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Genome Wide Association Study and Genomic Selection of Amino Acid Concentrations in Soybean Seeds

文献类型: 外文期刊

作者: Qin, Jun 1 ; Shi, Ainong 2 ; Song, Qijian 3 ; Li, Song 4 ; Wang, Fengmin 1 ; Cao, Yinghao 5 ; Ravelombola, Waltram 2 ; So 1 ;

作者机构: 1.Hebei Acad Agr & Forestry Sci, Natl Soybean Improvement Ctr Shijiazhuang Sub Ctr, Cereal & Oil Crop Inst,North China Key Lab Biol &, Minist Agr,Lab Crop Genet & Breeding Hebei, Shijiazhuang, Hebei, Peoples R China

2.Univ Arkansas, Dept Hort, Fayetteville, AR 72701 USA

3.USDA ARS, Soybean Genom & Improvement Lab, Beltsville, MD USA

4.Virginia Tech, Crop & Soil Environm Sci, Blacksburg, VA USA

5.Allife Med Sci & Technol Co Ltd, Bioinformat Ctr, Beijing, Peoples R China

关键词: Glycine max; genome-wide association study; genomic selection; genotyping by sequencing; amino acid concentration; single nucleotide polymorphism

期刊名称:FRONTIERS IN PLANT SCIENCE ( 影响因子:5.753; 五年影响因子:6.612 )

ISSN: 1664-462X

年卷期: 2019 年 10 卷

页码:

收录情况: SCI

摘要: Soybean is a major source of protein for human consumption and animal feed. Releasing new cultivars with high nutritional value is one of the major goals in soybean breeding. To achieve this goal, genome-wide association studies of seed amino acid contents were conducted based on 249 soybean accessions from China, US, Japan, and South Korea. The accessions were evaluated for 15 amino acids and genotyped by sequencing. Significant genetic variation was observed for amino acids among the accessions. Among the 231 single nucleotide polymorphisms (SNPs) significantly associated with variations in amino acid contents, fifteen SNPs localized near 14 candidate genes involving in amino acid metabolism. The amino acids were classified into two groups with five in one group and seven amino acids in the other. Correlation coefficients among the amino acids within each group were high and positive, but the correlation coefficients of amino acids between the two groups were negative. Twenty-five SNP markers associated with multiple amino acids can be used to simultaneously improve multi-amino acid concentration in soybean. Genomic selection analysis of amino acid concentration showed that selection efficiency of amino acids based on the markers significantly associated with all 15 amino acids was higher than that based on random markers or markers only associated with individual amino acid. The identified markers could facilitate selection of soybean varieties with improved seed quality.

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