ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2013
Statistical epistasis networks reduce the computational complexity of searching three-locus genetic models.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Synergistic Epistasis and Systems Biology Approaches to Uncover a Pharmacogenomic Map Linked to Pain, Anti-Inflammatory and Immunomodulating Agents (PAIma) in a Healthy Cohort.Cellular and molecular neurobiology · 2024Article
- EpistasisRank and EpistasisKatz: interaction network centrality methods that integrate prior knowledge networks.Bioinformatics (Oxford, England) · 2019Article
- How to increase our belief in discovered statistical interactions via large-scale association studies?Human genetics · 2019Review
- Integrative information theoretic network analysis for genome-wide association study of aspirin exacerbated respiratory disease in Korean population.BMC medical genomics · 2017Article
- CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions.BMC bioinformatics · 2016Article
- Heuristic identification of biological architectures for simulating complex hierarchical genetic interactions.Genetic epidemiology · 2015Article
- Functional dyadicity and heterophilicity of gene-gene interactions in statistical epistasis networks.BioData mining · 2015Article
- Practical aspects of genome-wide association interaction analysis.Human genetics · 2014Review
- The post GWAS era: strategies to identify gene-gene and gene-environment interactions in urinary bladder cancer.EXCLI journal · 2014Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
The rapid development of sequencing technologies makes thousands to millions of genetic attributes available for testing associations with various biological traits. Searching this enormous high-dimensional data space imposes a great computational challenge in genome-wide association studies. We introduce a network-based approach to supervise the search for three-locus models of disease susceptibility. Such statistical epistasis networks (SEN) are built using strong pairwise epistatic interactions and provide a global interaction map to search for higher-order interactions by prioritizing genetic attributes clustered together in the networks. Applying this approach to a population-based bladder cancer dataset, we found a high susceptibility three-way model of genetic variations in DNA repair and immune regulation pathways, which holds great potential for studying the etiology of bladder cancer with further biological validations. We demonstrate that our SEN-supervised search is able to find a small subset of three-locus models with significantly high associations at a substantially reduced computational cost.
Indexed as
Identifiers
23424144PMC3587773W1561391947What OpenQuestion holds
Registered trials
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.