AUTHOR=Li Wen-Xia , Wang Ping , Zhao Hengxing , Sun Xu , Yang Tao , Li Haoran , Hou Yongqin , Liu Cuiqiao , Siyal Mahfishan , Raja veesar Rameez , Hu Bo , Ning Hailong TITLE=QTL for Main Stem Node Number and Its Response to Plant Densities in 144 Soybean FW-RILs JOURNAL=Frontiers in Plant Science VOLUME=Volume 12 - 2021 YEAR=2021 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.666796 DOI=10.3389/fpls.2021.666796 ISSN=1664-462X ABSTRACT=

Although the main stem node number of soybean [Glycine max (L.) Merr. ] is an important yield-related trait, there have been limited studies on the effect of plant density on the identification of quantitative trait loci (QTL) for main stem node number (MSNN). To address this issue, here, 144 four-way recombinant inbred lines (FW-RILs) derived from Kenfeng 14, Kenfeng 15, Heinong 48, and Kenfeng 19 were used to identify QTL for MSNN with densities of 2.2 × 105 (D1) and 3 × 105 (D2) plants/ha in five environments by linkage and association studies. As a result, the linkage and association studies identified 40 and 28 QTL in D1 and D2, respectively, indicating the difference in QTL in various densities. Among these QTL, five were common in the two densities; 36 were singly identified for response to density; 12 were repeatedly identified by both response to density and phenotype of two densities. Thirty-one were repeatedly detected across various methods, densities, and environments in the linkage and association studies. Among the 24 common QTL in the linkage and association studies, 15 explained a phenotypic variation of more than 10%. Finally, Glyma.06G094400, Glyma.06G147600, Glyma.19G160800.1, and Glyma.19G161100 were predicted to be associated with MSNN. These findings will help to elucidate the genetic basis of MSNN and improve molecular assistant selection in high-yield soybean breeding.