Correlated multiple testing is widely performed in genetic research, particularly in multilocus analyses of complex diseases. Failure to control appropriately for the effect of multiple testing will either result in a flood of false-positive claims or in true hits being overlooked. Cheverud proposed the idea of adjusting correlated tests as if they were independent, according to an 'effective number' (M eff ) of independent tests. However, our experience has indicated that Cheverud's estimate of the M eff is overly large and will lead to excessively conservative results. We propose a more accurate estimate of the M eff , and design M eff -based procedures to control the experiment-wise significant level and the false discovery rate. In an evaluation, based on both real and simulated data, the M eff -based procedures were able to control the error rate accurately and consequently resulted in a power increase, especially in multilocus analyses. The results confirm that the M eff is a useful concept in the error-rate control of correlated tests. With its efficiency and accuracy, the M eff method provides an alternative to computationally intensive methods such as the permutation test. Heredity (2005) 95, 221-227.

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