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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.


Analysis of the population structure of 146 soybean accessions.A is for the estimated Δk over 10 repeats of STRUCTURE analysis; and B is for the population structure estimated by STRUCTURE. Each individual is represented by a vertical bar, partitioned into colored segments with the length of each segment representing the proportion of the individual’s genome when k = 2.
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pone.0158602.g004: Analysis of the population structure of 146 soybean accessions.A is for the estimated Δk over 10 repeats of STRUCTURE analysis; and B is for the population structure estimated by STRUCTURE. Each individual is represented by a vertical bar, partitioned into colored segments with the length of each segment representing the proportion of the individual’s genome when k = 2.

Mentions: To avoid false-positive associations due to population stratification, three statistical methods, including STRUCTRUE, NJ tree-based, and PCA (Figs 4 and 5) were exploited to estimate the relatedness among 146 accessions using 4032 SNPs. The distribution of LnP(D) value for each given k did not show a clear trend (Fig 4A). The Δk reached to the highest value when k was at 2 (Fig 4A), which indicated that the 146 accessions could be divided into two subpopulations (Fig 4B). The measurement of population differentiation, FST, was estimated at 0.18 (P<0.001) between the two subpopulations, suggesting high level of genetic difference (S3 Table). The result of AMOVA showed that 17.64% of the total genetic variation was among subpopulations, whereas 82.36% was within subpopulations (S3 Table).


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)

Analysis of the population structure of 146 soybean accessions.A is for the estimated Δk over 10 repeats of STRUCTURE analysis; and B is for the population structure estimated by STRUCTURE. Each individual is represented by a vertical bar, partitioned into colored segments with the length of each segment representing the proportion of the individual’s genome when k = 2.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0158602.g004: Analysis of the population structure of 146 soybean accessions.A is for the estimated Δk over 10 repeats of STRUCTURE analysis; and B is for the population structure estimated by STRUCTURE. Each individual is represented by a vertical bar, partitioned into colored segments with the length of each segment representing the proportion of the individual’s genome when k = 2.
Mentions: To avoid false-positive associations due to population stratification, three statistical methods, including STRUCTRUE, NJ tree-based, and PCA (Figs 4 and 5) were exploited to estimate the relatedness among 146 accessions using 4032 SNPs. The distribution of LnP(D) value for each given k did not show a clear trend (Fig 4A). The Δk reached to the highest value when k was at 2 (Fig 4A), which indicated that the 146 accessions could be divided into two subpopulations (Fig 4B). The measurement of population differentiation, FST, was estimated at 0.18 (P<0.001) between the two subpopulations, suggesting high level of genetic difference (S3 Table). The result of AMOVA showed that 17.64% of the total genetic variation was among subpopulations, whereas 82.36% was within subpopulations (S3 Table).

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.