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

Research output: Contribution to journalArticle

Original languageEnglish
Article numbergiy090
Number of pages10
JournalGigaScience
Volume7
Issue number8
Early online date24 Aug 2018
DOIs
DateAccepted/In press - 16 Jul 2018
DateE-pub ahead of print - 24 Aug 2018
DatePublished (current) - Aug 2018

Abstract

Background: Identifying phenotypic correlations between complex traits and diseases can provide useful etiological insights. Restricted access to much individual-level phenotype data makes it difficult to estimate large-scale phenotypic correlation across the human phenome. Two state-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate phenotypic correlation using only genome-wide association study (GWAS) summary results.
Results: Here, we present an integrated R toolkit, PhenoSpD, to 1) use LD score regression to estimate phenotypic cor-relations using GWAS summary statistics; and 2) 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 1) it is possible to identify non-independence of phenotypes using samples with partial overlap, as overlap decreases the estimated phenotypic correlations will attenuate towards zero and multiple testing correction will be more stringent than in perfectly overlapping samples; 2) in contrast to LD score regression, metaCCA will provide approximate genetic corre-lations rather than phenotypic correlation, which limits its application for multiple testing correction. In a case study, PhenoSpD using UK Biobank GWAS results suggested 399.6 independent tests among 487 human traits, which is close to the 352.4 independent tests estimated using true phenotypic correlation. We further applied PhenoSpD to an estimat-ed 5618 pair-wise phenotypic correlations among 107 metabolites using GWAS summary statistics from Kettunen et al. and PhenoSpD suggested the equivalent of 33.5 independent tests for theses metabolites.
Conclusion: PhenoSpD extends the use of summary level results, providing a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics. This is particularly valuable in the age of large-scale biobank and consortia studies, where GWAS results are much more accessible than individual-level data.
Availability: R code and documentation for PhenoSpD V1.0.0 is available online https://github.com/MRCIEU/PhenoSpD.

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via OUP at https://academic.oup.com/gigascience/article/7/8/giy090/5078867 . Please refer to any applicable terms of use of the publisher.

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