biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Pirinen, Matti
dc.contributor.author Benner, Christian
dc.contributor.author Marttinen, Pekka
dc.contributor.author Järvelin, Marjo-Riitta
dc.contributor.author Rivas, Manuel A
dc.contributor.author Ripatti, Samuli
dc.date.accessioned 2017-08-03T12:09:58Z
dc.date.available 2017-08-03T12:09:58Z
dc.date.issued 2017
dc.identifier.citation Pirinen , M , Benner , C , Marttinen , P , Järvelin , M-R , Rivas , M A & Ripatti , S 2016 , ' biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements ' Bioinformatics , vol 2017 , pp. 1-3 . DOI: 10.1093/bioinformatics/btx166 en
dc.identifier.issn 1367-4803
dc.identifier.issn 1460-2059
dc.identifier.other PURE UUID: a3cc4f10-264b-4cae-9fdf-cd181574bd6c
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/bimm-efficient-estimation-of-genetic-variances-and-covariances-for-cohorts-with-highdimensional-phenotype-measurements(a3cc4f10-264b-4cae-9fdf-cd181574bd6c).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/14298883/btx166.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/27406
dc.description.abstract Genetic research utilizes a decomposition of trait variances and covariances into genetic and environmental parts. Our software package biMM is a computationally efficient implementation of a bivariate linear mixed model for settings where hundreds of traits have been measured on partially overlapping sets of individuals. en
dc.format.extent 1-3
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries Bioinformatics en
dc.relation.ispartofseries Volume 2017 en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department University of Helsinki
dc.contributor.department Department of Computer Science
dc.contributor.department University of Oulu
dc.contributor.department Stanford University
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201708036374
dc.identifier.doi 10.1093/bioinformatics/btx166
dc.type.version publishedVersion


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