ArticlePloS one2017
The performance of a new local false discovery rate method on tests of association between coronary artery disease (CAD) and genome-wide genetic variants.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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
3 citing papers in PubMed.
- Association of Thymidylate Synthase (International journal of molecular sciences · 2023Article
- Reciprocal Perspective for Improved Protein-Protein Interaction Prediction.Scientific reports · 2018Article
- Estimating the local false discovery rate via a bootstrap solution to the reference class problem.PloS one · 2018Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The maximum entropy (ME) method is a recently-developed approach for estimating local false discovery rates (LFDR) that incorporates external information allowing assignment of a subset of tests to a category with a different prior probability of following the null hypothesis. Using this ME method, we have reanalyzed the findings from a recent large genome-wide association study of coronary artery disease (CAD), incorporating biologic annotations. Our revised LFDR estimates show many large reductions in LFDR, particularly among the genetic variants belonging to annotation categories that were known to be of particular interest for CAD. However, among SNPs with rare minor allele frequencies, the reductions in LFDR were modest in size.
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