scholarly journals Additive genetic prediction for F3 families of common black beans (Phaseolus vulgaris L.) using linear mixed models

2019 ◽  
pp. 1950-1958
Author(s):  
Ivan Ricardo Carvalho ◽  
Vinícius Jardel Szareski ◽  
Luiz Leonardo Ferreira ◽  
Gustavo Henrique Demari ◽  
Maurício Horbach Barbosa ◽  
...  
Keyword(s):  
Plant Height ◽  
Mixed Models ◽  
Variance Components ◽  
Seed Mass ◽  
Phaseolus Vulgaris L ◽  
Future Studies ◽  
Black Beans ◽  
Genetic Value ◽  
Additive Genetic Effects ◽  
Selection Of

The aim of study is to estimate the variance components and genetic parameters (REML), as well as to predict the genetic value (BLUP) of F3 families of common black beans for the components of seed production. The experimental design was augmented blocks, where the 83 F3 families were arranged only in each block, and the commercial controls were organized in three replicates. The additive genetic effects were determinant for plant height and first pod insertion height. The pronounced effects of the environment are expressed for the number of pods, seeds and seed mass per plant. Potentiality in the selection of higher F3 families are revealed through the 2CBRS population for plant height and first pod insertion height, for the components of seed yield the selections should be directed to the populations 2CARS and 1FVRS. The number of seeds and seed mass per plant were potentiated in more than 28% of the selected F3 families, and pronounceable genetic gains are obtained by the selection of families 66, 65 and 67. The inferences obtained in this study present theoretical and practical foundation, and can be applied in future studies of breeding and production of common black bean seeds.

scholarly journals REML/BLUP applied to characterize important agronomic traits in segregating generations of bean (Phaseolus vulgaris L.)

2020 ◽  
pp. 391-399
Author(s):  
Ivan Ricardo Carvalho ◽  
José Antonio Gonzalez da Silva ◽  
Luiz Leonardo Ferreira ◽  
Vinícius Jardel Szareski ◽  
Gustavo Henrique Demari ◽  
...  
Keyword(s):  
Agronomic Traits ◽  
Seed Mass ◽  
Genetic Contribution ◽  
Phaseolus Vulgaris L ◽  
Genetic Studies ◽  
Black Bean ◽  
Quantitative Genetic ◽  
Genetic Value ◽  
Genetic Profiles ◽  
Selection Of

The objective of this work was to estimate and predict the genetic gains through the REML/BLUP methodology for the characters of agronomic interest of common black beanas at segregating generations. The experiments were conducted in 2015, 2016 and 2017 in experimental design consisted of augmented blocks in four replicates. Superior genetic variability was identified for the first pod insertion height and plant height in the F4 segregating generation. For the number of pods, seeds and seed mass per plant, genetic contribution superior to the phenotype is expressed in F3 segregating generation. The prediction of genetic value defines that the number of pods, seeds and seed mass per plant can be potentiated in F2 segregating generation by selection of population 26, selection of F3families 64 and 49, F4 families 33 and 19. Through the estimations and genetic predictions of the characters measured in the segregating generations F2, F3 and F4 it is possible to define six multivariate genetic profiles. These strategies can be used in quantitative genetic studies aimed at increasing the production of common black bean seeds.

scholarly journals Genetic progress in maize from advanced cycles of reciprocal recurrent selection through REML/BLUP

2020 ◽  
pp. 1295-1301
Author(s):  
José Arantes Ferreira Júnior ◽  
Gabriel Moreno Bernardo Gonçalves ◽  
Jocarla Ambrosim Crevelari ◽  
Julio Cesar Fiorio Vettorazzi ◽  
Vivane Mirian Lanhellas Gonçalves ◽  
...  
Keyword(s):  
Grain Yield ◽  
Mixed Models ◽  
Variance Components ◽  
Genetic Parameters ◽  
Recurrent Selection ◽  
Yield Potential ◽  
Reciprocal Recurrent Selection ◽  
Genetic Progress ◽  
Advanced Cycles ◽  
Selection Of

Breeding methods such as reciprocal recurrent selection (RRS) is very important alternative to breeders. This strategy, besides allowing continuous genetic progress, helps maintaining the genetic variability of populations. The aim of the present study was to estimate genetic parameters, as well as to predict selection gains in advanced cycles of reciprocal recurrent selection of maize progenies through mixed models. Two selection cycles were considered. We evaluated 196 and 169 progenies in the 15 and 16 cycles, respectively. The yield potential of the progenies was evaluated from the following characteristics: grain yield, number of ears, weight of ears and weight of 100 seeds. The estimate of variance components and the prediction of genetic values were based on the REML/BLUP method. Overall, the genetic parameters were increased in the 16th cycle estimates compared to the previous cycle. It is worth highlighting that heritability coefficient for the grain yield was (0.81) in the 16th cycle, since it resulted in 21% of selection gain. Results showed that, even after sixteen recurrent selection cycles, the populations under selection have high potential for satisfactory genetic gains.

scholarly journals Simultaneous selection for yield, stability, and adaptability of carioca and black beans

2018 ◽  
Vol 53 (6) ◽  
pp. 736-745 ◽  
Author(s):  
Paulo Ricardo dos Santos ◽  
Kleyton Danilo da Silva Costa ◽  
Maxwel Rodrigues Nascimento ◽  
Thalyson Vasconcelos Lima ◽  
Yure Pequeno de Souza ◽  
...  
Keyword(s):  
Grain Yield ◽  
Mixed Models ◽  
Phenotypic Expression ◽  
Yield Stability ◽  
Specific Adaptation ◽  
Black Beans ◽  
Black Bean ◽  
Simultaneous Selection ◽  
Selection For ◽  
Genetic Value

Abstract: The objective of this work was to determine the efficiency of a simultaneous selection for yield, stability, and adaptability of bean genotypes of the carioca and black groups. In the 2016 harvest, two experiments were carried out in the state of Pernambuco, Brazil: one for the carioca group, with 20 genotypes, in the municipalities of Caruaru, Arcoverde, and Belém de São Francisco; and the other for the black group, with 12 genotypes, in the municipalities of Caruaru and Arcoverde. The parameters were estimated by mixed models, and selection was performed by the harmonic mean of the relative performance of genetic values, using three strategies: selection based on the predicted genetic value, without interaction; selection based on the predicted genetic value, considering each location; and simultaneous selection for grain yield, stability, and adaptability. The environments affected the phenotypic expression of the carioca bean genotypes, indicating specific adaptation. The average heritability for grain yield showed high values for black bean genotypes, which is a favorable condition for selection, and low values for carioca bean genotypes. The black bean genotypes CNFP 15684, 'BRS Esteio', CNFP 15678, CNFP 15697, CNFP 15695, and 'IPR Uirapuru' show the best performances in the studied environments, simultaneously considering grain yield, adaptability, and stability.

scholarly journals Purebred and Crossbred Genomic Evaluation and Mate Allocation Strategies To Exploit Dominance in Pig Crossbreeding Schemes

2020 ◽  
Vol 10 (8) ◽  
pp. 2829-2841
Author(s):  
David González-Diéguez ◽  
Llibertat Tusell ◽  
Alban Bouquet ◽  
Andres Legarra ◽  
Zulma G. Vitezica
Keyword(s):  
Variance Components ◽  
Genetic Variance ◽  
Sources Of Information ◽  
Genomic Evaluation ◽  
Multiple Trait ◽  
Genetic Value ◽  
Additive Genetic Effects ◽  
Positive Heterosis ◽  
Allocation Strategies ◽  
Qtl Effects

We investigated the effectiveness of mate allocation strategies accounting for non-additive genetic effects to improve crossbred performance in a two-way crossbreeding scheme. We did this by computer simulation of 10 generations of evaluation and selection. QTL effects were simulated as correlated across purebreds and crossbreds, and (positive) heterosis was simulated as directional dominance. The purebred-crossbred correlation was 0.30 or 0.68 depending on the genetic variance component used. Dominance and additive marker effects were estimated simultaneously for purebreds and crossbreds by multiple trait genomic BLUP. Four scenarios that differ in the sources of information (only purebred data, or purebred and crossbred data) and mate allocation strategies (mating at random, minimizing expected future inbreeding, or maximizing the expected total genetic value of crossbred animals) were evaluated under different cases of genetic variance components. Selecting purebred animals for purebred performance yielded a response of 0.2 genetic standard deviations of the trait “crossbred performance” per generation, whereas selecting purebred animals for crossbred performance doubled the genetic response. Mate allocation strategy to maximize the expected total genetic value of crossbred descendants resulted in a slight increase (0.8%, 4% and 0.5% depending on the genetic variance components) of the crossbred performance. Purebred populations increased homozygosity, but the heterozygosity of the crossbreds remained constant. When purebred-crossbred genetic correlation is low, selecting purebred animals for crossbred performance using crossbred information is a more efficient strategy to exploit heterosis and increase performance at the crossbred commercial level, whereas mate allocation did not improve crossbred performance.

Combining Ability of Plant Height and Yield components in Indian Mustard (Brassica junceaL.Czern & Coss.) under Salt affected Soil using Line×Tester Analysis

2016 ◽  
Vol 3 (2) ◽  
Author(s):  
SHAILESH CHAND GAUTAM ◽  
MP Chauhan
Keyword(s):  
Plant Height ◽  
Seed Yield ◽  
Combining Ability ◽  
Yield Components ◽  
Indian Mustard ◽  
Heritability Estimate ◽  
Genetic Effects ◽  
Narrow Sense ◽  
Narrow Sense Heritability ◽  
Additive Genetic Effects

Line × tester analysis of twenty lines and three testers of Indian mustard (Brassica juncea L. Czern & Coss.) cultivars were used to estimate general combining ability (GCA), specific combining ability (SCA) effects, high parent heterosis and narrow-sense heritability estimate for plant height, yield components and seed yield. Significant variance of line x tester for the traits like pods per plant and seed yield indicating non additive genetic effects have important role for controlling these traits. Significant mean squares of parents v/s crosses which are indicating significant average heterosis were also significant for all the traits except seeds per pod. High narrow-sense heritability estimates for all the traits except seeds per pod exhibited the prime importance of additive genetic effects for these traits except seeds per pod. Most of the crosses with negative SCA effect for plant height had at least one parent with significant negative or negative GCA effect for this trait. For most of the traits except pods per plant, the efficiency of high parent heterosis effect was more than SCA effect for determining superior cross combinations.

scholarly journals Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Akio Onogi ◽  
Toshio Watanabe ◽  
Atsushi Ogino ◽  
Kazuhito Kurogi ◽  
Kenji Togashi
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Variance Components ◽  
Variance Component ◽  
Predictive Accuracy ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Additive Effects ◽  
Additive Variance ◽  
Additive Genetic Effects

Abstract Background Genomic prediction is now an essential technology for genetic improvement in animal and plant breeding. Whereas emphasis has been placed on predicting the breeding values, the prediction of non-additive genetic effects has also been of interest. In this study, we assessed the potential of genomic prediction using non-additive effects for phenotypic prediction in Japanese Black, a beef cattle breed. In addition, we examined the stability of variance component and genetic effect estimates against population size by subsampling with different sample sizes. Results Records of six carcass traits, namely, carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, yield rate and beef marbling score, for 9850 animals were used for analyses. As the non-additive genetic effects, dominance, additive-by-additive, additive-by-dominance and dominance-by-dominance effects were considered. The covariance structures of these genetic effects were defined using genome-wide SNPs. Using single-trait animal models with different combinations of genetic effects, it was found that 12.6–19.5 % of phenotypic variance were occupied by the additive-by-additive variance, whereas little dominance variance was observed. In cross-validation, adding the additive-by-additive effects had little influence on predictive accuracy and bias. Subsampling analyses showed that estimation of the additive-by-additive effects was highly variable when phenotypes were not available. On the other hand, the estimates of the additive-by-additive variance components were less affected by reduction of the population size. Conclusions The six carcass traits of Japanese Black cattle showed moderate or relatively high levels of additive-by-additive variance components, although incorporating the additive-by-additive effects did not improve the predictive accuracy. Subsampling analysis suggested that estimation of the additive-by-additive effects was highly reliant on the phenotypic values of the animals to be estimated, as supported by low off-diagonal values of the relationship matrix. On the other hand, estimates of the additive-by-additive variance components were relatively stable against reduction of the population size compared with the estimates of the corresponding genetic effects.

scholarly journals Predictors of Urgent Cancer Care Clinic and Emergency Department Visits for Individuals Diagnosed with Cancer

2021 ◽  
Vol 28 (3) ◽  
pp. 1773-1789
Author(s):  
Kathleen Decker ◽  
Pascal Lambert ◽  
Katie Galloway ◽  
Oliver Bucher ◽  
Marshall Pitz ◽  
...  
Keyword(s):  
Emergency Department ◽  
Mixed Models ◽  
Cancer Care ◽  
Median Number ◽  
Emergency Department Visits ◽  
Planning Service ◽  
Care Clinic ◽  
Future Studies ◽  
Acute Complications ◽  
Ed Visits

In 2013, CancerCare Manitoba (CCMB) launched an urgent cancer care clinic (UCC) to meet the needs of individuals diagnosed with cancer experiencing acute complications of cancer or its treatment. This retrospective cohort study compared the characteristics of individuals diagnosed with cancer that visited the UCC to those who visited an emergency department (ED) and determined predictors of use. Multivariable logistic mixed models were run to predict an individual’s likelihood of visiting the UCC or an ED. Scaled Brier scores were calculated to determine how greatly each predictor impacted UCC or ED use. We found that UCC visits increased up to 4 months after eligibility to visit and then decreased. ED visits were highest immediately after eligibility and then decreased. The median number of hours between triage and discharge was 2 h for UCC visits and 9 h for ED visits. Chemotherapy had the strongest association with UCC visits, whereas ED visits prior to diagnosis had the strongest association with ED visits. Variables related to socioeconomic status were less strongly associated with UCC or ED visits. Future studies would be beneficial to planning service delivery and improving clinical outcomes and patient satisfaction.

scholarly journals A novel metric of reliability in pressure pain threshold measurement

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bernard Liew ◽  
Ho Yin Lee ◽  
David Rügamer ◽  
Alessandro Marco De Nunzio ◽  
Nicola R. Heneghan ◽  
...  
Keyword(s):  
Mixed Models ◽  
Variance Components ◽  
Pain Threshold ◽  
Intraclass Correlation ◽  
Pressure Pain Threshold ◽  
Total Variance ◽  
Lumbar Region ◽  
Pressure Pain ◽  
Pressure Algometer ◽  
Testing Protocols

AbstractThe inter-session Intraclass Correlation Coefficient (ICC) is a commonly investigated and clinically important metric of reliability for pressure pain threshold (PPT) measurement. However, current investigations do not account for inter-repetition variability when calculating inter-session ICC, even though a PPT measurement taken at different sessions must also imply different repetitions. The primary aim was to evaluate and report a novel metric of reliability in PPT measurement: the inter-session-repetition ICC. One rater recorded ten repetitions of PPT measurement over the lumbar region bilaterally at two sessions in twenty healthy adults using a pressure algometer. Variance components were computed using linear mixed-models and used to construct ICCs; most notably inter-session ICC and inter-session-repetition ICC. At 70.1% of the total variance, the source of greatest variability was between subjects ($${\sigma }_{subj}^{2}$$ σ subj 2 = 222.28 N2), whereas the source of least variability (1.5% total variance) was between sessions ($${\sigma }_{sess}^{2}$$ σ sess 2 = 4.83 N2). Derived inter-session and inter-session-repetition ICCs were 0.88 (95%CI: 0.77 to 0.94) and 0.73 (95%CI: 0.53 to 0.84) respectively. Inter-session-repetition ICC provides a more conservative estimate of reliability than inter-session ICC, with the magnitude of difference being clinically meaningful. Quantifying individual sources of variability enables ICC construction to be reflective of individual testing protocols.

130 Utilization of Nutrack to Assess Variance Components and Heritability of Activity Traits

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 17-17
Author(s):  
Lexi M Ostrand ◽  
Melanie D Trenhaile-Grannemann ◽  
Garrett See ◽  
Ty B Schmidt ◽  
Eric Psota ◽  
...  
Keyword(s):  
Animal Model ◽  
Mixed Models ◽  
Variance Components ◽  
Average Speed ◽  
Genetic Variances ◽  
Sow Longevity ◽  
Selection Decisions ◽  
Recorded Data ◽  
Residual Variances ◽  
And Behavior

Abstract Overall activity and behavior are integral components of sows remaining productive in the herd. This investigation studied overall activity of group housed replacement gilts and the heritability of various activity traits. Beginning around 20 wk of age, video recorded data of approximately 75 gilts/group for a total of 2,378 gilts over 32 groups was collected for 7 consecutive d using the NUtrack System, which tracks distance travelled (m), avg speed (m/s), angle rotated (degrees), and time standing (s), sitting (s), eating (s), and laying (s). The recorded phenotypes were standardized to the distribution observed within a pen for each group. The final values used for analysis were the average daily standardized values. Data were analyzed using mixed models (RStudio V 1.2.5033) including effects of sire, dam, dam’s sire and dam, dam’s grandsire and granddam, farrowing group, barn, pen, and on-test date. Sire had an effect on every activity trait P < 0.001), and dam had an effect on average speed (P < 0.001). The dam’s sire had an effect on all activity traits (P < 0.001) and the dam’s grandsire had an effect on average speed (P < 0.001). Heritabilities and variance components of activity traits were estimated in ASReml 4 using an animal model with a two-generation pedigree. Genetic variances are 0.17 +/- 0.029, 0.19 +/- 0.034, and 0.11 +/- 0.024, residual variances are 0.37 +/- 0.023, 0.41 +/- 0.027, and 0.41 +/- 0.022, phenotypic variances are 0.54 +/- 0.018, 0.60 +/- 0.020, and 0.52 +/- 0.016, and heritabilities are 0.32 +/- 0.048, 0.32 +/- 0.049, and 0.21 +/- 0.044 for average speed, distance, and lie respectively. NUtrack offers potential to aid in selection decisions. Given the results presented herein, continued investigation into these activity traits and their association with sow longevity is warranted.

scholarly journals Gradient boosting for linear mixed models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Colin Griesbach ◽  
Benjamin Säfken ◽  
Elisabeth Waldmann
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Selection Procedure ◽  
Classification Theory ◽  
Gradient Boosting ◽  
Random Structure ◽  
Boosting Algorithm ◽  
The One ◽  
Biased Estimates ◽  
Selection Of

Abstract Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory. Current boosting approaches also offer methods accounting for random effects and thus enable prediction of mixed models for longitudinal and clustered data. However, these approaches include several flaws resulting in unbalanced effect selection with falsely induced shrinkage and a low convergence rate on the one hand and biased estimates of the random effects on the other hand. We therefore propose a new boosting algorithm which explicitly accounts for the random structure by excluding it from the selection procedure, properly correcting the random effects estimates and in addition providing likelihood-based estimation of the random effects variance structure. The new algorithm offers an organic and unbiased fitting approach, which is shown via simulations and data examples.

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