@article{PENG2026, 
author = {TingShen PENG and JiuYan LU and MeiLin WU and YuXin YAN and HongZhou LIU and WenBin NAN and XiaoJian QIN and Ming LI and JunYi GONG and YongShu LIANG},
title = {QTL Analysis of Yield-Related Traits in Both Huangnuo2# and Changbai7# of Perennial Chinese Rice},
year = {2026},
journal = {Scientia Agricultura Sinica},
volume = {59},
number = {7},
pages = {1361-1379},
keywords = {perennial Chinese rice, yield-related traits, major crop, ratooning crop, genetic analysis, QTL analysis},
url = {https://www.sciopen.com/article/10.3864/j.issn.0578-1752.2026.07.001},
doi = {10.3864/j.issn.0578-1752.2026.07.001},
abstract = {ObjectiveThe analysis of quantitative trait loci (QTL) underlying yield-related traits of perennial Chinese rice laid a good foundation for fine mapping, cloning, and functional research of yield-related traits genes. Meanwhile, it also provided technical support for revealing the genetic mechanism of yield-related traits in perennial Chinese rice and breeding perennial rice variety.MethodTwo perennial Chinese japonica rice, namely, Huangnuo2# (HN2#) and Changbai7# (CB7#), and two half-sib (Huangnuo2#/XieqingzaoB and Changbai7#/XieqingzaoB) F2 populations and their bi-parents were selected as experimental materials. Sixteen yield-related traits, including heading date, plant height, and thousand-grain weight of HN2# and CB7# in major crop (MC) and ratooning crop (RC) of 2024, were investigated for phenotypic analysis. Fifteen yield-related traits, including plant height, panicle plant-1, and thousand-grain weight, in HN2# and CB7#-populations and their bi-parents were investigated for phenotypic analysis and QTL mapping.ResultBetween MC and RC of 2024, seven yield-related traits of HN2#, including heading date, plant height, and thousand-grain weight, exhibited significant phenotypic differences (P<0.05). Three yield-related traits of CB7#, including plant height, grain setting density, and grain weight panicle-1, displayed significant phenotypic differences. Among 15 yield-related traits, 34 pairs of significantly positive correlations were calculated in the HN2#-population. A total of 39 pairs of significantly positive correlations were calculated in the CB7#-population. Exactly 29 QTLs were detected in the HN2#-population, accounting for 2.61% to 29.41% of the phenotypic variation. Thirteen novel QTLs were detected in the HN2#-population. Of these, seven QTLs with additive effects were derived from HN2#, and the other six with additive effects were derived from XQZB. Five pleiotropic QTL were detected in the HN2# HN2#-population. A total of 22 QTLs were detected in the CB7#-population, accounting for 2.77% to 27.94% of the phenotypic variation. Ten novel QTLs were detected in the CB7#-population. Of those, six QTLs with additive effects were derived from CB7#, and the other four with additive effects as well were derived from XQZB. Five pleiotropic QTL were detected in the CB7#-population.ConclusionThese novel and pleiotropic QTL are unique to HN2# and CB7#, which should be the primary focus of future research.}
}