title: Estimating heritability in pharmacogenetic studies.
abstract: OBJECTIVE
  This article aims to evaluate the performance of a recent method to estimate heritability of continuous and binary traits, specifically in the context of pharmacogenetic studies.
  
  
  METHODS
  The approach to be evaluated was designed to estimate heritability in large-scale disease studies. Extensive simulation studies designed to emulate common scenarios seen in pharmacogenetic studies were performed to elucidate the potential utility of this approach outside of disease genetics. The simulations cover continuous and binary traits with small-to-moderate heritability values across a variety of samples sizes in genome-wide, as well as candidate gene, settings.
  
  
  RESULTS
  On a genome-wide scale, a combination of relatively large sample sizes (i.e., n ≥ 1000) and at least moderate underlying heritability (i.e., ≥ 0.25) are needed in order to attain reasonable statistical power. However, in candidate gene studies, reasonable power can be attained across a more broad range of scenarios.
  
  
  CONCLUSIONS
  Our simulation studies show that the proposed approach has clear utility in the context of pharmacogenetic studies, especially in candidate gene settings, and provides novel supplementary information that can be used to inform decision-making in the pharmaceutical industry.
authors: BioStat Solutions, Inc., 114 S Main St Ste 002, Mt Airy, MD 21771, USA.
authors: Scott L
authors: SL
authors: Marshall
authors: Tobias
authors: T
authors: Guennel
authors: Jared
authors: J
authors: Kohler
authors: Michael
authors: M
authors: Man
authors: Stewart
authors: S
authors: Fossceco
mesh_terms: D003198:Computer Simulation; D056726:Genetic Association Studies; D055106:Genome-Wide Association Study; D006801:Humans; D015233:Models, Statistical; D010597:Pharmacogenetics; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable; D018401:Sample Size