A new structural approach to genomic discovery of disease: example of adult-onset diabetes

Abstract

This paper reports on an investigation of disease discovery from genomic data, by methods which depart substantially from customary practices found in the investigation of genome-wide association studies. Such data in general are composed of the genomic content from two contrasting phenotypes, e.g., disease versus control populations, and the analysis proceeds under the hypothesis that populational dissimilarities might reveal disease risk alleles. The proposed suite of new methods is in part based on information theory (Shannon in Bell Syst Tech J 27:379–423, 1948a; Bell Syst Tech J 27:623–656, 1948b; Jaynes in Phys Rev 106:620–630, 1957), and strong evidence will be given of the effectiveness of this new approach. The methodology extends naturally and successfully to predicting genomic disposition to disease arising from large collections of weakly contributing genomic loci. Evidence will be advanced that the example of adult-onset diabetes (“type 2 diabetes”) is such a candidate disease, and in this case, probably for the first time, it can be demonstrated that disease prediction is possible. Another novel element of this study is the search and identification of potential beneficial genomic loci that may counter a disease. The generality of the methodology suggests that it might extend to other diseases.