Evidence map›Paper›PMID 38136959›Full record

ArticleGenes2023

Assessing the Potential for Genome-Assisted Breeding in Red Perilla Using Quantitative Trait Locus Analysis and Genomic Prediction.

Sei Kinoshita, Kengo Sakurai, Kosuke Hamazaki, Takahiro Tsusaka, Miki Sakurai, Terue Kurosawa, Youichi Aoki, Kenta Shirasawa, Sachiko Isobe, Hiroyoshi Iwata

Abstract read
In one paragraph

Article in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Chromosome-scale genome assembly and genomic prediction of essential oil compounds in Atractylodes lancea for genomics-assisted breeding.DNA research : an international journal for rapid publication of reports on genes and genomes · 2026
    Article
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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

10 authors.

Sei KinoshitaGraduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo 113-8657, Japan.ORCID 0009-0002-1063-5467
Kengo SakuraiGraduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo 113-8657, Japan.ORCID 0000-0001-7773-289X
Kosuke HamazakiRIKEN Center for Advanced Intelligence Project, Kashiwa, Chiba 227-0871, Japan.ORCID 0000-0002-7486-7438
Takahiro TsusakaTSUMURA & CO., Ami, Ibaraki 300-1155, Japan.
Miki SakuraiTSUMURA & CO., Ami, Ibaraki 300-1155, Japan.
Terue KurosawaTSUMURA & CO., Ami, Ibaraki 300-1155, Japan.
Youichi AokiTSUMURA & CO., Ami, Ibaraki 300-1155, Japan.
Kenta ShirasawaKazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.ORCID 0000-0001-7880-6221
Sachiko IsobeKazusa DNA Research Institute, Kisarazu, Chiba 292-0818, Japan.ORCID 0000-0002-9555-5054
Hiroyoshi IwataGraduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo 113-8657, Japan.ORCID 0000-0002-6747-7036

Funding

Japan Science and Technology Agency JPMJOP1851
6 · The paper itself

Abstract

Red perilla is an important medicinal plant used in Kampo medicine. The development of elite varieties of this species is urgently required. Medicinal compounds are generally considered target traits in medicinal plant breeding; however, selection based on compound phenotypes (i.e., conventional selection) is expensive and time consuming. Here, we propose genomic selection (GS) and marker-assisted selection (MAS), which use marker information for selection, as suitable selection methods for medicinal plants, and we evaluate the effectiveness of these methods in perilla breeding. Three breeding populations generated from crosses between one red and three green perilla genotypes were used to elucidate the genetic mechanisms underlying the production of major medicinal compounds using quantitative trait locus analysis and evaluating the accuracy of genomic prediction (GP). We found that GP had a sufficiently high accuracy for all traits, confirming that GS is an effective method for perilla breeding. Moreover, the three populations showed varying degrees of segregation, suggesting that using these populations in breeding may simultaneously enhance multiple target traits. This study contributes to research on the genetic mechanisms of the major medicinal compounds of red perilla, as well as the breeding efficiency of this medicinal plant.

Indexed as

PerillaPlants, MedicinalGenomicsPhenotypePlant BreedingQuantitative Trait Locibreedinggenomic selectionmedicinal plantsquantitative trait locus analysisred perilla

Identifiers

PMID38136959
PMCPMC10742415

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.