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Identification of novel soybean oil content-related genes using QTLbased collinearity analysis from the collective soybean genome
XU Ming-yue, LIU Zhang-xiong, QIN Hong-tao, QI Hui-dong, WANG Zhong-yu, MAO Xin-rui, XIN Dawei, HU Zhen-bang, WU Xiao-xia, JIANG Hong-wei, QI Zhao-ming, CHEN Qing-shan
2018, 17 (08): 1727-1735.   DOI: 10.1016/S2095-3119(17)61862-8
Abstract413)      PDF in ScienceDirect      
Soybean is a global principal source of edible plant oil.  As more soybean oil-related quantitative trait loci (QTLs) have been located in the collective genome, it is urgent to establish a classification system for these distributed QTLs.  A collinear platform may be useful to characterize and identify relationships among QTLs as well as aid in novel gene discovery.  In this study, the collinearity MCScanX algorithm and collective soybean genomic information were used to construct collinearity blocks, to which soybean oil-related QTLs were mapped.  The results demonstrated that 666 collinearity blocks were detected in the soybean genome across 20 chromosomes, and 521 collinearity relationships existed in 231 of the 242 effective soybean oil-related QTLs.  This included 214 inclusion relationships and 307 intersecting relationships.  Among them, the collinearity among QTLs that are related to soybean oil content was shown on a maximum of seven chromosomes and minimum of one chromosome, with the majority of QTLs having collinearity on two chromosomes.  Using overlapping hotspot regions in the soybean oil QTLs with collinearity, we mined for novel oil content-related genes.  Overall, we identified 23 putatively functional genes associated with oil content in soybean and annotated them using a number of annotation databases.  Our findings provide a valuable framework for elucidating evolutionary relationships between soybean oil-related QTLs and lay a foundation for functional marker-assisted breeding relating to soybean oil content.
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Meta-analysis of soybean amino acid QTLs and candidate gene mining
GONG Qian-chun, YU Hong-xiao, MAO Xin-rui, QI Hui-dong, SHI Yan, XIANG Wei, CHEN Qing-shan,
2018, 17 (05): 1074-1084.   DOI: 10.1016/S2095-3119(17)61783-0
Abstract471)      PDF in ScienceDirect      
The composition and quantity of amino acids influence the protein content and nutritional value of soybeans and also have an important impact upon soybean quality.  After integrating and proofreading 140 original QTLs associated with amino acid contentfrom soybase (http://www.soybase.org/), 138 QTLs were further analyzed to determine high-confidence QTL regions.  Meta-analysis was first carried out using the BioMercator ver. 2.1 software, yielding 33 consensus QTLs.  The consensus QTL confidence intervals (CIs) ranged from 0.07 to 19.85 Mb.  Next, the overview method was used to optimize the CIs, and 57 “real” QTLs were mapped.  Candidate genes in the consensus QTL regions were obtained from Phytozome and were annotated using the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Swissprot, and gene annotation databases.  Finally, 16 unpublished candidate genes controlling the content of five types of amino acids were identified with Blast.  These results laid the foundation for fine mapping of soybean amino acid-related QTLs and marker-assisted selection.
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