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Journal of Applied Genetics

, Volume 59, Issue 4, pp 493–501 | Cite as

Genomic prediction ability for beef fatty acid profile in Nelore cattle using different pseudo-phenotypes

  • Hermenegildo Lucas Justino Chiaia
  • Elisa Peripolli
  • Rafael Medeiros de Oliveira Silva
  • Fabiele Loise Braga Feitosa
  • Marcos Vinícius Antunes de Lemos
  • Mariana Piatto Berton
  • Bianca Ferreira Olivieri
  • Rafael Espigolan
  • Rafael Lara Tonussi
  • Daniel Gustavo Mansan Gordo
  • Lucia Galvão de Albuquerque
  • Henrique Nunes de Oliveira
  • Adrielle Mathias Ferrinho
  • Lenise Freitas Mueller
  • Sabrina Kluska
  • Humberto Tonhati
  • Angélica Simone Cravo Pereira
  • Ignacio Aguilar
  • Fernando Baldi
Animal Genetics • Original Paper

Abstract

The aim of the present study was to compare the predictive ability of SNP-BLUP model using different pseudo-phenotypes such as phenotype adjusted for fixed effects, estimated breeding value, and genomic estimated breeding value, using simulated and real data for beef FA profile of Nelore cattle finished in feedlot. A pedigree with phenotypes and genotypes of 10,000 animals were simulated, considering 50% of multiple sires in the pedigree. Regarding to phenotypes, two traits were simulated, one with high heritability (0.58), another with low heritability (0.13). Ten replicates were performed for each trait and results were averaged among replicates. A historical population was created from generation zero to 2020, with a constant size of 2000 animals (from generation zero to 1000) to produce different levels of linkage disequilibrium (LD). Therefore, there was a gradual reduction in the number of animals (from 2000 to 600), producing a “bottleneck effect” and consequently, genetic drift and LD starting in the generation 1001 to 2020. A total of 335,000 markers (with MAF greater or equal to 0.02) and 1000 QTL were randomly selected from the last generation of the historical population to generate genotypic data for the test population. The phenotypes were computed as the sum of the QTL effects and an error term sampled from a normal distribution with zero mean and variance equal to 0.88. For simulated data, 4000 animals of the generations 7, 8, and 9 (with genotype and phenotype) were used as training population, and 1000 animals of the last generation (10) were used as validation population. A total of 937 Nelore bulls with phenotype for fatty acid profiles (Sum of saturated, monounsaturated, omega 3, omega 6, ratio of polyunsaturated and saturated and polyunsaturated fatty acid profile) were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. To compare the accuracy and bias of direct genomic value (DGV) for different pseudo-phenotypes, the correlation between true breeding value (TBV) or DGV with pseudo-phenotypes and linear regression coefficient of the pseudo-phenotypes on TBV for simulated data or DGV for real data, respectively. For simulated data, the correlations between DGV and TBV for high heritability traits were higher than obtained with low heritability traits. For simulated and real data, the prediction ability was higher for GEBV than for Yc and EBV. For simulated data, the regression coefficient estimates (b(Yc,DGV)), were on average lower than 1 for high and low heritability traits, being inflated. The results were more biased for Yc and EBV than for GEBV. For real data, the GEBV displayed less biased results compared to Yc and EBV for SFA, MUFA, n-3, n-6, and PUFA/SFA. Despite the less biased results for PUFA using the EBV as pseudo-phenotype, the b(Yi,DGV estimates obtained for the different pseudo-phenotypes (Yc, EBV and GEBV) were very close. Genomic information can assist in improving beef fatty acid profile in Zebu cattle, since the use of genomic information yielded genomic values for fatty acid profile with accuracies ranging from low to moderate. Considering both simulated and real data, the ssGBLUP model is an appropriate alternative to obtain more reliable and less biased GEBVs as pseudo-phenotype in situations of missing pedigree, due to high proportion of multiple sires, being more adequate than EBV and Yc to predict direct genomic value for beef fatty acid profile.

Keywords

Bos indicus Lipid profile Genomic prediction Single-step SNP-BLUP 

Notes

Authors’ contributions

HLJC, EP, FLBF, MVAL, RLT, and RMOS contributed to the design and performance of the study, the interpretation of data, and writing of the manuscript. MPB, BFO, RE, and MVAL contributed to the laboratorial analysis. SK, DGMG, LGA, HNO, AMF, LFM, and HT contributed to reviewing the manuscript. RMOS, IA, and RLT contributed to the statistical analysis. FB and ASCP designed the study and were in charge of the overall project.

Funding

This work was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) with grant numbers (#2011/2141-0 and #2009/16118-5). HLJ Chiaia, MP Berton, S Kluska, and BF Olivieri received scholarships from the Coordination Office for Advancement of University-level Personnel (CAPES; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) in conjunction with the Postgraduate Program on Genetics and Animal Breeding, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista (FCAV, UNESP). MVA Lemos, FLB Feitosa, and E Peripolli received scholarships from FAPESP, Fundação de Amparo à Pesquisa do Estado de São Paulo. F Baldi and LG Albuquerque received support from FAPESP, Fundação de Amparo à Pesquisa do Estado de São Paulo grant #2011/21241-0 and Grant #2009/16118-5, respectively.

Compliance with ethical standards

Ethics approval

All procedures performed in studies involving animals were in accordance with the ethical standards of the Faculdade de Ciências Agrárias e Veterinárias (FCAV), Universidade Estadual Paulista (UNESP), Jaboticabal-SP, Brazil.

Consent for publication

Not applicable

Conflict of interest

The authors declare that they have no competing interests.

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Copyright information

© Institute of Plant Genetics, Polish Academy of Sciences, Poznan 2018

Authors and Affiliations

  • Hermenegildo Lucas Justino Chiaia
    • 1
  • Elisa Peripolli
    • 1
  • Rafael Medeiros de Oliveira Silva
    • 1
  • Fabiele Loise Braga Feitosa
    • 1
  • Marcos Vinícius Antunes de Lemos
    • 1
  • Mariana Piatto Berton
    • 1
  • Bianca Ferreira Olivieri
    • 1
  • Rafael Espigolan
    • 1
  • Rafael Lara Tonussi
    • 1
  • Daniel Gustavo Mansan Gordo
    • 1
  • Lucia Galvão de Albuquerque
    • 1
  • Henrique Nunes de Oliveira
    • 1
  • Adrielle Mathias Ferrinho
    • 2
  • Lenise Freitas Mueller
    • 3
  • Sabrina Kluska
    • 1
  • Humberto Tonhati
    • 1
  • Angélica Simone Cravo Pereira
    • 2
  • Ignacio Aguilar
    • 4
  • Fernando Baldi
    • 1
  1. 1.Faculdade de Ciências Agrárias e VeterináriasUNESPJaboticabalBrazil
  2. 2.Faculdade de Medicina Veterinária e Zootecnia, USPPirassunungaBrazil
  3. 3.Faculdade de Zootecnia e Engenharia de Alimentos, USPPirassunungaBrazil
  4. 4.Instituto Nacional de Investigación AgropecuariaMontevideoUruguay

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