Simulated data of a gene with multi-conditions (cell-type/tissues) gene expression level matrix(Y) and genotype matrix(X) from 400 individuals, plus mixure prior matrices, prior grid, as well as summary statistics from univariate regression and GWAS summary statistics that is ready for use for TWAS analysis. Genotype matrix is centered and scaled, expression matrix is normalized.
Format
multitrait_data is a list with the following elements:
- X
Centered and scaled n x p matrix of genotype, where n is the total number of individuals and p denotes the number of SNPs.
- Y
Normalized n x r matrix of residual for expression, where n is the total number of individuals and r is the total number of conditions (tissue/cell-types).
- prior_matrices
A list of data-driven covariance matrices.
- prior_grid
A vector of scaling factors to be used in fitting mr.mash model.
- prior_matrices_cv
A list of list containing data-driven covariance matrices for 5-fold cross validation.
- prior_grid_cv
A list of vectors of scaling factors for 5-fold cross validation via sample partition.
- gwas_sumstats
A data frame for GWAS summary statistics.
- sumstat
Summary statistics of Bhat and Sbhat from univariate regression for a gene.
- sumstat_cv
A list of 5 fold cross-validation summary statistics based on sample partition for a gene.
References
Morgante, F., Carbonetto, P., Wang, G., Zou, Y., Sarkar, A. & Stephens, M. (2023). A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes. PLoS Genetics 19(7): e1010539. https://doi.org/10.1371/journal.pgen.1010539
Examples
data(multitrait_data)