Power for Association With Error

This version of PAWE-PH considers random phenotype misclassification errors. These errors are sometimes referred to as diagnostic errors (Zheng and Tian 2005). Recent research (Zheng and Tian 2005; Edwards et al. 2005) indicates that phenotype misclassification errors can substantially decrease the asymptotic power to detect association between a trait locus and a marker locus. As with genotype misclassification error (http://linkage.rockefeller.edu/pawe/), the purpose of our program is two fold: (i) to compute power and sample size calculations for genetic case-control association studies in the presence of phenotype misclassification 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, phenotype misclassification errors cost the researcher performing genetic association studies with cases and controls. Results from the PAWE-PH program will be either asymptotic power or sample size values.

Important note: When discussing case/control studies in the presence of phenotype misclassification, we make a distinction between the terms case and affected, and similarly between the terms control and unaffected. A case individual is someone who is diagnosed as being affected, whether or not the individual is affected. An affected individual is someone who is truly affected. It is assumed that we only collect cases and controls; that is, some random proportion of cases have unaffected individuals among the cases and similarly for controls. For more information, see (Edwards et al. 2005).

This program is designed to perform asymptotic power and sample size calculations for genetic case-control studies with a di-allelic marker locus (for example, a SNP) in the presence of errors. The test statistics considered are the linear trend test (Armitage 1955) (functional in Version 2.1) and the chi-square test of independence applied to genotypes. In the genetic model-free setting, we assume there is a di-allelic marker locus with two alleles, denoted by 1 and 2, that has different genotype frequencies in the affected and control unaffected populations. In the genetic model-based setting, we assume that there is an unobserved di-allelic trait locus for a discrete trait with two alleles: a wild-type allele or low risk allele, denoted by +, and a trait or high-risk allele, denoted by d. Also, it is assumed that there is a marker locus with two alleles, denoted by 1 and 2, that is in linkage disequilibrium with the trait locus.

PAWE-PH version 2.0 March 2005 Written by Chad Haynes and Derek Gordon Run PAWE-PH - Phenotype Edition This version of PAWE-PH considers random phenotype misclassification errors. These errors are sometimes referred to as diagnostic errors (Zheng and Tian 2005). Recent research (Zheng and Tian 2005; Edwards et al. 2005) indicates that phenotype misclassification errors can substantially decrease the asymptotic power to detect association between a trait locus and a marker locus. As with genotype misclassification error (http://linkage.rockefeller.edu/pawe/), the purpose of our program is two fold: (i) to compute power and sample size calculations for genetic case-control association studies in the presence of phenotype misclassification 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, phenotype misclassification errors cost the researcher performing genetic association studies with cases and controls. Results from the PAWE-PH program will be either asymptotic power or sample size values. Important note: When discussing case/control studies in the presence of phenotype misclassification, we make a distinction between the terms case and affected, and similarly between the terms control and unaffected. A case individual is someone who is diagnosed as being affected, whether or not the individual is affected. An affected individual is someone who is truly affected. It is assumed that we only collect cases and controls; that is, some random proportion of cases have unaffected individuals among the cases and similarly for controls. For more information, see (Edwards et al. 2005). This program is designed to perform asymptotic power and sample size calculations for genetic case-control studies with a di-allelic marker locus (for example, a SNP) in the presence of errors. The test statistics considered are the linear trend test (Armitage 1955) (functional in Version 2.1) and the chi-square test of independence applied to genotypes. In the genetic model-free setting, we assume there is a di-allelic marker locus with two alleles, denoted by 1 and 2, that has different genotype frequencies in the affected and control unaffected populations. In the genetic model-based setting, we assume that there is an unobserved di-allelic trait locus for a discrete trait with two alleles: a wild-type allele or low risk allele, denoted by +, and a trait or high-risk allele, denoted by d. Also, it is assumed that there is a marker locus with two alleles, denoted by 1 and 2, that is in linkage disequilibrium with the trait locus. References Please cite the first two references when reporting results using PAWE-PH: Edwards BJ, Haynes C, Levenstien MA, Finch SJ, and Gordon D (2005) Power and sample size calculations in the presence of phenotype errors for case/control genetic association studies. BMC Genet 6(1):18. 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 Armitage P (1955) Tests for linear trends in proportions and frequencies. Biometrics 11:375-386. Zheng G, Tian X (2005) The impact of diagnostic error on testing genetic association in case-control studies. Stat Med 24:869-82. Pawe Pawe-3D Run PAWE-PH

	PAWE-PH is maintained by Chad Haynes Laboratory of Statistical Genetics The Rockefeller University

References