RT Journal Article
SR Electronic
T1 MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.05.26.116814
DO 10.1101/2020.05.26.116814
A1 Daniel E Runcie
A1 Jiayi Qu
A1 Hao Cheng
A1 Lorin Crawford
YR 2020
UL http://biorxiv.org/content/early/2020/07/08/2020.05.26.116814.abstract
AB Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.Competing Interest StatementThe authors have declared no competing interest.