BMC Genomics (Jun 2017)

Construction of high-density genetic map and QTL mapping of yield-related and two quality traits in soybean RILs population by RAD-sequencing

  • Nianxi Liu,
  • Mu Li,
  • Xiangbao Hu,
  • Qibin Ma,
  • Yinghui Mu,
  • Zhiyuan Tan,
  • Qiuju Xia,
  • Gengyun Zhang,
  • Hai Nian

DOI
https://doi.org/10.1186/s12864-017-3854-8
Journal volume & issue
Vol. 18, no. 1
pp. 1 – 13

Abstract

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Abstract Background One of the overarching goals of soybean breeding is to develop lines that combine increased yield with improved quality characteristics. High-density-marker QTL mapping can serve as an effective strategy to identify novel genomic information to facilitate crop improvement. In this study, we genotyped a recombinant inbred line (RIL) population (Zhonghuang 24 × Huaxia 3) using a restriction-site associated DNA sequencing (RAD-seq) approach. A high-density soybean genetic map was constructed and used to identify several QTLs that were shown to influence six yield-related and two quality traits. Results A total of 47,472 single-nucleotide polymorphisms (SNPs) were detected for the RILs that were integrated into 2639 recombination bin units, with an average distance of 1.00 cM between adjacent markers. Forty seven QTLs for yield-related traits and 13 QTLs for grain quality traits were found to be distributed on 16 chromosomes in the 2 year studies. Among them, 18 QTLs were stable, and were identified in both analyses. Twenty six QTLs were identified for the first time, with a single QTL (qNN19a) in a 56 kb region explaining 32.56% of phenotypic variation, and an additional 10 of these were novel, stable QTLs. Moreover, 8 QTL hotpots on four different chromosomes were identified for the correlated traits. Conclusions With RAD-sequencing, some novel QTLs and important QTL clusters for both yield-related and quality traits were identified based on a new, high-density bin linkage map. Three predicted genes were selected as candidates that likely have a direct or indirect influence on both yield and quality in soybean. Our findings will be helpful for understanding common genetic control mechanisms of co-localized traits and to select cultivars for further analysis to predictably modulate soybean yield and quality simultaneously.

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