ArticleGenetic epidemiology2010
Bayesian mixture models for the incorporation of prior knowledge to inform genetic association studies.
Article in Genetic epidemiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Response and Toxicity to Cytarabine Therapy in Leukemia and Lymphoma: From Dose Puzzle to Pharmacogenomic Biomarkers.Cancers · 2021Review
- Leveraging genome characteristics to improve gene discovery for putamen subcortical brain structure.Scientific reports · 2017Article
- pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies.Heredity · 2017Article
- Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects.Genetic epidemiology · 2017Article
- Replication of breast cancer susceptibility loci in whites and African Americans using a Bayesian approach.American journal of epidemiology · 2014Article
- Article
- Cancer pharmacogenomics: strategies and challenges.Nature reviews. Genetics · 2013Review
- Incorporating prior information into association studies.Bioinformatics (Oxford, England) · 2012Article
- A strategy analysis for genetic association studies with known inbreeding.BMC genetics · 2011Article
- Gene-based tests of association.PLoS genetics · 2011Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
In the last decade, numerous genome-wide linkage and association studies of complex diseases have been completed. The critical question remains of how to best use this potentially valuable information to improve study design and statistical analysis in current and future genetic association studies. With genetic effect size for complex diseases being relatively small, the use of all available information is essential to untangle the genetic architecture of complex diseases. One promising approach to incorporating prior knowledge from linkage scans, or other information, is to up- or down-weight P-values resulting from genetic association study in either a frequentist or Bayesian manner. As an alternative to these methods, we propose a fully Bayesian mixture model to incorporate previous knowledge into on-going association analysis. In this approach, both the data and previous information collectively inform the association analysis, in contrast to modifying the association results (P-values) to conform to the prior knowledge. By using a Bayesian framework, one has flexibility in modeling, and is able to comprehensively assess the impact of model specification on posterior inferences. We illustrate the use of this method through a genome-wide linkage study of colorectal cancer, and a genome-wide association study of colorectal polyps.
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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.