MTG2: An efficient algorithm for multivariate linear mixed model analysis based on genomic information

S. H. Lee, J. H.J. Van Der Werf

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)

Abstract

Summary: We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations.

Original languageEnglish
Pages (from-to)1420-1422
Number of pages3
JournalBioinformatics
Volume32
Issue number9
DOIs
Publication statusPublished or Issued - 1 May 2016
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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