`R/boot_pvl.R`

`boot_pvl.Rd`

Create a bootstrap sample, perform multivariate QTL scan, and calculate log10 LRT statistic

boot_pvl( probs, pheno, addcovar = NULL, kinship = NULL, start_snp = 1, n_snp, pleio_peak_index, nboot = 1, max_iter = 10000, max_prec = 1/1e+08, cores = 1 )

probs | founder allele probabilities three-dimensional array for one chromosome only (not a list) |
---|---|

pheno | n by d matrix of phenotypes |

addcovar | n by c matrix of additive numeric covariates |

kinship | a kinship matrix, not a list |

start_snp | positive integer indicating index within probs for start of scan |

n_snp | number of (consecutive) markers to use in scan |

pleio_peak_index | positive integer index indicating genotype matrix for bootstrap sampling. Typically acquired by using `find_pleio_peak_tib`. |

nboot | number of bootstrap samples to acquire and scan |

max_iter | maximum number of iterations for EM algorithm |

max_prec | stepwise precision for EM algorithm. EM stops once incremental difference in log likelihood is less than max_prec |

cores | number of cores to use when calling mclapply to parallelize the bootstrap analysis. |

numeric vector of (log) likelihood ratio test statistics from `nboot_per_job` bootstrap samples

Performs a parametric bootstrap method to calibrate test statistic values in the test of pleiotropy vs. separate QTL. It begins by inferring parameter values at the `pleio_peak_index` index value in the object `probs`. It then uses these inferred parameter values in sampling from a multivariate normal distribution. For each of the `nboot` sampled phenotype vectors, a two-dimensional QTL scan, starting at the marker indexed by `start_snp` within the object `probs` and extending for a total of `n_snp` consecutive markers. The two-dimensional scan is performed via the function `scan_pvl_clean`. For each two-dimensional scan, a log10 likelihood ratio test statistic is calculated. The outputted object is a vector of `nboot` log10 likelihood ratio test statistics from `nboot` distinct bootstrap samples.

Knott SA, Haley CS (2000) Multitrait least squares for quantitative trait loci detection. Genetics 156: 899–911.

Walling GA, Visscher PM, Haley CS (1998) A comparison of bootstrap methods to construct confidence intervals in QTL mapping. Genet. Res. 71: 171–180.

n <- 50 pheno <- matrix(rnorm(2 * n), ncol = 2) rownames(pheno) <- paste0("s", 1:n) colnames(pheno) <- paste0("tr", 1:2) probs <- array(dim = c(n, 2, 5)) probs[ , 1, ] <- rbinom(n * 5, size = 1, prob = 0.2) probs[ , 2, ] <- 1 - probs[ , 1, ] rownames(probs) <- paste0("s", 1:n) colnames(probs) <- LETTERS[1:2] dimnames(probs)[[3]] <- paste0("m", 1:5) boot_pvl(probs = probs, pheno = pheno, start_snp = 1, n_snp = 5, pleio_peak_index = 3, nboot = 1, cores = 1)#> [1] 1.826634