Multifactorial methods integrating haplotype and epistasis effects for genomic estimation and prediction of quantitative traits

• DOI: 10.3389/fgene.2022.922369
• Publication Date: 2022-10-14T07:00:34Z
• Authors: Yang Da; Zuoxiang Liang; Dzianis Prakapenka
• Keywords: Epistasis; Genetic architecture; Restricted maximum likelihood

The rapid growth in genomic selection data provides unprecedented opportunities to discover and utilize complex genetic effects for improving phenotypes, but the methodology is lacking. Epistasis are interaction effects, haplotype may contain local high-order epistasis effects. Multifactorial methods with SNP, haplotype, up third-order developed investigate contributions of global low-order phenotypic variance accuracy prediction quantitative traits. These include best linear unbiased (GBLUP) associated reliability individuals without observations, including a computationally efficient GBLUP method large validation populations, restricted maximum estimation (GREML) heritability using combination EM-REML AI-REML iterative algorithms. were two models, Model-I 10 effect types Model-II 13 types, intra- inter-chromosome pairwise that replace Model-I. GREML estimate each an type derived, except multifactorial models evaluate based on values adjusted remaining can use more than separate providing capability phenotypes