Patent Document ID: 9672271
Application ID: 14268982
Patent Flag: 1

Claim One:
1. A method of identifying susceptibility of a patient to a predetermined complex hereditary disease derived from a large-scale genomic data first database composed of single nucleotide polymorphisms (SNPs) expressed as base pair symbols comprising the steps of: accessing said first database of genomic data for said predetermined complex hereditary disease in the form of a matrix D, the rows of which are genomic sequences of disease subjects, and a comparison (control) matrix, C, of genomic sequences of control subjects deemed not to have the disease, the two matrices D &C having a common set of SNP columns and rows of numbers N d & N c respectively; transforming the genomic data in said first database by organizing or registering it in terms of allele pairs to provide a modified second database and accompanied by listing of all SNP identities included in the first database through rs numbers that specify chromosomes, chromosome locations and the two nucleotide (base pair) symbols taken from [A,C,G,T] or it's integer alias [1, 2, 3, 4], unique pairs of SNP symbols (alleles) being arranged in anti-alphabetical (anti-integer) order, each SNP becoming a unique pair of alleles and pair of first and second nucleotide symbols replaced, without loss of information, by the integer 2 for the first symbol and the integer 1 for the second symbol so that, for example, if two relevant symbols are A and T, T=2 and A=1 the first allele becoming T=2 unless a SNP is homozygous, e.g., (A, A), in which case the allele pair is (1, 1); digitizing said data in said modified second database by vectorization to enable analysis by a programmed computer by embedding symbol representations into vector form under the transformation 1→[1,0], 2→[0,1], whereby a SNP such as (A, T) transforms as (A,T)→(T,A)→(2,1)→[0,1,1,0]; determining from said digitized data high value markers or loci of said predetermined complex hereditary disease through construction of Incremental Information, If, from D & C, by first determining probabilities based on symbol frequency at a particular locus, denoted by p d & p c , respectively, and If is calculated from: 
 If=−([ p d ln 2 p d +(1− p d )ln 2 (1− p d )]−[ p c ln 2 p c +(1− p c )ln 2 (1− p c )]), which determines the excess of information in the diseased set over the controls that resides in an allele and leads to a reduced set of high value loci and symbols; determining from said high value markers or loci an indicator vector determined by the criterion that this vector be optimally correlated with the disease population and minimally correlated with the control population, resulting in an eigenvector v defined by 1 N d ⁢ D † ⁢ Dv - 1 N c ⁢ C † ⁢ Cv = λ ⁢ ⁢ v , where λ the largest eigenvalue; determining from said indicator vector it's Alleles and Symbols to form a disease classifier as a two row matrix as follows: Classifier = [ Alleles Symbols ] , for said predetermined complex hereditary disease calculating the amount of agreement of the disease sequences with the Classifier, termed the Score, and similarly calculating the Scores of normal (control) populations; generating normal and disease probability distributions of database-derived disease and control scores over the derived range of genomic loci for the disease and normal (control) populations, the intersection of the two probability distributions defining a transition score that separates said database derived disease and control scores, said transition score serving as a disease/control threshold determinant for any putative sequence; using a gene array that includes elements which report on the activity of the classifier loci; obtaining a specimen from a patient containing the patient's genomic information; analyzing the patient's genomic information with said gene array to obtain a patient-derived score from specific sites determined by said disease classifier; and predicting the patient's likelihood of having or not having the disease by comparing the patient-derived score with said transition score and predicting that the degree of probability of the patient not contracting the disease increases as the patient-derived score increasingly deviates from said transition score in the direction of increasing database-derived control scores and that the degree of probability of the patient contracting the disease increases as the patient-derived score increasingly deviates from said transition score in the direction of increasing database-derived disease scores.