ArticleBMC genomics2012
iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies.
Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Complex genetic effects linked to plasma protein abundance in the UK Biobank.Nature communications · 2025Article
- A Novel Detection Method for High-Order SNP Epistatic Interactions Based on Explicit-Encoding-Based Multitasking Harmony Search.Interdisciplinary sciences, computational life sciences · 2024Article
- Deep integrative models for large-scale human genomics.Nucleic acids research · 2023Article
- Genome-Wide Association Study Reveals Candidate Genes for Root-Related Traits in Rice.Current issues in molecular biology · 2022Article
- Predictive SNPs for βScientific reports · 2021Observational
- How to increase our belief in discovered statistical interactions via large-scale association studies?Human genetics · 2019Review
- Prioritizing tests of epistasis through hierarchical representation of genomic redundancies.Nucleic acids research · 2017Article
- CollapsABEL: an R library for detecting compound heterozygote alleles in genome-wide association studies.BMC bioinformatics · 2016Article
- FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm.PloS one · 2016Article
- Detecting epistasis in human complex traits.Nature reviews. Genetics · 2014Review
- Practical aspects of genome-wide association interaction analysis.Human genetics · 2014Review
- Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering.BMC bioinformatics · 2014Article
- Approaching "phantom heritability" in psychiatry by hypothesis-driven gene-gene interactions.Frontiers in human neuroscience · 2013Article
- Advances in translational bioinformatics and population genomics in the Asia-Pacific.BMC genomics · 2012Article
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8 authors.
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Abstract
backgroundGenome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD).
resultsIn this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles.
conclusioniLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at http://www4a.biotec.or.th/GI/tools/iloci.
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