Abstract
ABSTRACT Statistical analysis of Coffea arabica L. progeny production has been a great challenge. In this species, genotypes may present differential biennial behaviors due to different physiological responses to the environmental conditions, indicating a mixture of two subpopulations in the tested progenies. Previously proposed statistical methods are unable to handle data overdispersion and/or bimodality because they assume the same stochastic process generating different phenotypes. This study proposes a finite mixture mixed model for modeling the biennial patterns. Production data for 21 S 0:1 progenies, evaluated through eight harvests, were used. Individual (per harvest) and repeated measures analyses were performed using conventional mixed models and Gaussian mixture mixed models. The proposed methodology is also illustrated in a simulation study. On a real dataset, the approximated prediction error variance, CV, and residual variance were drastically reduced using mixture mixed models, resulting in a higher estimated heritability and expected gain from selection. Residual dependence across years was lower for the mixture model, but no differences were observed in genetic correlations. The posterior probability matrix captured the biennial pattern, which indicates the probability of a progeny's physiological stage. The Spearman correlation coefficient (0.87) indicates that selection based on grouped means may not be efficient. In general, the proposed model was more efficient for higher subpopulations means differences. The results suggest that for analysis of C. arabica progenies exhibiting different biennial patterns, mixture mixed models are superior to traditional mixed models and to models that structure biennial effects using covariance matrices.
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Claims 9
Claim · model-written summary
Mixture models drastically reduced prediction error variance, CV and residual variance on real coffee data.
- Outcome
- prediction error variance, CV, and residual variancee0
- Direction
- reducede0
- Comparator
- mixture mixed modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“approximated prediction error variance, CV, and residual variance were drastically reduced using mixture mixed models”
t0“Coffee Bean Progenies”
Claim · model-written summary
Mixture models produced higher estimated heritability.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“higher estimated heritability and expected gain from selection”
t0“Coffee Bean Progenies”
Claim · model-written summary
Mixture models produced higher expected gain from selection.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“higher estimated heritability and expected gain from selection”
t0“Coffee Bean Progenies”
Claim · model-written summary
Residual dependence across years was lower for the mixture model.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Residual dependence across years was lower for the mixture model”
t0“Coffee Bean Progenies”
Claim · model-written summary
Genetic correlations did not differ between models.
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“no differences were observed in genetic correlations”
t0“Coffee Bean Progenies”
Claim · model-written summary
The posterior probability matrix captured progeny biennial patterns.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“posterior probability matrix captured the biennial pattern, which indicates the probability of a progeny's physiological stage”
t0“Coffee Bean Progenies”
Claim · model-written summary
Spearman correlation of 0.87 suggested grouped-mean selection may be inefficient.
Not reportedMaterialDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Spearman correlation coefficient (0.87) indicates that selection based on grouped means may not be efficient”
t0“Coffee Bean Progenies”
Claim · model-written summary
The proposed model was more efficient when subpopulation means differed more.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“proposed model was more efficient for higher subpopulations means differences”
t0“Coffee Bean Progenies”
Claim · model-written summary
The authors conclude mixture mixed models are superior for progenies with distinct biennial patterns.
- Taxon
- C. arabica
Coffea arabicae0 - Outcome
- mixture mixed models are superiore0
- Comparator
- traditional mixed modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“for analysis of C. arabica progenies exhibiting different biennial patterns, mixture mixed models are superior to traditional mixed models and to models that structure biennial effects using covariance matrices”