ArticleAnnals of human genetics2011
Bayesian analysis of genetic interactions in case-control studies, with application to adiponectin genes and colorectal cancer risk.
Article in Annals of human genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 6 of them syntheses that pooled it.
What it found
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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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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.
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Who cites it
25 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Detecting fitness epistasis in recently admixed populations with genome-wide data.BMC genomics · 2020Pooled it
- A population-specific correlation between ADIPOQ rs2241766 and rs 1501299 and colorectal cancer risk: a meta-analysis for debate.International journal of clinical oncology · 2017Pooled it
- Three ADIPOR1 Polymorphisms and Cancer Risk: A Meta-Analysis of Case-Control Studies.PloS one · 2015Pooled it
- Associations between variants on ADIPOQ and ADIPOR1 with colorectal cancer risk: a Chinese case-control study and updated meta-analysis.BMC medical genetics · 2014Pooled it
- Five common haplotype-tagging variants of adiponectin (ADIPOQ) and cancer susceptibility: a meta-analysis.Genetic testing and molecular biomarkers · 2014Pooled it
- The roles of ADIPOQ genetic variations in cancer risk: evidence from published studies.Molecular biology reports · 2013Pooled it
- Gene-gene interaction analysis identifies a new genetic risk factor for colorectal cancer.Journal of biomedical science · 2015Trial
- BHCox: Bayesian heredity-constrained Cox proportional hazards models for detecting gene-environment interactions.BMC bioinformatics · 2025Article
- Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.Cancer genomics & proteomics · 2023Review
- EM Adaptive LASSO-A Multilocus Modeling Strategy for Detecting SNPs Associated with Zero-inflated Count Phenotypes.Frontiers in genetics · 2016Article
- Filter-free exhaustive odds ratio-based genome-wide interaction approach pinpoints evidence for interaction in the HLA region in psoriasis.BMC genetics · 2015Article
- A Bayesian approach to identify genes and gene-level SNP aggregates in a genetic analysis of cancer data.Statistics and its interface · 2015Article
- Evaluating methods for modeling epistasis networks with application to head and neck cancer.Cancer informatics · 2015Article
- Detecting epistasis in human complex traits.Nature reviews. Genetics · 2014Review
- Multiple comparisons in genetic association studies: a hierarchical modeling approach.Statistical applications in genetics and molecular biology · 2014Article
- Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report.Genetic epidemiology · 2013Article
- Hierarchical shrinkage priors and model fitting for high-dimensional generalized linear models.Statistical applications in genetics and molecular biology · 2012Article
- Hierarchical generalized linear models for multiple groups of rare and common variants: jointly estimating group and individual-variant effects.PLoS genetics · 2011Article
- Polymorphisms of ADIPOQ and ADIPOR1 and prostate cancer risk.Metabolism: clinical and experimental · 2011Article
- The role of the fat mass and obesity associated gene (FTO) in breast cancer risk.BMC medical genetics · 2011Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Complex diseases such as cancers are influenced by interacting networks of genetic and environmental factors. However, a joint analysis of multiple genes and environmental factors is challenging, owing to potentially large numbers of correlated and complex variables. We describe Bayesian generalized linear models for simultaneously analyzing covariates, main effects of numerous loci, gene-gene and gene-environment interactions in population case-control studies. Our Bayesian models use Student-t prior distributions with different shrinkage parameters for different types of effects, allowing reliable estimates of main effects and interactions and hence increasing the power for detection of real signals. We implement a fast and stable algorithm for fitting models by extending available tools for classical generalized linear models to the Bayesian case. We propose a novel method to interpret and visualize models with multiple interactions by computing the average predictive probability. Simulations show that the method has the potential to dissect interacting networks of complex diseases. Application of the method to a large case-control study of adiponectin genes and colorectal cancer risk highlights the previous results and detects new epistatic interactions and sex-specific effects that warrant follow-up in independent studies.
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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.