calc_m

This function performs the reparameterization
step originally described in
Carter, G. W., Hays, M., Sherman, A. &amp; Galitski, T. 
Use of pleiotropy to model genetic interactions in a 
population. PLoS Genet. 8, e1003010 (2012).

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

calc_m function

<dl><dt>markers</dt>
<dd>A vector of length two holding the
names of the markers being tested.</dd>
<dt>beta_m</dt>
<dd>The matrix of beta values from the 
pairwise linear regression testing the interaction
between the markers.</dd></dl>

Arguments

Calculate m — calc_m

<dl>

<dt>markers</dt>
<dd>A vector of length two holding the
names of the markers being tested.</dd>


<dt>beta_m</dt>
<dd>The matrix of beta values from the 
pairwise linear regression testing the interaction
between the markers.</dd>

</dl>

Calculate m

calc_m: Calculate m

Description

Usage

Value

Arguments