ArticleFrontiers in genetics2019
Bayesian, Likelihood-Free Modelling of Phenotypic Plasticity and Variability in Individuals and Populations.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Strategic risk analysis for the selection of stable and high-potential maize genotypes in multi-environment trials.PloS one · 2025Article
- Characterization, costs, cues and future perspectives of phenotypic plasticity.Annals of botany · 2022Review
- A Novel Estimation of Unobserved Pig Growth Traits for the Purposes of Precision Feeding Methods.Frontiers in veterinary science · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a paradigm shift from the traditional focus on the "average" individual towards the definition and analysis of trait variation within individual life-history and among individuals in populations. This is a result of increasing availability of individual phenotypic data. The shift allows the use of genetic and environment-driven variations to assess robustness to challenge, gain greater understanding of organismal biological processes, or deliver individual-targeted treatments or genetic selection. These consequences apply, in particular, to variation in ontogenetic growth. We propose an approach to parameterise mathematical models of individual traits (e.g., reaction norms, growth curves) that address two challenges: 1) Estimation of individual traits while making minimal assumptions about data distribution and correlation, addressed
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