Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix.

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 Meff 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.

journal_name

Heredity (Edinb)

journal_title

Heredity

authors

Li J,Ji L

doi

10.1038/sj.hdy.6800717

keywords:

subject

Has Abstract

pub_date

2005-09-01 00:00:00

pages

221-7

issue

3

eissn

0018-067X

issn

1365-2540

pii

6800717

journal_volume

95

pub_type

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