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Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants
Author(s) -
Guey Lin T.,
Kravic Jasmina,
Melander Olle,
Burtt Noël P.,
Laramie Jason M.,
Lyssenko Valeriya,
Jonsson Anna,
Lindholm Eero,
Tuomi Tiinamaija,
Isomaa Bo,
Nilsson Peter,
Almgren Peter,
Kathiresan Sekar,
Groop Leif,
Seymour Albert B.,
Altshuler David,
Voight Benjamin F.
Publication year - 2011
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.20572
Subject(s) - sample size determination , statistical power , replication (statistics) , sampling (signal processing) , statistics , population , selection (genetic algorithm) , biology , computational biology , type i and type ii errors , trait , genetics , computer science , mathematics , artificial intelligence , medicine , programming language , environmental health , filter (signal processing) , computer vision
Abstract Next‐generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost‐effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two‐stage design. Two‐stage designs include a broad‐based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow‐up; third, the impact of extreme and random sampling in (Phase 2) replication . We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false‐negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well‐cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling. Genet. Epidemiol . 35: 236‐246, 2011. © 2011 Wiley‐Liss, Inc.