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Based on the normal distribution and its properties, i.e., average andvariance, Fisher works have provided a conceptual framework to identifygenotype-phenotype associations. While Fisher intuition has proved fruitfulover the past century, the current demands for higher mapping precisions haveled to the formulation of a new genotype-phenotype association method a.k.a.GIFT (Genomic Informational Field Theory). Not only is the method more powerfulin extracting information from genotype and phenotype datasets, GIFT can alsodeal with any phenotype distribution density function. Here we apply GIFT to ahypothetical Cauchy-distributed phenotype. As opposed to the normaldistribution that restricts fluctuations to a finite variance defined by thebulk of the distribution, Cauchy distribution embraces large phenotypicfluctuations and as a result, averages and variances from Cauchy-distributedphenotypes cannot be defined mathematically. While classic genotype-phenotypeassociation methods (GWAS) are unable to function without proper average andvariance, it is demonstrated here that GIFT can associate genotype to phenotypein this case. As phenotypic plasticity, i.e., phenotypic fluctuation, iscentral to surviving sudden environmental changes, by applying GIFT the uniquecharacteristic of the genotype permitting evolution of biallelic organisms totake place is determined in this case.

Genomic Informational Field Theory (GIFT) to characterize genotypes involved in large phenotypic fluctuations