Power calculations for a general class of family-based association tests: dichotomous traits.
Using large-sample theory, we present a unified approach to power calculations for family-based association tests. Currently available methods for power calculations are restricted to special designs or require approximations or simulations. Our analytical approach to power calculations is broadly applicable in many settings. We discuss power calculations for two scenarios that have high practical relevance and in which power previously could only be assessed by simulation studies or by approximations: (1) studies using both affected and unaffected offspring and (2) studies with missing parental information. When the population prevalence is high, it can be worthwhile to genotype unaffected offspring. For many scenarios, high power can be achieved with reasonable sample sizes, even when no parental information is available.
Department of Biostatistics, Harvard School of Public Health, Boston, MA, 02115, USA. clange@hsph.harvard.edu
Christoph
C
Lange
Nan M
NM
Laird
D016009:Chi-Square Distribution; D002874:Chromosome Mapping / Q000379:methods* / Q000706:statistics & numerical data*; D003198:Computer Simulation; D005787:Gene Frequency; D005819:Genetic Markers; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D009679:Nuclear Family; D010641:Phenotype; D018401:Sample Size
D005819:Genetic Markers