Prediction of Disease and Phenotype Associations from Genome-Wide Association Studies

Abstract

BackgroundGenome wide association studies (GWAS) have proven useful as a method for identifying genetic variations associated with diseases. In this study, we analyzed GWAS data for 61 diseases and phenotypes to elucidate common associations based on single nucleotide polymorphisms (SNP). The study was an expansion on a previous study on identifying disease associations via data from a single GWAS on seven diseases.Methodology/Principal FindingsAdjustments to the originally reported study included expansion of the SNP dataset using Linkage Disequilibrium (LD) and refinement of the four levels of analysis to encompass SNP, SNP block, gene, and pathway level comparisons. A pair-wise comparison between diseases and phenotypes was performed at each level and the Jaccard similarity index was used to measure the degree of association between two diseases/phenotypes. Disease relatedness networks (DRNs) were used to visualize our results. We saw predominant relatedness between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis for the first three levels of analysis. Expected relatedness was also seen between lipid- and blood-related traits.Conclusions/SignificanceThe predominant associations between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis can be validated by clinical studies. The diseases have been proposed to share a systemic inflammation phenotype that can result in progression of additional diseases in patients with one of these three diseases. We also noticed unexpected relationships between metabolic and neurological diseases at the pathway comparison level. The less significant relationships found between diseases require a more detailed literature review to determine validity of the predictions. The results from this study serve as a first step towards a better understanding of seemingly unrelated diseases and phenotypes with similar symptoms or modes of treatment.

Meta-Analysis of Genome-Wide Association Studies to Understand Disease Relatedness

“…Suggested associations were verified by principal components analysis (PCA) and minor data mining for clinical relevance. Complete details of these methods were previously described by Lewis et al (Lewis et al, 2011).…”

“…The lowest p-values in each category Table 1. List of diseases and phenotypes considered for this study and the previous study (Lewis et al, 2011) with corresponding abbreviations.…”

“…JPT was also missing the (HBF and HT) association that was observed in the other populations. Further details regarding the results of this portion of the study were previously submitted for publication (Lewis et al, 2011 …”

DermaGene and VitmiRS: a comprehensive systems analysis of genetic dermatological disorders

“…Due to the contribution of disease comorbidities as a burden on societies at large, it becomes crucial for risk stratification and charting treatment course. One of the most powerful approaches in the present times, the Genome-Wide Association Studies (GWAS), serves as the primary pipeline for identifying disease variants and researching association of diseases and their phenotypes (Lewis et al 2011). The complete sequen…”

Association of Human Leukocyte Antigen with Interstitial Lung Disease in Rheumatoid Arthritis: A Protective Role for Shared Epitope

“…RA pathogenesis is multifactorial and the disease susceptibility is associated with genetic and environmental factors [1], [2], [3]. Human Leukocyte Antigen (HLA) is known to be associated with RA in most ethnic groups.…”