Last updated: 2020-01-17
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Rmd | 2ba18cc | Yanyu Liang | 2020-01-17 | workflowr::wflow_git_commit(all = T) |
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Rmd | 8eeb085 | Hae Kyung Im | 2020-01-15 | power calc |
html | 8eeb085 | Hae Kyung Im | 2020-01-15 | power calc |
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
✓ ggplot2 3.2.1 ✓ purrr 0.3.3
✓ tibble 2.1.3 ✓ dplyr 0.8.3
✓ tidyr 1.0.0 ✓ stringr 1.4.0
✓ readr 1.3.1 ✓ forcats 0.4.0
── Conflicts ───────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
fastlm = function(xx,yy)
{
## compute betahat (regression coef) and pvalue with Ftest
## for now it does not take covariates
df1 = 2
df0 = 1
ind = !is.na(xx) & !is.na(yy)
xx = xx[ind]
yy = yy[ind]
n = sum(ind)
xbar = mean(xx)
ybar = mean(yy)
xx = xx - xbar
yy = yy - ybar
SXX = sum( xx^2 )
SYY = sum( yy^2 )
SXY = sum( xx * yy )
betahat = SXY / SXX
RSS1 = sum( ( yy - xx * betahat )^2 )
RSS0 = SYY
fstat = ( ( RSS0 - RSS1 ) / ( df1 - df0 ) ) / ( RSS1 / ( n - df1 ) )
pval = 1 - pf(fstat, df1 = ( df1 - df0 ), df2 = ( n - df1 ))
res = list(betahat = betahat, pval = pval)
return(res)
}
In this vignette, we’d like to:
As a concrete example, we pre-specify the model for continuous trait \(Y\) and genotype \(X\) as
\[Y = \beta X + \epsilon, \epsilon ~ N(0, \sigma_\epsilon^2)\]
where we assume
Note that the proportion of variation explained, minor allele frequency, effect size (\(\beta\)) are related under the model above and you will be/have already been asked to work it out in assignment.
To simulate genotype, we assume the locus is bialleilic and each individual is diploid. So that \(X \sim Binomial(2, f)\) with \(f\) as minor allele frequency (here we encode minor allele as 1 and major allele as 0).
Given genotype, to simulate phenotype, we need to know \(\beta\) and \(\sigma_\epsilon^2\). So, we first calculate the effect size and the variance of the noise term from the proportion of variation explained, variance of trait, and minor allele frequency of the SNP.
simulate_genotype = function(maf, num_individuals, num_replicates) {
# maf: minor allele frequency
# num_individuals: the number of individuals in each replicates
# num_replicates: the number of replicates
# it returns a matrix with num_individuals rows and num_replicates columns
genotype = matrix(
rbinom(num_individuals * num_replicates, 2, maf),
nrow = num_individuals,
ncol = num_replicates
)
return(genotype)
}
simulate_phenotype = function(genotype, beta, sig2epsi) {
# genotype: each column is one replicate
# beta: effect size of the linear model
# sig2epsi: the variance of the noise term
num_individuals = nrow(genotype)
num_replicates = ncol(genotype)
epsilon = matrix(
rnorm(num_individuals * num_replicates, mean = 0, sd = sqrt(sig2epsi)),
nrow = num_individuals,
ncol = num_replicates
)
phenotype = genotype * beta + epsilon
return(phenotype)
}
get_beta_and_sig2epsi = function(r2, sig2Y, maf) {
# r2: proportion of variation explained by SNP
# sig2Y: variance of trait
# maf: minor allele frequency of SNP
# it returns a list of beta and variance of noise term
## effect size is calculated as r2 = beta^2 *2*maf*(1-maf)
beta = sqrt( r2 * sig2Y / (2 * maf * (1 - maf)) )
sig2epsi = sig2Y * (1 - r2)
return(list(beta = beta, sig2epsi = sig2epsi))
}
linear_model_simulator = function(num_replicates, num_individuals, maf, r2, sig2Y) {
# simulate genotype
X = simulate_genotype(maf, num_individuals, num_replicates)
# calculate model parameters
params = get_beta_and_sig2epsi(r2, sig2Y, maf)
# simulate phenotype given genotype and model parameters
Y = simulate_phenotype(X, params$beta, params$sig2epsi)
return(list(Y = Y, X = X))
}
Here we simulate 1000 individuals per replicate and 10000 replicates in total. With parameters:
# specify paramters
nindiv = 1000
nreplicate = 10000
maf = 0.30
r2 = 0.01
sig2Y = 1
# run simulator
## under the alternative
data_alt = linear_model_simulator(nreplicate, nindiv, maf, r2, sig2Y)
## under the null
data_null = linear_model_simulator(nreplicate, nindiv, maf, 0, sig2Y)
The following chunk of R code implement hypothesis test procedure based on linear regression. Essentially, the R function calcz
takes genotype X
and Y
and returns test statistic z-score.
runassoc = function(X,Y)
{
pvec = rep(NA,ncol(X))
bvec = rep(NA,ncol(X))
for(ss in 1:ncol(X))
{
fit = fastlm(X[,ss], Y[,ss])
pvec[ss] = fit$pval
bvec[ss] = fit$betahat
}
list(pvec=pvec, bvec=bvec)
}
p2z = function(b,p)
{
## calculate zscore from p-value and sign of effect size
sign(b) * abs(qnorm(p/2))
}
calcz = function(X,Y)
{
tempo = runassoc(X,Y)
p2z(tempo$bvec,tempo$pvec)
}
Now that we can calculate test statistics under the null and alternative.
Zalt = calcz(data_alt$X, data_alt$Y)
Znull = calcz(data_null$X, data_null$Y)
tibble(Y = c(Zalt,Znull), type=c(rep("alt",length(Zalt)),rep("null",length(Znull))) ) %>% ggplot(aes(Y,fill=type)) + geom_density(color=NA,alpha=0.6) + theme_bw(base_size = 15)
Version | Author | Date |
---|---|---|
c5dd6b8 | Yanyu Liang | 2020-01-17 |
## define significance level
alpha = 0.01
## find threshold for rejection; we want P(Znull > alpha/2) two-sided
threshold = quantile(Znull, 1 - alpha/2)
## calculate proportion of Zalt above threshold
mean(Zalt > threshold)
[1] 0.7337
## install.packages("pwr")
library(pwr)
pwr.r.test(n = nindiv, r= sqrt(r2), sig.level = alpha)
approximate correlation power calculation (arctangh transformation)
n = 1000
r = 0.1
sig.level = 0.01
power = 0.7234392
alternative = two.sided
sessionInfo()
R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] pwr_1.2-2 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3
[5] purrr_0.3.3 readr_1.3.1 tidyr_1.0.0 tibble_2.1.3
[9] ggplot2_3.2.1 tidyverse_1.3.0 workflowr_1.6.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.11 haven_2.2.0 lattice_0.20-38
[5] colorspace_1.4-1 vctrs_0.2.1 generics_0.0.2 htmltools_0.4.0
[9] yaml_2.2.0 rlang_0.4.2 later_1.0.0 pillar_1.4.3
[13] withr_2.1.2 glue_1.3.1 DBI_1.1.0 dbplyr_1.4.2
[17] modelr_0.1.5 readxl_1.3.1 lifecycle_0.1.0 munsell_0.5.0
[21] gtable_0.3.0 cellranger_1.1.0 rvest_0.3.5 evaluate_0.14
[25] labeling_0.3 knitr_1.26 httpuv_1.5.2 fansi_0.4.0
[29] broom_0.5.3 Rcpp_1.0.3 promises_1.1.0 backports_1.1.5
[33] scales_1.1.0 jsonlite_1.6 farver_2.0.1 fs_1.3.1
[37] hms_0.5.3 digest_0.6.23 stringi_1.4.3 grid_3.6.2
[41] rprojroot_1.3-2 cli_2.0.0 tools_3.6.2 magrittr_1.5
[45] lazyeval_0.2.2 crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3
[49] zeallot_0.1.0 xml2_1.2.2 reprex_0.3.0 lubridate_1.7.4
[53] rstudioapi_0.10 assertthat_0.2.1 rmarkdown_2.0 httr_1.4.1
[57] R6_2.4.1 nlme_3.1-143 git2r_0.26.1 compiler_3.6.2