Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering

Abstract

BackgroudTaking the advan tage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger.ResultsIn this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions.ConclusionsExperimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS.

Omics Biomarkers in Ophthalmology

“…These higher-order interaction effects may explain additional genetic variability in common eye diseases. [6][7][8] The genetic code alone only partially contributes to the development of a complex disease. Environmental factors influence gene expression levels and metabolism that ultimately triggers pathogenic mechanisms (Fig.…”

Detection of SNP-SNP interaction based on the generalized particle swarm optimization algorithm

“…Suppose there are N data sets with the same parameter settings and i Q ground-truth SNPs in data set i , detection power 1 is defined as We exemplify six commonly used SNP-SNP interaction models (Model1 ~ Model6) for this study [11,12,[15][16][17]. The first two models (Model1 and Model2) are the SNP-SNP interaction models displaying both marginal effects and interactive effects.…”

A Comprehensive Review of Emerging Computational Methods for Gene Identification

“…One of the feasible solutions, compatible with big data environments, is to design a concurrent pipeline or a novel parallel framework (e.g., MapReduce) [187] to integrate outstanding ML methods into modern high performance context.…”

“…Some particular cloud computing algorithms have been developed to resolve biological problems [187][188][189], especially for handling the issue of a large volume of data and processing compute-intensive tasks.…”