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Bioinformatics. 2017 Aug 1;33(15):2405-2407. doi: 10.1093/bioinformatics/btx166.

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

Author information

1
Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
2
Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
3
Department of Public Health, University of Helsinki, Helsinki, Finland.
4
Helsinki Institute for Information Technology HIIT and Department of Computer Science, Aalto University, Espoo, Finland.
5
Biocenter Oulu, University of Oulu, Oulu, Finland.
6
Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
7
Center for Life Course and Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland.
8
Unit of Primary Care, Oulu University Hospital, Oulu, Finland.
9
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.

Abstract

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 .

Contact:

matti.pirinen@helsinki.fi.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28369165
PMCID:
PMC5860115
DOI:
10.1093/bioinformatics/btx166
[Indexed for MEDLINE]
Free PMC Article

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