The MAX Statistic is Less Powerful for Genome Wide Association Studies Under Most Alternative Hypotheses - Shifflett, Benjamin; Huang, Rong; Edland, Steven D. (International Journal of Statistics in Medical Research, 2017)
Genotypic association studies are prone to inflated type I error rates if multiple hypothesis testing is performed, e.g., sequentially testing for recessive, multiplicative, and dominant risk. Alternatives to multiple hypothesis testing include the model independent genotypic c2 test, the efficiency robust MAX statistic, which corrects for multiple comparisons but with some loss of power, or a single Armitage test for multiplicative trend, which has optimal power when the multiplicative model holds but with some loss of power when dominant or recessive models underlie the genetic association. We used Monte Carlo simulations to describe the relative performance of these three approaches under a range of scenarios. All three approaches maintained their nominal type I error rates. The genotypic c2 and MAX statistics were more powerful when testing a strictly recessive genetic effect or when testing a dominant effect when the allele frequency was high. The Armitage test for multiplicative trend was most powerful for the broad range of scenarios where heterozygote risk is intermediate between recessive and dominant risk. Moreover, all tests had limited power to detect recessive genetic risk unless the sample size was large, and conversely all tests were relatively well powered to detect dominant risk. Taken together, these results suggest the general utility of the multiplicative trend test when the underlying genetic model is unknown.