ArticleBMC bioinformatics2014
Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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20 citing papers in PubMed, 1 synthesis or guideline pooled it, 83 citations in OpenAlex.
- GWAS of longevity in CHARGE consortium confirms APOE and FOXO3 candidacy.The journals of gerontology. Series A, Biological sciences and medical sciences · 2015Pooled it
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- JS-MA: A Jensen-Shannon Divergence Based Method for Mapping Genome-Wide Associations on Multiple Diseases.Frontiers in genetics · 2020Article
- HiSSI: high-order SNP-SNP interactions detection based on efficient significant pattern and differential evolution.BMC medical genomics · 2019Article
- Locating potentially lethal genes using the abnormal distributions of genotypes.Scientific reports · 2019Article
- FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies.Genes · 2018Article
- ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information.International journal of molecular sciences · 2018Article
- Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations.Scientific reports · 2017Article
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- Integrative information theoretic network analysis for genome-wide association study of aspirin exacerbated respiratory disease in Korean population.BMC medical genomics · 2017Article
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- Combinations of Genetic Variants Occurring Exclusively in Patients.Computational and structural biotechnology journal · 2017Review
- FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm.PloS one · 2016Article
- VariantSpark: population scale clustering of genotype information.BMC genomics · 2015Article
- An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.BioMed research international · 2015Article
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4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundTaking the advantage 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.