A New Methodology to Associate SNPs with Human Diseases According to Their Pathway Related Context

Abstract

Genome-wide association studies (GWAS) with hundreds of żthousands of single nucleotide polymorphisms (SNPs) are popular strategies to reveal the genetic basis of human complex diseases. Despite many successes of GWAS, it is well recognized that new analytical approaches have to be integrated to achieve their full potential. Starting with a list of SNPs, found to be associated with disease in GWAS, here we propose a novel methodology to devise functionally important KEGG pathways through the identification of genes within these pathways, where these genes are obtained from SNP analysis. Our methodology is based on functionalization of important SNPs to identify effected genes and disease related pathways. We have tested our methodology on WTCCC Rheumatoid Arthritis (RA) dataset and identified: i) previously known RA related KEGG pathways (e.g., Toll-like receptor signaling, Jak-STAT signaling, Antigen processing, Leukocyte transendothelial migration and MAPK signaling pathways); ii) additional KEGG pathways (e.g., Pathways in cancer, Neurotrophin signaling, Chemokine signaling pathways) as associated with RA. Furthermore, these newly found pathways included genes which are targets of RA-specific drugs. Even though GWAS analysis identifies 14 out of 83 of those drug target genes; newly found functionally important KEGG pathways led to the discovery of 25 out of 83 genes, known to be used as drug targets for the treatment of RA. Among the previously known pathways, we identified additional genes associated with RA (e.g. Antigen processing and presentation, Tight junction). Importantly, within these pathways, the associations between some of these additionally found genes, such as HLA-C, HLA-G, PRKCQ, PRKCZ, TAP1, TAP2 and RA were verified by either OMIM database or by literature retrieved from the NCBI PubMed module. With the whole-genome sequencing on the horizon, we show that the full potential of GWAS can be achieved by integrating pathway and network-oriented analysis and prior knowledge from functional properties of a SNP.

Identification of possible pathogenic pathways in Behçet’s disease using genome-wide association study data from two different populations

“…8,19,20,[26][27][28] PANOGA is publicly available at http://panoga.sabanciuniv.edu. In this study, we further improved PANOGA to identify SNP-targeted pathways with the genes responsible for BD susceptibility.…”

“…In this regard, pathway-based approaches to GWAS may be more helpful in a search for multiple genes involved in the same biological pathway, where the common variations in each of these genes have little correlation with disease risk. [8][9][10][11][12][13][14] It has been observed that genes that have aberrations associated with a given complex disease tend to be part of the same subnetwork of the overall protein-protein interaction (PPI) network. 15,16 Hence, to be able to explain the connections between genotypic and phenotypic data, perturbed network modules need to be detected.…”

“…15,16 In this respect, the use of PPI networks has demonstrated great success in enhancing the outcome of GWASs. [8][9][10]13,[17][18][19][20] Another important piece of information that could improve the analysis of GWAS data sets is the functional effect of a SNP. 21,22 Although DNA variations that alter protein function can have significant effects, such as NOD2 mutations in inflammatory bowel disease 23 and FLG mutations in eczema, 24 other types of SNPs do not have such serious consequences in disease development mechanisms.…”

“…In this respect, we analyzed the GWAS data from two different populations using the methodology that we had developed to identify disease-associated pathways by combining nominally significant evidence of genetic association with the known biochemical pathways, PPI networks, and the functional information of selected SNPs. 8,[26][27][28] In the following sections, we will discuss our findings.…”

GenomeRunner web server: regulatory similarity and differences define the functional impact of SNP sets

“…To our knowledge, only three studies have investigated the use of similarity of patient-specific genomic information for classification purposes (Bakir-Gungor and Sezerman, 2011;Hofree et al, 2013;Wang et al, 2014). However, these studies use gene-centric reference to map SNPs to pathways and gene interaction networks, the idea we have also independently explored (https://github.com/ mdozmorov/PathwayRunner).…”