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| Author(s): |
Camila da Costa Barros
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | Jaboticabal. 2017-08-23. |
| Institution: | Universidade Estadual Paulista (Unesp). Faculdade de Ciências Agrárias e Veterinárias. Jaboticabal |
| Defense date: | 2017-07-21 |
| Advisor: | Humberto Tonhati; Rusbel Raul Aspilcueta Borquis; Daniel Jordan de Abreu Santos |
| Abstract | |
The aim of this study was to compare different Bayesian methods of genomic prediction for milk yield (MY), fat (%F) and protein (%P) percentages in dairy buffaloes in Brazil, and to perform a genome-wide association study for the purpose of identify chromosomal regions and genes possibly related to the these traits, using information from genotyped and non-genotyped individuals. The number of animals with phenotype was 3,355, the pedigree file contained 15,495 animals, of which 322 were genotyped. The animals were genotyped using a 90K SNP panel (Axiom® Buffalo Genotyping Array). The following criteria for quality control of SNPs were used: MAF < 0.05, Call Rate < 0.95 and Hardy-Weinberg Equilibrium p-value < 10-6 . In relation to the sample, a Call Rate <0.90 was used. Four methods for genomic prediction were used: Bayes A (BA), Bayes B (BB), Bayes C (BC) and Bayes LASSO (BL). Phenotypes for the fixed effects (Y*) were used as response variables. The predictive ability of the different models was evaluated using a leave-one-out cross-validation approach. The prediction accuracy was calculated by Pearson's correlation between estimated genomic genetic value (GEBV) and response variable (Y*) for each model. In relation to genome-wide association studies, an iterative process was performed to derive SNP weights as function of squares of SNP effects and allele frequencies (ssGWAS). In general, all Bayesian models showed similar prediction accuracy, ranging from 0.41 to 0.42, 0.38 to 0.39 and 0,39 to 0,40 for MY, %F and %P, respectively. Therefore, the methods BA, BB, BC and BL can be used in the predictions of the effects of SNPs, obtaining, practically, the same prediction accuracy. The proportions of variance explained by the top 10 SNPs for MY, %F and %P were: 7.48, 9.94 and 6.56%, respectively. The results of ssGWAS revealed chromosomal regions and genes that may be related with the analyzed traits. These regions and genes may contribute to a better understanding of their influence on milk yield and fat and protein percentages in buffalo milk. (AU) | |
| FAPESP's process: | 13/24427-3 - Genomic selection for yield and quality of milk buffalo |
| Grantee: | Camila da Costa Barros |
| Support Opportunities: | Scholarships in Brazil - Doctorate |