biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements
Pirinen, Matti; Benner, Christian; Marttinen, Pekka; Järvelin, Marjo-Riitta; Rivas, Manuel A.; Ripatti, Samuli (2017-08-01)
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
© 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.
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe201708298243
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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.
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