boot_pvl: Perform bootstrap sampling and calculate test statistic for...
In qtl2pleio: Testing Pleiotropy in Multiparental Populations

Description Usage Arguments Details Value References Examples

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.

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.

numeric vector of (log) likelihood ratio test statistics from 'nboot_per_job' 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)