“Good Enough Solutions” and the Genetics of Complex Diseases

Abstract

In this Emerging Science Review, we discuss a systems genetics strategy, which we call gene module association study (GMAS), as a novel approach complementing genome-wide association studies (GWAS), to understand complex diseases by focusing on how genes work together in groups rather than singly. The first step is to characterize phenotypic differences among a genetically diverse population. The second step is to use gene expression microarray (or other high-throughput) data from the population to construct gene coexpression networks. Coexpression analysis typically groups 20 000 genes into 20 to 30 modules containing tens to hundreds of genes, whose aggregate behavior can be represented by the module's “eigengene.” The third step is to correlate expression patterns with phenotype, as in GWAS, only applied to eigengenes instead of single nucleotide polymorphisms. The goal of the GMAS approach is to identify groups of coregulated genes that explain complex traits from a systems perspective. From an evolutionary standpoint, we hypothesize that variability in eigengene patterns reflects the “good enough solution” concept, that biological systems are sufficiently complex so that many possible combinations of the same elements (in this case eigengenes) can produce an equivalent output, that is, a “good enough solution” to accomplish normal biological functions. However, when faced with environmental stresses, some “good enough solutions” adapt better than others, explaining individual variability to disease and drug susceptibility. If validated, GMAS may imply that common polygenic diseases are related as much to group interactions between normal genes, as to multiple gene mutations.