Convergent phenotypic evolution, the independent acquisition of similar or nearly identical traits in multiple species, is widespread throughout the tree of life. These cases of repeated evolution offer an opportunity to investigate shared genetic changes underlying shared traits, thereby linking genotypes to phenotypes. Genetic convergence can take many forms: identical amino acid or nucleotide substitutions; non-identical changes in orthologous genes or other elements; losses or gains of the same genetic elements; or convergent shifts in molecular evolutionary characteristics, such as substitution rates, amino acid preferences and selection strength. However, identifying adaptive genetic convergence, whereby evolved traits provide a fitness advantage, is challenging due to a pervasive background of random convergence that causes low signal-to-noise ratios. Numerous computational methods, including machine learning and artificial intelligence approaches, have been developed to detect, interpret and predict molecular convergence across multiple levels of genetic organization in multicellular organisms. These emerging approaches offer novel avenues to uncover the genetic foundations of complex and biomedically important traits.
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