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, https://doi.org/10.1093/bioinformatics/btx166
biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements
|Author:||Pirinen, Matti1,2,3; Benner, Christian1,3; Marttinen, Pekka2,4;|
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
|Online Access:||PDF Full Text (PDF, 0.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201708298243
Oxford University Press,
|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 www.iki.fi/mpirinen.
Supplementary information: Supplementary data are available at Bioinformatics online.
|Pages:||2405 - 2407|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
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 (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)
Supplementary data are available at Bioinformatics online.
© The Author 2017. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.