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

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)2405-2407
Number of pages3
JournalBioinformatics
Volume33
Issue number15
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed

Researchers

  • Matti Pirinen
  • Christian Benner
  • Pekka Marttinen

  • Marjo-Riitta Järvelin
  • Manuel A Rivas
  • Samuli Ripatti

Research units

  • University of Helsinki
  • University of Oulu
  • Imperial College London
  • Stanford University

Abstract

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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