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

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 use LD score regression to estimate phenotypic correlations using GWAS summary statistics and 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 that it is possible to identify nonindependence of phenotypes using samples with partial overlap; as overlap decreases, the estimated phenotypic correlations will attenuate toward zero and multiple testing correction will be more stringent than in perfectly overlapping samples. Also, in contrast to LD score regression, metaCCA will provide approximate genetic correlations 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 estimated 5,618 pair-wise phenotypic correlations among 107 metabolites using GWAS summary statistics from Kettunen's publication and PhenoSpD suggested the equivalent of 33.5 independent tests for these metabolites.

Conclusions

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.

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Now published in GigaScience doi: 10.1093/gigascience/giy090

Jie Zheng 1MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence: jie.zheng@bristol.ac.uk tom.gaunt@bristol.ac.ukTom G. Richardson 1MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteLouise A. C. Millard 1MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK;2Intelligent Systems Laboratory, University of Bristol, Bristol, UK;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteGibran Hemani 1MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteChristopher Raistrick Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteBjarni Vilhjalmsson 3Århus Center for Bioinformatics BIRC, Aarhus UniversityFind this author on Google ScholarFind this author on PubMedSearch for this author on this sitePhilip Haycock 1MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteTom R Gaunt 1MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK;Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence: jie.zheng@bristol.ac.uk tom.gaunt@bristol.ac.uk

A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giy090 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

These peer reviews were as follows:

Reviewer 1: http://dx.doi.org/10.5524/REVIEW.101321 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.101322

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