PAWE - Power for Association With Error

PAWE version 2.1, May 2014

Written by Jeffrey Kutcher and Derek Gordon

(Based on Version 1.2, February 2003
written by Derek Gordon and Michael Nothnagel)

The name of this program is PAWE, which stands for Power for Association With Errors. Because it has been previously documented (Mote and Anderson 1965; Gordon et al. 2002) that genotyping errors can substantially decrease the asymptotic power to detect association between a trait locus and a marker locus, the purpose of the PAWE program is two-fold: (i) to compute power and sample size calculations for genetic case-control association studies in the presence of genotyping errors, and (ii) to determine quantitatively how much, in terms of decrease in asymptotic power for a fixed sample size, or increase in sample size to maintain constant asymptotic power, genotyping errors cost the researcher performing genetic association studies with cases and controls. Thus, results from the PAWE program will be either asymptotic power or sample size values. In this updated version of the program, we remove the test for allelic association and replace it by the linear trend test (Cochran 1954; Armitage 1955). The reason is that the allelic test is not robust to genotype errors; that is, the true null distribution of the allelic test is unknown in the presence of genotype misclassification errors.

This program is designed to perform asymptotic power and sample size calculations for genetic case-control studies with a di-allelic locus (for example, a SNP) in the presence of errors. Here, we specify that there is a di-allelic trait locus for a discrete trait with two alleles: a wild-type or low risk allele, denoted by +, and a trait or high-risk allele, denoted by d. Also, we specify that there is a marker locus with two alleles, denoted by 1 and 2 in linkage disequilibrium with the trait locus.

Please cite the following two references when reporting results using PAWE:

Gordon D., Finch S.J., Nothnagel M., and Ott J.  (2002) Power and sample size calculations for case-control genetic association tests when errors present: application to single nucleotide polymorphisms. Human Heredity 54:22-33.

Gordon D., Levenstien M.A., Finch S.J., and Ott J. (2003) Errors and linkage disequilibrium interact multiplicatively when computing sample sizes for genetic case-control association studies. Pacific Symposium on Biocomputing: 490-501.

Cochran W.G. (1954) Some methods for strengthening the common chi-squared tests. Biometrics 10: 417 - 451.

Armitage P. (1955) Tests for linear trends in proportions and frequencies. Biometrics 11: 375 - 386.