RT Journal Article
SR Electronic
T1 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 Runcie
A1 Hao Cheng
A1 Lorin Crawford
YR 2020
UL http://biorxiv.org/content/early/2020/05/29/2020.05.26.116814.abstract
AB Plant breeding, like other fields in applied quantitative genetics, has embraced large-scale phenotype data as a way to rapidly and accurately create the next generations of crops. High-throughput phenotyping technologies and large-scale multi-environment breeding trials generate an unprecedented scale of data for breeders to use to make selections and crosses in breeding programs. However, the statistical foundation of multi-trait breeding 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 that facilitates flexible mixed model analyses on a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can efficiently model the genetic architecture of thousands of traits at once while significantly improving genetic value prediction accuracy.Competing Interest StatementThe authors have declared no competing interest.