科研之友
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摘要
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, inter-phenotypic 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).
- 出版日期2016-6
