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Power in the Phenotypic Extremes: A Simulation Study of Power in Discovery and Replication of Rare Variants

Guey, Lin T.; Kravic, Jasmina LU ; Melander, Olle LU ; Burtt, Noel P.; Laramie, Jason M.; Lyssenko, Valeriya LU ; Jonsson, Anna LU ; Lindholm, Eero LU ; Tuomi, Tiinamaija and Isomaa, Bo, et al. (2011) In Genetic Epidemiology 35(4). p.236-246
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... (More)
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. (c) 2011 Wiley-Liss, Inc. (Less)
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published
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keywords
* next-generation sequencing * liability ascertainment * variant discovery * replication of association * phenotype extremes
in
Genetic Epidemiology
volume
35
issue
4
pages
236 - 246
publisher
John Wiley & Sons
external identifiers
  • wos:000289375400004
  • scopus:79959557454
ISSN
0741-0395
DOI
10.1002/gepi.20572
language
English
LU publication?
yes
id
a5d54699-2b4d-4be9-b152-cae4de0b066d (old id 1965707)
date added to LUP
2011-05-23 14:33:56
date last changed
2017-08-27 03:42:52
@article{a5d54699-2b4d-4be9-b152-cae4de0b066d,
  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. (c) 2011 Wiley-Liss, Inc.},
  author       = {Guey, Lin T. and Kravic, Jasmina and Melander, Olle and Burtt, Noel P. and Laramie, Jason M. and Lyssenko, Valeriya and Jonsson, Anna and Lindholm, Eero and Tuomi, Tiinamaija and Isomaa, Bo and Nilsson, Peter and Almgren, Peter and Kathiresan, Sekar and Groop, Leif and Seymour, Albert B. and Altshuler, David and Voight, Benjamin F.},
  issn         = {0741-0395},
  keyword      = {* next-generation sequencing
* liability ascertainment
* variant discovery
* replication of association
* phenotype extremes},
  language     = {eng},
  number       = {4},
  pages        = {236--246},
  publisher    = {John Wiley & Sons},
  series       = {Genetic Epidemiology},
  title        = {Power in the Phenotypic Extremes: A Simulation Study of Power in Discovery and Replication of Rare Variants},
  url          = {http://dx.doi.org/10.1002/gepi.20572},
  volume       = {35},
  year         = {2011},
}