Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/18977
Title: | MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information | Contributor(s): | Lee, Sang Hong (author); Van Der Werf, Julius H (author)![]() |
Publication Date: | 2016 | Open Access: | Yes | DOI: | 10.1093/bioinformatics/btw012 | Handle Link: | https://hdl.handle.net/1959.11/18977 | Open Access Link: | http://dx.doi.org/10.1093/bioinformatics/btw012 | 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. | Publication Type: | Journal Article | Grant Details: | NHMRC/APP1080157 ARC/DP160102126 ARC/DE130100614 |
Source of Publication: | Bioinformatics, 32(9), p. 1420-1422 | Publisher: | Oxford University Press | Place of Publication: | United Kingdom | ISSN: | 1367-4803 1367-4811 |
Field of Research (FOR): | 070201 Animal Breeding 060408 Genomics 060412 Quantitative Genetics (incl. Disease and Trait Mapping Genetics) |
Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal | Statistics to Oct 2018: | Visitors: 137 Views: 205 Downloads: 1 |
Appears in Collections: | Journal Article |
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