title: Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants.
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.
authors.affiliation: Applied Quantitative Genotherapeutics, Pfizer Biotherapeutics, Cambridge, MA 02144, USA.
authors.forename: Lin T
authors.initials: LT
authors.lastname: Guey
authors.forename: Jasmina
authors.initials: J
authors.lastname: Kravic
authors.forename: Olle
authors.initials: O
authors.lastname: Melander
authors.forename: Noël P
authors.initials: NP
authors.lastname: Burtt
authors.forename: Jason M
authors.initials: JM
authors.lastname: Laramie
authors.forename: Valeriya
authors.initials: V
authors.lastname: Lyssenko
authors.forename: Anna
authors.initials: A
authors.lastname: Jonsson
authors.forename: Eero
authors.initials: E
authors.lastname: Lindholm
authors.forename: Tiinamaija
authors.initials: T
authors.lastname: Tuomi
authors.forename: Bo
authors.initials: B
authors.lastname: Isomaa
authors.forename: Peter
authors.initials: P
authors.lastname: Nilsson
authors.forename: Peter
authors.initials: P
authors.lastname: Almgren
authors.forename: Sekar
authors.initials: S
authors.lastname: Kathiresan
authors.forename: Leif
authors.initials: L
authors.lastname: Groop
authors.forename: Albert B
authors.initials: AB
authors.lastname: Seymour
authors.forename: David
authors.initials: D
authors.lastname: Altshuler
authors.forename: Benjamin F
authors.initials: BF
authors.lastname: Voight
mesh_terms: D003198:Computer Simulation; D056726:Genetic Association Studies / Q000379:methods*; D014644:Genetic Variation; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D018401:Sample Size; D012494:Sampling Studies; D017422:Sequence Analysis, DNA