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.
Highlights
Genome-wide association study (GWAS) has been proved to be a powerful genomic and statistical inference tool, and its goal is to identify genetic susceptibility through statistical tests on associations between a trait of interests and the genetic information of unrelated individuals [1]
We first give definitions of 6 simulated disease models and the power metric used to evaluate the effectiveness of DCHE in comparison with other 4 popular epistatic interactions detecting methods, i.e. TEAM [29], SNPRuler [24], EDCF [20], BOOST [18]
Three reasons for choosing above 4 approaches are as follows: (1) TEAM, EDCF and BOOST all use the exhaustive search strategy for detecting two-locus interactions, so the comparison of their performance is fair; (2) a recent review tested five available methods and recommended BOOST and TEAM as a powerful tool for searching epistatic interactions on a genome-wide scale [15]; (3) our goal is to discover highorder epistatic interactions from GWAS data, and among 4 detectors excluding DCHE, only SNPRuler and EDCF are equipped the ability to search interactions with more than 2 single nucleotide polymorphism (SNP)
Summary
Genome-wide association study (GWAS) has been proved to be a powerful genomic and statistical inference tool, and its goal is to identify genetic susceptibility through statistical tests on associations between a trait of interests and the genetic information of unrelated individuals [1]. Genotype-phenotype association studies have established that single nucleotide polymorphisms (SNPs) [2], one type of genetic variants, are associated with a variety of diseases [3]. The primary analysis paradigm for GWAS is dominated by the analysis on susceptibility of individual SNPs, which can only explain a small part of genetic causal effects for complex diseases [4]. For better understanding underlying causes of complex disease traits, identifying joint genetic effects (epistasis) across the whole genome has attracted more attentions [5]. Many studies [8,9,10,11]
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