# Genome-Wide Regression P-Value (gwrpv) in R Computes the sample probability value (p-value) for the estimated coefficient from a standard genome-wide univariate regression. It computes the exact finite-sample p-value under the assumption that the measured phenotype (the dependent variable in the regression) has a known Bernoulli-normal mixture distribution. ```r gwrpv(beta, n0, n1, n2, mua, siga, mub, sigb, pa, pb, logdelta = -16, lognearnorm = -5, logtopsum = 8) ``` ## Arguments - `beta`: the beta being tested - `n0`: number of major allele homozygotes - `n1`: number of major allele heterozygotes - `n2`: number of minor allele zygotes - `mua`: parameter of the mixture distribution, can be any real number - `siga`: parameter of the mixture distribution, can be any real number - `mub`: parameter of the mixture distribution, can be any real number - `sigb`: parameter of the mixture distribution, can be any real number - `pa`: parameter of the mixture distribution, a real number between zero and one with pa+pb=1 - `pb`: parameter of the mixture distribution, a real number between zero and one with pa+pb=1 - `logdelta`: must be in log base 10 format, with default value set to -16 - `lognearnorm`: must be in log base 10 format, with default value set to -5 - `logtopsum`: must be in log base 10 format, with default value set to 8 ## Returns gwrpv returns a list containing: - **$pvalue**: p-value of a two-sided hypothesis test for a true coefficient of zero - **$skew**: skewness - **$kurt**: kurtosis of the coefficient estimate under assumed model - **$skiptype**: type of trimming/skip which took place (zero means no trimming) - **$totnobs**: total number of observations - **$loopruns**: number of sums in the main computation for each regression case . ## Examples ```r beta <- 6.05879 n0 <- 499 n1 <- 1 n2 <- 0 mua <- 13.87226 siga <- 2.58807 mub <- 4.62829 sigb <- 2.51803 pa <- 0.96544 pb <- 0.03456 # alternatively: pb <- 1.0 - pa gwrpv(beta,n0,n1,n2,mua,siga,mub,sigb,pa,pb) # note default values have been used for the trim parameters above # in the following example we explicitly set the trim parameters # g <- gwrpv(beta,n0,n1,n2,mua,siga,mub,sigb,pa,pb,logdelta=-16,lognearnorm=-5,logtopsum=8) g$pvalue ```

gwrpv function