ph.large

A large quad-variate data set was simulated assuming an underlying Cholesky model, 
with 5000 observations per trait and high polygenicity (5,000 SNPs per genetic factor).
The data set is described in full in the vignette. Genetic and residual
covariances ar assumed to be influenced by four independent genetic factors
(A1, A2, A3 and A4, based on 5000 SNPs each) and four independent residual factors (E1, E2, E3 and E4), 
respectively.

datasets

Quantitative genetics tool supporting the modelling of multivariate
genetic variance structures in quantitative data. It allows fitting different
models through multivariate genetic-relationship-matrix (GRM)
structural equation modelling (SEM) in unrelated individuals,
using a maximum likelihood approach. Specifically,
it combines genome-wide genotyping information, as captured by GRMs,
with twin-research-based SEM techniques,
St Pourcain et al. (2017) <doi:10.1016/j.biopsych.2017.09.020>,
Shapland et al. (2020) <doi:10.1101/2020.08.14.251199>.

Beate StPourcain

grmsem

Genetic-Relationship-Matrix Structural Equation Modelling
(GRMSEM)

Alexander Klassmann

ph.large function

A data frame with 5000 rows and 4 columns<dl class="dl-horizontal">
 <dt>Y1</dt><dd>trait one</dd>
 <dt>Y2</dt><dd>trait two</dd>
 <dt>Y3</dt><dd>trait three</dd>
 <dt>Y4</dt><dd>trait four</dd>
</dl>

Format

Phenotype data: large data set — ph.large

A data frame with 5000 rows and 4 columns<dl class='dl-horizontal'>
 <dt>Y1</dt><dd>trait one</dd>
 <dt>Y2</dt><dd>trait two</dd>
 <dt>Y3</dt><dd>trait three</dd>
 <dt>Y4</dt><dd>trait four</dd>
</dl>

ph.large: Phenotype data: large data set

Description

Usage

Arguments

Format