Mixture Mixed Models: Biennial Growth as a Latent Variable in Coffee Bean Progenies

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 S₀:₁ 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.

Plant phenotyping relevance

コーヒー progeny の生産データから二年結果性という植物の生理状態・表現型を推定する混合モデルを提案し、従来法と比較・シミュレーション検証している。統計モデル自体が中心的な方法論的貢献である。

Code and data availability

公開本文の所在を確認できませんでした。非公開または購読が必要な可能性があります。

No evidence-backed public reproduction asset is currently recorded.