Repeated measurement sampling in genetic association analysis with genotyping errors

Abstract

Genotype misclassification occurs frequently in human genetic association studies. When cases and controls are subject to the same misclassification model, Pearson's chi-square test has the correct type I error but may lose power. Most current methods adjusting for genotyping errors assume that the misclassification model is known a priori or can be assessed by a gold standard instrument. But in practical applications, the misclassification probabilities may not be completely known or the gold standard method can be too costly to be available. The repeated measurement design provides an alternative approach for identifying misclassification probabilities. With this design, a proportion of the subjects are measured repeatedly (five or more repeats) for the genotypes when the error model is completely unknown. We investigate the applications of the repeated measurement method in genetic association analysis. Cost-effectiveness study shows that if the phenotyping-to-genotyping cost ratio or the misclassification rates are relatively large, the repeat sampling can gain power over the regular case-control design. We also show that the power gain is not sensitive to the genetic model, genetic relative risk and the population high-risk allele frequency, all of which are typically important ingredients in association studies. An important implication of this result is that whatever the genetic factors are, the repeated measurement method can be applied if the genotyping errors must be accounted for or the phenotyping cost is high.