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A diagnostic marker kit for Fusarium wilt and sterility mosaic diseases resistance in pigeonpea.

Rachit K SaxenaAnil HakeAbhishek BohraAamir W KhanAnupama HinganeRafat SultanaIndra Prakash SinghS J Satheesh NaikRajeev Kumar Varshney
Published in: TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik (2020)
Fusarium wilt (FW) and sterility mosaic diseases (SMD) are key biotic constraints to pigeonpea production. Occurrence of these two diseases in congenial conditions is reported to cause complete yield loss in susceptible pigeonpea cultivars. Various studies to elucidate genomic architecture of the two traits have revealed significant marker-trait associations for use in breeding programs. However, these DNA markers could not be used effectively in genomics-assisted breeding for developing FW and SMD resistant varieties primarily due to pathogen variability, location or background specificity, lesser phenotypic variance explained by the reported QTL and cost-inefficiency of the genotyping assays. Therefore, in the present study, a novel approach has been used to develop a diagnostic kit for identification of suitable FW and SMD resistant lines. This kit was developed with 10 markers each for FW and SMD resistance. Investigation of the diversity of these loci has shown the role of different alleles in different resistant genotypes. Two genes (C.cajan_03691 and C.cajan_18888) for FW resistance and four genes (C.cajan_07858, C.cajan_20995, C.cajan_21801 and C.cajan_17341) for SMD resistance have been identified. More importantly, we developed a customized and cost-effective Kompetitive allele-specific PCR genotyping assay for the identified genes in order to encourage their downstream applications in pigeonpea breeding programs. The diagnostic marker kit developed here will offer great strength to pigeonpea varietal development program, since the resistance against these two diseases is essentially required for nominating an improved line in varietal release pipeline.
Keyphrases
  • genome wide
  • high throughput
  • dna methylation
  • bioinformatics analysis
  • public health
  • single cell
  • risk assessment
  • quality improvement
  • gene expression
  • single molecule
  • cell free
  • high density