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Weighted genomic prediction by candidate genomic regions for traits of economic importance in nelore cattle

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Author(s):
João Barbosa da Silva Neto
Total Authors: 1
Document type: Master's Dissertation
Press: Jaboticabal. 2021-05-11.
Institution: Universidade Estadual Paulista (Unesp). Faculdade de Ciências Agrárias e Veterinárias. Jaboticabal
Defense date:
Advisor: Fernando Sebastián Baldi Rey
Abstract

This study aimed to evaluate the impact on the accuracy and bias of genomic predictions by including and differentiately weighting candidate genomic regions for production characteristics in Nelore cattle. We used 244,254 animals born between 1977 and 2016. The data comprise approximately 176,000 phenotypic records for adjusted weight at 450 (P450) days of age, loin eye area (AOL), marbling (MARB) subcutaneous fat thickness (EGS) and croup fat thickness (EGP8). A total of 7,769 animals were genotyped with the low density panel (30k). All genotypes were imputed to a panel containing 735,044 markers. For all traits a linear animal model was considered to obtain the genetic parameter estimates and run GWAS. Direct and residual additive genetic effects were included in the model as random effects, the fixed effects for carcass traits were contemporary groups (CG) and animal age (covariate) and for growth trait the contemporary groups (CG). For all characteristics a single-step genomic BLUP procedure (ssGBLUP) was used. The G Matrix was constructed using different combinations of SNPs and weights. To evaluate the GEBV prediction capability of the models for each data set, it was measured by Pearson's correlation between the estimated genetic genetic value (GEBV) and the fixed effect adjusted phenotype (Yc), divided by the square root of the heritability of the evaluated characteristic. The inflation of the genomic predictions was evaluated using the regression coefficient of the adjusted phenotype (Yc) on GEBV. There was a greater effect on bias of predictions by including candidate QTLs and weights in ssGLBUP than in prediction abilities. More gains in predictive ability were obtained for P450 by weighting the G matrix and including candidate QTLs from ssGWAS. Incorporating QTLs previously reported in the literature and weighting them differently may have limited success for carcass traits. For traits with little information and low heritability, predictive ability estimates were low, as possibly the number of observations limits the benefits of incorporating genomic information. (AU)

FAPESP's process: 19/06736-5 - Genomic prediction for reproductive, carcass and feed efficiency traits in Nelore cattle using biological information and different criteria in the incorporation of candidate genomic regions
Grantee:João Barbosa da Silva Neto
Support type: Scholarships in Brazil - Master