boot_pvl {qtl2pleio}

Perform bootstrap sampling and calculate test statistic for each bootstrap sample

Description

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

Usage

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
)

Arguments

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.

Details

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.

Value

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

References

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.

Examples

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)