Comparison of methods for predicting genomic breeding values for growth traits in Nellore cattle

Author(s)

Nascimento Terakado, Ana Paula ^[1] ; Costa, Raphael Bermal ^[2] ; Irano, Natalia ^[1] ; Bresolin, Tiago ^[1] ; de Oliveira, Henrique Nunes ^[1] ; Carvalheiro, Roberto ^[1] ; Baldi, Fernando ^[1] ; Solar Diaz, Iara Del Pilar ^[2] ; de Albuquerque, Lucia Galvao ^[1]

Affiliation

^[1] Sao Paulo State Univ Unesp, Sch Agr & Veterinarian Sci, BR-14884900 Jaboticabal, SP - Brazil

^[2] Univ Fed Bahia UFBA, Sch Vet & Anim Sci, BR-40170110 Salvador, BA - Brazil

• Document type: Journal article
• Source: TROPICAL ANIMAL HEALTH AND PRODUCTION; v. 53, n. 3 JUL 2021.
• Web of Science Citations: 0

Abstract

The objective of this study was to evaluate the accuracy of genomic predictions of growth traits in Nellore cattle. Data from 5064 animals belonging to farms that participate in the ConexAo DeltaGen and PAINT breeding programs were used. Genotyping was performed with the Illumina BovineHD BeadChip (777,962 SNPs). After quality control of the genomic data, 412,993 SNPs were used. Deregressed EBVs (DEBVs) were calculated using the estimated breeding values (EBVs) and accuracies of birth weight (BW), weight gain from birth to weaning (GBW), postweaning weight gain (PWG), yearling height (YH), and cow weight (CW) provided by GenSys. Three models were used to estimate marker effects: genomic best linear unbiased prediction (GBLUP), BayesC pi, and improved Bayesian least absolute shrinkage and selection operator (IBLASSO). The prediction ability of genomic estimated breeding value (GEBVs) was estimated by the average Pearson correlation between DEBVs and GEBVs, predicted with the different methodologies in the validation populations. The regression coefficients of DEBVs on GEBVs in the validation population were calculated and used as indicators of prediction bias of GEBV. In general, the Bayesian methods provided slightly more accurate predictions of genomic breeding values than GBLUP. The BayesC pi and IBLASSO were similar for all traits (BW, GBW, PWG, and YH), except for CW. Thus, there does not seem to be a more suitable method for the estimation of SNP effects and genomic breeding values. Bayesian regression models are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions. (AU)

• FAPESP's process: 09/16118-5 - Genomic tools to genetic improvement of direct economic important traits in Nelore cattle
• Grantee: Lucia Galvão de Albuquerque
• Support Opportunities: Research Projects - Thematic Grants