ArticleBMC medical genomics2019
Region-based interaction detection in genome-wide case-control studies.
Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Assessing the effect of interaction between gut microbiome and inflammatory bowel disease on the risks of depression.Brain, behavior, & immunity - health · 2022Article
- Tailored graphical lasso for data integration in gene network reconstruction.BMC bioinformatics · 2021Article
- Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities.Journal of genetics and genomics = Yi chuan xue bao · 2021Review
- Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies.Frontiers in cell and developmental biology · 2021Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn genome-wide association study (GWAS), conventional interaction detection methods such as BOOST are mostly based on SNP-SNP interactions. Although single nucleotides are the building blocks of human genome, single nucleotide polymorphisms (SNPs) are not necessarily the smallest functional unit for complex phenotypes. Region-based strategies have been proved to be successful in studies aiming at marginal effects.
methodsWe propose a novel region-region interaction detection method named RRIntCC (region-region interaction detection for case-control studies). RRIntCC uses the correlations between individual SNP-SNP interactions based on linkage disequilibrium (LD) contrast test.
resultsSimulation experiments showed that our method can achieve a higher power than conventional SNP-based methods with similar type-I-error rates. When applied to two real datasets, RRIntCC was able to find several significant regions, while BOOST failed to identify any significant results. The source code and the sample data of RRIntCC are available at http://bioinformatics.ust.hk/RRIntCC.html.
conclusionIn this paper, a new region-based interaction detection method with better performance than SNP-based interaction detection methods has been proposed.
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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.