Impact of genotyping errors on the type I error rate and the power of haplotype-based association methods

Background: We investigated the influence of genotyping errors on
the type I error rate and empirical power of two haplotype based
association methods applied to candidate regions. We compared the
performance of the Mantel Statistic Using Haplotype Sharing and the
haplotype frequency based score test with that of the Armitage
trend test. Our study is based on 1000 replication of simulated
case-control data settings with 500 cases and 500 controls,
respectively. One of the examined markers was set to be the disease
locus with a simulated odds ratio of 3. Differential and
non-differential genotyping errors were introduced following a
misclassification model with varying mean error rates per locus in
the range of 0.2% to 15.6%. Results: We found that the type I error
rate of all three test statistics hold the nominal significance
level in the presence of nondifferential genotyping errors and low
error rates. For high and differential error rates, the type I
error rate of all three test statistics was inflated, even when
genetic markers not in Hardy-Weinberg Equilibrium were removed. The
empirical power of all three association test statistics remained
high at around 89% to 94% when genotyping error rates were low, but
decreased to 48% to 80% for high and nondifferential genotyping
error rates. Conclusion: Currently realistic genotyping error rates
for candidate gene analysis (mean error rate per locus of 0.2%)
pose no significant problem for the type I error rate as well as
the power of all three investigated test statistics.