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cape (version 2.0.2)

pairscan.kin: Performs a pairscan with kinship correction.

Description

This function performs the pairwise regression on all selected marker pairs. The phenotypes used can be either eigentraits or raw phenotypes.

Usage

pairscan.kin(data.obj, geno.obj = NULL, covar, scan.what, marker.pairs, kin.full.geno, sample.kinship, num.kin.samples, n.per.sample, verbose = TRUE, n.cores = 2)

Arguments

data.obj
The object in which all results are stored. See read.population.
geno.obj
The object in which the genotype matrix and marker information are stored. See read.geno.
covar
A vector of character strings indicating which covariates to use.
scan.what
A character string uniquely identifying whether eigentraits or raw traits should be scanned.
marker.pairs
A two-column matrix indicating which marker pairs to test in the pairscan.
kin.full.geno
A logical value indicating whether a kinship matrix for the entire genome should be calculated. This is TRUE if phenotypic covariates are being tested.
sample.kinship
A logical value indicating whether kinship matrices should be sampled (TRUE) or calculated directly (FALSE). Sampling is faster when very large genotype matrices are being used.
num.kin.samples
An integer indicating how many samples to use in calculating the kinship matrices if sample.kinship is TRUE.
n.per.sample
An integer indicating how many markers should be sampled in calculating the kinship matrices if sample.kinship is TRUE.
verbose
A logical value indicating whether the progress of the scan should be printed to the screen.
n.cores
An integer specifying the number of cores to be used in parallel processing.

Value

This function adds an element to the pairscan object reporting the results of the pair-wise scan:
pairscan.effects
A matrix with five columns indicating the names of markers 1 and 2, their effect sizes, and the effect size of their interaction
pairscan.se
A table of the standard errors from the test on each marker pair. The columns are identical to those described for pairscan.effects
model.covariance
This is a table in which each row is the linearized matrix of the variance-covariance matrix of each pairwise regression.

References

Carter, G. W., Hays, M., Sherman, A., & Galitski, T. (2012). Use of pleiotropy to model genetic interactions in a population. PLoS genetics, 8(10), e1003010. doi:10.1371/journal.pgen.1003010

See Also

pairscan, pairscan.noKin