SoyBase, the USDA-ARS soybean genetics and genomics database

Grant, David M.; Nelson, Rex T.; Cannon, Steven B.; Shoemaker, Randy C.

doi:10.1093/nar/gkp798

Cited by 553 publications

(494 citation statements)

References 5 publications

(5 reference statements)

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“…A 0.05 cM sliding window with 100 bp steps across the whole genome was used for scanning. Individual SNPs were assigned at positions along the genetic map from the SoyBase 45 (http://www. soybase.org/) by assuming uniform recombination between mapped markers.…”

Section: Population Genetics Analysismentioning

confidence: 99%

Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean

et al. 2015

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Section: Population Genetics Analysismentioning

confidence: 99%

Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean

et al. 2015

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“…Análise de variância conjunta e percentuais médios de proteína e óleo de dois grupos de genótipos de soja, em base seca, em quatro ambientes. (Grant et al, 2010). Com os dados moleculares, as distâncias genéticas foram estimadas por meio do complemento do índice de similaridade ponderado, como apresentado a seguir:…”

Section: Methodsunclassified

Divergência em QTLs e variância genética para teores de proteína e óleo em soja

Rodrigues

Arruda

Cruz

et al. 2015

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar a relação entre os parâmetros de divergência em regiões de QTLs e a variância genética em genótipos de soja, quanto aos teores de proteína e óleo nos grãos. Dois grupos de genótipos foram avaliados, em diferentes ambientes, quanto aos teores de proteína e óleo e genotipados com marcadores moleculares de regiões de QTLs. A partir de cada grupo, estabeleceram-se subgrupos por critérios pré-definidos e avaliou-se a relação entre os parâmetros, tendo-se comparado a divergência média e a variância genética entre os subgrupos. Os subgrupos foram definidos com base nos critérios de diferença em divergência média, homogeneidade e heterogeneidade nos subgrupos e proximidade em uma projeção tridimensional da matriz de distância. As percentagens de concordância entre maiores valores de divergência média e de variância genética para o total de subgrupos de cada grupo inicial foram de 72,5 e de 73,4%, respectivamente. Portanto, nestes genótipos, há relação positiva entre as estimativas de divergência em regiões de QTL e variância genética para os teores de proteína e de óleo dos grãos. As distâncias genéticas com base nos marcadores moleculares de regiões de QTLs são eficientes para a predição da variabilidade genética em genótipos de soja para os teores de proteína e de óleo dos grãos.Termos para indexação: Glycine max, distância genética, marcadores moleculares, variabilidade genética. Divergence on QTLs and genetic variance for protein and oil contents in soybeanAbstract -The objective of this work was to evaluate the relationship between the parameters of divergence on QTL regions and the genetic variance in soybean genotypes for protein and oil contents. Two groups of genotypes were evaluated for protein and oil contents in different environments, and genotyped with molecular markers of QTL regions. Subgroups were established by predefined criteria from each group, and the relationship between the parameters was evaluated by comparing mean divergence and genetic variance between the subgroups. The subgroups were defined based on the criteria of difference of mean divergence, homogeneity and heterogeneity in the subgroups, and proximity in a three-dimensional projection of the distance matrix. Percentage agreement between the greater values for mean divergence and genetic divergence for the subgroup total of each initial group was 72.5 and 73.4%, respectively. Therefore, there is a positive relationship between the estimates on QTL regions and genetic variance for protein and oil contents in grains in these genotypes. The genetic distances based on molecular markers of QTL regions are efficient to predict the genetic variability in soybean genotypes, for protein and oil contents.

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“…These microsatellite markers were selected for analysis by being located on QTL regions for protein content in soybean. The QTLs for protein content in the region of the molecular markers are reported at SoyBase (Grant, Nelson, Cannon & Shoemaker, 2010) on linkage groups (LG) A1, B2, C1, C2, D1a, D2, E, F, I, K, L and M, respectively.…”

Section: Yijk=μ+gi+ej+geij+b⁄ejk +εIjk Equationmentioning

confidence: 99%

“…The genetic distance (Bonato, Calvo, Arias, Toledo & Geraldi, 2006) estimates obtained by the complement of the weighted similarity index from the 39 microsatellite markers were also compared to the estimates obtained by the same index from the microsatellites of linkage group I, linkage group most related to the protein content according to the literature (Grant et al, 2010). All mentioned analyzes were performed with the sotware GENES ® (Cruz, 2013), to identify significant difference among treatments and statistical significance for all comparisons was made at p<0.05.…”

Section: Equationmentioning

confidence: 99%

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et al. 2017

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Pre-mejoramiento vegetal para el volumen incrementado de proteína en soya Glycine max (L. ) AbstractThe aim of this research were to determine protein content and genetic divergence of soybean genotypes in QTL regions of the trait grain protein content for plant breeding purposes. Twenty-nine soybean genotypes were grown in the field in four environments (Viçosa-MG Dec/2009, Visconde do Rio Branco-MG Feb/2010, São Gotardo-MG Feb/2010, and São Gotardo-MG Oct/2011) using the randomized block design with three replicates. The grain protein content was quantified by the infrared spectrometry method. The genetic divergence was estimated by the analysis of 39 microsatellite markers from QTL regions for the soybean grain protein content. The pairs of genotypes with greater genetic distances and protein contents were as follows: BARC-8/CS3032PTA276-3-4 (D=0.71) (45.18%/44.31%); BARC-8/CS3032PTA276-1-2 (D=0.71) (45.18%/43.75%); BARC-8/CS3032PTA190-5-1 (D=0.71) (45.18%/43.63%); B3PTA382-2-10/CS3032PTA276-3-4 (D=0.62) (43.80%/44.31%); CS3032PTA276-1-2/ B3PTA382-2-10 (D=0.62) (43.75%/43.80%); B3PTA382-2-10/CS3032PTA190-5-1 (D=0.62) (43.80%/43.63%); B3PTA216-1-9/CS3032PTA276-3-4 (D=0.61) (43.48%/44.31%); and B3PTA216-1-9/CS3032PTA276-1-2 (D=0.61) (43.48%/43.75%), respectively. Other promising combinations for the improvement of protein content were as follows: BR8014887/CS3032PTA190-5-1 (D=0.57) (44.71%/43.63%); BARC-8/CS3032PTA182 (D=0.78) (45.18%/41.84%); BR8014887/CS3032PTA182 (D=0.65) (44.71%/41.84%); BR8014887/PI417360 (D=0.76) (44.71%/41.01%), respectively; and PI417360/B3PTA216-1-9 (D=0.80) (41.01%/43.48%). These pairs of genotypes when crossed should produce populations with higher means and genetic variances and greater gains with selection.

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SoyBase, the USDA-ARS soybean genetics and genomics database

Cited by 553 publications

References 5 publications

Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean

Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean

Divergência em QTLs e variância genética para teores de proteína e óleo em soja

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