The questions addressed in this paper are: What single nucleotide polymorphism (SNP) genotyping errors are most costly, in terms of minimum sample size necessary (MSSN) to maintain constant asymptotic power and significance level, when performing case-control studies of genetic association applying the Cochran-Armitage trend test? And which trend test or χ2 test is more powerful under standard genetic models with genotyping errors? Our strategy is to expand the non-centrality parameter of the asymptotic distribution of the trend test to approximate the MSSN using a Taylor series linear in the genotyping error rates. We apply our strategy to example scenarios that assume recessive, dominant, additive, or over-dominant disease models.
The most costly errors are recording the more common homozygote as the less common homozygote, and the more common homozygote as the heterozygote, with MSSN that become indefinitely large as the minor SNP allele frequency approaches zero. Misclassifying the heterozygote as the less common homozygote is costly when using the recessive trend test on data from a recessive model. The χ2 test has power close to, but less than, the optimal trend test and is never dominated over all genetic models studied by any specific trend test.