Genomic Informational Field Theory (GIFT) to characterize genotypes involved in large phenotypic fluctuations
Cyril Rauch, Panagiota Kyratzi, Andras Paldi

TL;DR
This paper introduces GIFT, a novel genotype-phenotype association method capable of handling arbitrary phenotype distributions, including those with large fluctuations like the Cauchy distribution, surpassing traditional GWAS limitations.
Contribution
GIFT is a new framework that extends genotype-phenotype association analysis to non-normal distributions, enabling insights into phenotypic plasticity and evolution under large phenotypic fluctuations.
Findings
GIFT successfully associates genotypes with Cauchy-distributed phenotypes.
Traditional GWAS cannot analyze distributions with undefined mean and variance.
GIFT reveals the evolutionary potential of genotypes in fluctuating environments.
Abstract
Based on the normal distribution and its properties, i.e., average and variance, Fisher works have provided a conceptual framework to identify genotype-phenotype associations. While Fisher intuition has proved fruitful over the past century, the current demands for higher mapping precisions have led to the formulation of a new genotype-phenotype association method a.k.a. GIFT (Genomic Informational Field Theory). Not only is the method more powerful in extracting information from genotype and phenotype datasets, GIFT can also deal with any phenotype distribution density function. Here we apply GIFT to a hypothetical Cauchy-distributed phenotype. As opposed to the normal distribution that restricts fluctuations to a finite variance defined by the bulk of the distribution, Cauchy distribution embraces large phenotypic fluctuations and as a result, averages and variances from…
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Taxonomy
TopicsEvolution and Genetic Dynamics · Gene expression and cancer classification · Gene Regulatory Network Analysis
