FIT-GWA: A new method for the genetic analysis of small gene effects, high precision in phenotype measurements and small sample sizes

Small gene effects involved in complex traits remains difficult to analyse using current genome-wide association methods (GWAS) due to the number of individuals required to return meaningful association(s), a.k.a. study power. Inspired by physics fields theory we provide a different method called Fields Informational Theory for Genome-Wide Associations (FIT-GWA). Contrary to GWAS, FIT-GWA that the phenotype is measured precisely enough and/or the number of individuals in the population is too small, to permit categories. To extract information FIT-GWA use the difference in the cumulated sum of gene microstates between two configurations: (i) when the individuals are taken at random without information on phenotype values and, (ii) when individuals are ranked as a function of their phenotype value. Such difference can be accounted through the emergence of ‘phenotypic fields’. We demonstrate that FIT-GWA recovers GWAS, i.e., Fisher’s theory, when the phenotypic fields are linear. However, unlike GWAS, FIT-GWA permits to demonstrate how the variance of microstate distribution density functions are also involved in genotype-phenotype associations. Using genotype-phenotype simulations based on Fisher’s theory we illustrate the application and power of the method with a small sample size of 1000 individuals. ### Competing Interest Statement The authors have declared no competing interest.