Pathway analysis of seven common diseases assessed by genome-wide association

Abstract

Recent genome-wide association studies (GWAS) have identified DNA sequence variations that exhibit unequivocal statistical associations with many common chronic diseases. However, the vast majority of these studies identified variations that explain only a very small fraction of disease burden in the population at large, suggesting that other factors, such as multiple rare or low-penetrance variations and interacting environmental factors, are major contributors to disease susceptibility. Identifying multiple low-penetrance variations (or "polygenes") contributing to disease susceptibility will be difficult. We present a pathway analysis approach to characterizing the likely polygenic basis of seven common diseases using the Wellcome Trust Case Control Consortium (WTCCC) GWAS results. We identify numerous pathways implicated in disease predisposition that would have not been revealed using standard single-locus GWAS statistical analysis criteria. Many of these pathways have long been assumed to contain polymorphic genes that lead to disease predisposition. Additionally, we analyze the genetic relationships between the seven diseases, and based upon similarities with respect to the associated genes and pathways affected in each, propose a new way of categorizing the diseases.

Figure 1

Tree diagram of disease based upon the distances calculated as one minus the correlation coefficient between each disease. BD = bipolar disorder, CAD = coronary artery disease, T2D = type 2 diabetes, HT = hypertension, CD = Crohn’s disease, RA = rheumatoid arthritis, T1D = type 1 diabetes. The analysis suggests three classes of diseases (see Discussion), differing from commonly accepted classifications.