run.positional.scans: Run single chromosome scans on parametric bootstrap samples...

In gkeele/miqtl: Suite of QTL Mapping Functions

Run single chromosome scans on parametric bootstrap samples from the alternative model of a particular locus

Description

This function runs single chromosome scans of parametric bootstrap samples from the alternative model of a particular locus. These association scans can then be used to estimate a confidence interval on QTL position.

Usage

run.positional.scans(
  sim.object,
  keep.full.scans = TRUE,
  genomecache,
  data,
  model = c("additive", "full"),
  use.par = "h2",
  use.multi.impute = TRUE,
  num.imp = 11,
  brute = TRUE,
  use.fix.par = FALSE,
  scan.seed = 1,
  do.augment = FALSE,
  use.augment.weights = FALSE,
  use.full.null = FALSE,
  added.data.points = 1,
  alpha = 0.05,
  ...
)

Arguments

sim.object	A sample object. This is primarily a matrix of parametric bootstrap samples from a locus.
keep.full.scans	DEFAULT: TRUE. If TRUE, the full scans from each sample are kept. This allows for them to be plotted out, but also increases the memory or storage needed.
genomecache	The path to the genome cache directory. The genome cache is a particularly structured directory that stores the haplotype probabilities/dosages at each locus. It has an additive model subdirectory and a full model subdirectory. Each contains subdirectories for each chromosome, which then store .RData files for the probabilities/dosages of each locus.
data	A data frame with outcome and potential covariates. Should also have IDs that link to IDs in the genome cache, often the individual-level ID named "SUBJECT.NAME".
model	DEFAULT: additive. Specifies how to model the founder haplotype probabilities. The additive options specifies use of haplotype dosages, and is most commonly used. The full option regresses the phenotype on the actual diplotype probabilities.
use.par	DEFAULT: "h2". The parameterization of the likelihood to be used.
use.multi.impute	DEFAULT: TRUE. If TRUE, use multiple imputations of genetic data. If FALSE, use ROP.
num.imp	DEFAULT: 11. If multiple imputations are used, this specifies the number of imputations to perform.
brute	DEFAULT: TRUE. During the optimization to find maximum likelihood parameters, this specifies checking the boundaries of h2=0 and h2=1. Slightly less efficient, but otherwise the optimization procedure will not directly check these values.
use.fix.par	DEFAULT: TRUE. This specifies an approximate fitting of mixed effect model (Kang et al. 2009). Much more efficient, as the optimization of h2 only needs to be performed once for the null model rather than every locus. Technically less powerful, though in practice it has proven to be almost equal to the exact procedure.
scan.seed	DEFAULT: 1.
do.augment	DEFAULT: FALSE. Augments the data with null observations for genotype groups. This is an approximately Bayesian approach to applying a prior to the data, and can help control highly influential data points.
use.augment.weights	DEFAULT: FALSE. Specify non-equal weights on the augmented data points. This allows for the inclusion of augmented data points to all genotype classes while reducing their overall contribution to the data.
use.full.null	DEFAULT: FALSE. Draws augmented data points from the null model. This allows for the inclusion of null data points that do not influence the estimation of other model parameters as much.
added.data.points	DEFAULT: 1. If augment weights are being used, this specifies how many data points should be added in total.

Examples

run.positional.scans()

gkeele/miqtl documentation built on Sept. 13, 2024, 5:49 a.m.