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26 February 2019 Genome-wide association analysis of 1-octen-3-ol content related to soymilk off-flavor in soybean seed
Ning Xia, Mingming Yang, Junhan Zhao, Yupeng Shao, Yanguo Shi, Wenbing Yan, Xiaoqi Wang, Yingpeng Han, Zhikun Wang
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Abstract

Chemical compounds in soybean (Glycine max (L.) Merr.) seed have great effects on the flavour and taste of soymilk. The 1-octen-3-ol content in soybean seed could produce sensory off-flavours (mushrooms, lavender, rose and hay flavour) during the processing of soymilk. The most effective way to reduce off-flavours of soymilk is the screening and utilisation of soybean cultivars with reduced 1-octen-3-ol content. However, genomic study of 1-octen-3-ol in soybean has not previously been reported. In the present work, a natural population of 90 diverse soybean accessions was used to dissect the genetic basis of 1-octen-3-ol content in soybean seed through genome-wide association analysis by using 26 822 single nucleotide polymorphisms (SNPs). Twenty-one novel quantitative trait nucleotides (QTNs) were thus identified to be associated with 1-octen-3-ol content in soybean seed. Among them, 13 QTNs overlapped with, or were located in, the linked regions of known QTNs for protein, oil and/or fatty acid content in soybean seed. The functional genes located in the 200-kb genomic region of each peak SNP were considered possible candidates related to 1-octen-3-ol, such as genes involved in amino acid metabolism, protein content, and hormone metabolism. The identified loci with beneficial alleles and the candidate genes may be valuable for improving the off-flavours of soymilk.

© CSIRO 2019
Ning Xia, Mingming Yang, Junhan Zhao, Yupeng Shao, Yanguo Shi, Wenbing Yan, Xiaoqi Wang, Yingpeng Han, and Zhikun Wang "Genome-wide association analysis of 1-octen-3-ol content related to soymilk off-flavor in soybean seed," Crop and Pasture Science 70(2), 133-139, (26 February 2019). https://doi.org/10.1071/CP18423
Received: 14 September 2018; Accepted: 7 January 2019; Published: 26 February 2019
KEYWORDS
candidate genes
genome-wide association mapping
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