ArticleBioinformatics (Oxford, England)2013
Network-guided sparse regression modeling for detection of gene-by-gene interactions.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meta-Analysis for Penalized Regression Methods with Multi-Cohort Genome-Wide Association Studies.Human heredity · 2016Pooled it
- Systems Genetics Analysis of Genome-Wide Association Study Reveals Novel Associations Between Key Biological Processes and Coronary Artery Disease.Arteriosclerosis, thrombosis, and vascular biology · 2015Pooled it
- A Bayesian model for detection of high-order interactions among genetic variants in genome-wide association studies.BMC genomics · 2015Article
- Regularized machine learning in the genetic prediction of complex traits.PLoS genetics · 2014Article
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Authors and funding
5 authors.
Funding
Abstract
motivationGenetic variants identified by genome-wide association studies to date explain only a small fraction of total heritability. Gene-by-gene interaction is one important potential source of unexplained total heritability. We propose a novel approach to detect such interactions that uses penalized regression and sparse estimation principles, and incorporates outside biological knowledge through a network-based penalty.
resultsWe tested our new method on simulated and real data. Simulation showed that with reasonable outside biological knowledge, our method performs noticeably better than stage-wise strategies (i.e. selecting main effects first, and interactions second, from those main effects selected) in finding true interactions, especially when the marginal strength of main effects is weak. We applied our method to Framingham Heart Study data on total plasma immunoglobulin E (IgE) concentrations and found a number of interactions among different classes of human leukocyte antigen genes that may interact to influence the risk of developing IgE dysregulation and allergy. AVAILABILITY: The proposed method is implemented in R and available at http://math.bu.edu/people/kolaczyk/software.html. CONTACT: chenlu@bu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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