcoRI-NN and MseI-CNN primer combinations were used in genetic analysis. The panel was weakly structured with two subpopulations. However, most entries were found to be admixtures. Survival of cuttings and number of roots per sapling were associated with root rot resistance. Association mapping was performed using different linear mixed models. Five AFLP markers explaining 9.6-12.7% of the total phenotypic variance were found to be significantly (p < 0.05) associated with root rot resistance. Significant associations were also detected in four AFLP markers for survival of cuttings, and these markers explained 10.7-14.2% of the total phenotypic variance. These markers should be validated using mapping populations derived from contrasting biparental combinations by linkage analysis for use in marker-assisted gene pyramiding for durable resistance. The resistant genotypes identified in this study will substantially contribute to genetic improvement of mulberry for charcoal rot resistance and can be integrated into conventional breeding programmes.

SUBMITTER: Pinto MV

PROVIDER: S-EPMC6034859 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Association mapping of quantitative resistance to charcoal root rot in mulberry germplasm.

Pinto Marian Vincent MV H S Poornima P M S Rukmangada R R Triveni T Naik V Girish VG

PloS one 20180706 7

Outbreaks of root rot disease in the productive South Indian sericulture belt have threatened the sustainability of the industry. Macrophomina phaseolina (Tassi) Goid. causing charcoal rot is the predominant pathogen to which all productive mulberry cultivars are susceptible. The present study was undertaken to identify molecular markers associated with charcoal rot resistance in mulberry. A mapping panel comprising 214 diverse entries from the Indian germplasm collection was assessed for charco ...[more]

PMID: 29979732

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Project description: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.

Project description:Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets (R 2 = 0.45-0.73 and RMSE = 0.66-1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section - computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ |r| ≤ 0.24, P < 0.05) and ten NDSIs for lentil root sections - computed from wavelengths in the range of 630-670, 700-840, and 1320-1530 nm (0.10 ≤ |r| ≤ 0.50, P < 0.05). Root-derived NDSIs were more accurate in predicting disease scores with an R 2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R 2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs - computed from wavelengths of 700, 710, 730, and 790 nm - had strong positive correlations with disease scores (0.35 ≤r ≤ 0.50, P < 0.0001), which was confirmed in field phenotyping with similar correlations using vegetation index with red edge wavelength (normalized difference red edge, 0.36 ≤ |r| ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes.

Dataset Information

Association mapping of quantitative resistance to charcoal root rot in mulberry germplasm.

Publications

Association mapping of quantitative resistance to charcoal root rot in mulberry germplasm.

Similar Datasets

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