| Title: | Power Calculations for GxE and GxG Interactions for GWAS |
|---|---|
| Description: | Analytical power calculations for GxE and GxG interactions for case-control studies of candidate genes and genome-wide association studies (GWAS). This includes power calculation for four two-step screening and testing procedures. It can also calculate power for GxE and GxG without any screening. |
| Authors: | Charles Kooperberg <[email protected]> and Li Hsu <[email protected]> |
| Maintainer: | Charles Kooperberg <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.1.3 |
| Built: | 2024-12-22 06:21:50 UTC |
| Source: | CRAN |
This routine carries out (analytical, approximate) power calculations for identifying Gene-Environment interactions in Genome Wide Association Studies
powerGE(n, power, model, caco, alpha, alpha1, maintain.alpha)powerGE(n, power, model, caco, alpha, alpha1, maintain.alpha)
n |
Sample size: combined number of cases and controls. Note: exactly one of |
power |
Power: targeted power. Note: exactly one of |
model |
List specifying the genetic model. This list contains the following objects:
|
caco |
Fraction of the sample that are cases (default = 0.5). |
alpha |
Overall (family-wise) Type 1 error (default = 0.05). |
alpha1 |
Significance level at which testing during the first stage (screening) takes place. If alpha1 = 1, there is no screening. |
maintain.alpha |
Some combinations of screening and GxE testing methods do not maintain the proper Type 1 error. Default is |
The routine computes power for a variety of two-stage procedures. Five different screening procedures are used:
No screening All SNPs are tested for interaction
Marginal screening Only SNPs that are marginally significant at level alpha1 are screened for interaction. See Kooperberg and LeBlanc (2010).
Correlation screening Only SNPs that are, combined over all cases and controls, associated with the environmental variable at level alpha 1 are screened for interaction. See Murcray et al. (2012).
Cocktail screening SNPs are screened on the most significant of marginal and correlation screening. See Hsu et al. (2012).
Chi-square screening SNPs are screened using a chi-square combination of correlation and marginal screening. See Gauderman et al. (2013).
After screening, the SNPs that pass the screen can be tested using
Case-control The standard case-control estimator.
Case-only The case-only estimator.
Empirical Bayes The empirical Bayes estimator of Mukherjee and Chatterjee (2010).
If screening took place using the correlation or chi-square screening, the Type 1 error won't be maintained if the final GxE testing is carried out using either the case-only or empirical Bayes estimator. See Dai et al. (2012). The cocktail screening maintains the Type 1 family wise error rate, since only those SNPs that pass on to the second stage using marginal screening will use the case-only or empirical Bayes estimator, the SNPs that pass on to the second stage using correlation screening will always use the case-control estimator.
When SNP and environment are correlated in the population (i.e. model$orGE does not equal 1) the case-only estimator does not maintain the Type 1 error.
The empirical Bayes estimator may also have a moderately inflated Type 1 error. When the disease is common either the case-only
estimator or the empirical Bayes estimator also may not estimate the GxE interaction.
Power calculations are described in Kooperberg, Dai, and Hsu (2014). Briefly, for a given genetic model we compute the expected p-values for all
screening statistics. We then use a normal approximation to compute the probability that this SNP passes the screening (e.g., if alpha1 equaled this expected p-value this probability would be exactly 0.5), and combine this with power calculations for the second stage of GxE testing.
A list with three components.
power |
A 5x3 matrix with estimated power for all testing approaches, only if |
samplesize |
A 5x3 matrix with required sample sizes for all testing approaches, only if |
expected.p |
A 5x3 matrix with the expected p value for the SNP to pass screening. This p-value depends on the sample size, but not on the second stage testing. |
prob.select |
A 5x3 matrix with the probability that the interacting SNP would pass the screening stage. This probability depends on the sample size, but not on the second stage testing. |
Li Hsu [email protected] and Charles Kooperberg [email protected].
Dai JY, Kooperberg C, LeBlanc M, Prentice RL (2012). Two-stage testing procedures with independent filtering for genome-wide gene-environment interaction. Biometrika, 99, 929-944.
Gauderman WJ, Zhang P, Morrison JL, Lewinger JP (2013). Finding novel genes by testing GxE interactions in a genome-wide association study. Genetic Epidemiology, 37, 603-613.
Hsu L, Jiao S, Dai JY, Hutter C, Peters U, Kooperberg C (2012). Powerful cocktail methods for detecting genome-wide gene-environment interaction. Genetic Epidemiology, 36, 183-194.
Kooperberg C, Dai, JY, Hsu L (2014). Two-stage procedures for the identification of gene x environment and gene x gene interactions in genome-wide association studies. To appear.
Kooperberg C, LeBlanc ML (2008). Increasing the power of identifying gene x gene interactions in genome-wide association studies. Genetic Epidemiology, 32, 255-263.
Mukherjee B, Chatterjee N (2008). Exploiting gene-environment inde- pendence for analysis of case-control studies: an empirical Bayes-type shrinkage estimator to trade-off between bias and efficiency Biometrics, 64, 685-694.
Murcray CE, Lewinger JP, Gauderman WJ (2009). Gene-environment interaction in genome-wide association studies. American Journalk of Epidemiology, 169, 219-226.
powerGG
mod1 <- list(prev=0.01,pGene=0.2,pEnv=0.2,beta.LOR=log(c(1.0,1.2,1.4)),orGE=1.2,nSNP=10^6) results <- powerGE(n=20000, model=mod1,alpha1=.01) print(results) mod2 <- list(prev=0.01,pGene=0.2,pEnv=0.2,beta.LOR=log(c(1.0,1.0,1.4)),orGE=1,nSNP=10^6) results <- powerGE(power=0.8, model=mod2,alpha1=.01) print(results)mod1 <- list(prev=0.01,pGene=0.2,pEnv=0.2,beta.LOR=log(c(1.0,1.2,1.4)),orGE=1.2,nSNP=10^6) results <- powerGE(n=20000, model=mod1,alpha1=.01) print(results) mod2 <- list(prev=0.01,pGene=0.2,pEnv=0.2,beta.LOR=log(c(1.0,1.0,1.4)),orGE=1,nSNP=10^6) results <- powerGE(power=0.8, model=mod2,alpha1=.01) print(results)
This routine carries out (analytical, approximate) power calculations for identifying Gene-Gene interactions in Genome Wide Association Studies
powerGG(n, power, model, caco, alpha, alpha1)powerGG(n, power, model, caco, alpha, alpha1)
n |
Sample size: combined number of cases and controls. Note: exactly one of |
power |
Power: targeted power. Note: exactly one of |
model |
List specifying the genetic model. This list contains the following objects:
|
caco |
Fraction of the sample that are cases (default = 0.5). |
alpha |
Overall (family-wise) Type 1 error (default = 0.05). |
alpha1 |
Significance level at which testing during the first stage (screening) takes place. If alpha1 = 1, there is no screening. |
The routine computes power calculations for a two-stage procedure with marginal screening followed by either case-control or case-only testing.
A data frame consisting of two numbers: the power for the case-control and case-only approaches if n is specified or the required combined sample size
for the case-control and case-only approaches if power is specified.
Charles Kooperberg, [email protected]
Kooperberg C, LeBlanc M (2008). Increasing the power of identifying gene x gene interactions in genome-wide association studies. Genetic Epidemiology, 32, 255-263.
powerGG
mod1 <- list(prev=0.05, pGene1=0.3, pGene2=0.3, beta.LOR=c(0,0,.6),nSNP=500000) powerGG(n=10000,mod=mod1,caco=0.5,alpha=.05,alpha1=.001) powerGG(power=0.8,mod=mod1,caco=0.5,alpha=.05,alpha1=.001)mod1 <- list(prev=0.05, pGene1=0.3, pGene2=0.3, beta.LOR=c(0,0,.6),nSNP=500000) powerGG(n=10000,mod=mod1,caco=0.5,alpha=.05,alpha1=.001) powerGG(power=0.8,mod=mod1,caco=0.5,alpha=.05,alpha1=.001)
This function is depreciated and has been replaced by powerGG and powerGE
powerGWASinteraction()powerGWASinteraction()
An error message is printed
Charles Kooperberg, [email protected]
powerGG,powerGE
powerGWASinteraction()powerGWASinteraction()