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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

文献类型： 外文期刊

作者： Yan, Long 1 ; Hofmann, Nicolle 2 ; Li, Shuxian 3 ; Ferreira, Marcio Elias 4 ; Song, Baohua 5 ; Jiang, Guoliang 6 ; Ren 1 ;

作者机构： 1.Hebei Acad Agr & Forestry Sci, Shijiazhuang Branch Natl Soybean Improvement Ctr, Inst Cereal & Oil Crops, Key Lab Crop Genet & Breeding Hebei, Shijiazhuang 050035, Hebei, Peoples R China

2.USDA ARS, Soybean Genom & Improvement Lab, 10300 Baltimore Ave,Bldg 006, Beltsville, MD 20705 USA

3.USDA ARS, Crop Genet Res Unit, Stoneville, MS 38776 USA

4.EMBRAPA, Genet Resources & Biotechnol, CP 02372, Brasilia, DF, Brazil

5.Univ North Carolina Charlotte, Dept Biol Sci, Charlotte, NC 28223 USA

6.Virginia State Univ, Agr Res Stn, POB 9061, Petersburg, VA 23806 USA

7.Or

关键词： Soybean;Selective population;Seed weight;GWAS;SNP;Fixation index analysis

期刊名称：BMC GENOMICS （ 影响因子：3.969； 五年影响因子：4.478 ）

ISSN： 1471-2164

年卷期： 2017 年 18 卷

页码：

收录情况： SCI

摘要： Background: 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. Results: 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. Conclusion: 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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