get_stats_multiallele

This function performs the multi-allele version of 
linear regression. It is used in <code>singlescan</code>
and <code>one_singlescanDO</code>. It performs marker-by-marker
linear regressions for each trait and adjusts for covariates.
It collects parameters from the linear models and returns
for downstream use.

internal

Combined Analysis of Pleiotropy and Epistasis infers predictive
networks between genetic variants and phenotypes. It can be used with
standard two-parent populations as well as multi-parent populations, such
as the Diversity Outbred (DO) mice, Collaborative Cross (CC) mice, or the
multi-parent advanced generation intercross (MAGIC) population of Arabidopsis
thaliana. It uses complementary information of pleiotropic gene variants across
different phenotypes to resolve models of epistatic interactions between alleles.
To do this, cape reparametrizes main effect and interaction coefficients from
pairwise variant regressions into directed influence parameters. These
parameters describe how alleles influence each other, in terms of suppression
and enhancement, as well as how gene variants influence phenotypes. All of the
final interactions are reported as directed interactions between pairs of
parental alleles. For detailed descriptions of the methods used in this package
please see the following references.
Carter, G. W., Hays, M., Sherman, A. & Galitski, T. (2012) <doi:10.1371/journal.pgen.1003010>.
Tyler, A. L., Lu, W., Hendrick, J. J., Philip, V. M. & Carter, G. W. (2013) <doi:10.1371/journal.pcbi.1003270>.

Anna L Tyler

cape

Combined Analysis of Pleiotropy and Epistasis for Diversity
Outbred Mice

Anna Tyler

Jake Emerson

Baha El Kassaby

Ann Wells

Georgi Kolishovski

Vivek Philip

Gregory Carter

get_stats_multiallele function

<dl><dt>phenotype</dt>
<dd>A phenotype vector</dd>
<dt>genotype</dt>
<dd>genotypes of the marker being tested. 
If this is a vector, it will be converted into a one-column 
matrix. If it is a matrix, each column represents an allele,
and each row represents an individual.</dd>
<dt>covar_table</dt>
<dd>The covariate matrix, with each covariate
in a column, and each individual in a row.</dd>
<dt>ph_family</dt>
<dd>a character string for a description of the 
error distribution. Can be either "gaussian" or "binomial"</dd>
<dt>ref_col</dt>
<dd>The column belonging to the reference allele.
The reference allele is removed from the genotype matrix so
that the matrix is linearly independent. There are only n-1
degrees of freedom in the genotype matrix, where n is the number
of alleles.</dd></dl>

Arguments

Perform linear regression on multi-allele markers. — get_stats_multiallele

<dl>

<dt>phenotype</dt>
<dd>A phenotype vector</dd>


<dt>genotype</dt>
<dd>genotypes of the marker being tested. 
If this is a vector, it will be converted into a one-column 
matrix. If it is a matrix, each column represents an allele,
and each row represents an individual.</dd>


<dt>covar_table</dt>
<dd>The covariate matrix, with each covariate
in a column, and each individual in a row.</dd>


<dt>ph_family</dt>
<dd>a character string for a description of the 
error distribution. Can be either "gaussian" or "binomial"</dd>


<dt>ref_col</dt>
<dd>The column belonging to the reference allele.
The reference allele is removed from the genotype matrix so
that the matrix is linearly independent. There are only n-1
degrees of freedom in the genotype matrix, where n is the number
of alleles.</dd>

</dl>

Perform linear regression on multi-allele markers.

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get_stats_multiallele: Perform linear regression on multi-allele markers.

Description

Usage

Value

Arguments