University of Oulu

Matti Pirinen, Christian Benner, Pekka Marttinen, Marjo-Riitta Järvelin, Manuel A. Rivas, Samuli Ripatti; biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements, Bioinformatics, Volume 33, Issue 15, 1 August 2017, Pages 2405–2407,

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

Saved in:
Author: Pirinen, Matti1,2,3; Benner, Christian1,3; Marttinen, Pekka2,4;
Organizations: 1Institute for Molecular Medicine Finland (FIMM), University of Helsinki
2Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki
3Department of Public Health, University of Helsinki
4Helsinki Institute for Information Technology HIIT and Department of Computer Science, Aalto University
5Biocenter Oulu, University of Oulu
6Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London
7Center for Life Course and Systems Epidemiology, Faculty of Medicine, University of Oulu
8Unit of Primary Care, Oulu University Hospital
9Department of Biomedical Data Science, Stanford University
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.1 MB)
Persistent link:
Language: English
Published: Oxford University Press, 2017
Publish Date: 2017-08-29


Summary: 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.

Availability and Implementation: Implementation in R freely available at


Supplementary information: Supplementary data are available at Bioinformatics online.

see all

Series: Bioinformatics
ISSN: 1367-4803
ISSN-E: 1460-2059
ISSN-L: 1367-4803
Volume: 33
Issue: 15
Pages: 2405 - 2407
DOI: 10.1093/bioinformatics/btx166
Type of Publication: A1 Journal article – refereed
Field of Science: 3111 Biomedicine
Funding: This work was supported by the Academy of Finland [257654 and 288509 to M.P.; 286607 and 294015 to P.M.; 251217 and 255847 to S.R.]. S.R. was supported by EU FP7 projects ENGAGE (201413) and BioSHaRE (261433), the Finnish Foundation for Cardiovascular Research, Biocentrum Helsinki and the Sigrid Juselius Foundation. NFBC1966 received financial support from University of Oulu Grant no. 65354, Oulu University Hospital Grant no. 2/97, 8/97, Ministry of Health and Social Affairs Grant no. 23/251/97, 160/97, 190/97, National Institute for Health and Welfare, Helsinki Grant no. 54121, Regional Institute of Occupational Health, Oulu, Finland Grant no. 50621, 54231.
Academy of Finland Grant Number: 257654
Detailed Information: 257654 (Academy of Finland Funding decision)
288509 (Academy of Finland Funding decision)
286607 (Academy of Finland Funding decision)
294015 (Academy of Finland Funding decision)
251217 (Academy of Finland Funding decision)
255847 (Academy of Finland Funding decision)
Dataset Reference: Supplementary data are available at Bioinformatics online.
Copyright information: © The Author 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.