The Possibilities of Filtering Pairs of SNPs in GWAS Studies Exploratory Study on Public Protein-interaction and Pathway Data

LEXA, Matej and Stanislav ŠTEFANIČ. The Possibilities of Filtering Pairs of SNPs in GWAS Studies Exploratory Study on Public Protein-interaction and Pathway Data. In Pastor, O Sinoquet, C Plantier, G Schultz, T Fred, A Gamboa, H. BIOINFORMATICS 2014: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIOINFORMATICS MODELS, METHODS AND ALGORITHMS. SETUBAL: SCITEPRESS, 2014, p. 259-264. ISBN 978-989-758-012-3.

Basic information

• Original name: The Possibilities of Filtering Pairs of SNPs in GWAS Studies Exploratory Study on Public Protein-interaction and Pathway Data
• Authors: LEXA, Matej (703 Slovakia, guarantor, belonging to the institution) and Stanislav ŠTEFANIČ (703 Slovakia, belonging to the institution).
• Edition: SETUBAL, BIOINFORMATICS 2014: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIOINFORMATICS MODELS, METHODS AND ALGORITHMS, p. 259-264, 6 pp. 2014.
• Publisher: SCITEPRESS

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: France
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: printed version "print"
• RIV identification code: RIV/00216224:14330/14:00094268
• Organization unit: Faculty of Informatics
• ISBN: 978-989-758-012-3
• UT WoS: 000345686400039
• Keywords in English: GWAS; SNPs; Biological Knowledge; Databases; Genotyping; Filtering

Abstract

Genome-wide association studies have become a standard way of discovering novel causative alleles by looking for statisticaly significant associations in patient genotyping data. The present challenge for these methods is to discover associations involving multiple interacting loci, a common phenomenon in diseases often related to epistasis. The main problem is the exponential increase in necessary computational power for every additional interacting locus considered in association tests. Several approaches have been proposed to manage this problem, including limiting analysis to interacting pairs and filtering SNPs according to external biological knowledge. Here we explore the possibilities of using public protein interaction data and pathway maps to filter out only pairs of SNPs that are likely to interact, perhaps because of epistatic mechanisms working at the protein level. After filtering all possible pairs of SNPs by their presence in common protein-protein interactions or proteins sharing a metabolic or signalling pathway, we calculate the possible reduction in computational requirements under different scenarios. We discuss these exploratory results in the context of the so-called "lost heredity" and the usefulness of this approach for similar scenarios.