Evidence map›Paper›PMID 33543373›Full record

ArticleGenes & genomics2021

Bulk segregant analysis identifies SSR markers associated with leaf- and seed-related traits in Perilla crop (Perilla frutescens L.).

Su Eun Lim, Kyu Jin Sa, Ju Kyong Lee

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Article in Genes & genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
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12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Pigment Diversity in Leaves ofInternational journal of molecular sciences · 2024
    Article
  8. Utilization of NovelPlants (Basel, Switzerland) · 2022
    Article
  9. Article
  10. Article
  11. Genetic Variation of NativePlants (Basel, Switzerland) · 2021
    Article
  12. Article
4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Su Eun Lim *Department of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon-si, 24341, Korea.
Kyu Jin Sa *Department of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon-si, 24341, Korea.
Ju Kyong LeeDepartment of Applied Plant Sciences, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon-si, 24341, Korea. jukyonglee@kangwon.ac.kr.ORCID http://orcid.org/0000-0002-2769-0799

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBulk segregant analysis (BSA) is another method of identifying significant molecular markers linked to the target gene or region for specific traits. BSA is easier and less expensive than other methods; it does not require genetic map construction and needs fewer markers than the number needed to construct a genetic map for QTL mapping.

objectivesThe purpose of our study was to identify simple sequence repeat (SSR) markers linked with leaf- and seed-related traits in Perilla crop, and to allow the selection of better accessions in Perilla breeding programs with marker-assisted selection (MAS).

methodsThe genotypes of the 25 SSR markers and phenotypic data for the eight qualitative traits were used to confirm significant marker-trait associations (SMTAs) using TASSEL software. To detect SSR markers associated with leaf color, the 16 individuals of the F

resultsThis study detected 18 significant marker-trait associations (SMTAs) involving 12 SSR markers associated with six agronomic traits. The SSR markers KNUPF15, KNUPF21, KNUPF29, and KNUPF60 were associated with leaf surface color, and KNUPF11, KNUPF15, KNUPF21, and KNUPF60 were associated with leaf reverse side color. In addition, five SSR markers were associated with seed-related traits. KNUPF11 and KNUPF29 were associated with seed coat color, while KNUPF29 was associated with seed size. KNUPF12, KNUPF16, and KNUPF42 were associated with seed hardness. To verify the selected significant SSR markers associated with leaf color and seed-related traits, a UPGMA dendrogram for 11 individuals in the F

conclusionThese results are very important for understanding the characteristics of Perilla leaves and seeds; they may also support opportunities to effectively preserve and utilize existing accessions and to allow Perilla breeders to improve crop quality by mean of MAS.

Indexed as

Microsatellite RepeatsGenetic MarkersPerilla frutescensPhenotypePlant LeavesSeedsGenetic MarkersAssociation analysisBulk segregant analysisPerilla frutescensSSR marker

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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.