The paired species contrast (PSC) design. A : An example phylogeny with one set of selected species (solid blue and red lines). Extraneous lineages (black dotted lines) and shared evolutionary history (gray dotted lines). B : A schematic depiction of the four species selected for ESL-PSC analysis. In the ESL experiment, the response variable refers to the binary phenotype, where +1 represents the convergent trait, and -1 represents the ancestral trait.

The paired species contrast (PSC) design. A : An example phylogeny with one set of selected species (solid blue and red lines). Extraneous lineages (black dotted lines) and shared evolutionary history (gray dotted lines). B : A schematic depiction of the four species selected for ESL-PSC analysis. In the ESL experiment, the response variable refers to the binary phenotype, where +1 represents the convergent trait, and -1 represents the ancestral trait.

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Cases abound in which nearly identical traits have appeared in distant species facing similar environments. These unmistakable examples of adaptive evolution offer opportunities to gain insight into their genetic origins and mechanisms through comparative analyses. Here, we present a novel comparative genomics approach to build genetic models that...

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Context 1
... PSC, species pairs are required to be from evolutionarily independent clades to avoid introducing evolutionary correlations among pairs due to shared evolutionary history, which is known to cause spurious associations 5,6,9 . As an example, we could select trait-positive species A 1 and D 1 and trait-negative species B 1 and C, respectively, to satisfy the above conditions ( Fig. 1A ) . ...
Context 2
... be more recent than the MRCA of either member of the pair with any of the other species in the analysis. In the above example, the MRCA of A 1 and B 1 (Y) is more recent than that of A 1 and F (W). Also, ESL-PSC automatically excludes all branches in the phylogeny that are unrelated to the evolution of the convergent trait (dotted branches in Fig. 1A ). This means that the model learning is directly focused on the molecular evolutionary changes between trait-positive and trait-negative species (solid blue and red branches, respectively). If there are multiple species in some trait-positive and trait-negative clades, different combinations of training sets may be used to build ...