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

Matti Pirinen; Christian Benner; Pekka Marttinen; Marjo-Riitta Järvelin; Manuel A Rivas; Samuli Ripatti

doi:10.1093/bioinformatics/btx166

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

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

Authors

Matti Pirinen^{1

2

3}, Christian Benner^{1

3}, Pekka Marttinen^{2

4}, Marjo-Riitta Järvelin^{5

6

7

8}, Manuel A Rivas⁹, Samuli Ripatti^{1

3}

Affiliations

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.

MeSH terms

Genetic Variation*
Genetics, Population / methods*
Genomics / methods
Humans
Models, Statistical*
Software*