snpDRAC: snpDRAC

In SharonLutz/snpDRAC: Examines dominant, recessive, additive and co-dominant models

snpDRAC

Description

Determines the proportion of simulations the null hypothesis is rejected to detect a genetic association for dominant, recessive, additive, and co-dominant models.

Usage

snpDRAC(n = 1000, MAF = 0.3, mu0 = 0.6, mu1 = 0.8, mu2 = 1, varY = 1, nSim = 1000, alpha = 0.05, SEED = 1)

Arguments

n	is the sample size
MAF	is the minor allele frequency for the SNP X
mu0	is the means of Y given the SNP X=0
mu1	is the means of Y given the SNP X=1
mu2	is the means of Y given the SNP X=2
varY	is the variance of Y
nSim	is the number of simulations
alpha	is the significance level, 0.05
SEED	is seed for reproducibility

Value

For n subjects, the SNP x is generated from a binomial distribution with minor allele frequency inputted by the user (input:MAF). The outcome y is generated form a normal distribution with a mean for x=0 (input:mu0), x=1 (input:mu1), x=2 (input:mu2). The user can specify the number of simulations (input: nSim), the significance level (input: alpha), and the seed(input: SEED). After the SNP x and the outcome y are generated, then 4 linear regression models are fit: (1) a dominant model for the SNP x, (2) a recessive model for the SNP x, (3) an additive model for the SNP x, and (4) a co-dominant for the SNP x. Then, the proportion of simulations the null hypothesis is rejected is outputted for all for models (i.e. the proportion of simulations the null hypothesis is rejected at the specified alpha level).

Author(s)

Sharon Lutz

Examples

snpDRAC(n=1000,MAF=0.3,mu0=0.6,mu1=0.7,mu2=0.8)

SharonLutz/snpDRAC documentation built on July 21, 2024, 4:35 p.m.