MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 26755623.
- Also identified by DOI 10.1093/bioinformatics/btw012 and PMC identifier 4848406.
- Licence recorded as CC BY-NC.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
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. MTG2 is available in https://sites.google.com/site/honglee0707/mtg2 CONTACT: hong.lee@une.edu.au Supplementary data are available at Bioinformatics online.
Medical subject headings
- Algorithms
- Genomics