scholarly journals Research Article Genetic parameters and combined selection for seed coat color and macrominerals in Mesoamerican common bean lines

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
N.D. Ribeiro ◽  
H.C. Mezzomo ◽  
G.G. dos Santos
Keyword(s):  
Common Bean ◽  
Seed Coat ◽  
Genetic Parameters ◽  
Coat Color ◽  
Seed Coat Color ◽  
Research Article ◽  
Selection For ◽  
Combined Selection

scholarly journals TECHNOLOGICAL-NUTRITIONAL QUALITY TRAITS AND RELATIONSHIP TO BIOACTIVE COMPOUNDS IN MESOAMERICAN AND ANDEAN BEANS

2021 ◽  
Vol 34 (2) ◽  
pp. 266-275
Author(s):  
NERINÉIA DALFOLLO RIBEIRO ◽  
GREICE ROSANA KLÄSENER ◽  
HENRIQUE CALETTI MEZZOMO ◽  
SKARLET DE MARCO STECKLING
Keyword(s):  
Common Bean ◽  
Bioactive Compounds ◽  
Seed Coat ◽  
Coat Color ◽  
Nutritional Composition ◽  
Quality Traits ◽  
Seed Coat Color ◽  
Technological Quality ◽  
Mineral Concentrations ◽  
Potassium Magnesium

ABSTRACT The common bean exhibits wide genetic variability for technological quality traits, mineral concentrations, and bioactive compounds. For this reason, investigating the correlations between those traits in common bean lines of different gene pools contributes to the progress of biofortification programs. In the present study, two recombinant inbred line populations of Mesoamerican and Andean common bean were evaluated at the F5:7 generation. Technological quality was evaluated based on mass of 100 grains and seed coat color, which was determined using a colorimeter that analyzed the L* (white to black), a* (green to red) and b* (blue to yellow) values. The concentration of six minerals and bioactive compounds (phytates and phenolic compounds) was evaluated and used to characterize the nutritional composition of the lines. The evaluated common bean lines differed (p ≤ 0.05) for all technological quality traits, mineral concentrations, and bioactive compounds, except for the zinc concentration in Andean beans. Seed coat color (L*, a*, and b* values) was highly correlated with most of the evaluated minerals and with the phytates in Mesoamerican beans. In Andean beans, seed coat color (L*, a*, and b* values) was correlated with the concentrations of potassium, magnesium, iron, and phenolic compounds. The nutritional composition of common bean lines of different classes is variable: black beans have higher concentrations of potassium, phosphorus, calcium, zinc, and phytates; carioca beans stand out with high magnesium concentration; and cranberry beans have higher concentrations of potassium, magnesium, and iron.

scholarly journals How does Computer vision compare to standard colorimeter in assessing the seed coat color of common bean (Phaseolus vulgaris L.)?

2019 ◽  
Vol 20 (4) ◽  
pp. 1169-1178
Author(s):  
Filip Varga ◽  
Monika Vidak ◽  
Ksenija Ivanović ◽  
Boris Lazarević ◽  
Ivan Širić ◽  
...  
Keyword(s):  
Computer Vision ◽  
Phaseolus Vulgaris ◽  
Common Bean ◽  
Seed Coat ◽  
Coat Color ◽  
Seed Coat Color ◽  
Phaseolus Vulgaris L

scholarly journals Genetic Mapping and Identification of the Candidate Gene for White Seed Coat in Cucurbita maxima

2021 ◽  
Vol 22 (6) ◽  
pp. 2972
Author(s):  
Yuzi Shi ◽  
Meng Zhang ◽  
Qin Shu ◽  
Wei Ma ◽  
Tingzhen Sun ◽  
...  
Keyword(s):  
Seed Coat ◽  
Molecular Breeding ◽  
Coat Color ◽  
Recessive Gene ◽  
Myb Transcription Factor ◽  
Cucurbita Maxima ◽  
Seed Coat Color ◽  
Rna Seq ◽  
Edible Seed ◽  
Segregating Population

Seed coat color is an important agronomic trait of edible seed pumpkin in Cucurbita maxima. In this study, the development pattern of seed coat was detected in yellow and white seed coat accessions Wuminglv and Agol. Genetic analysis suggested that a single recessive gene white seed coat (wsc) is involved in seed coat color regulation in Cucurbita maxima. An F2 segregating population including 2798 plants was used for fine mapping and a candidate region containing nine genes was identified. Analysis of 54 inbred accessions revealed four main Insertion/Deletion sites in the promoter of CmaCh15G005270 encoding an MYB transcription factor were co-segregated with the phenotype of seed coat color. RNA-seq analysis and qRT-PCR revealed that some genes involved in phenylpropanoid/flavonoid metabolism pathway displayed remarkable distinction in Wuminglv and Agol during the seed coat development. The flanking InDel marker S1548 was developed to predict the seed coat color in the MAS breeding with an accuracy of 100%. The results may provide valuable information for further studies in seed coat color formation and structure development in Cucurbitaceae crops and help the molecular breeding of Cucurbita maxima.

scholarly journals Explore the genetics of weedy traits using rice 3K database

2021 ◽  
Vol 62 (1) ◽  
Author(s):  
Yu-Lan Lin ◽  
Dong-Hong Wu ◽  
Cheng-Chieh Wu ◽  
Yung-Fen Huang
Keyword(s):  
Seed Coat ◽  
Coat Color ◽  
Rice Genome ◽  
Genome Project ◽  
Weedy Rice ◽  
Seed Coat Color ◽  
Allele Mining ◽  
Cultivated Rice ◽  
Population Structure Analysis ◽  
Public Data

Abstract Background Weedy rice, a conspecific weedy counterpart of the cultivated rice (Oryza sativa L.), has been problematic in rice-production area worldwide. Although we started to know about the origin of some weedy traits for some rice-growing regions, an overall assessment of weedy trait-related loci was not yet available. On the other hand, the advances in sequencing technologies, together with community efforts, have made publicly available a large amount of genomic data. Given the availability of public data and the need of “weedy” allele mining for a better management of weedy rice, the objective of the present study was to explore the genetic architecture of weedy traits based on publicly available data, mainly from the 3000 Rice Genome Project (3K-RGP). Results Based on the results of population structure analysis, we have selected 1378 individuals from four sub-populations (aus, indica, temperate japonica, tropical japonica) without admixed genomic composition for genome-wide association analysis (GWAS). Five traits were investigated: awn color, seed shattering, seed threshability, seed coat color, and seedling height. GWAS was conducted for each sub-population × trait combination and we have identified 66 population-specific trait-associated SNPs. Eleven significant SNPs fell into an annotated gene and four other SNPs were close to a putative candidate gene (± 25 kb). SNPs located in or close to Rc were particularly predictive of the occurrence of seed coat color and our results showed that different sub-populations required different SNPs for a better seed coat color prediction. We compared the data of 3K-RGP to a publicly available weedy rice dataset. The profile of allele frequency, phenotype-genotype segregation of target SNP, as well as GWAS results for the presence and absence of awns diverged between the two sets of data. Conclusions The genotype of trait-associated SNPs identified in this study, especially those located in or close to Rc, can be developed to diagnostic SNPs to trace the origin of weedy trait occurred in the field. The difference of results from the two publicly available datasets used in this study emphasized the importance of laboratory experiments to confirm the allele mining results based on publicly available data.

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