ArticleBMC genetics2014
GWGGI: software for genome-wide gene-gene interaction analysis.
Article in BMC genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions.BMC genomics · 2024Article
- A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction.Frontiers in bioinformatics · 2022Review
- Article
- A Three-Way Interaction among Maternal and Fetal Variants Contributing to Congenital Heart Defects.Annals of human genetics · 2016Article
- Mixed Linear Model Approaches of Association Mapping for Complex Traits Based on Omics Variants.Scientific reports · 2015Article
- A survey about methods dedicated to epistasis detection.Frontiers in genetics · 2015Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundWhile the importance of gene-gene interactions in human diseases has been well recognized, identifying them has been a great challenge, especially through association studies with millions of genetic markers and thousands of individuals. Computationally efficient and powerful tools are in great need for the identification of new gene-gene interactions in high-dimensional association studies.
resultWe develop C++ software for genome-wide gene-gene interaction analyses (GWGGI). GWGGI utilizes tree-based algorithms to search a large number of genetic markers for a disease-associated joint association with the consideration of high-order interactions, and then uses non-parametric statistics to test the joint association. The package includes two functions, likelihood ratio Mann-Whitney (LRMW) and Tree Assembling Mann-Whitney (TAMW). We optimize the data storage and computational efficiency of the software, making it feasible to run the genome-wide analysis on a personal computer. The use of GWGGI was demonstrated by using two real data-sets with nearly 500 k genetic markers.
conclusionThrough the empirical study, we demonstrated that the genome-wide gene-gene interaction analysis using GWGGI could be accomplished within a reasonable time on a personal computer (i.e., ~3.5 hours for LRMW and ~10 hours for TAMW). We also showed that LRMW was suitable to detect interaction among a small number of genetic variants with moderate-to-strong marginal effect, while TAMW was useful to detect interaction among a larger number of low-marginal-effect genetic variants.
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