37 An Assessment of Genomic Relatedness across Management Units. (10th April 2018)

• Record Type: Journal Article
• Authors: Yu, H; Spangler, M L; Lewis, R M; Morota, G
• Is Part Of: Journal of animal science. Volume 96(2018)Supplement 2
• Page Start: 20
• Page End: 20
• Publication Date: 2018-04-10
• Subjects: genomic connectedness -- genomic prediction -- relatedness; Livestock -- Periodicals; Livestock; Electronic journals; Periodicals; 636.005
• DOI: 10.1093/jas/sky073.035
• Languages: English
• ISSNs: 0021-8812

Abstract

Abstract: Genetic connectedness assesses the extent to which estimated breeding values can be fairly compared across management units. Ranking of individuals across units based on best linear unbiased prediction (BLUP) is reliable when there is a sufficient level of connectedness due to a better disentangling of genetic signal from noise. Although genetic connectedness has been successfully applied to pedigree-based BLUP, relatively little attention has been paid to studying the importance of genomic information in estimating genetic connectedness across management units. First, we assessed genome-based genetic 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). Relationship matrices were constructed from three different sources: pedigree (A), genomics (G), and a hybrid of these two. We found that genomic information increased the estimate of connectedness among individuals from different management units compared to that of pedigree, and a disconnected design benefited the greatest. In the well-structured mice data (full-sib families) all 3 statistics inferred increased connectedness across-units when using G- rather than A-based relationships and the highest increase was 0.26 with CD in heritability 0.2. With the cattle data, genomic relationships decreased PEVD across-units suggesting stronger connectedness. With r, once scaling G to values between 0 and 2, which is intrinsic to A, connectedness also increased with genomic information. However, PEVD often increased and r often decreased when obtained using the alternative form of G, instead suggesting less connectedness. Such inconsistencies were not found with CD. Second, we examined whether increased measures of connectedness led to higher prediction accuracies evaluated by a cross-validation. We applied PEVD, CD, and BLUP-type prediction models to data simulated under various scenarios. We found that the greater extent of connectedness enhanced accuracy of whole-genome prediction. A pedigree-based relationship matrix yielded better capturing of connectedness and higher prediction accuracies than those of genomic relationship counterparts when the assumed numbers of genetic markers and quantitative trait loci (QTLs) were small. The impact of genomics was more marked when large numbers of markers and QTLs were used to infer connectedness and evaluate prediction accuracy. We observed up to 6.49% and 13.74% increases in CD for prediction accuracy and connectedness, respectively. We contend that genomic relatedness enhances prediction accuracy across management units by strengthening connectedness measures and has a potential to aid genomic evaluation of livestock species.