scholarly journals Navy Bean Canning Quality: Correlations, Heritability Estimates, and Randomly Amplified Polymorphic DNA Markers Associated with Component Traits

1997 ◽  
Vol 122 (3) ◽  
pp. 338-343 ◽  
Author(s):  
Kimberly J. Walters ◽  
George L. Hosfield ◽  
Mark A. Uebersax ◽  
James D. Kelly
Keyword(s):  
Dna Markers ◽  
Linear Models ◽  
Random Amplified Polymorphic Dna ◽  
Recombinant Inbred Lines ◽  
Coefficient Of Determination ◽  
Phenotypic Traits ◽  
Multiple Traits ◽  
Navy Bean ◽  
Heritability Estimates ◽  
Canning Quality

Three populations of navy bean (Phaseolus vulgaris L.), consisting of recombinant inbred lines, were grown at two locations for 2 years and were used to study canning quality. The traits measured included visual appeal (VIS), texture (TXT), and washed drained mass (WDM). Genotype mean squares were significant for all three traits across populations, although location and year mean squares were higher. We found a positive correlation (r = 0.19 to 0.66) between VIS and TXT and a negative correlation (r = -0.26 to -0.66) between VIS and WDM and between TXT and WDM (r = -0.53 to -0.83) in all three populations. Heritability estimates were calculated for VIS, TXT, and WDM, and these values were moderate to high (0.48 to 0.78). Random amplified polymorphic DNA markers associated with quantitative trait loci (QTL) for the same canning quality traits were identified and studied in each population. Marker-QTL associations were established using the general linear models procedure with significance set at P=0.05. Location and population specificity was common among the marker-QTL associations identified. Coefficient of determination (R2) values for groups of markers used in multiple regression analyses ranged from 0.2 to 0.52 for VIS, 0.11 to 0.38 for TXT, and 0.25 to 0.38 for WDM. Markers were identified that were associated with multiple traits and those associations supported correlations between phenotypic traits. MAS would offer no advantage over phenotypic selection for the improvement of negatively associated traits.

scholarly journals Quantitative Trait Loci (QTL) Analysis of Canning Quality Traits in Kidney Bean (Phaseolus vulgaris L.)

2002 ◽  
Vol 127 (4) ◽  
pp. 608-615 ◽  
Author(s):  
Maria-Carmela T. Posa-Macalincag ◽  
George L. Hosfield ◽  
Kenneth F. Grafton ◽  
Mark A. Uebersax ◽  
James D. Kelly
Keyword(s):  
Phaseolus Vulgaris ◽  
Linkage Group ◽  
Rapd Markers ◽  
Random Amplified Polymorphic Dna ◽  
Recombinant Inbred Lines ◽  
Kidney Bean ◽  
Major Genes ◽  
The Core ◽  
Two Populations ◽  
Canning Quality

Canning quality of dry bean (Phaseolus vulgaris L.), of which the degree of splitting (SPLT) and overall appearance (APP) of canned beans are major components, is a complex trait that exhibits quantitative inheritance. The objectives of this study were to identify major genes that affect APP and SPLT in kidney bean, and map the location of these loci to the integrated core map of common bean. The analysis was performed using random amplified polymorphic DNA (RAPD) markers and two populations of kidney bean, consisting of 75 and 73 recombinant inbred lines (RILs), respectively. The two populations—`Montcalm' × `California Dark Red Kidney 82' and `Montcalm' × `California Early Light Red Kidney'—were planted in six year-location combinations in Michigan, Minnesota and North Dakota from 1996 to 1999. Correlations between APP and SPLT were high (0.91 to 0.97). Heritability estimates for APP and SPLT ranged from 0.83 to 0.85 in the two populations. Major genes for these traits were identified on two linkage groups. The first QTL, associated with seven RAPD markers, was putatively mapped to the B8 linkage group of the core bean linkage map. Desirable canning quality appeared to be derived from Montcalm at this locus. The second QTL, associated with four markers, appeared to be derived from the California parents. The second linkage group was not assigned to a linkage group in the core map. Population and environment-specificity were observed for the markers identified.

scholarly journals Prediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation study

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0257213
Author(s):  
Antônio Carlos da Silva Júnior ◽  
Michele Jorge da Silva ◽  
Cosme Damião Cruz ◽  
Isabela de Castro Sant’Anna ◽  
Gabi Nunes Silva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Plant Breeding ◽  
Computational Intelligence ◽  
Coefficient Of Determination ◽  
Phenotypic Traits ◽  
Linkage Groups ◽  
Breeding Programs ◽  
A Genome ◽  
Heritability Estimates ◽  
Differential Action

The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant breeding. Data of an F2 population represented by 500 individuals, obtained from a cross between contrasting homozygous parents, were simulated. Phenotypic traits were simulated based on previously established means and heritability estimates (30%, 50%, and 80%); traits were distributed in a genome with 10 linkage groups, considering two alleles per marker. Four different scenarios were considered. For the principal trait, heritability was 50%, and 40 control loci were distributed in five linkage groups. Another phenotypic control trait with the same complexity as the principal trait but without any genetic relationship with it and without pleiotropy or a factorial link between the control loci for both traits was simulated. These traits shared a large number of control loci with the principal trait, but could be distinguished by the differential action of the environment on them, as reflected in heritability estimates (30%, 50%, and 80%). The coefficient of determination were considered to evaluate the proposed methodologies. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the tested traits. Computational intelligence and machine learning were superior in extracting nonlinear information from model inputs and quantifying the relative contributions of phenotypic traits. The R2 values ranged from 44.0% - 83.0% and 79.0% - 94.0%, for computational intelligence and machine learning, respectively. In conclusion, the relative contributions of auxiliary traits in different scenarios in plant breeding programs can be efficiently predicted using computational intelligence and machine learning.

scholarly journals Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction

2021 ◽  
Vol 11 (13) ◽  
pp. 6030
Author(s):  
Daljeet Singh ◽  
Antonella B. Francavilla ◽  
Simona Mancini ◽  
Claudio Guarnaccia
Keyword(s):  
Machine Learning ◽  
Linear Models ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Volume Percentage

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

Comparison of various multivariate models to estimate structural properties by means of non-destructive techniques (NDTs) in Pinus sylvestris L. timber

Holzforschung ◽  
2019 ◽  
Vol 73 (4) ◽  
pp. 331-338
Author(s):  
Antonio Villasante ◽  
Guillermo Íñiguez-González ◽  
Lluis Puigdomenech
Keyword(s):  
Pinus Sylvestris ◽  
Linear Models ◽  
Longitudinal Vibration ◽  
Modulus Of Rupture ◽  
Coefficient Of Determination ◽  
Pullout Resistance ◽  
Density Prediction ◽  
Learning Software ◽  
Artificial Neural Network Ann ◽  
Non Destructive

AbstractThe predictability of modulus of elasticity (MOE), modulus of rupture (MOR) and density of 120 samples of Scots pine (Pinus sylvestrisL.) were investigated using various non-destructive variables (such as time of flight of stress wave, natural frequency of longitudinal vibration, penetration depth, pullout resistance, visual grading and concentrated knot diameter ratio), and based on multivariate algorithms, applying WEKA as machine learning software. The algorithms used were: multivariate linear regression (MLR), Gaussian, Lazy, artificial neural network (ANN), Rules and decision Tree. The models were quantified based on the root-mean-square error (RMSE) and the coefficient of determination (R2). To avoid model overfitting, the modeling was built and the results validated via the so-called 10-fold cross-validation. MLR with the “greedy method” for variable selection based on the Akaike information metric (MLRak) significantly reduced the RMSE of MOR and MOE compared to univariate linear regressions (ULR). However, this reduction was not significant for density prediction. The predictability of MLRak was not improved by any other of the tested algorithms. Specifically, non-linear models, such as multilayer perceptron, did not contribute any significant improvements over linear models. Finally, MLRak models were simplified by discarding the variables that produce the lowest RMSE increment. The resulted models could be even further simplified without significant RMSE increment.

scholarly journals Nonlinear modeling growth body weight of Mangalarga Marchador horses

2017 ◽  
Vol 47 (4) ◽  
Author(s):  
Felipe Amorim Caetano Souza ◽  
Tales Jesus Fernandes ◽  
Raquel Silva de Moura ◽  
Sarah Laguna Conceição Meirelles ◽  
Rafaela Aparecida Ribeiro ◽  
...  
Keyword(s):  
Body Weight ◽  
Linear Models ◽  
Nonlinear Modeling ◽  
Growth Curves ◽  
Live Weight ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Cross Sectional ◽  
Adult Body Weight ◽  
The Cross

ABSTRACT: The analysis of the growth and development of various species has been done using the growth curves of the specific animal based on non-linear models. The objective of the current study was to evaluate the fit of the Brody, Gompertz, Logistic and von Bertalanffy models to the cross-sectional data of the live weight of the MangalargaMarchador horses to identify the best model and make accurate predictions regarding the growth and maturity in the males and females of this breed. The study involved recording the weight of 214 horses, of which 94 were males and 120 were non-pregnant females, between 6 and 153 months of age. The parameters of the model were estimated by employing the method of least squares, using the iteratively regularized Gauss-Newton method and the R software package. Comparison of the models was done based on the following criteria: coefficient of determination (R²); Residual Standard Deviation (RSD); corrected Akaike Information Criterion (AICc). The estimated weight of the adult horses by the models ranged between 431kg and 439kg for males and between 416kg and 420kg for females. The growth curves were studied using the cross-sectional data collection method. For males the von Bertalanffymodel was found to be the most effective in expressing growth, while in females the Brody model was more suitable. The MangalargaMarchador females achieve adult body weight earlier than the males.

Genotype and environment effect on canning quality of dry bean grown in Saskatchewan

1999 ◽  
Vol 79 (3) ◽  
pp. 335-342 ◽  
Author(s):  
P. Balasubramanian ◽  
A. Slinkard ◽  
R. Tyler ◽  
A. Vandenberg
Keyword(s):  
Environment Interaction ◽  
Quality Traits ◽  
Navy Bean ◽  
Dry Bean ◽  
Black Bean ◽  
Genotype Environment Interaction ◽  
Pinto Bean ◽  
Bean Genotype ◽  
Early Fall ◽  
Canning Quality

Canning quality traits of dry bean are affected by both the genotype and the environment. This study was conducted to determine the effects of genotype, environment and the genotype × environment interaction on canning quality traits of selected navy bean, black bean and pinto bean cultivars. Three cultivars each of navy bean and black bean and two cultivars of pinto bean were grown at several sites across Saskatchewan in the summer of 1995 and 1996. Dry bean seed samples from five sites for navy bean, four sites for black bean and six sites for pinto bean grown over 2 yr were evaluated for canning quality traits using a modified laboratory canning protocol. The cultivar effect was significant for most canning quality traits in all three bean classes. For most canning quality traits, the cultivar × year × site interaction variance predominated over the corresponding cultivar × year or cultivar × site variances and, hence, the first order interactions were considered relatively unimportant. The occurrence of early fall frost at several sites resulted in frost-damaged seed, which affected both the genetic and environmental effects on the canning quality traits. Identification of cultivar × site interactions for a few canning quality traits does not justify dividing the province into subareas for breeding and testing purposes. Key words: Phaseolus vulgaris, common bean, genotype, environment, canning quality

Dynamic Predictive Model for Growth of Bacillus cereus from Spores in Cooked Beans

2017 ◽  
Vol 81 (2) ◽  
pp. 308-315 ◽  
Author(s):  
Vijay K. Juneja ◽  
Abhinav Mishra ◽  
Abani K. Pradhan
Keyword(s):  
Bacillus Cereus ◽  
Growth Rates ◽  
Linear Models ◽  
Growth Models ◽  
Maximum Growth ◽  
Effect Of Temperature ◽  
Coefficient Of Determination ◽  
Growth Data ◽  
Three Phase ◽  
Primary Model

ABSTRACT Kinetic growth data for Bacillus cereus grown from spores were collected in cooked beans under several isothermal conditions (10 to 49°C). Samples were inoculated with approximately 2 log CFU/g heat-shocked (80°C for 10 min) spores and stored at isothermal temperatures. B. cereus populations were determined at appropriate intervals by plating on mannitol–egg yolk–polymyxin agar and incubating at 30°C for 24 h. Data were fitted into Baranyi, Huang, modified Gompertz, and three-phase linear primary growth models. All four models were fitted to the experimental growth data collected at 13 to 46°C. Performances of these models were evaluated based on accuracy and bias factors, the coefficient of determination (R2), and the root mean square error. Based on these criteria, the Baranyi model best described the growth data, followed by the Huang, modified Gompertz, and three-phase linear models. The maximum growth rates of each primary model were fitted as a function of temperature using the modified Ratkowsky model. The high R2 values (0.95 to 0.98) indicate that the modified Ratkowsky model can be used to describe the effect of temperature on the growth rates for all four primary models. The acceptable prediction zone (APZ) approach also was used for validation of the model with observed data collected during single and two-step dynamic cooling temperature protocols. When the predictions using the Baranyi model were compared with the observed data using the APZ analysis, all 24 observations for the exponential single rate cooling were within the APZ, which was set between −0.5 and 1 log CFU/g; 26 of 28 predictions for the two-step cooling profiles also were within the APZ limits. The developed dynamic model can be used to predict potential B. cereus growth from spores in beans under various temperature conditions or during extended chilling of cooked beans.

scholarly journals Genetic basis of phenotypic plasticity and genotype x environment interaction in a multi-parental population

2020 ◽  
Author(s):  
Isidore Diouf ◽  
Laurent Derivot ◽  
Shai Koussevitzky ◽  
Yolande Carretero ◽  
Frédérique Bitton ◽  
...  
Keyword(s):  
Phenotypic Plasticity ◽  
Genetic Architecture ◽  
Genetic Basis ◽  
Linear Models ◽  
Global Climate ◽  
Functional Characterization ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Multiple Traits ◽  
Genotype X Environment

AbstractDeciphering the genetic basis of phenotypic plasticity and genotype x environment interaction (GxE) is of primary importance for plant breeding in the context of global climate change. Tomato is a widely cultivated crop that can grow in different geographical habitats and which evinces a great capacity of expressing phenotypic plasticity. We used a multi-parental advanced generation intercross (MAGIC) tomato population to explore GxE and plasticity for multiple traits measured in a multi-environment trial (MET) design comprising optimal cultural conditions and water deficit, salinity and heat stress over 12 environments. Substantial GxE was observed for all the traits measured. Different plasticity parameters were estimated through the Finlay-Wilkinson and factorial regression models and used together with the genotypic means for quantitative trait loci (QTL) mapping analyses. Mixed linear models were further used to investigate the presence of interactive QTLs (QEI). The results highlighted a complex genetic architecture of tomato plasticity and GxE. Candidate genes that might be involved in the occurrence of GxE were proposed, paving the way for functional characterization of stress response genes in tomato and breeding for climate-adapted crop.HighlightThe genetic architecture of tomato response to several abiotic stresses is deciphered. QTL for plasticity and QTL x Environment were identified in a highly recombinant MAGIC population.

scholarly journals Assessment of genetic diversity among surian Toona sinensis Roem in progenies test using random amplified polymorphic DNA markers

2018 ◽  
Vol 22 (1) ◽  
pp. 22
Author(s):  
Jayusman Jayusman ◽  
Muhammad Na’iem ◽  
Sapto Indrioko ◽  
Eko Bhakti Hardiyanto ◽  
ILG Nurcahyaningsih
Keyword(s):  
Genetic Diversity ◽  
Dna Markers ◽  
Rapd Markers ◽  
Random Amplified Polymorphic Dna ◽  
Management Strategies ◽  
Genetic Distances ◽  
Sampled Data ◽  
Toona Sinensis ◽  
The Mean ◽  
Individual Trees

Surian Toona sinensis Roem is one of the most widely planted species in Indonesia. This study aimed to estimate the genetic diversity between a number of surian populations in a progeny test using RAPD markers, with the goal of proposing management strategies for a surian breeding program. Ninety-six individual trees from 8 populations of surian were chosen as samples for analysis. Eleven polymorphic primers (OP-B3, OP-B4, OP-B10, OP-H3, OP-Y6, OP-Y7, OP-Y8, OP-Y10, OP-Y11, OP-Y14, and OP-06) producing reproducible bands were analyzed for the 96 trees, with six trees per family sampled. Data were analyzed using GenAlEx 6.3, NTSYS 2.02. The observed percentage of polymorphic loci ranged from 18.2% to 50%. The mean level of genetic diversity among the surian populations was considered to be moderate (He 0.304). Cluster analysis grouped the genotypes into two main clusters, at similarity levels of 0.68 and 0.46. The first two axes of the PCoA explained 46.16% and 25.54% of the total variation, respectively. The grouping of samples into clusters and subclusters did not correspond with family and their distances, but the grouping was in line with the genetic distances of the samples.

Sign in / Sign up

Export Citation Format

Share Document