Evidence map›Paper›PMID 37351213›Full record

ArticleFrontiers in plant science2023

High resolution mapping of QTLs for fruit color and firmness in Amrapali/Sensation mango hybrids.

Manish Srivastav, Nidhi Radadiya, Sridhar Ramachandra, Pawan Kumar Jayaswal, Nisha Singh, Sangeeta Singh, Ajay Kumar Mahato, Gitanjali Tandon, Ankit Gupta, Rajni Devi and 14 more

Abstract read
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Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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0cells of the map it votes in
10citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
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  9. A high-density linkage map construction in guava (Frontiers in plant science · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors.

Manish SrivastavDivision of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Nidhi RadadiyaDivision of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Sridhar RamachandraDivision of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Pawan Kumar JayaswalGenomics Laboratory, Indian Council of Agricultural Research (ICAR)- National Institute for Plant Biotechnology, New Delhi, India.
Nisha SinghGenomics Laboratory, Indian Council of Agricultural Research (ICAR)- National Institute for Plant Biotechnology, New Delhi, India.
Sangeeta SinghGenomics Laboratory, Indian Council of Agricultural Research (ICAR)- National Institute for Plant Biotechnology, New Delhi, India.
Ajay Kumar MahatoGenomics Laboratory, Indian Council of Agricultural Research (ICAR)- National Institute for Plant Biotechnology, New Delhi, India.
Gitanjali TandonDivision of Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Statistics Research Institute, New Delhi, India.
Ankit GuptaDivision of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Rajni DeviDivision of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Sreekanth Halli SubrayagowdaDivision of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Gulshan KumarDivision of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Pragya PrakashDivision of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Shivani SinghDivision of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Nimisha SharmaDivision of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
A NagarajaDivision of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Abhijit KarDivision of Food Science and Postharvest Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Shalini Gaur RudraDivision of Food Science and Postharvest Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Shruti SethiDivision of Food Science and Postharvest Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Sarika JaiswalDivision of Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Statistics Research Institute, New Delhi, India.
Mir Asif IquebalDivision of Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Statistics Research Institute, New Delhi, India.
Rakesh SinghDivision of Genomic Resources, Indian Council of Agricultural Research (ICAR)- National Bureau of Plant Genetic Resources, New Delhi, India.
Sanjay Kumar SinghDivision of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India.
Nagendra Kumar SinghGenomics Laboratory, Indian Council of Agricultural Research (ICAR)- National Institute for Plant Biotechnology, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Mango (Mangifera indica L.), acclaimed as the 'king of fruits' in the tropical world, has historical, religious, and economic values. It is grown commercially in more than 100 countries, and fresh mango world trade accounts for ~3,200 million US dollars for the year 2020. Mango is widely cultivated in sub-tropical and tropical regions of the world, with India, China, and Thailand being the top three producers. Mango fruit is adored for its taste, color, flavor, and aroma. Fruit color and firmness are important fruit quality traits for consumer acceptance, but their genetics is poorly understood. Methods: For mapping of fruit color and firmness, mango varieties Amrapali and Sensation, having contrasting fruit quality traits, were crossed for the development of a mapping population. Ninety-two bi-parental progenies obtained from this cross were used for the construction of a high-density linkage map and identification of QTLs. Genotyping was carried out using an 80K SNP chip array. Results and discussion: Initially, we constructed two high-density linkage maps based on the segregation of female and male parents. A female map with 3,213 SNPs and male map with 1,781 SNPs were distributed on 20 linkages groups covering map lengths of 2,844.39 and 2,684.22cM, respectively. Finally, the integrated map was constructed comprised of 4,361 SNP markers distributed on 20 linkage groups, which consisted of the chromosome haploid number in Mangifera indica (n =20). The integrated genetic map covered the entire genome of Mangifera indica cv. Dashehari, with a total genetic distance of 2,982.75 cM and an average distance between markers of 0.68 cM. The length of LGs varied from 85.78 to 218.28 cM, with a mean size of 149.14 cM. Phenotyping for fruit color and firmness traits was done for two consecutive seasons. We identified important consistent QTLs for 12 out of 20 traits, with integrated genetic linkages having significant LOD scores in at least one season. Important consistent QTLs for fruit peel color are located at Chr 3 and 18, and firmness on Chr 11 and 20. The QTLs mapped in this study would be useful in the marker-assisted breeding of mango for improved efficiency.

Indexed as

Colorfirmnessfruit quality QTLsMangifera indica L.molecular linkage mapSNP markers

Identifiers

PMID37351213
PMCPMC10282835

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