ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2009
Structures and Assumptions: Strategies to Harness Gene × Gene and Gene × Environment Interactions in GWAS.
Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 26 citations in OpenAlex.
- Biomarker interaction selection and disease detection based on multivariate gain ratio.BMC bioinformatics · 2022Article
- Approaches to treatment effect heterogeneity in the presence of confounding.Statistics in medicine · 2019Article
- Article
- Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA.Journal of the American Statistical Association · 2014Article
- Testing gene-gene interactions in genome wide association studies.Genetic epidemiology · 2014Article
- Informative gene selection and direct classification of tumor based on Chi-square test of pairwise gene interactions.BioMed research international · 2014Article
- Variation in HNF1B and Obesity May Influence Prostate Cancer Risk in African American Men: A Pilot Study.Prostate cancer · 2013Article
- Interaction-based feature selection and classification for high-dimensional biological data.Bioinformatics (Oxford, England) · 2012Article
- Multivariate detection of gene-gene interactions.Genetic epidemiology · 2012Article
- Molecular basis of obesity: current status and future prospects.Current genomics · 2011Article
- Adaptive tests for detecting gene-gene and gene-environment interactions.Human heredity · 2011Article
- Bayesian analysis of genetic interactions in case-control studies, with application to adiponectin genes and colorectal cancer risk.Annals of human genetics · 2011Article
- Computational Analysis of Molecular Interaction Networks Underlying Change of HIV-1 Resistance to Selected Reverse Transcriptase Inhibitors.Bioinformatics and biology insights · 2010Article
- Statistical analysis of genetic interactions.Genetics research · 2010Review
- Boosting predictions of treatment success.Proceedings of the National Academy of Sciences of the United States of America · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Genome-wide association studies, in which as many as a million single nucleotide polymorphisms (SNP) are measured on several thousand samples, are quickly becoming a common type of study for identifying genetic factors associated with many phenotypes. There is a strong assumption that interactions between SNPs or genes and interactions between genes and environmental factors substantially contribute to the genetic risk of a disease. Identification of such interactions could potentially lead to increased understanding about disease mechanisms; drug × gene interactions could have profound applications for personalized medicine; strong interaction effects could be beneficial for risk prediction models. In this paper we provide an overview of different approaches to model interactions, emphasizing approaches that make specific use of the structure of genetic data, and those that make specific modeling assumptions that may (or may not) be reasonable to make. We conclude that to identify interactions it is often necessary to do some selection of SNPs, for example, based on prior hypothesis or marginal significance, but that to identify SNPs that are marginally associated with a disease it may also be useful to consider larger numbers of interactions.
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.