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

Matti Pirinen | Zendy; Christian Benner | Zendy; Pekka Marttinen | Zendy; MarjoRiitta Järvelin | Zendy; Manuel A. Rivas | Zendy; Samuli Ripatti | Zendy

Open Access

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

Author(s) -

Matti Pirinen,

Christian Benner,

Pekka Marttinen,

MarjoRiitta Järvelin,

Manuel A. Rivas,

Samuli Ripatti

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx166

Subject(s) - bivariate analysis , computer science , trait , r package , software , estimation , statistics , data mining , mathematics , machine learning , computational science , engineering , systems engineering , programming language

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.

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