The rapid growth in genomic selection data provides unprecedented opportunities to
discover and utilize complex genetic effects for improving phenotypes, but the methodology …

Abstract Key Message The accuracy of genomic prediction of phenotypes can be increased
by including the top-ranked pairwise SNP interactions into the prediction model. Abstract We …

Genomic prediction methods based on multiple markers have potential to include
nonadditive effects in prediction and analysis of complex traits. However, most …

We established a genomic model of quantitative trait with genomic additive and dominance
relationships that parallels the traditional quantitative genetics model, which partitions a …

Epistasis effects (gene interactions) have been increasingly recognized as important genetic
factors underlying complex traits. The existence of a large number of single nucleotide …

The accuracy of predicting seven human phenotypes of 3657–7564 individuals using global
epistasis effects was evaluated and compared to the accuracy of haplotype genomic …

For most complex traits, results from genome-wide association studies show that the
proportion of the phenotypic variance attributable to the additive effects of individual SNPs …

The concept of genomic selection (Meuwissen et al., 2001) generated great excitement in
the animal breeding community. With genomic information from SNP panels, one can …

Whole-genome resequencing technology has improved rapidly during recent years and is
expected to improve further such that the sequencing of an entire human genome sequence …

Genome-wide prediction approaches represent versatile tools for the analysis and
prediction of complex traits. Mostly they rely on marker-based information, but scenarios …