ArticleGenomics & informatics2016
Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes.
Article in Genomics & informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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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Who cites it
3 citing papers in PubMed.
- Associating complex traits with genetic variants: polygenic risk scores, pleiotropy and endophenotypes.Genetica · 2022Article
- A new method for exploring gene-gene and gene-environment interactions in GWAS with tree ensemble methods and SHAP values.BMC bioinformatics · 2021Article
- Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect.DNA research : an international journal for rapid publication of reports on genes and genomes · 2019Article
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Authors and funding
2 authors.
Funding
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Abstract
Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named "BOolean Operation-based Screening and Testing" (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.
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Registered trials
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