ArticleGenetic epidemiology2014
Testing gene-gene interactions in genome wide association studies.
Article in Genetic epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- MR-GGI: accurate inference of gene-gene interactions using Mendelian randomization.BMC bioinformatics · 2024Article
- Poor statistical power in population-based association study of gene interaction.BMC medical genomics · 2024Article
- Atrial fibrillation and left atrial size and function: a Mendelian randomization study.Scientific reports · 2021Article
- Region-based interaction detection in genome-wide case-control studies.BMC medical genomics · 2019Article
- Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes.Genomics & informatics · 2016Article
- GCORE-sib: An efficient gene-gene interaction tool for genome-wide association studies based on discordant sib pairs.BMC bioinformatics · 2016Article
- An efficient gene-gene interaction test for genome-wide association studies in trio families.Bioinformatics (Oxford, England) · 2016Article
- Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.PLoS genetics · 2016Article
- Genome-Wide Analysis of Gene-Gene and Gene-Environment Interactions Using Closed-Form Wald Tests.Genetic epidemiology · 2015Article
- Review
- Detecting epistasis in human complex traits.Nature reviews. Genetics · 2014Review
- Lack of replication of interactions between polymorphisms in rheumatoid arthritis susceptibility: case-control study.Arthritis research & therapy · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Detection of gene-gene interaction has become increasingly popular over the past decade in genome wide association studies (GWAS). Besides traditional logistic regression analysis for detecting interactions between two markers, new methods have been developed in recent years such as comparing linkage disequilibrium (LD) in case and control groups. All these methods form the building blocks of most screening strategies for disease susceptibility loci in GWAS. In this paper, we are interested in comparing the competing methods and providing practical guidelines for selecting appropriate testing methods for interaction in GWAS. We first review a series of existing statistical methods to detect interactions, and then examine different definitions of interactions to gain insight into the theoretical relationship between the existing testing methods. Lastly, we perform extensive simulations to compare powers of various methods to detect either interaction between two markers at two unlinked loci or the overall association allowing for both interaction and main effects. This investigation reveals informative characteristics of various methods that are helpful to GWAS investigators.
Indexed as
Identifiers
What OpenQuestion holds
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.