biMM: Efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements.
File(s)btx166.pdf (114.76 KB)
Published version
Author(s)
Type
Journal Article
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.
Date Issued
2017-03-28
Date Acceptance
2017-03-22
Citation
Bioinformatics, 2017, 33 (15), pp.2405-2407
ISSN
1367-4803
Publisher
Oxford University Press
Start Page
2405
End Page
2407
Journal / Book Title
Bioinformatics
Volume
33
Issue
15
Copyright Statement
© 2017 The Author(s). 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.
4.0/), which permits
unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
PII: 3091808
Subjects
Science & Technology
Life Sciences & Biomedicine
Technology
Physical Sciences
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability
Biochemistry & Molecular Biology
Computer Science
Mathematics
LINEAR MIXED-MODEL
ASSOCIATION
DISEASES
TRAITS
01 Mathematical Sciences
06 Biological Sciences
08 Information And Computing Sciences
Bioinformatics
Publication Status
Published