In population-based case-control association studies, the regular chi-square test is 
often used to investigate association between a candidate locus and disease. However, it 
is well known that this test may be biased in the presence of population stratification 
and/or genotyping error. Unlike some other biases, this bias will not go away with 
increasing sample size. On the contrary, the false-positive rate will be much larger 
when the sample size is increased. The usual family-based designs are robust against 
population stratification, but they are sensitive to genotype error. We propose a novel 
method of simultaneously correcting for the bias arising from population stratification 
and/or for the genotyping error in case-control studies. In addition, it is simple to 
apply. We considered a simulation study to investigate the performance of the new 
method, using parameter values similar to those found in real-data examples. The 
results show that the corrected test approximately maintains the expected type I 
error rate under various simulation conditions. It also improves the power of the 
association test in the presence of population stratification and/or genotyping 
error. The discrepancy in power between the tests with correction and those without 
correction tends to be more extreme as the magnitude of the bias becomes larger. 
Therefore, the bias-correction method that we proposed should be useful for the 
genetic analysis of complex traits.