Example_output_file

This file is an example of the output obtained with
 <code>multi_meta</code> function. The input files were
 Example_file_1.txt and Example_file_2.txt, the command
 line was the one in the example (see <code>multi_meta</code>
 help) The columns are: <ul>
<li>chr Chromosome
 </li>
<li>SNP SNP name </li>
<li>Position
 Position </li>
<li>allele1 Effect allele
 </li>
<li>allele0 Non-effect allele
 </li>
<li>tot_af Effect-allele frequency computed on
 all the cohorts analysed </li>
<li>n_pops Number of
 cohorts (populations) analysed </li>
<li>pops
 String of 1 or 0 values indicating which populations were
 analysed: e.g. if the first number is 1, the first cohort
 was included in the analysis etc. </li>
<li>beta_1,
 beta_2, ..., beta_p Effect sizes for
 each of the p traits; in this example p=6 
 </li>
<li>Vbeta_1_1, Vbeta_1_2, ...,
 Vbeta_1_p, Vbeta_2_2, ...,
 Vbeta_2_p, ..., Vbeta_p_p
 Variance-covariance matrix entries (diagonal and upper
 triangle values only, since this matrix is symmetric)
 </li>
<li>p_value P-value </li>
</ul>

datasets

data

Allows running a meta-analysis of multivariate Genome Wide
Association Studies (GWAS) and easily visualizing results through custom
plotting functions. The multivariate setting implies that results for each
single nucleotide polymorphism (SNP) include several effect sizes (also
known as "beta coefficients", one for each trait), as well as related
variance values, but also covariance between the betas. The main goal of
the package is to provide combined beta coefficients across different
cohorts, together with the combined variance/covariance matrix. The method
is inverse-variance based, thus each beta is weighted by the inverse of its
variance-covariance matrix, before taking the average across all betas. The
default options of the main function \code{multi_meta} will work with files
obtained from GEMMA multivariate option for GWAS (Zhou & Stephens, 2014).
It will work with any other output, as soon as columns are formatted to
have the according names. The package also provides several plotting
functions for QQ-plots, Manhattan Plots and custom summary plots.

Dragana Vuckovic

MultiMeta

Meta-analysis of Multivariate Genome Wide Association Studies

Example_output_file function

This file is an example of the output obtained with
 <code>multi_meta</code> function. The input files were
 Example_file_1.txt and Example_file_2.txt, the command
 line was the one in the example (see <code>multi_meta</code>
 help)

 The columns are: <ul>
<li>chr Chromosome
 </li>
<li>SNP SNP name </li>
<li>Position
 Position </li>
<li>allele1 Effect allele
 </li>
<li>allele0 Non-effect allele
 </li>
<li>tot_af Effect-allele frequency computed on
 all the cohorts analysed </li>
<li>n_pops Number of
 cohorts (populations) analysed </li>
<li>pops
 String of 1 or 0 values indicating which populations were
 analysed: e.g. if the first number is 1, the first cohort
 was included in the analysis etc. </li>
<li>beta_1,
 beta_2, ..., beta_p Effect sizes for
 each of the p traits; in this example p=6 
 </li>
<li>Vbeta_1_1, Vbeta_1_2, ...,
 Vbeta_1_p, Vbeta_2_2, ...,
 Vbeta_2_p, ..., Vbeta_p_p
 Variance-covariance matrix entries (diagonal and upper
 triangle values only, since this matrix is symmetric)
 </li>
<li>p_value P-value </li>
</ul>

Example_output_file: Example output file

Description

Arguments