Description Usage Arguments Details Value References
Performs heritability informed power optimization (HIPO) to conduct powerful association testing across multiple traits.
1 2 3 |
sumstats |
A list of K containing summary statistics for K traits.
Each element is a data frame that contains at least 6 columns<ef><bc><9a> |
out.path |
The path where the LDSC intermediate outputs are stored. |
HIPOD.num |
Number of HIPO components for which to calculate z statistics and p-values. Default 2. |
ldsc.path |
The path to LDSC software. |
python.path |
The path to Python, if you need to use a version other than the default one. |
ldscore.path |
The path containing the LDSC LD score files. Default to be |
maf.thr |
MAF threshold for quality control. SNPs with MAF < |
mergeallele |
Corresponds to the |
truncate |
Used only for high-dimensional phenotypes. If |
This function fits LD score regression to estimate the genetic covariance matrix and the covariance matrix of GWAS parameter estimates. Suitable eigendecomposition is then performed to find the weights that lead to the largest average non-centrality parameter of the underlying test statistic (HIPO-D1), as well as subsequent HIPO components. Z-statistics of HIPO components are computed for association testing.
A list that contains
sumstats.all |
A data.frame containing the merged individual trait summary statistics and the z statistics ( |
coherit.mat |
Genetic covariance matrix. |
ldscint.mat |
Matrix of LD score regression intercepts. |
eigenvalue |
Eigenvalues from HIPO eigendecomposition. Proportional to the average non-centrality parameter. |
HIPOD.mat |
A matrix of which the columns are the HIPO components. |
Qi, Guanghao, and Nilanjan Chatterjee. "Heritability Informed Power Optimization (HIPO) Leads to Enhanced Detection of Genetic Associations Across Multiple Traits." bioRxiv (2017): 218404.
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