Premium
Flexible Designs for Genomewide Association Studies
Author(s) -
Scherag André,
Hebebrand Johannes,
Schäfer Helmut,
Müller HansHelge
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01174.x
Subject(s) - flexibility (engineering) , computer science , type i and type ii errors , sample size determination , genotyping , set (abstract data type) , genetic association , sample (material) , association (psychology) , data mining , statistics , machine learning , mathematics , genetics , biology , genotype , single nucleotide polymorphism , chemistry , chromatography , gene , programming language , philosophy , epistemology
Summary Genomewide association studies attempting to unravel the genetic etiology of complex traits have recently gained attention. Frequently, these studies employ a sequential genotyping strategy: A large panel of markers is examined in a subsample of subjects, and the most promising markers are genotyped in the remaining subjects. In this article, we introduce a novel method for such designs enabling investigators to, for example, modify marker densities and sample proportions while strongly controlling the family‐wise type I error rate. Loss of efficiency is avoided by redistributing conditional type I error rates of discarded markers. Our approach can be combined with cost optimal designs and entails a greater flexibility than all previously suggested designs. Among other features, it allows for marker selections based upon biological criteria instead of statistical criteria alone, or the option to modify the sample size at any time during the course of the project. For practical applicability, we develop a new algorithm, subsequently evaluate it by simulations, and illustrate it using a real data set.