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

ABSTRACT: 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 effects are interaction effects, and haplotype effects may contain local high-order epistasis effects. Multifactorial methods with SNP, haplotype, and epistasis effects up to the third-order are developed to investigate the contributions of global low-order and local high-order epistasis effects to the phenotypic variance and the accuracy of genomic prediction of quantitative traits. These methods include genomic best linear unbiased prediction (GBLUP) with associated reliability for individuals with and without phenotypic observations, including a computationally efficient GBLUP method for large

SUBMITTER: Da Y 

PROVIDER: S-EPMC9614238 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature