Information theory enables tractable quantification of genetic interactions

Traditional statistical genetic models require parameter counts that grow quadratically with the number of loci when modeling gene-gene interactions, rapidly becoming intractable due to limited sample size. In contrast, information theory avoids assumption constraints, offering measures such as entropy and mutual information that allow direct quantification and partitioning of nonlinear dependencies among genetic and phenotypic factors without explosive growth in parameters.

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