¹The Ministry of Education (MOE) Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing, People's Republic of China

Correspondence: L Ji, The Ministry of Education (MOE) Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing 100084, People's Republic of China. E-mail: zc-sa@mail.tsinghua.edu.cn

Received 24 January 2005; Accepted 21 June 2005; Published online 3 August 2005.

Abstract

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.