Animal Science Department

 

Genomic Relatedness Strengthens Genetic Connectedness Across Management Units

Haipeng Yu, University of Nebraska - Lincoln
Matthew L. Spangler, University of Nebraska - Lincoln
Gota Morota, University of Nebraska- Lincoln

Document Type Article

Copyright © 2017 Yu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License

Abstract

Genetic connectedness refers to a measure of genetic relatedness across management units (e.g., herds and flocks). With the presence of high genetic connectedness in management units, best linear unbiased prediction (BLUP) is known to provide reliable comparisons between estimated genetic values. Genetic connectedness has been studied for pedigree-based BLUP; however, relatively little attention has been paid to using genomic information to measure connectedness. In this study, we assessed genomebased connectedness across management units by applying prediction error variance of difference (PEVD), coefficient of determination (CD), and prediction error correlation r to a combination of computer simulation and real data (mice and cattle). We found that genomic information (G) increased the estimate of connectedness among individuals from different management units compared to that based on pedigree (A). A disconnected design benefited the most. In both datasets, PEVD and CD statistics inferred increased connectedness across units when using G- rather than A-based relatedness, suggesting stronger connectedness. With r once using allele frequencies equal to one-half or scaling G to values between 0 and 2, which is intrinsic to A; connectedness also increased with genomic information. However, PEVD occasionally increased, and r decreased when obtained using the alternative form of G; instead suggesting less connectedness. Such inconsistencies were not found with CD. We contend that genomic relatedness strengthens measures of genetic connectedness across units and has the potential to aid genomic evaluation of livestock species.