henhaobio
Sign in

GenesGenomicsPeer-reviewed journal article

Assessing the potential for genome-assisted breeding in red perilla using quantitative trait locus analysis and genomic prediction

S. KinoshitaiD, Kengo SakuraiiD, Kosuke HamazakiiD, Takahiro TsusakaiD, Miki SAKURAIiD, Terue Kurosawa, Youichi Aoki, Kenta ShirasawaiD, Sachiko IsobeiD, Hiroyoshi IwataiD

PDFDOIPreprint

Abstract

Red perilla is an important medicinal plant used in Kampo medicine, and the development of elite varieties of this species is urgently necessary. Medicinal compounds are generally considered target traits in medicinal plant breeding; however, selection based on the phenotypes of the compounds (i.e., conventional selection) is expensive and time-consuming. Here, we proposed genomic selection (GS) and marker-assisted selection (MAS) as suitable selection methods for medicinal plants, and evaluated the effectiveness of GS and MAS in red 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 to evaluate the accuracy of genomic prediction (GP). We found that GP had sufficiently high accuracy for all traits, confirming that GS was an effective method for perilla breeding. Moreover, the three populations showed varying degrees of segregation, suggesting that use of these populations in breeding may yield simultaneous enhancements of different target traits. This study can contribute to research on the genetic mechanisms of the major medicinal compounds of red perilla as well as the breeding efficiency of this medicinal plant.

Citations
9
FWCI
1.49
Field percentile
top 17%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISKinoshita '26Esfandyari '20Tamura '22Brault '24Klein '26Runcie '26
85% semantic matchGenetics
S. Kinoshita, Kengo Sakurai, Takahiro Tsusaka+4 more·Molecular Breeding·Aug 1, 2026