g a suite of eight models following this approach. The data, parameters, and predictions made by these eight models are described in Table 1 , with model equations identified in Table 2 . Each model has a coded name based on the assumptions therein (Table 3 ). The model and implementation codes used for analysis are provided at https://github.com/jrpleban/Bayes_Farquhar_Models_2_level_Hierarchy . Priors on parameters are shown in Table 4 . We have chosen to estimate some parameters often set as constants ( K c , K o ) to evaluate a given model's ability to discern traits expected to be conserved in this population. A literature survey for each parameter was used to provide statistical distri
Open resource ↗jrpleban/Bayes_Farquhar_Models_2_level_Hierarchy · lines:45-184Unverified paper record
Phenotypic Trait Identification Using a Multimodel Bayesian Method: A Case Study Using Photosynthesis in Brassica rapa Genotypes.
Frontiers in plant science · 17 Apr 2018 · 10.3389/fpls.2018.00448
Abstract
Agronomists have used statistical crop models to predict yield on a genotype-by-genotype basis. Mechanistic models, based on fundamental physiological processes common across plant taxa, will ultimately enable yield prediction applicable to diverse genotypes and crops. Here, genotypic information is combined with multiple mechanistically based models to characterize photosynthetic trait differentiation among genotypes of Brassica rapa . Infrared leaf gas exchange and chlorophyll fluorescence observations are analyzed using Bayesian methods. Three advantages of Bayesian approaches are employed: a hierarchical model structure, the testing of parameter estimates with posterior predictive checks and a multimodel complexity analysis. In all, eight models of photosynthesis are compared for fit to data and penalized for complexity using deviance information criteria (DIC) at the genotype scale. The multimodel evaluation improves the credibility of trait estimates using posterior distributions. Traits with important implications for yield in crops, including maximum rate of carboxylation ( V cmax ) and maximum rate of electron transport ( J max ) show genotypic differentiation. B. rapa shows phenotypic diversity in causal traits with the potential for genetic enhancement of photosynthesis. This multimodel screening represents a statistically rigorous method for characterizing genotypic differences in traits with clear biophysical consequences to growth and productivity within large crop breeding populations with application across plant processes.
Plant phenotyping relevance
光合成形質を推定するマルチモデル・ベイズ統計手法が研究の中心であり、遺伝子型間の形質差を定量化・検証しているため。
titlePhenotypic Trait Identification Using a Multimodel Bayesian Method: A Case Study Using Photosynthesis in Brassica rapa Genotypes.
abstractThis multimodel screening represents a statistically rigorous method for characterizing genotypic differences in traits with clear biophysical consequences to growth and productivity within large crop breeding populations with application across plant processes.
abstractInfrared leaf gas exchange and chlorophyll fluorescence observations are analyzed using Bayesian methods.
Code and data availability
The paper's authors provide the model and implementation code used for the Bayesian photosynthesis phenotyping analysis in a public GitHub repository, explicitly stated in the text. No public phenotype dataset deposit is mentioned; the raw A/Ci curve data availability is not stated.
This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.