Discovering phenotypic causal structure from nonexperimental data

2016/03/23 by Jun Otsuka, J. Otsuka
Biochemistry, Genetics and Molecular Biology · #Genetic Mapping and Diversity in Plants and Animals #Genetic and phenotypic traits in livestock #Genetic diversity and population structure

paper · pdf · doi:10.1111/jeb.12869

openalex publication_date 2016/03/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The evolutionary potential of organisms depends on how their parts are structured into a cohesive whole. A major obstacle for empirical studies of phenotypic organization is that observed associations among characters usually confound different causal pathways such as pleiotropic modules, interphenotypic causal relationships and environmental effects. The present article proposes causal search algorithms as a new tool to distinguish these different modes of phenotypic integration. Without assuming an a priori structure, the algorithms seek a class of causal hypotheses consistent with independence relationships holding in observational data. The technique can be applied to discover causal relationships among a set of measured traits and to distinguish genuine selection from spurious correlations. The former application is illustrated with a biological data set of rat morphological measurements previously analysed by Cheverud et al. (Evolution 1983, 37, 895).

Citations

• Causal Inference in the Presence of Latent Variables and Selection Bias
• The adaptive landscape as a conceptual bridge between micro- and macroevolution

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