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

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...

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Autores principales: Pirinen, Matti, Benner, Christian, Marttinen, Pekka, Järvelin, Marjo-Riitta, Rivas, Manuel A, Ripatti, Samuli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860115/
https://www.ncbi.nlm.nih.gov/pubmed/28369165
http://dx.doi.org/10.1093/bioinformatics/btx166
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author Pirinen, Matti
Benner, Christian
Marttinen, Pekka
Järvelin, Marjo-Riitta
Rivas, Manuel A
Ripatti, Samuli
author_facet Pirinen, Matti
Benner, Christian
Marttinen, Pekka
Järvelin, Marjo-Riitta
Rivas, Manuel A
Ripatti, Samuli
author_sort Pirinen, Matti
collection PubMed
description 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.
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spelling pubmed-58601152018-03-23 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 Bioinformatics Applications Notes 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. Oxford University Press 2017-08-01 2017-03-28 /pmc/articles/PMC5860115/ /pubmed/28369165 http://dx.doi.org/10.1093/bioinformatics/btx166 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Applications Notes
Pirinen, Matti
Benner, Christian
Marttinen, Pekka
Järvelin, Marjo-Riitta
Rivas, Manuel A
Ripatti, Samuli
biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements
title biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements
title_full biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements
title_fullStr biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements
title_full_unstemmed biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements
title_short biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements
title_sort bimm: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860115/
https://www.ncbi.nlm.nih.gov/pubmed/28369165
http://dx.doi.org/10.1093/bioinformatics/btx166
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