`corrected_cs.Rd`

Corrected credible set using Z-scores and MAFs

corrected_cs(z, f, N0, N1, Sigma, W = 0.2, lower = 0, upper = 1, desired.cov, acc = 0.005, max.iter = 20, pp0min = 0.001)

z | Z-scores |
---|---|

f | Minor allele frequencies |

N0 | Number of controls |

N1 | Number of cases |

Sigma | Correlation matrix of SNPs |

W | Prior for the standard deviation of the effect size parameter, beta (default 0.2) |

lower | Lower threshold (default = 0) |

upper | Upper threshold (default = 1) |

desired.cov | The desired coverage of the causal variant in the credible set |

acc | Accuracy of corrected coverage to desired coverage (default = 0.005) |

max.iter | Maximum iterations (default = 20) |

pp0min | Only average over SNPs with pp0 > pp0min |

List of variants in credible set, required threshold, the corrected coverage and the size of the credible set

# \donttest{ # this is a long running example # In this example, the function is used to find a corrected 95% credible set # using Z-scores and MAFs, that is the smallest set of variants # required such that the resultant credible set has coverage close to (/within # some accuracy of) the "desired coverage" (here set to 0.95). Max.iter parameter # defines the maximum number of iterations to try in the root bisection algorithm, # this should be increased to ensure convergence to the desired coverage, but is set # to 1 here for speed (and thus the resultant credible set will not be accurate). set.seed(2) nsnps = 200 N0 = 1000 N1 = 1000 z_scores <- rnorm(nsnps, 0, 1) # simulate a vector of Z-scores ## generate example LD matrix library(mvtnorm) nsamples = 1000 simx <- function(nsnps, nsamples, S, maf=0.1) { mu <- rep(0,nsnps) rawvars <- rmvnorm(n=nsamples, mean=mu, sigma=S) pvars <- pnorm(rawvars) x <- qbinom(1-pvars, 1, maf) } S <- (1 - (abs(outer(1:nsnps,1:nsnps,`-`))/nsnps))^4 X <- simx(nsnps,nsamples,S) LD <- cor2(X) maf <- colMeans(X) names(z_scores) <- seq(1,length(z_scores)) corrected_cs(z = z_scores, f = maf, N0, N1, Sigma = LD, desired.cov = 0.9, max.iter = 1)#> [1] "thr: 0.5 , cov: 0.471664648318262"#> $credset #> [1] "26" "16" "85" "139" "168" "21" "167" "52" "106" "62" "134" "108" #> [13] "67" "46" "9" "56" "42" "97" "24" "44" "86" "61" "15" "130" #> [25] "129" "37" "123" "82" "92" "60" "180" "91" "23" "3" "95" "140" #> [37] "80" "84" "114" "94" "156" "193" "165" "77" "155" "191" "54" "182" #> [49] "169" "121" #> #> $req.thr #> [1] 0.5 #> #> $corr.cov #> [1] 0.4716646 #> #> $size #> [1] 0.5004184 #># max.iter set low for speed, should be set to at least # the default to ensure convergence to desired coverage # }