PhenoSpD: an integrated toolkit for phenotypic correlation estimation and multiple testing correction using GWAS summary statistics

BackgroundIdentifying phenotypic correlations between complex traits and diseases can provide useful etiological insights. Restricted access to individual-level phenotype data makes it difficult to estimate large-scale phenotypic correlation across the human phenome. State-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate phenotypic correlation using genome-wide association study (GWAS) summary statistics.\n\nResultsHere, we present an integrated R toolkit, PhenoSpD, to 1) apply metaCCA (or LD score regression) to estimate phenotypic correlations using GWAS summary statistics; and 2) to utilize the estimated phenotypic correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest it is possible to estimate phenotypic correlation using samples with only a partial overlap, but as overlap decreases correlations will attenuate towards zero and multiple testing correction will be more stringent than in perfectly overlapping samples. In a case study, PhenoSpD using GWAS results suggested 324.4 independent tests among 452 metabolites, which is close to the 296 independent tests estimated using true phenotypic correlation. We further applied PhenoSpD to estimated 7,503 pair-wise phenotypic correlations among 123 metabolites using GWAS summary statistics from Kettunen et al. and PhenoSpD suggested 44.9 number of independent tests for theses metabolites.\n\nConclusionPhenoSpD integrates existing methods and provides a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics, which is particularly valuable for post-GWAS analysis of complex molecular traits.\n\nAvailabilityR code and documentation for PhenoSpD V1.0.0 is available online (https://github.com/MRCIEU/PhenoSpD).