Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers

PLoS One. 2014 Jan 30;9(1):e87666. doi: 10.1371/journal.pone.0087666. eCollection 2014.


We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005-0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Genetic Markers / genetics*
  • Genome, Human / genetics*
  • Genomics / methods
  • Genotype
  • Humans
  • Likelihood Functions
  • Models, Genetic
  • Pedigree
  • Phenotype
  • Polymorphism, Single Nucleotide / genetics*
  • Quantitative Trait, Heritable


  • Genetic Markers

Grant support

This research was supported by USDA National Institute of Food and Agriculture Grant no. 2011-67015-30333 and by project MN-16-043 of the Agricultural Experiment Station at the University of Minnesota. The Holstein SNP data were supported by National Research Initiative Competitive Grant no. 2008-35205-18846 from the USDA National Institute of Food and Agriculture and by a financial contribution from Holstein Association USA. Supercomputer computing time was provided by the Minnesota Supercomputer Institute at the University of Minnesota. Guo Hu was supported by Heilongjiang River Fishery Research Institute of Chinese Academy of Fishery Sciences, Harbin, China. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.