An integrated framework for local genetic correlation analysis

Josefin Werme; Sophie van der Sluis; Danielle Posthuma; Christiaan A. de Leeuw

doi:10.1038/s41588-022-01017-y

An integrated framework for local genetic correlation analysis

Josefin Werme^*, Sophie van der Sluis, Danielle Posthuma, Christiaan A. de Leeuw

^*Corresponding author for this work

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Abstract

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

Original language	English
Pages (from-to)	274-282
Number of pages	9
Journal	Nature genetics
Volume	54
Issue number	3
Early online date	14 Mar 2022
DOIs	https://doi.org/10.1038/s41588-022-01017-y
Publication status	Published - Mar 2022

Bibliographical note

Funding Information:
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.

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

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.

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An integrated framework for local genetic correlation analysisFinal published version, 3.49 MBLicence: Article 25fa Dutch Copyright Act

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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.",

author = "Josefin Werme and {van der Sluis}, Sophie and Danielle Posthuma and {de Leeuw}, {Christiaan A.}",

note = "Funding Information: 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. Funding Information: C.A.d.L. is funded by Hoffman-La Roche. The other authors declare no competing interests. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.",

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An integrated framework for local genetic correlation analysis

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