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

  1. Jie Zheng
  2. Tom G. Richardson
  3. Louise A. C. Millard
  4. Gibran Hemani
  5. Christopher Raistrick
  6. Bjarni Vilhjalmsson
  7. Philip Haycock
  8. Tom R Gaunt

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Abstract

Background

Identifying 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.

Results

Here, 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.

Conclusion

PhenoSpD 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.

Availability

R code and documentation for PhenoSpD V1.0.0 is available online ( https://github.com/MRCIEU/PhenoSpD ).

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  1. GigaScience

    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

  2. Version published to 10.1101/148627 on bioRxiv