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dc.contributor.authorFrei, Oleksandr
dc.contributor.authorHolland, Dominic
dc.contributor.authorSmeland, Olav Bjerkehagen
dc.contributor.authorShadrin, Alexey A.
dc.contributor.authorFan, Chun Chieh
dc.contributor.authorMaeland, Steffen
dc.contributor.authorO'Connell, Kevin S.
dc.contributor.authorWang, Yunpeng
dc.contributor.authorDjurovic, Srdjan
dc.contributor.authorThompson, Wesley Kurt
dc.contributor.authorAndreassen, Ole Andreas
dc.contributor.authorDale, Anders
dc.identifier.citationFrei 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.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.eng
dc.publisherSpringer Natureeng
dc.rightsAttribution CC BYeng
dc.titleBivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlationeng
dc.typeJournal articleeng
dc.rights.holderCopyright 2019 The Author(s)eng
bora.peerreviewedPeer reviewedeng
dc.type.documentJournal article
dc.relation.journalNature Communications

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