Genomic Selection for Yield and Seed Protein Content in Soybean: A Study of Breeding Program Data and Assessment of Prediction Accuracy

Crop Science ◽  
2017 ◽  
Vol 57 (3) ◽  
pp. 1325-1337 ◽  
Author(s):  
Alexandra Duhnen ◽  
Amandine Gras ◽  
Simon Teyssèdre ◽  
Michel Romestant ◽  
Bruno Claustres ◽  
...  
Keyword(s):  
Protein Content ◽  
Genomic Selection ◽  
Prediction Accuracy ◽  
Seed Protein ◽  
Breeding Program ◽  
Seed Protein Content ◽  
Selection For ◽  
Program Data

Effects of divergent selection for seed protein content in high-protein vs. food-grade populations of early maturity soybean

2013 ◽  
Vol 133 (1) ◽  
pp. 74-79 ◽  
Author(s):  
Takashi Sato ◽  
Melanie Van Schoote ◽  
Helmut Wagentristl ◽  
Johann Vollmann
Keyword(s):  
Protein Content ◽  
Seed Protein ◽  
Divergent Selection ◽  
High Protein ◽  
Seed Protein Content ◽  
Early Maturity ◽  
Food Grade ◽  
Selection For

EARLY GENERATION SELECTION FOR PROTEIN IN Glycine max × G. soja CROSSES

1981 ◽  
Vol 61 (4) ◽  
pp. 901-908 ◽  
Author(s):  
L. R. ERICKSON ◽  
W. D. BEVERSDORF ◽  
H. D. VOLDENG
Keyword(s):  
Protein Content ◽  
Seed Protein ◽  
Glycine Soja ◽  
Mass Selection ◽  
Seed Protein Content ◽  
Family Selection ◽  
Early Generation ◽  
Early Generation Selection ◽  
The Mean ◽  
Selection For

Early generation selection for high seed-protein content would be desirable in breeding programs attempting to combine high yield and high protein content in soybeans (Gycine max (L.) Merr.). The heritability of seed-protein content in the F2 and F3 was measured in four crosses and mass and family selection for protein were compared in the F3 of these crosses. Glycine soja Sieb. and Zucc., a putative wild ancestor of the soybean, was the male parent in all crosses and selection intensity was at 10%. The heritability of protein in the F2, calculated by parent-offspring regression, was 27%. The broad-sense heritability of protein in F3 families grown over two locations in one season was 78%. Mean protein content for every selected population was greater (P ≤ 0.01) than the mean for the non-selected control population (45.3%). Mean protein contents of the F4 populations resulting from these selection methods were: mass selection in the F2 and F3, 48.0%; mass selection in the F3 following one generation of single seed descent, 48.8%; selection among F3 families, 47.6%; selection among and within F3 families, 47.5%. Either method of mass selection was superior to either method of family selection at the 0.01 level. Although early selection (mass or family) raised the mean protein content of a population, segregation continued to give rise to low-protein genotypes thereby requiring further selection.

EFFECT OF SELECTION FOR PROTEIN ON LENGTHS OF GROWTH STAGES IN Glycine max × Glycine soja CROSSES

1982 ◽  
Vol 62 (2) ◽  
pp. 293-298 ◽  
Author(s):  
L. R. ERICKSON ◽  
W. D. BEVERSDORF
Keyword(s):  
Glycine Max ◽  
Protein Content ◽  
Growth Stage ◽  
Seed Protein ◽  
Glycine Soja ◽  
Growth Stages ◽  
Seed Protein Content ◽  
Glycine Max L ◽  
The Mean ◽  
Selection For

The effect of selection for high seed protein content on plant development in soybeans (Glycine max (L.) Merr.) was investigated by comparing the lengths of growth stages of four selected populations with those of a nonselected control. Each population, grown at two locations in one season, was a composite of four crosses between G. max and its putative wild ancestor, Glycine soja Sieb. and Zucc. The growth stages were planting to emergence, emergence to flowering and flowering to maturity. The length of each growth stage was measured in soybean development units (SDU) and in days. On average, populations selected for protein alone required more SDUs from planting to emergence and from emergence to flowering and fewer SDUs from flowering to maturity than did the control. The mean temperature in the stage from flowering to maturity was negatively correlated with protein content in all populations. The correlations ranged from −0.22* to −0.34**.

Shikimate Dehydrogenase ‐1 2 Allozyme as a Marker for High Seed Protein Content in Rice

Crop Science ◽  
1990 ◽  
Vol 30 (4) ◽  
pp. 937-940 ◽  
Author(s):  
V. V. Shenoy ◽  
D. V. Seshu ◽  
J. K. S. Sachan
Keyword(s):  
Protein Content ◽  
Seed Protein ◽  
Seed Protein Content ◽  
Shikimate Dehydrogenase

scholarly journals Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 210
Author(s):  
Sang V. Vu ◽  
Cedric Gondro ◽  
Ngoc T. H. Nguyen ◽  
Arthur R. Gilmour ◽  
Rick Tearle ◽  
...  
Keyword(s):  
Moisture Content ◽  
Genomic Selection ◽  
Shell Length ◽  
Prediction Accuracy ◽  
Genomic Information ◽  
Morphometric Traits ◽  
Shell Weight ◽  
Shell Width ◽  
Crassostrea Angulata ◽  
Selection For

Genomic selection has been widely used in terrestrial animals but has had limited application in aquaculture due to relatively high genotyping costs. Genomic information has an important role in improving the prediction accuracy of breeding values, especially for traits that are difficult or expensive to measure. The purposes of this study were to (i) further evaluate the use of genomic information to improve prediction accuracies of breeding values from, (ii) compare different prediction methods (BayesA, BayesCπ and GBLUP) on prediction accuracies in our field data, and (iii) investigate the effects of different SNP marker densities on prediction accuracies of traits in the Portuguese oyster (Crassostrea angulata). The traits studied are all of economic importance and included morphometric traits (shell length, shell width, shell depth, shell weight), edibility traits (tenderness, taste, moisture content), and disease traits (Polydora sp. and Marteilioides chungmuensis). A total of 18,849 single nucleotide polymorphisms were obtained from genotyping by sequencing and used to estimate genetic parameters (heritability and genetic correlation) and the prediction accuracy of genomic selection for these traits. Multi-locus mixed model analysis indicated high estimates of heritability for edibility traits; 0.44 for moisture content, 0.59 for taste, and 0.72 for tenderness. The morphometric traits, shell length, shell width, shell depth and shell weight had estimated genomic heritabilities ranging from 0.28 to 0.55. The genomic heritabilities were relatively low for the disease related traits: Polydora sp. prevalence (0.11) and M. chungmuensis (0.10). Genomic correlations between whole weight and other morphometric traits were from moderate to high and positive (0.58–0.90). However, unfavourably positive genomic correlations were observed between whole weight and the disease traits (0.35–0.37). The genomic best linear unbiased prediction method (GBLUP) showed slightly higher accuracy for the traits studied (0.240–0.794) compared with both BayesA and BayesCπ methods but these differences were not significant. In addition, there is a large potential for using low-density SNP markers for genomic selection in this population at a number of 3000 SNPs. Therefore, there is the prospect to improve morphometric, edibility and disease related traits using genomic information in this species.

scholarly journals Modeling the Competitive Effect of Euphorbia dracunculoides and Astragalus sp. in Zero Input Rainfed Chickpea

2018 ◽  
Vol 36 (0) ◽  
Author(s):  
R.M. IKRAM ◽  
A. TANVEER ◽  
R. MAQBOOL ◽  
M.A. NADEEN
Keyword(s):  
Protein Content ◽  
Critical Period ◽  
Field Experiments ◽  
Seed Protein ◽  
Yield Loss ◽  
Seed Protein Content ◽  
Yield Losses ◽  
Competition Period ◽  
Crop Competition ◽  
Weed Removal

ABSTRACT: Brown chickpea (Cicer arietinum L.) is one of the two chickpea types grown in Pakistan and other countries. The critical period for weed removal in a rainfed chickpea system is an important consideration in devising weed management strategies. Field experiments were conducted in the winter season of 2011 and 2012 to determine the extent of yield loss with different periods of weed crop competition. Seven weed crop competition periods (0, 45, 60, 75, 90, 105 and 160 days after sowing - DAS) were used to identify the critical period for weed removal in rainfed chickpea. Experimental plots were naturally infested with Euphorbia dracunculoides and Astragalus sp. in both years. Individual, composite density and dry weights of E. dracunculoides and Astragalussp. increased significantly with an increase in the competition period. However, yield and yield-contributing traits of chickpea significantly decreased with an increase in the competition period. Chickpea seed yield loss was 11-53% in different weed crop competition periods. Euphorbia dracunculoides and Astragalus sp. removed 39.9 and 36.9 kg ha-1 of N, 9.61 and 7.27 kg ha-1 of P and 38.3 and 36.9 kg ha-1 of K, respectively. Season long weed competition (160 days after sowing) resulted in 19.5% seed protein content compared with 24.5% seed protein content in weed-free chickpea. A Logistic equation was fitted to yield data in response to increasing periods of weed crop competition. The critical timing of weed removal at 5 and 10% acceptable yield losses were 26 and 39 DAS, respectively. The observed critical period suggests that in rainfed chickpea, a carefully timed weed removal could prevent grain yield losses.

scholarly journals A Rapid Method for Measuring Seed Protein Content in Cowpea (<i>Vigna unguiculata</i> (L.) Walp)

2017 ◽  
Vol 08 (10) ◽  
pp. 2387-2396 ◽  
Author(s):  
Yuejin Weng ◽  
Ainong Shi ◽  
Waltram Second Ravelombola ◽  
Wei Yang ◽  
Jun Qin ◽  
...  
Keyword(s):  
Protein Content ◽  
Rapid Method ◽  
Vigna Unguiculata ◽  
Seed Protein ◽  
Seed Protein Content

Performance of multi-trait genomic selection for Eucalyptus robusta breeding program

2018 ◽  
Vol 14 (5) ◽  
Author(s):  
Tahina Rambolarimanana ◽  
Lolona Ramamonjisoa ◽  
Daniel Verhaegen ◽  
Jean-Michel Leong Pock Tsy ◽  
Laval Jacquin ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Breeding Program ◽  
Selection For

scholarly journals Intra-plant variation in seed weight and seed protein content of cowpea

2019 ◽  
Vol 23 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Kohtaro Iseki ◽  
Olajumoke Olaleye ◽  
Haruki Ishikawa
Keyword(s):  
Protein Content ◽  
Seed Weight ◽  
Seed Protein ◽  
Seed Protein Content ◽  
Plant Variation
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