ArticleGenetic epidemiology2011
Analysis of exome sequences with and without incorporating prior biological knowledge.
Article in Genetic epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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Who cites it
4 citing papers in PubMed.
- A method to incorporate prior information into score test for genetic association studies.BMC bioinformatics · 2014Article
- Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data.BMC systems biology · 2014Review
- HapFABIA: identification of very short segments of identity by descent characterized by rare variants in large sequencing data.Nucleic acids research · 2013Article
- Lessons learned from Genetic Analysis Workshop 17: transitioning from genome-wide association studies to whole-genome statistical genetic analysis.Genetic epidemiology · 2011Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Next-generation sequencing technology provides new opportunities and challenges in the search for genetic variants that underlie complex traits. It will also presumably uncover many new rare variants, but exactly how these variants should be incorporated into the data analysis remains a question. Several papers in our group from Genetic Analysis Workshop 17 evaluated different methods of rare variant analysis, including single-variant, gene-based, and pathway-based analyses and analyses that incorporated biological information. Although the performance of some of these methods strongly depends on the underlying disease model, integration of known biological information is helpful in detecting causal genes. Two work groups demonstrated that use of a Bayesian network and a collapsing receiver operating characteristic curve approach improves risk prediction when a disease is caused by many rare variants. Another work group suggested that modeling local rather than global ancestry may be beneficial when controlling the effect of population structure in rare variant association analysis.
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Registered trials
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