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dc.contributor.authorFrei, Oleksandren_US
dc.contributor.authorHolland, Dominicen_US
dc.contributor.authorSmeland, Olav Bjerkehagenen_US
dc.contributor.authorShadrin, Alexey A.en_US
dc.contributor.authorFan, Chun Chiehen_US
dc.contributor.authorMaeland, Steffenen_US
dc.contributor.authorO'Connell, Kevin S.en_US
dc.contributor.authorWang, Yunpengen_US
dc.contributor.authorDjurovic, Srdjanen_US
dc.contributor.authorThompson, Wesley Kurten_US
dc.contributor.authorAndreassen, Ole Andreasen_US
dc.contributor.authorDale, Andersen_US
dc.date.accessioned2020-05-22T17:15:19Z
dc.date.available2020-05-22T17:15:19Z
dc.date.issued2019-06-03
dc.PublishedFrei O, Holland D, Smeland OB, Shadrin AA, Fan CC, Maeland, O'Connell, Wang Y, Djurovic S, Thompson WK, Andreassen OA, Dale A. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nature Communications. 2019;10(2417)eng
dc.identifier.issn2041-1723
dc.identifier.urihttps://hdl.handle.net/1956/22357
dc.description.abstractAccumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures.en_US
dc.language.isoengeng
dc.publisherSpringer Natureeng
dc.rightsAttribution CC BYeng
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/eng
dc.titleBivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlationen_US
dc.typePeer reviewed
dc.typeJournal article
dc.date.updated2020-01-14T11:31:41Z
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2019 The Author(s)
dc.identifier.doihttps://doi.org/10.1038/s41467-019-10310-0
dc.identifier.cristin1714307
dc.source.journalNature Communications


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