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False Discovery Rates for Rare Variants From Sequenced Data
Author(s) -
Capanu Marinela,
Seshan Venkatraman E.
Publication year - 2015
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21880
Subject(s) - false discovery rate , bayesian probability , multiple comparisons problem , computational biology , biology , locus (genetics) , genome wide association study , genetic association , computer science , evolutionary biology , genetics , artificial intelligence , statistics , genotype , mathematics , gene , single nucleotide polymorphism
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.