library(bigsnpr)
chr22 <- snp_attach("../Dubois2010_data/celiac_chr22.rds")
G <- chr22$genotypes$copy(code = c(0, 1, 2, 0, rep(NA, 252)))
dim(G) # 11402 x 4945
big_counts(G, ind.col = 1:10)
CHR <- chr22$map$chromosome
POS <- chr22$map$physical.pos
stats <- big_scale()(G)
POS2 <- snp_asGeneticPos(CHR, POS, dir = "tmp-data/")
plot(POS, POS2, pch = 20)
corr <- runonce::save_run(
snp_cor(chr22$genotypes, infos.pos = POS2, size = 3 / 1000, ncores = 6),
file = "tmp-data/corr_chr22.rds"
)
# hist(log10(abs(corr@x)))
# Simu phenotype
set.seed(1)
(M <- round(ncol(G) * 10^-runif(1, 1, 2))) # 268
simu <- snp_simuPheno(G, h2 = 0.1, M = M)
y <- simu$pheno
# GWAS
set.seed(1)
ind.gwas <- sample(nrow(G), 8e3)
ind.val <- setdiff(rows_along(G), ind.gwas)
gwas <- big_univLinReg(G, y[ind.gwas], ind.train = ind.gwas)
plot(gwas)
plot(gwas, type = "Manhattan")
df_beta <- data.frame(beta = gwas$estim, beta_se = gwas$std.err,
n_eff = length(ind.gwas))
# input parameters
n_vec <- df_beta$n_eff
beta_hat <- with(df_beta, beta / sqrt(n_eff * beta_se^2 + beta^2))
mean_ld <- mean(Matrix::colSums(corr^2))
burn_in <- 100
num_iter <- 100
p_init <- 0.05
h2_init <- 0.1
#### LDpred2-auto ####
corr2 <- as.matrix(corr)
m <- length(beta_hat)
{
curr_beta <- dotprods <- avg_beta <- avg_postp <- avg_beta_hat <-
rep(0, m)
num_iter_tot <- burn_in + num_iter
p_est <- h2_est <- rep(NA_real_, num_iter_tot)
id <- matrix(0, m, 1)
cur_h2_est <- 0
p <- p_init
h2 <- h2_init
gap0 <- crossprod(beta_hat)
shrink_corr <- 0.999
for (k in seq_len(num_iter_tot)) {
inv_odd_p = (1 - p) / p
sigma2 = h2 / (m * p)
gap = 0
nb_causal <- 0
for (j in seq_len(m)) {
# print(j)
dotprod = dotprods[j];
resid = beta_hat[j] - dotprod;
gap = gap + resid * resid;
res_beta_hat_j = beta_hat[j] + shrink_corr * (curr_beta[j] - dotprod);
C1 = sigma2 * n_vec[j];
C2 = 1 / (1 + 1 / C1);
C3 = C2 * res_beta_hat_j;
C4 = C2 / n_vec[j];
postp = 1 / (1 + inv_odd_p * sqrt(1 + C1) * exp(-C3 * C3 / C4 / 2));
dotprod_shrunk = shrink_corr * dotprod + (1 - shrink_corr) * curr_beta[j];
if (k > burn_in) {
avg_postp[j] = avg_postp[j] + postp;
avg_beta[j] = avg_beta[j] + C3 * postp;
avg_beta_hat[j] = avg_beta_hat[j] + dotprod_shrunk;
}
if (postp > runif(1)) {
samp_beta = rnorm(1, C3, sqrt(C4));
diff = samp_beta - curr_beta[j];
curr_beta[j] = samp_beta;
nb_causal <- nb_causal + 1
} else {
diff = -curr_beta[j];
curr_beta[j] = 0;
}
if (diff != 0) {
cur_h2_est = cur_h2_est + diff * (2 * dotprod_shrunk + diff);
dotprods = dotprods + corr2[, j] * diff
# id[[j]] <- diff
# dotprods = dotprods + Matrix::solve(prec2, id)[, 1]
# id[[j]] <- 0
}
}
if (gap > gap0) stop("Divergence!")
# p = rbeta(1, 1 + nb_causal / mean_ld, 1 + (m - nb_causal) / mean_ld)
# h2 = cur_h2_est
print(c(k, p, h2))
p_est[k] = p;
h2_est[k] = h2;
}
}
plot(p_est, log = "y", pch = 20); abline(h = M / m, col = "red")
plot(h2_est, pch = 20); abline(h = 0.2, col = "red")
pred <- big_prodVec(G, avg_beta, ind.row = ind.val)
cor(pred, y[ind.val])^2 # 6.1%
# using the normal corr leads to divergence
## Variational inference (based on https://bit.ly/ldpred_VI)
{
curr_beta <- dotprods <- avg_beta <- avg_postp <- avg_beta_hat <- postp <-
rep(0, m)
num_iter_tot <- burn_in + num_iter
p_est <- h2_est <- rep(NA_real_, num_iter_tot)
id <- matrix(0, m, 1)
cur_h2_est <- 0
p <- p_init
h2 <- h2_init
gap0 <- crossprod(beta_hat)
shrink_corr <- 0.999
tol <- median(abs(beta_hat)) * 1e-2
for (k in seq_len(num_iter_tot)) {
print(k)
stop <- TRUE
inv_odd_p = (1 - p) / p
sigma2 = h2 / (m * p)
gap = 0
nb_causal <- 0
prev_beta <- curr_beta
for (j in seq_len(m)) {
# print(j)
dotprod = dotprods[j];
resid = beta_hat[j] - dotprod;
gap = gap + resid * resid;
res_beta_hat_j = beta_hat[j] + shrink_corr * (curr_beta[j] - dotprod);
C1 = sigma2 * n_vec[j];
C2 = 1 / (1 + 1 / C1);
C3 = C2 * res_beta_hat_j;
C4 = C2 / n_vec[j];
postp[j] = 1 / (1 + inv_odd_p * sqrt(1 + C1) * exp(-C3 * C3 / C4 / 2));
dotprod_shrunk = shrink_corr * dotprod + (1 - shrink_corr) * curr_beta[j];
if (k > burn_in) {
avg_postp[j] = avg_postp[j] + postp[j];
avg_beta[j] = avg_beta[j] + C3 * postp[j];
avg_beta_hat[j] = avg_beta_hat[j] + dotprod_shrunk;
}
new_beta <- C3 * postp[j]
diff <- new_beta - prev_beta[j]
if (abs(diff) > tol) {
curr_beta[j] = new_beta
dotprods = dotprods + corr2[, j] * diff
stop <- FALSE
}
}
if (stop) break
# nb_causal <- sum(postp)
# p = rbeta(1, 1 + nb_causal / mean_ld, 1 + (m - nb_causal) / mean_ld)
}
}
pred3 <- big_prodVec(G, curr_beta, ind.row = ind.val)
cor(pred3, y[ind.val])^2 # 5.8%
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