Evidence map›Paper›PMID 39670092›Full record

ArticlePeerJ2024

Associations between SNPs and vegetation indices: unraveling molecular insights for enhanced cultivation of tea plant (

Daria Kuzmina, Lyudmila S Malyukova, Karina Manakhova, Tatyana Kovalenko, Jaroslava Fedorina, Aleksandra O Matskiv, Alexey V Ryndin, Maya V Gvasaliya, Yuriy L Orlov, Lidiia S Samarina

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Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Daria KuzminaFederal Research Centre the Subtropical Scientific Centre of the Russian Academy of Sciences, Sochi, Russia.
Lyudmila S MalyukovaFederal Research Centre the Subtropical Scientific Centre of the Russian Academy of Sciences, Sochi, Russia.
Karina ManakhovaFederal Research Centre the Subtropical Scientific Centre of the Russian Academy of Sciences, Sochi, Russia.
Tatyana KovalenkoFederal Research Centre the Subtropical Scientific Centre of the Russian Academy of Sciences, Sochi, Russia.
Jaroslava FedorinaFederal Research Centre the Subtropical Scientific Centre of the Russian Academy of Sciences, Sochi, Russia.
Aleksandra O MatskivFederal Research Centre the Subtropical Scientific Centre of the Russian Academy of Sciences, Sochi, Russia.
Alexey V RyndinFederal Research Centre the Subtropical Scientific Centre of the Russian Academy of Sciences, Sochi, Russia.
Maya V GvasaliyaFederal Research Centre the Subtropical Scientific Centre of the Russian Academy of Sciences, Sochi, Russia.
Yuriy L OrlovInstitute of Biodesign and Complex Systems Modeling, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
Lidiia S SamarinaFederal Research Centre the Subtropical Scientific Centre of the Russian Academy of Sciences, Sochi, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Breeding programs for nutrient-efficient tea plant varieties could be advanced by the combination of genotyping and phenotyping technologies. This study was aimed to search functional SNPs in key genes related to the nitrogen-assimilation in the collection of tea plant Methods: The study was conducted on the tea plant collection of Results: PCA and regression analysis revealed significant vegetation indices with high R2 values (more than 0.5) and the most reliable indices to select ND-tolerant genotypes were established: ZMI, CNDVI, RENDVI, VREI1, GM2, GM1, PRI, and Ctr2, VREI3, VREI2. The largest SNPs frequency was observed in several genes, namely Conclusions: The results will be useful to identify tolerant and susceptible tea genotypes under nitrogen deficiency. Revealed missense SNPs and associations with vegetation indices improve our understanding of nitrogen effect on tea quality. The findings in our study would provide new insights into the genetic basis of tea quality variation under the N-deficiency and facilitate the identification of elite genes to enhance tea quality.

Indexed as

Camellia sinensisPolymorphism, Single NucleotideGenotypeNitrogenPhenotypePlant BreedingPlant LeavesNitrogenCamellia sinensisFlavonoid biosynthesisL-theanineNitrogen deficiencyPhenotypingSNPTea qualityVegetation indices

Identifiers

PMID39670092
PMCPMC11636977

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.