Genotyping of Soybean Cultivars With Medium-Density Array Reveals the Population Structure and QTNs Underlying Maturity and Seed Traits

Wang, Yaying; Li, Yu-Qiu; Wu, Hongyan; Hu, Bo; Zheng, Jiajia; Zhai, Hong; Lv, Shi-Xiang; Liu, Xinlei; Chen, Xin; Qiu, Hongmei; Yang, Jason; Zong, Chunmei; Han, Dejun; Zhang, Wen; Wang, Dechun; Xia, Zhengjun

doi:10.3389/fpls.2018.00610

Cited by 30 publications

(23 citation statements)

References 96 publications

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“…Diers et al (2018) found a total of 19 regions for maturity on chromosome 16. However, Wang et al (2018) reported a significant discrepancy in SNPs associated with maturity across multiple environments, which was also consistent with that of reported in this study where different SNPs were reported in different years with different environmental conditions. A cluster of significant SNPs were identified in a 232-kb region of chromosome 20.…”

Section: Discussionsupporting

confidence: 92%

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Ravelombola

Qin²,

Shi

et al. 2019

Preprint

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Background Soybean [ Glycine max (L.) Merr.] is a legume of great interest worldwide. Enhancing genetic gain for agronomic traits via molecular approaches has been long considered as the main task for soybean breeders and geneticists. The objectives of this study were to evaluate maturity, plant height, seed weight, and yield in a diverse soybean accession panel, to conduct a genome-wide association study (GWAS) for these traits and identify SNP markers associated with the four traits, and to assess genomic selection (GS) accuracy. Results A total of 250 soybean accessions were evaluated for maturity, plant height, seed weight, and yield over three years. This panel was genotyped with a total of 10,259 high quality SNPs postulated from genotyping by sequencing (GBS). GWAS was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model, and GS was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that a total of 20, 31, 37, 31, and 23 SNPs were significantly associated with the average 3-year data for maturity, plant height, seed weight, and yield, respectively; some significant SNPs were mapped into previously described loci ( E2 , E4 , and Dt1 ) affecting maturity and plant height in soybean and a new locus mapped on chromosome 20 was significantly associated with plant height; Glyma.10g228900 , Glyma.19g200800 , Glyma.09g196700 , and Glyma.09g038300 were candidate genes found in the vicinity of the top or the second best SNP for maturity, plant height, seed weight, and yield, respectively; a 11.5-Mb region of chromosome 10 was associated with both seed weight and yield; and GS accuracy was trait-, year-, and population structure-dependent. Conclusions The SNP markers identified from this study for plant height, maturity, seed weight and yield can be used to improve the four agronomic traits through marker-assisted selection (MAS) and GS in soybean breeding programs. After validation, the candidate genes can be transferred to new cultivars using SNP markers through MAS. The high GS accuracy has confirmed that the four agronomic traits can be selected in molecular breeding through GS.

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Section: Discussionsupporting

confidence: 92%

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Ravelombola

Qin²,

Shi

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Our previous study introduced FarmCPU method, which is a multiple loci model and split the MLM into separated fixed effect model and random effect model and iteratively uses above two models (Liu et al, 2016). A series of studies in both livestock and plant indicated that FarmCPU detected more candidate genes by solving the confounding problem (Kaler et al, 2017; Kusmec et al, 2017; Meng et al, 2017; Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association Study Reveals Candidate Genes for Growth Relevant Traits in Pigs

Tang

Yin

et al. 2019

Front. Genet.

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Improvement of the growth rate is a challenge in the pig industry, the Average Daily Gain (ADG) and Days (AGE) to 100 kg are directly related to growth performance. We performed genome-wide association study (GWAS) and genetic parameters estimation for ADG and AGE using the genomic and phonemic from four breed (Duroc, Yorkshire, Landrace, and Pietrain) populations. All analyses were performed by a multi-loci GWAS model, FarmCPU. The GWAS results of all four breeds indicate that five genome-wide significant SNPs were associated with ADG, and the nearby genomic regions explained 4.08% of the genetic variance and 1.90% of the phenotypic variance, respectively. For AGE, six genome-wide significant SNPs were detected, and the nearby genomic regions explained 8.09% of the genetic variance and 3.52% of phenotypic variance, respectively. In total, nine candidate genes were identified to be associated with growth and metabolism. Among them, TRIB3 was reported to associate with pig growth, GRP, TTR, CNR1, GLP1R, BRD2, HCRTR2, SEC11C, and ssc-mir-122 were reported to associate with growth traits in human and mouse. The newly detected candidate genes will advance the understanding of growth related traits and the identification of the novel variants will suggest a potential use in pig genomic breeding programs.

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“…To date, it has been used to identify molecular markers for a broad range of complex traits in different plant species including Arabidopsis (Angelovici et al, 2017), wheat (Peng et al, 2018), maize (Li et al, 2013; Deng et al, 2017), rice (Huang et al, 2010; Yano et al, 2016), soybean (Fang et al, 2017); sorghum (Morris et al, 2013). In soybean research, GWAS were used in agronomic traits (Zatybekov et al, 2017; Chang et al, 2018), seed quality (Zhang et al, 2018), seed traits (Xia et al, 2018), phosphorus efficiency (Lü et al, 2018), disease resistance (Qin et al, 2017b; Hanson et al, 2018) etc. As soybean is globally cultivated primarily for its protein and oil, and soybean protein is a complete protein as it contains all the essential amino acid that are required for human health.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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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Genotyping of Soybean Cultivars With Medium-Density Array Reveals the Population Structure and QTNs Underlying Maturity and Seed Traits

Cited by 30 publications

References 96 publications

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Genome-Wide Association Study Reveals Candidate Genes for Growth Relevant Traits in Pigs

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

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