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Identification of QTL with large effect on seed weight in a selective population of soybean with genome-wide association and fixation index analyses

Overview of attention for article published in BMC Genomics, July 2017
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Title
Identification of QTL with large effect on seed weight in a selective population of soybean with genome-wide association and fixation index analyses
Published in
BMC Genomics, July 2017
DOI 10.1186/s12864-017-3922-0
Pubmed ID
Authors

Long Yan, Nicolle Hofmann, Shuxian Li, Marcio Elias Ferreira, Baohua Song, Guoliang Jiang, Shuxin Ren, Charles Quigley, Edward Fickus, Perry Cregan, Qijian Song

Abstract

Soybean seed weight is not only a yield component, but also a critical trait for various soybean food products such as sprouts, edamame, soy nuts, natto and miso. Linkage analysis and genome-wide association study (GWAS) are two complementary and powerful tools to connect phenotypic differences to the underlying contributing loci. Linkage analysis is based on progeny derived from two parents, given sufficient sample size and biological replication, it usually has high statistical power to map alleles with relatively small effect on phenotype, however, linkage analysis of the bi-parental population can't detect quantitative trait loci (QTL) that are fixed in the two parents. Because of the small seed weight difference between the two parents in most families of previous studies, these populations are not suitable to detect QTL that have considerable effects on seed weight. GWAS is based on unrelated individuals to detect alleles associated with the trait under investigation. The ability of GWAS to capture major seed weight QTL depends on the frequency of the accessions with small and large seed weight in the population being investigated. Our objective was to identify QTL that had a pronounced effect on seed weight using a selective population of soybean germplasm accessions and the approach of GWAS and fixation index analysis. We selected 166 accessions from the USDA Soybean Germplasm Collection with either large or small seed weight and could typically grow in the same location. The accessions were evaluated for seed weight in the field for two years and genotyped with the SoySNP50K BeadChip containing >42,000 SNPs. Of the 17 SNPs on six chromosomes that were significantly associated with seed weight in two years based on a GWAS of the selective population, eight on chromosome 4 or chromosome 17 had significant Fst values between the large and small seed weight sub-populations. The seed weight difference of the two alleles of these eight significant SNPs varied from 8.1 g to 11.7 g/100 seeds in two years. We also identified haplotypes in three haplotype blocks with significant effects on seed weight. These findings were validated in a panel with 3753 accessions from the USDA Soybean Germplasm Collection. This study highlighted the usefulness of selective genotyping populations coupled with GWAS and fixation index analysis for the identification of QTL with substantial effects on seed weight in soybean. This approach may help geneticists and breeders to more efficiently identify major QTL controlling other traits. The major regions and haplotypes we have identified that control seed weight differences in soybean will facilitate the identification of genes regulating this important trait.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 69 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 19%
Student > Master 8 12%
Student > Doctoral Student 8 12%
Researcher 7 10%
Student > Postgraduate 3 4%
Other 7 10%
Unknown 23 33%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 42%
Biochemistry, Genetics and Molecular Biology 7 10%
Unspecified 1 1%
Psychology 1 1%
Medicine and Dentistry 1 1%
Other 4 6%
Unknown 26 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 July 2017.
All research outputs
#20,434,884
of 22,988,380 outputs
Outputs from BMC Genomics
#9,318
of 10,690 outputs
Outputs of similar age
#272,528
of 312,615 outputs
Outputs of similar age from BMC Genomics
#197
of 225 outputs
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