CORE GREML: MTG2 code and instruction

Lee, Sang Hong, University of South Australia, https://orcid.org/0000-0001-9701-2718

Zhou, Xuan, University of South Australia, https://orcid.org/0000-0003-3882-2408

hong.lee@unisa.edu.au, xuan.zhou@unisa.edu.au

Publication date: August 17, 2020

Publisher: Dryad

https://doi.org/10.5061/dryad.bk3j9kd8c

Citation

Lee, Sang Hong; Zhou, Xuan (2020), CORE GREML: MTG2 code and instruction , Dryad, Dataset, https://doi.org/10.5061/dryad.bk3j9kd8c

Abstract

Linear mixed models (LMMs) using genome-based restricted maximum likelihood (GREML) allow both fixed and random effects. Classic LMMs assume independence between random effects, which can be violated. To relax the assumption, we introduced a generalised GREML, named CORE GREML, that explicitly estimates the covariance between random effects. This dataset includes MTG2 code and instruction for CORE GREML.

Methods

The code was written in fortan program language.