1. A blind genetics classification method in a computing device for use in blind genetic trait classification of an individual sample outside of a sample population involving analysis of at least two genes associated with a genetic trait, the method comprising the acts of: receiving, in the computing device from the individual sample, each genotype of a plurality of genotypes of the at least two genes found in the individual sample; receiving, in the computing device from the individual sample, each genotype combination of one or more predetermined genotype combinations of the at least two genes found in the individual sample; calculating, by the computing device, a value for classifying the individual sample outside of the sample population into one of a plurality of trait classes associated with the genetic trait based on a summation of: a first sum of contributions associated with each said genotype of the plurality of genotypes of the at least two genes in the individual sample, the contribution for each said genotype being a function of a calculated statistical value for the genotype determined from coupts of individuals in the sample population associated with each trait class of the genetic trait, the calculated statistical value comprising a p-value and the first sum of contributions being representable by the mathematical expression “Σnv i ” for all genotypes i, where v i =log (c/p i ), p i is the p-value, c is a constant greater than or equal to 0.05, and n is a variable for each different trait class, and a second sum of contributions associated with each said genotype combination of the one or more predetermined genotype combinations in the individual sample, each said one or more predetermined genotype combination being selected from the sample population based on having a statistically significant association with one of the trait classes of the genetic trait; classifying the individual sample outside of the sample population into one of the plurality of trait classes of the genetic trait based on the calculated value, and providing a classification result corresponding to the classified individual sample; and providing the classification result to an output device for observation.