ArticleBMC bioinformatics2008
Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 59 citations in OpenAlex.
- Identification of High-Order Single-Nucleotide Polymorphism Barcodes in Breast Cancer Using a Hybrid Taguchi-Genetic Algorithm: Case-Control Study.JMIR medical informatics · 2020Article
- Modulation on Glutamic Pathway of Frontal-Striatum-Thalamus by rs11146020 and rs3813296 Gene Polymorphism in First-Episode Negative Schizophrenia.Frontiers in neuroscience · 2020Article
- Epistasis of polymorphisms related to the articular cartilage extracellular matrix in knee osteoarthritis: Analysis-based multifactor dimensionality reduction.Genetics and molecular biology · 2020Article
- Impact of the gene-gene interactions related to the HIF-1α signaling pathway with the knee osteoarthritis development.Clinical rheumatology · 2019Article
- Testing Differential Networks with Applications to Detecting Gene-by-Gene Interactions.Biometrika · 2015Article
- Oxido-reductive regulation of vascular remodeling by receptor tyrosine kinase ROS1.The Journal of clinical investigation · 2014Article
- Double-bottom chaotic map particle swarm optimization based on chi-square test to determine gene-gene interactions.BioMed research international · 2014Article
- Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer.Journal of clinical bioinformatics · 2013Article
- Interpreting noncoding genetic variation in complex traits and human disease.Nature biotechnology · 2012Review
- Mining functional gene modules linked with rheumatoid arthritis using a SNP-SNP network.Genomics, proteomics & bioinformatics · 2012Article
- Article
- Gene-environment interplay in common complex diseases: forging an integrative model—recommendations from an NIH workshop.Genetic epidemiology · 2011Article
- A genetic ensemble approach for gene-gene interaction identification.BMC bioinformatics · 2010Article
- Prioritizing GWAS results: A review of statistical methods and recommendations for their application.American journal of human genetics · 2010Review
- The interaction of reward genes with environmental factors in contribution to alcoholism in mexican americans.Alcoholism, clinical and experimental research · 2009Article
- Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses.BMC bioinformatics · 2009Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThe risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives.
resultsWe developed the Polymorphism Interaction Analysis tool (PIA version 2.0) to include different approaches for ranking and scoring SNP combinations, to account for imbalances between case and control ratios, stratify on particular factors, and examine associations of user-defined pathways (based on SNP or gene) with case status. PIA v. 2.0 detected 2-SNP interactions as the highest ranking model 77% of the time, using simulated data sets of genetic models of interaction (minor allele frequency = 0.2; heritability = 0.01; N = 1600) generated previously [Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007, 31:306-315.]. Interacting SNPs were detected in both balanced (20 SNPs) and imbalanced data (case:control 1:2 and 1:4, 10 SNPs) in the context of non-interacting SNPs.
conclusionPIA v. 2.0 is a useful tool for exploring gene*gene or gene*environment interactions and identifying a small number of putative associations which may be investigated further using other statistical methods and in replication study populations.
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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.