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EcoTILLING-Based Association Mapping Efficiently Delineates Functionally Relevant Natural Allelic Variants of Candidate Genes Governing Agronomic Traits in Chickpea.

Bajaj D, Srivastava R, Nath M, Tripathi S, Bharadwaj C, Upadhyaya HD, Tyagi AK, Parida SK - Front Plant Sci (2016)

Bottom Line: The associated individual and combination of all SNPs explained 10-15 and 31% phenotypic variation for seed weight, respectively.The EcoTILLING-based large-scale allele mining and genotyping strategy implemented for association mapping is found much effective for a diploid genome crop species like chickpea with narrow genetic base and low genetic polymorphism.The seed weight-associated natural allelic variants and candidate TF genes delineated have potential to accelerate marker-assisted genetic improvement of chickpea.

View Article: PubMed Central - PubMed

Affiliation: Govt. of India, Plant Genomics and Molecular Breeding Lab, Department of Biotechnology, National Institute of Plant Genome Research New Delhi, India.

ABSTRACT
The large-scale mining and high-throughput genotyping of novel gene-based allelic variants in natural mapping population are essential for association mapping to identify functionally relevant molecular tags governing useful agronomic traits in chickpea. The present study employs an alternative time-saving, non-laborious and economical pool-based EcoTILLING approach coupled with agarose gel detection assay to discover 1133 novel SNP allelic variants from diverse coding and regulatory sequence components of 1133 transcription factor (TF) genes by genotyping in 192 diverse desi and kabuli chickpea accessions constituting a seed weight association panel. Integrating these SNP genotyping data with seed weight field phenotypic information of 192 structured association panel identified eight SNP alleles in the eight TF genes regulating seed weight of chickpea. The associated individual and combination of all SNPs explained 10-15 and 31% phenotypic variation for seed weight, respectively. The EcoTILLING-based large-scale allele mining and genotyping strategy implemented for association mapping is found much effective for a diploid genome crop species like chickpea with narrow genetic base and low genetic polymorphism. This optimized approach thus can be deployed for various genomics-assisted breeding applications with optimal expense of resources in domesticated chickpea. The seed weight-associated natural allelic variants and candidate TF genes delineated have potential to accelerate marker-assisted genetic improvement of chickpea.

No MeSH data available.


Related in: MedlinePlus

Schematic depicting the major steps followed in an agarose gel-based EcoTILLING assay for efficient identification of functionally relevant molecular tags governing useful agronomic traits in chickpea. This strategy is optimized for successful large-scale mining of novel SNP allelic variants from the target genomic regions (genes) by genotyping in a constituted field-phenotyped association panel (desi and kabuli core/mini-core germplasm lines). A, Accessions; SP, Superpool; F, (Forward); and R, (Reverse) primers.
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Figure 1: Schematic depicting the major steps followed in an agarose gel-based EcoTILLING assay for efficient identification of functionally relevant molecular tags governing useful agronomic traits in chickpea. This strategy is optimized for successful large-scale mining of novel SNP allelic variants from the target genomic regions (genes) by genotyping in a constituted field-phenotyped association panel (desi and kabuli core/mini-core germplasm lines). A, Accessions; SP, Superpool; F, (Forward); and R, (Reverse) primers.

Mentions: In light of the above, the present study employed a simpler non-laborious and rapid yet cost-effective agarose gel-based EcoTILLING assay (Figure 1) for high-throughput mining of natural allelic variants derived from diverse coding and non-coding regulatory sequence components of 1133 TF genes by genotyping in 192 core/mini-core germplasm accessions constituting a seed weight association panel. As a proof of concept, the high-throughput genotyping data of 1133 TF gene-derived SNPs was correlated with seed weight field phenotypic information of the 192 accessions to delineate functionally relevant natural allelic variants in the candidate TF genes regulating 100-seed weight in chickpea.


EcoTILLING-Based Association Mapping Efficiently Delineates Functionally Relevant Natural Allelic Variants of Candidate Genes Governing Agronomic Traits in Chickpea.

Bajaj D, Srivastava R, Nath M, Tripathi S, Bharadwaj C, Upadhyaya HD, Tyagi AK, Parida SK - Front Plant Sci (2016)

Schematic depicting the major steps followed in an agarose gel-based EcoTILLING assay for efficient identification of functionally relevant molecular tags governing useful agronomic traits in chickpea. This strategy is optimized for successful large-scale mining of novel SNP allelic variants from the target genomic regions (genes) by genotyping in a constituted field-phenotyped association panel (desi and kabuli core/mini-core germplasm lines). A, Accessions; SP, Superpool; F, (Forward); and R, (Reverse) primers.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835497&req=5

Figure 1: Schematic depicting the major steps followed in an agarose gel-based EcoTILLING assay for efficient identification of functionally relevant molecular tags governing useful agronomic traits in chickpea. This strategy is optimized for successful large-scale mining of novel SNP allelic variants from the target genomic regions (genes) by genotyping in a constituted field-phenotyped association panel (desi and kabuli core/mini-core germplasm lines). A, Accessions; SP, Superpool; F, (Forward); and R, (Reverse) primers.
Mentions: In light of the above, the present study employed a simpler non-laborious and rapid yet cost-effective agarose gel-based EcoTILLING assay (Figure 1) for high-throughput mining of natural allelic variants derived from diverse coding and non-coding regulatory sequence components of 1133 TF genes by genotyping in 192 core/mini-core germplasm accessions constituting a seed weight association panel. As a proof of concept, the high-throughput genotyping data of 1133 TF gene-derived SNPs was correlated with seed weight field phenotypic information of the 192 accessions to delineate functionally relevant natural allelic variants in the candidate TF genes regulating 100-seed weight in chickpea.

Bottom Line: The associated individual and combination of all SNPs explained 10-15 and 31% phenotypic variation for seed weight, respectively.The EcoTILLING-based large-scale allele mining and genotyping strategy implemented for association mapping is found much effective for a diploid genome crop species like chickpea with narrow genetic base and low genetic polymorphism.The seed weight-associated natural allelic variants and candidate TF genes delineated have potential to accelerate marker-assisted genetic improvement of chickpea.

View Article: PubMed Central - PubMed

Affiliation: Govt. of India, Plant Genomics and Molecular Breeding Lab, Department of Biotechnology, National Institute of Plant Genome Research New Delhi, India.

ABSTRACT
The large-scale mining and high-throughput genotyping of novel gene-based allelic variants in natural mapping population are essential for association mapping to identify functionally relevant molecular tags governing useful agronomic traits in chickpea. The present study employs an alternative time-saving, non-laborious and economical pool-based EcoTILLING approach coupled with agarose gel detection assay to discover 1133 novel SNP allelic variants from diverse coding and regulatory sequence components of 1133 transcription factor (TF) genes by genotyping in 192 diverse desi and kabuli chickpea accessions constituting a seed weight association panel. Integrating these SNP genotyping data with seed weight field phenotypic information of 192 structured association panel identified eight SNP alleles in the eight TF genes regulating seed weight of chickpea. The associated individual and combination of all SNPs explained 10-15 and 31% phenotypic variation for seed weight, respectively. The EcoTILLING-based large-scale allele mining and genotyping strategy implemented for association mapping is found much effective for a diploid genome crop species like chickpea with narrow genetic base and low genetic polymorphism. This optimized approach thus can be deployed for various genomics-assisted breeding applications with optimal expense of resources in domesticated chickpea. The seed weight-associated natural allelic variants and candidate TF genes delineated have potential to accelerate marker-assisted genetic improvement of chickpea.

No MeSH data available.


Related in: MedlinePlus