Flexible designs for genomewide association studies.
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.
Scherag
André
A
Institute of Medical Biometry and Epidemiology, Philipps-University, Marburg, Germany.
Hebebrand
Johannes
J
Schäfer
Helmut
H
Müller
Hans-Helge
HH
D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003627:Data Interpretation, Statistical; D005819:Genetic Markers / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000453:epidemiology* / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D012107:Research Design
D005819:Genetic Markers