Abstract
ABSTRACTThe soybean (Glycine max) node number on the main stem (NNMS) is closely related to seed yield of soybean. The aim of this study was to identify important loci affecting soybean NNMS using meta-analysis based on a reference physical map. Twenty-nine NNMS-related QTLs were mapped across 8 years with a recombination inbred line (RIL) population. Fifty-four QTLs related to NNMS of soybean identified from the database and our research were collected and each QTL was projected onto the soybean physical map. Through meta-analysis, 11 consensus soybean NNMS QTLs were obtained and located on LG D1b, LG C2, LG B1, LG F, LG L, and LG I. The map distance was from 0.02 to 4.22 Mb, the variation of original QTLs was from 2 to 4, and the mean R2 values ranged from 6.90% to 27.40%. Furthermore, 488 candidate genes were located in these consensus QTLs, 6 of which had a relationship with NNMS. These results may help lay a foundation for fine mapping of QTLs/genes related to NNMS that can be used to help breed high-yield soybean cultivars.
Highlights
Soybean (Glycine max) is one of the most important crops in the worldfor its use as a source of both edible oil and vegetable protein [1,2]
Most of the 29 QTLs were consistent over different years, indicating that the high-density soybean genetic map constructed by SLAF-seq plays an important role in detecting number on the main stem (NNMS) traits in soybean
29 NNMS related QTLs were firstly mapped across 8 years within a recombination inbred line population based on a high-density soybean genetic map constructed by SLAF-seq
Summary
Soybean (Glycine max) is one of the most important crops in the worldfor its use as a source of both edible oil and vegetable protein [1,2]. In the development of the soybean genetic map, many quantitative trait loci (QTLs) underlying the node number on the main stem (NNMS) have been identified in different genetic backgrounds, environments and statistical methods [3,5,6,7,8,9,10]. These QTLs play only a minor role in soybean breeding programmes because the distribution of the QTLs in different populations varies, with confidence intervals (CIs) too long and LOD scores too low [11]. A statistical approach is needed to confirm whether a cluster of original QTLs detected in different backgrounds are referencing the same locus
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