Megalmm: mega-scale linear mixed models for genomic predictions with thousands of traits

HIGHLIGHTS

• who: Daniel E. Runcie from the Department of Plant Sciences, University of California Davis, Davis, CA, USA have published the research: MegaLMM: mega-scale linear mixed models for genomic predictions with thousands of traits, in the Journal: (JOURNAL)
• what: Using three examples with real plant data the authors show that can leverage thousands of traits at once to significantly improve genetic value prediction accuracy. Although the authors focus on plant breeding applications for concreteness, the method can be broadly applied wherever multi-trait linear mixed models are used (e_g, human genetics, industrial experiments, psychology, linguistics . . .
  

   

  

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