association.test: Association Test

Description

Association tests between phenotype and SNPs.

Usage

association.test(x, Y = x@ped$pheno, X = matrix(1, nrow(x)), 
    eigenK, beg = 1, end = ncol(x), p = 0, test = c("wald", "lrt"),
    tol = .Machine$double.eps^0.25, multithreaded = FALSE)

Arguments

Value

A named list of vectors, giving for each considered SNP:h2Estimated value of $\tau \over {\tau + \sigma^2}$beta(only whith test = "wald") Estimated value of $\beta$sd(only whith test = "wald") Estimated standard deviation of the $\beta$ estimationLRT(only whith test = "lrt") Value of the Likelihood Ratio TestpThe corresponding p-value

Details

Tests the association between the phenotype and requested SNPs in x. For each SNP, the following model in considered $$Y = (X|PC)\alpha + G\beta + \omega + \varepsilon$$ with $\omega \sim N(0,\tau K)$ and $\varepsilon \sim N(0,\sigma^2 I_n)$. $G$ is the genotype vector of the SNP, $K$ is the Genetic Relationship Matrix (GRM) computed on the whole genome (specified via the parameter eigenK), $X$ the covariable matrix, and $PC$ the matrix of the first $p$ principal components. All parameters are estimated with the same procedure as in lmm.diago.

Note: in the present implementation, using several threads is likely to be slower than using only one, hence the multithreaded parameter. This might change in the near future.

Examples

Run this code

# Load data
data(AGT)
x <- as.bed.matrix(AGT.gen, AGT.fam, AGT.bim)
x <- set.stats(x)
standardize(x) <- 'mu'

# generate a random positive matrix (to play the role of the GRM)
set.seed(1)
R <- random.pm(nrow(x))

# simulate phenotype with effect of SNP #351 and a polygenic component 
y <- x %*% c(rep(0,350),0.25,rep(0,ncol(x)-351)) + lmm.simu(0.3,1,eigenK=R$eigen)$y

# association test
t <- association.test(x, y, eigenK = R$eigen)

# (mini) Manhattan plot
plot(-log10(t$p), xlab="SNP index", ylab = "-log(p)")

# link between p-values and LD with SNP #351
lds <- LD(x, 351, c(1,ncol(x)))
plot(lds, -log10(t$p), xlab="r^2", ylab="-log(p)")