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Phenotypic Characterization and Genetic Dissection of Growth Period Traits in Soybean (Glycine max) Using Association Mapping.

Liu Z, Li H, Fan X, Huang W, Yang J, Li C, Wen Z, Li Y, Guan R, Guo Y, Chang R, Wang D, Wang S, Qiu LJ - PLoS ONE (2016)

Bottom Line: The whole accessions could be clearly clustered into two subpopulations based on their genetic relatedness, and accessions in the same group were almost from the same province.GWAS based on the unified mixed model identified 19 significant SNPs distributed on 11 soybean chromosomes, 12 of which can be consistently detected in both planting densities, and 5 of which were pleotropic QTL.Of 19 SNPs, 7 SNPs located in or close to the previously reported QTL or genes controlling growth period traits.

View Article: PubMed Central - PubMed

Affiliation: National Key Facility for Gene Resources and Genetic Improvement, Key Laboratory of Crop Germplasm Utilization, Ministry of Agriculture, Institute of Crop Sciences, Chinese Academy of Agricultural Science, Beijing, China.

ABSTRACT
The growth period traits are important traits that affect soybean yield. The insights into the genetic basis of growth period traits can provide theoretical basis for cultivated area division, rational distribution, and molecular breeding for soybean varieties. In this study, genome-wide association analysis (GWAS) was exploited to detect the quantitative trait loci (QTL) for number of days to flowering (ETF), number of days from flowering to maturity (FTM), and number of days to maturity (ETM) using 4032 single nucleotide polymorphism (SNP) markers with 146 cultivars mainly from Northeast China. Results showed that abundant phenotypic variation was presented in the population, and variation explained by genotype, environment, and genotype by environment interaction were all significant for each trait. The whole accessions could be clearly clustered into two subpopulations based on their genetic relatedness, and accessions in the same group were almost from the same province. GWAS based on the unified mixed model identified 19 significant SNPs distributed on 11 soybean chromosomes, 12 of which can be consistently detected in both planting densities, and 5 of which were pleotropic QTL. Of 19 SNPs, 7 SNPs located in or close to the previously reported QTL or genes controlling growth period traits. The QTL identified with high resolution in this study will enrich our genomic understanding of growth period traits and could then be explored as genetic markers to be used in genomic applications in soybean breeding.

No MeSH data available.


Number of QTL detected under each of low and high densities and both two densities.ETF is for trait of number of days to flowering; FTM is for trait of number of days from flowering to maturity; and ETM is for trait of number of days to maturity.
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pone.0158602.g008: Number of QTL detected under each of low and high densities and both two densities.ETF is for trait of number of days to flowering; FTM is for trait of number of days from flowering to maturity; and ETM is for trait of number of days to maturity.

Mentions: From GWAS analysis based on MLM model involved with Q matrix and Kinship matrix (S1 Fig), 19 QTL regions can be clearly identified from the Manhattan plots (Figs 6–8; Table 4). They were distributed on 11 chromosomes of soybean genome, three of each on chromosomes 3, 4, and 13, 2 on each of chromosomes 11 and 15, and one on each of chromosomes 3, 5, 9, 16, 18, and 19. The largest QTL, explaining 14.62% of the phenotypic variance, was ss245775380 on chromosome 5 associated with ETF (Table 4). Of 19 QTLs, 5 QTLs could explain more than 10% of the phenotypic variance, and the other 14 QTL could explain more than 5% of the phenotypic variance. On average, 9.53% of the phenotypic variance could be explained by each QTL.


Phenotypic Characterization and Genetic Dissection of Growth Period Traits in Soybean (Glycine max) Using Association Mapping.

Liu Z, Li H, Fan X, Huang W, Yang J, Li C, Wen Z, Li Y, Guan R, Guo Y, Chang R, Wang D, Wang S, Qiu LJ - PLoS ONE (2016)

Number of QTL detected under each of low and high densities and both two densities.ETF is for trait of number of days to flowering; FTM is for trait of number of days from flowering to maturity; and ETM is for trait of number of days to maturity.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4930185&req=5

pone.0158602.g008: Number of QTL detected under each of low and high densities and both two densities.ETF is for trait of number of days to flowering; FTM is for trait of number of days from flowering to maturity; and ETM is for trait of number of days to maturity.
Mentions: From GWAS analysis based on MLM model involved with Q matrix and Kinship matrix (S1 Fig), 19 QTL regions can be clearly identified from the Manhattan plots (Figs 6–8; Table 4). They were distributed on 11 chromosomes of soybean genome, three of each on chromosomes 3, 4, and 13, 2 on each of chromosomes 11 and 15, and one on each of chromosomes 3, 5, 9, 16, 18, and 19. The largest QTL, explaining 14.62% of the phenotypic variance, was ss245775380 on chromosome 5 associated with ETF (Table 4). Of 19 QTLs, 5 QTLs could explain more than 10% of the phenotypic variance, and the other 14 QTL could explain more than 5% of the phenotypic variance. On average, 9.53% of the phenotypic variance could be explained by each QTL.

Bottom Line: The whole accessions could be clearly clustered into two subpopulations based on their genetic relatedness, and accessions in the same group were almost from the same province.GWAS based on the unified mixed model identified 19 significant SNPs distributed on 11 soybean chromosomes, 12 of which can be consistently detected in both planting densities, and 5 of which were pleotropic QTL.Of 19 SNPs, 7 SNPs located in or close to the previously reported QTL or genes controlling growth period traits.

View Article: PubMed Central - PubMed

Affiliation: National Key Facility for Gene Resources and Genetic Improvement, Key Laboratory of Crop Germplasm Utilization, Ministry of Agriculture, Institute of Crop Sciences, Chinese Academy of Agricultural Science, Beijing, China.

ABSTRACT
The growth period traits are important traits that affect soybean yield. The insights into the genetic basis of growth period traits can provide theoretical basis for cultivated area division, rational distribution, and molecular breeding for soybean varieties. In this study, genome-wide association analysis (GWAS) was exploited to detect the quantitative trait loci (QTL) for number of days to flowering (ETF), number of days from flowering to maturity (FTM), and number of days to maturity (ETM) using 4032 single nucleotide polymorphism (SNP) markers with 146 cultivars mainly from Northeast China. Results showed that abundant phenotypic variation was presented in the population, and variation explained by genotype, environment, and genotype by environment interaction were all significant for each trait. The whole accessions could be clearly clustered into two subpopulations based on their genetic relatedness, and accessions in the same group were almost from the same province. GWAS based on the unified mixed model identified 19 significant SNPs distributed on 11 soybean chromosomes, 12 of which can be consistently detected in both planting densities, and 5 of which were pleotropic QTL. Of 19 SNPs, 7 SNPs located in or close to the previously reported QTL or genes controlling growth period traits. The QTL identified with high resolution in this study will enrich our genomic understanding of growth period traits and could then be explored as genetic markers to be used in genomic applications in soybean breeding.

No MeSH data available.