tmp-tests/test-ldpred-approx.R

In privefl/mypack: Analysis of Massive SNP Arrays

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  4945
big_counts(G, ind.col = 1:10)
CHR <- chr22$map$chromosome
POS <- chr22$map$physical.pos

corr <- snp_cor(chr22$genotypes, infos.pos = POS, size = 3000,
                ncores = 4, alpha = 1)
# ld <- Matrix::colMeans(corr ** 2 - (1 - corr ** 2) / (nrow(G) - 2))
ld <- Matrix::colMeans(corr ** 2)
plot(ld, pch = 20)
plot(bigutilsr::rollmean(ld, 100), pch = 20)

# Simu phenotype
set.seed(1)
h2 <- 0.1; M <- 100
set <- sort(sample(ncol(G), size = M))
effects <- rnorm(M, sd = sqrt(h2 / M))
y <- drop(scale(G[, set]) %*% effects)       ## G
y2 <- y + rnorm(nrow(G), sd = sqrt(1 - h2))  ## G + E
var(y) / var(y2)                             ## H2

# GWAS
ind.gwas <- sample(nrow(G), 8e3)
N <- length(ind.gwas)
gwas <- big_univLinReg(G, y2[ind.gwas], ind.train = ind.gwas)
beta_gwas <- gwas$estim

ind.val <- setdiff(rows_along(G), ind.gwas)

# LDpred-inf
m <- length(beta_gwas)
coeff <- N * h2 / m
corr2 <- corr + Matrix::Diagonal(ncol(corr), 1 / coeff)
betas_blup <- as.vector(Matrix::solve(corr2, beta_gwas))
pred <- big_prodVec(G, betas_blup, ind.row = ind.val)
plot(pred, y2[ind.val], pch = 20); abline(0, 1, col = "red", lwd = 3)
cor(pred, y2[ind.val])**2  # 0.376 -> 0.38


# LDpred-fast

p_seq <- seq_log(1e-4, 0.9, length.out = 20)
ldpred_approx <- sapply(setNames(nm = p_seq), function(p) {

  print(p)

  C1 <- (coeff + 1) / (coeff + p)
  C2 <- coeff / (coeff + 1)
  V_nc <- C2 / N * (1 - C2 * h2)
  V_c <- C2 * h2 / (m * p) + V_nc
  # Should use p-transformed betas instead?
  d_beta_ncaus <- dnorm(betas_blup, sd = sqrt(V_nc))
  d_beta_caus <- dnorm(betas_blup, sd = sqrt(V_c))
  d_caus_beta <- d_beta_caus * p / (d_beta_caus * p + d_beta_ncaus * (1 - p))
  beta_jms <- betas_blup * C1 * d_caus_beta

  pred5 <- big_prodVec(G, beta_jms, ind.row = ind.val)
  plot(pred5, y2[ind.val], pch = 20); abline(0, 1, col = "red", lwd = 3)
  round(100 * cor(pred5, y2[ind.val])**2, 1)
})



chi2 <- qchisq(predict(gwas) * log(10), df = 1, lower.tail = FALSE, log.p = TRUE)
betas_hat <- sqrt(chi2) * sign(beta_gwas) / sqrt(N)

Rcpp::sourceCpp('src/ldpred2.cpp')
all_ldpred2 <- ldpred2_gibbs(
  corr      = corr,
  betas_hat = betas_hat,
  order     = order(betas_hat ** 2, decreasing = TRUE) - 1L,
  n_vec     = `if`(length(N) == 1, rep(N, m), N),
  h2        = rep(var(y) / var(y2), length(p_seq)),
  p         = p_seq,
  burn_in   = 100,
  num_iter  = 200,
  sparse    = FALSE,
  ncores    = 4
)

pred6 <- big_prodMat(G, do.call("cbind", all_ldpred2), ind.row = ind.val)
setNames(drop(round(100 * cor(pred6, y2[ind.val])**2, 1)),  p_seq)

plot(p_seq, 100 * cor(pred6, y2[ind.val])**2, log = "x", pch = 20)
abline(v = M / ncol(G), col = "red")

points(p_seq, ldpred_approx, pch = 20, col = "blue")