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An integrated framework for local genetic correlation analysis

Nature Genetics volume 54, pages 274–282 (2022)Cite this article

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Abstract

Genetic correlation (rg) analysis is used to identify phenotypes that may have a shared genetic basis. Traditionally, rg is studied globally, considering only the average of the shared signal across the genome, although this approach may fail when the rg is confined to particular genomic regions or in opposing directions at different loci. Current tools for local rg analysis are restricted to analysis of two phenotypes. Here we introduce LAVA, an integrated framework for local rg analysis that, in addition to testing the standard bivariate local rgs between two phenotypes, can evaluate local heritabilities and analyze conditional genetic relations between several phenotypes using partial correlation and multiple regression. Applied to 25 behavioral and health phenotypes, we show considerable heterogeneity in the bivariate local rgs across the genome, which is often masked by the global rg patterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations.

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Fig. 1: Overview of the number of significant bivariate local rgs between all 25 phenotype pairs.
Fig. 2: Comparison between the global genetic correlations estimated using LDSC and the mean local genetic correlations from LAVA across tested loci.
Fig. 3: Local genetic correlation hotspots within the MHC.

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Data availability

All analyses in this study relied on publicly available summary statistics that, in all but three cases, were downloaded from the GWAS Atlas2 (https://atlas.ctglab.nl). Original sources and Atlas IDs (when applicable) are referenced in Table 1. As a reference for the estimation of LD, we used the European subset of the 1000 Genomes26 data as downloaded from https://ctg.cncr.nl/software/magma. The locus file used for all the LAVA analyses can be accessed at https://github.com/josefin-werme/lava-scripts2021 (ref. 61).

Code availability

LAVA is implemented as an R package, which is publicly available at the LAVA website (https://ctg.cncr.nl/software/lava) and LAVA GitHub repository (https://github.com/josefin-werme/LAVA). Analysis scripts and the exact package version (v0.0.6) used for the generation of the main results can be downloaded from https://github.com/josefin-werme/lava-scripts2021 (ref. 61). The method used for genome partitioning is available at https://github.com/cadeleeuw/lava-partitioning (ref. 62).

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Acknowledgements

This work was funded by COSYN (Comorbidity and Synapse Biology in Clinically Overlapping Psychiatric Disorders: Horizon 2020 Program of the European Union under RIA grant agreement 667301, D.P.) and the Netherlands Organization for Scientific Research (VICI 435-14-005, D.P.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The analyses were carried out on the Genetic Cluster Computer, which is financed by the Netherlands Organization for Scientific Research (480-05-003, D.P.), VU University (Amsterdam, the Netherlands) and the Dutch Brain Foundation, hosted by the Dutch National Computing and Networking Services SurfSARA.

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Authors and Affiliations

  1. Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, the Netherlands

    Josefin Werme, Danielle Posthuma & Christiaan A. de Leeuw

  2. Section Complex Trait Genetics, Department of Child and Adolescent Psychology and Psychiatry, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, the Netherlands

    Sophie van der Sluis & Danielle Posthuma

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  1. Josefin Werme
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Contributions

J.W., S.v.d.S., D.P. and C.A.d.L. conceived of the study. J.W. and C.A.d.L. developed the statistical framework and implemented the software. J.W. performed the analyses, simulations and wrote the manuscript with contributions from C.A.d.L. J.W., S.v.d.S., D.P. and C.A.d.L. participated in the interpretation of the results and revision of the manuscript. All authors provided meaningful contributions at each stage of the project.

Corresponding authors

Correspondence to Josefin Werme or Christiaan A. de Leeuw.

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C.A.d.L. is funded by Hoffman-La Roche. The other authors declare no competing interests.

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Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–18, Tables 1–3, 157 and 158, Methods and Note.

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Supplementary Tables

Tables containing the results for all genetic correlation hotspots with accompanying network plots.

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Werme, J., van der Sluis, S., Posthuma, D. et al. An integrated framework for local genetic correlation analysis. Nat Genet 54, 274–282 (2022). https://doi.org/10.1038/s41588-022-01017-y

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