False discovery rates for rare variants from sequenced data Journal Article


Authors: Capanu, M.; Seshan, V. E.
Article Title: False discovery rates for rare variants from sequenced data
Abstract: The detection of rare deleterious variants is the preeminent current technical challenge in statistical genetics. Sorting the deleterious from neutral variants at a disease locus is challenging because of the sparseness of the evidence for each individual variant. Hierarchical modeling and Bayesian model uncertainty are two techniques that have been shown to be promising in pinpointing individual rare variants that may be driving the association. Interpreting the results from these techniques from the perspective of multiple testing is a challenge and the goal of this article is to better understand their false discovery properties. Using simulations, we conclude that accurate false discovery control cannot be achieved in this framework unless the magnitude of the variants' risk is large and the hierarchical characteristics have high accuracy in distinguishing deleterious from neutral variants.
Keywords: sequence analysis; gene deletion; bayes theorem; genetic variability; risk assessment; genetic procedures; receiver operating characteristic; mathematical analysis; rare variants; local false discovery rate; false discovery rate; article; hierarchical model
Journal Title: Genetic Epidemiology
Volume: 39
Issue: 2
ISSN: 0741-0395
Publisher: John Wiley & Sons, Inc.  
Date Published: 2015-02-01
Start Page: 65
End Page: 76
Language: English
DOI: 10.1002/gepi.21880
PROVIDER: scopus
PUBMED: 25556339
PMCID: PMC4711769
DOI/URL:
Notes: Export Date: 2 February 2015 -- Source: Scopus
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  1. Venkatraman Ennapadam Seshan
    385 Seshan
  2. Marinela Capanu
    390 Capanu