scholarly journals Multiple-trait model through Bayesian inference applied to Jatropha curcas breeding for bioenergy

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247775
Author(s):  
Marco Antônio Peixoto ◽  
Jeniffer Santana Pinto Coelho Evangelista ◽  
Igor Ferreira Coelho ◽  
Rodrigo Silva Alves ◽  
Bruno Gâlveas Laviola ◽  
...  
Keyword(s):  
Jatropha Curcas ◽  
Repeated Measures ◽  
Bayesian Model ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Monte Carlo Algorithm ◽  
Multiple Trait ◽  
Trait Model

Multiple-trait model tends to be the best alternative for the analysis of repeated measures, since they consider the genetic and residual correlations between measures and improve the selective accuracy. Thus, the objective of this study was to propose a multiple-trait Bayesian model for repeated measures analysis in Jatropha curcas breeding for bioenergy. To this end, the grain yield trait of 730 individuals of 73 half-sib families was evaluated over six harvests. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. Genetic correlation between pairs of measures were estimated and four selective intensities (27.4%, 20.5%, 13.7%, and 6.9%) were used to compute the selection gains. The full model was selected based on deviance information criterion. Genetic correlations of low (ρg ≤ 0.33), moderate (0.34 ≤ ρg ≤ 0.66), and high magnitude (ρg ≥ 0.67) were observed between pairs of harvests. Bayesian analyses provide robust inference of genetic parameters and genetic values, with high selective accuracies. In summary, the multiple-trait Bayesian model allowed the reliable selection of superior Jatropha curcas progenies. Therefore, we recommend this model to genetic evaluation of Jatropha curcas genotypes, and its generalization, in other perennials.

scholarly journals Random regression for modeling yield genetic trajectories in Jatropha curcas breeding

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244021
Author(s):  
Marco Antônio Peixoto ◽  
Rodrigo Silva Alves ◽  
Igor Ferreira Coelho ◽  
Jeniffer Santana Pinto Coelho Evangelista ◽  
Marcos Deon Vilela de Resende ◽  
...  
Keyword(s):  
Jatropha Curcas ◽  
Repeated Measures ◽  
Variance Components ◽  
High Reliability ◽  
Genetic Selection ◽  
Information Criterion ◽  
Random Regression ◽  
Ratio Test ◽  
Sib Families ◽  
Over Time

Random regression models (RRM) are a powerful tool to evaluate genotypic plasticity over time. However, to date, RRM remains unexplored for the analysis of repeated measures in Jatropha curcas breeding. Thus, the present work aimed to apply the random regression technique and study its possibilities for the analysis of repeated measures in Jatropha curcas breeding. To this end, the grain yield (GY) trait of 730 individuals of 73 half-sib families was evaluated over six years. Variance components were estimated by restricted maximum likelihood, genetic values were predicted by best linear unbiased prediction and RRM were fitted through Legendre polynomials. The best RRM was selected by Bayesian information criterion. According to the likelihood ratio test, there was genetic variability among the Jatropha curcas progenies; also, the plot and permanent environmental effects were statistically significant. The variance components and heritability estimates increased over time. Non-uniform trajectories were estimated for each progeny throughout the measures, and the area under the trajectories distinguished the progenies with higher performance. High accuracies were found for GY in all harvests, which indicates the high reliability of the results. Moderate to strong genetic correlation was observed across pairs of harvests. The genetic trajectories indicated the existence of genotype × measurement interaction, once the trajectories crossed, which implies a different ranking in each year. Our results suggest that RRM can be efficiently applied for genetic selection in Jatropha curcas breeding programs.

Genetic parameters for daily live-weight gain, live fleshiness and bone thinness in station-tested Piemontese young bulls

2001 ◽  
Vol 72 (3) ◽  
pp. 449-456 ◽  
Author(s):  
A. Albera ◽  
R. Mantovani ◽  
G. Bittante ◽  
A. F. Groen ◽  
P. Carnier
Keyword(s):  
Weight Gain ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Genetic Correlations ◽  
Live Weight ◽  
Adaptation Period ◽  
Production Traits ◽  
Multiple Trait ◽  
Live Weight Gain ◽  
Total Gain

AbstractEstimates of genetic parameters for beef production traits were obtained for Piemontese cattle. Data were from 988 young bulls station-tested from 1989 till 1998. Bulls entered the station at 6 to 8 weeks of age and, after an adaptation period of 3 months, were tested for growth, live fleshiness and bone thinness. Length of test was 196 days. Growth traits considered were gain at farm, gain during the adaptation period, gain on test and total gain at the station. Six different fleshiness traits and bone thinness were scored on live animals at the end of the test using a linear system. Live evaluations of fleshiness were adjusted for the weight at scoring in order to provide an assessment of conformation independent of body size. Genetic parameters were estimated using animal models. Heritability of live-weight gain ranged from 0·20 in the adaptation period to 0·60 for total gain at the station. Genetic correlations between gains at station in different periods were high (from 0·63 to 0·97). Residual correlation between gain during the adaptation period and gain during test was negative, probably due to the occurrence of compensatory growth of the animals.Live fleshiness traits and bone thinness were of moderate to high heritability (from 0·34 to 0·55) and highly correlated indicating that heavy muscled bulls also have thin bones. Accuracy of breeding values and therefore response to selection were improved by multiple trait analysis of the live fleshiness traits and bone thinness. Overall weight gain at the station had a moderate negative genetic correlation with all live fleshiness traits and bone thinness (from –0·11 to –0·39).

scholarly journals Estimation of Genetic Parameters for Peak Yield, Yield and Persistency Traits in Murciano-Granadina Goats Using Multi-Traits Models

Animals ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 411
Author(s):  
Judith C. Miranda ◽  
José M. León ◽  
Camillo Pieramati ◽  
Mayra M. Gómez ◽  
Jesús Valdés ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Selection Criteria ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Multiple Trait ◽  
Lactation Curve ◽  
Derivative Free ◽  
Selection For ◽  
Estimated Breeding Values ◽  
Estimation Of Genetic Parameters

This paper studies parameters of a lactation curve such as peak yield (PY) and persistency (P), which do not conform to the usual selection criteria in the Murciano-Granadina (MG) breed, but are considered to be an alternative to benefit animal welfare without reducing production. Using 315,663 production records (of 122,883 animals) over a period of 24 years (1990–2014), genetic parameters were estimated with uni-, bi- and multivariate analysis using multiple trait derivative free restricted maximum likelihood (MTDFREML). The heritability (h2)/repeatability (re) of PY, yield (Y) and P was estimated as 0.13/0.19, 0.16/0.25 and 0.08/0.09 with the uni-trait and h2 of bi- and multi-traits analysis ranging from 0.16 to 0.17 of Y, while that of PY and Y remained constant. Genetic correlations were high between PY–Y (0.94 ± 0.011) but low between PY–P (–0.16 ± 0.054 to –0.17 ± 0.054) and between Y–P (–0.06 ± 0.058 to –0.05 ± 0.058). Estimates of h2/re were low to intermediate. The selection for Y–PY or both can be implemented given the genetic correlation between these traits. PY–P and Y–P showed low to negligible correlation values indicating that if these traits are implemented in the early stages of evaluation, they would not be to the detriment of PY–Y. The combination of estimated breeding values (EBVs) for all traits would be a good criterion for selection.

scholarly journals Model comparison and estimation of genetic parameters for body weight in commercial broilers

2013 ◽  
Vol 93 (1) ◽  
pp. 67-77 ◽  
Author(s):  
G. Maniatis ◽  
N. Demiris ◽  
A. Kranis ◽  
G. Banos ◽  
A. Kominakis
Keyword(s):  
Body Weight ◽  
Akaike Information Criterion ◽  
Model Evaluation ◽  
Model Comparison ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Information Criterion ◽  
Biological Properties ◽  
Genetic Covariance ◽  
Estimation Of Genetic Parameters

Maniatis, G., Demiris, N., Kranis, A., Banos, G. and Kominakis, A. 2013. Model comparison and estimation of genetic parameters for body weight in commercial broilers. Can. J. Anim. Sci. 93: 67–77. The availability of powerful computing and advances in algorithmic efficiency allow for the consideration of increasingly complex models. Consequently, the development and application of appropriate statistical procedures for model evaluation is becoming increasingly important. This paper is concerned with the application of an alternative model determination criterion (conditional Akaike Information Criterion, cAIC) in a large dataset comprising 203 323 body weights of broilers, pertaining to 7 (BW7) and 35 (BW35) days of age. Seven univariate and seven bivariate models were applied. Direct genetic, maternal genetic and maternal environmental (c2) effects were estimated via REML. The model evaluation criteria included conditional Akaike Information Criterion (cAIC), Bayesian Information Criterion (BIC) and the standard Akaike Information Criterion (henceforth marginal; mAIC). According to cAIC the best-fitting model included direct genetic, maternal genetic and c2 effects. Maternal heritabilities were low (0.10 and 0.03) compared to the direct heritabilities (0.17 and 0.21), while c2 was 0.05 and 0.04 for BW7 and BW35, respectively. BIC and mAIC favoured a model that additionally included a direct-maternal genetic covariance, resulting in highly negative direct-maternal genetic correlations (−0.47 and −0.64 for BW7 and BW35, respectively) and higher direct heritabilities (0.25 and 0.28 for BW7 and BW35, respectively). Results suggest that cAIC can select different animal models than mAIC and BIC with different biological properties.

Estimates of genetic parameters for ultrasound-measured carcass traits in sheep

2004 ◽  
Vol 84 (3) ◽  
pp. 361-365 ◽  
Author(s):  
T. L. Fernandes ◽  
J. W. Wilton ◽  
J. J. Tosh
Keyword(s):  
Genetic Improvement ◽  
Variance Component ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Backfat Thickness ◽  
Additive Genetic Effect ◽  
Multiple Trait ◽  
Breed Group ◽  
Variance Component Estimates

Data on ultrasound traits (loin depth, average backfat thickness, and loin width) were collected from lambs (n = 3483) across Ontario, born between 1997 and 1999. The data were analysed with a REML procedure in a multiple-trait mixed-animal model to obtain (co)variance component estimates. Analyses of all traits included the additive genetic effect of the lamb, sex of the lamb, contemporary group, and breed group effects. Weight or age was included as a covariate in two separate analyses. Estimates of direct additive heritabilities for loin depth, average backfat thickness, and loin width were 0.29, 0.29 and 0.26 respectively, with genetic correlations of -0.17 between loin depth and average backfat thickness, 0.43 between loin depth and loin width, and 0.23 between loin width and average backfat thickness for the weight constant analysis. When the data were analysed using age in the regression analysis, corresponding estimates of direct additive heritabilities were 0.38, 0.35 and 0.30, and genetic correlations between traits were all positive, 0.29 between loin depth and average backfat thickness, 0.61 between loin depth and loin width, and 0.44 between loin width and average backfat thickness. Results indicate that it is possible to make genetic improvement if selection is based on ultrasound information. Key words: Sheep, genetic parameters, heritability, ultrasound

scholarly journals Genetic parameters for direct and maternal effects on growth traits of sheep

2012 ◽  
Vol 55 (6) ◽  
pp. 603-611 ◽  
Author(s):  
F. Ghafouri-Kesbi ◽  
H. Baneh
Keyword(s):  
Maternal Effects ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Selection Criterion ◽  
Information Criterion ◽  
Phenotypic Variance ◽  
Environmental Variance ◽  
Daily Gain

Abstract. The aim of the present study was to estimate (co)variance components and corresponding genetic parameters for birth weight (BW), weaning weight (WW), 6-month weight (W6), 9-month weight (W9), average daily gain from birth to weaning (WWDG), average daily gain from weaning to 6 months (W6DG) and average daily gain from 6 months to 9 months (W9DG) for a nucleus flock of Iranian Makooei sheep. Genetic parameters were estimated by REML procedure fitting six animal models including various combinations of maternal effects. The Akaike information criterion (AIC) was used to determine the most appropriate model. Estimates of direct heritability (h2) ranged from 0.13 (W6DG) to 0.32 (BW). Maternal effects were found to be important in the growth performance of the Makooei sheep, indicating the necessity of including maternal effects in the model to obtain accurate estimates of direct heritability. Estimates of maternal heritability (m2) ranged from 0.05 (W6) to 0.16 (WWDG) and the estimates of proportion of maternal permanent environmental variance to phenotypic variance (c2) were in the range between 0.05 (BW) and 0.10 (W6). Direct additive genetic correlations were positive in all cases and ranged from 0.00 (BW/W9DG) to 0.99 (WW/WWDG). Phenotypic correlations showed a broad range from −0.27 (WW/W9DG) to 0.99 (WW/WWDG). Estimates of genetic parameters showed that genetic improvement through selection programs is possible. WW would be a suitable selection criterion since it has acceptable direct heritability and relatively high genetic correlation with other traits.

scholarly journals Comparison between methods for measuring fecal egg count and estimating genetic parameters for gastrointestinal parasite resistance traits in sheep

2021 ◽  
Author(s):  
M N Boareki ◽  
F S Schenkel ◽  
O Willoughby ◽  
A Suarez-Vega ◽  
D Kennedy ◽  
...  
Keyword(s):  
Genetic Parameters ◽  
Body Condition Score ◽  
Genetic Correlations ◽  
Gastrointestinal Parasite ◽  
Parasite Resistance ◽  
Multiple Trait ◽  
Fecal Egg Count ◽  
Counting Methods ◽  
Mean And Variance ◽  
Resistance Traits

Abstract Fecal egg count (FEC) is an indicative measurement for parasite infection in sheep. Different FEC methods may show inconsistent results. Not accounting for inconsistencies can be problematic when integrating measurements from different FEC methods for genetic evaluation. The objectives of this study were to evaluate the difference in means and variances between two fecal egg counting methods used in sheep, the Modified McMaster (LMMR) and the Triple Chamber McMaster (LTCM); to estimate variance components for the two FEC methods, treating them as two different traits; and to integrate FEC data from the two different methods and estimate genetic parameters for FEC and other gastrointestinal parasite resistance traits. Fecal samples were collected from a commercial Rideau-Arcott sheep farm in Ontario. Fecal egg counting was performed using both Modified McMaster and the Triple Chamber McMaster methods. Other parasite resistance trait records were collected from the same farm including eye score (FAMACHA ©), body condition score (BCS), and body weight (WT). The two FEC methods were highly genetically (0.94) and phenotypically (0.88) correlated. However, the mean and variance between the two FEC methods were significantly different (P < 0.0001). Therefore, re-scaling is required prior to integrating data from the different methods. For the multiple trait analysis, data from the two fecal egg counting methods were integrated (LFEC) by using records for the LMMR when available and replacing missing records with re-standardized LTCM records converted to the same mean and variance of LMMR. Heritability estimates were 0.12 ± 0.04, 0.07 ± 0.05 , 0.17 ± 0.06, and 0.24 ± 0.07 for LFEC egg count, FAMACHA ©, BCS, and WT, respectively. The estimated genetic correlations between fecal egg count and the other parasite resistance traits were low and not significant (P>0.05) for FAMACHA © (r= 0.24 ± 0.32) and WT (r= 0.22 ± 0.19), and essentially zero for BCS (r= -0.03 ± 0.25), suggesting little to no benefit of using such traits as indicators for LFEC.

scholarly journals Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice

2021 ◽  
Author(s):  
Antônio Carlos da Silva Júnior ◽  
Isabela de Castro Sant’Anna ◽  
Michele Jorge Silva ◽  
Cosme Damião Cruz ◽  
Camila Ferreira Azevedo ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Genetic Parameters ◽  
Block Design ◽  
Genetic Parameter ◽  
Monte Carlo Algorithm ◽  
Reliable Estimate ◽  
Irrigated Rice ◽  
Biotic Stresses ◽  
Randomized Block Design ◽  
Rice Genotypes

The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment.Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: [[EQUATION]] = 0.039- 0.80, and 0.02- 0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of [[EQUATION]] and [[EQUATION]] higher for flowering in the reduced model ( [[EQUATION]] = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: [[EQUATION]] = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.

scholarly journals Estimates of genetic parameters and cluster analyses for carcass and meat quality traits in birds

2017 ◽  
Vol 52 (3) ◽  
pp. 205-213 ◽  
Author(s):  
Adriane Molardi Bainy ◽  
Rodrigo Pelicioni Savegnago ◽  
Luara Afonso de Freitas ◽  
Beatriz do Nascimento Nunes ◽  
Jaqueline Oliveira Rosa ◽  
...  
Keyword(s):  
Meat Quality ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Meat Production ◽  
Quality Traits ◽  
Production Traits ◽  
Multiple Trait ◽  
Breeding Values ◽  
Cluster Analyses ◽  
Meat Quality Traits

Abstract: The objective of this work was to estimate genetic parameters for bird carcass and meat quality traits, as well as to explore the genetic patterns of the breeding values of this population using cluster analyses. Data from 1,846 birds were used to estimate the genetic parameters of production and quality traits using the multiple-trait animal model, and cluster analyses were performed. The heritability estimates ranged from 0.08± 0.03 for meat pH measured 24 hours after slaughter to 0.85± 0.09 for body weight. The genetic correlations between production traits were high and positive. The genetic correlations between meat quality traits were low and were not informative due to the high standard errors (same magnitudes as those of the genetic correlations). The genetic correlations between meat production and quality traits were negative, except between production traits and meat lightness intensity. Based on breeding values (EBVs), the evaluated population can be divided into four groups through cluster analyses, and one group is suitable for selection because the birds presented EBVs above and around the average of the population, respectively, for production and quality traits. Therefore, it is possible to obtain genetic gains for production-related traits without decreasing meat quality.

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