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Study of the genetic variability of meat fatty acid profile in Nelore cattle finished in feedlot

Grant number: 11/21241-0
Support type:Research Grants - Young Investigators Grants
Duration: April 01, 2012 - March 31, 2015
Field of knowledge:Agronomical Sciences - Animal Husbandry
Principal Investigator:Fernando Sebastián Baldi Rey
Grantee:Fernando Sebastián Baldi Rey
Home Institution: Faculdade de Medicina Veterinária e Zootecnia (FMVZ). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Assoc. researchers:Angélica Simone Cravo Pereira ; Arione Augusti Boligon ; Fabio Ricardo Pablos de Souza ; Francisco Palma Rennó ; Guilherme Jordão de Magalhães Rosa ; Henrique Nunes de Oliveira ; Humberto Tonhati ; Lucia Galvão de Albuquerque ; Luis Artur Loyola Chardulo ; Luis Felipe Prada e Silva ; Roberta Carrilho Canesin ; Roberto Carvalheiro ; Rusbel Raúl Aspilcueta Borquis
Associated scholarship(s):13/11853-4 - Analysis of copy number variations in the genome of Nellore and its associations with the fatty acid profile of the meat, BP.DR
12/23979-0 - Genetic association between meat fatty acid profile with carcass and meat trait in Nelore cattle, BP.MS
12/15098-3 - Study of the gene expression associated to fatty acid profile in Nelore cattle feedlot, BP.MS

Abstract

Among the attributes of beef meat, the fatty acid profile is important because it affects not only the meat palatability, but also the human health. In recent years, fatty acids harmful to human health have received considerable attention. Several studies, working with taurine breeds, showed that there is genetic variability for meat fatty acid profile and, therefore the possibility of genetic improvement of fatty acid composition in beef cattle. Moreover, the results of these studies showed favorable genetic correlations estimates between fatty acids. However, genetic parameter estimates for fatty acid profile in zebu cattle are scarce. The meat fatty acid profile is difficult and costly to measure. For this type of trait is indicated the application of genomic selection, which is a type of marker-assisted selection. Despite the major advances in genetic molecular techniques that allow the genotyping of hundreds of animals in less time and in an automated fashion, there are still being developed methodologies that allow the incorporation of genomic information in animal breeding programs. The objective of this project is to study the genetic variability of meat fatty acid profile in Nelore cattle finished in feedlot conditions, and implement models and methods that use genomic information to improve the fatty acid composition of beef meat. To attain this objective we propose the following specific objectives: 1) Study and characterize the profile of meat fatty acids of Nelore cattle finished in feedlot 2) Estimate genetic parameters for meat fatty acid composition in Nelore cattle finished in feedlot 3) Implement genome wide association studies between single nucleotide polymorphisms markers (SNPs) with meat fatty acid composition 4) Predict the genomic breeding values for meat fatty acid profile considering different models 5) Verified the expression patterns of genes involved in lipid metabolism and fatty acid synthesis 6) Study the influence of polymorphisms (functional polymorphisms) on the expression of several genes of interest. Approximately from 800 to 1,000 Nelore males finished in feedlot conditions (minimum 90 days), aged around two years old, it were utilized. From the individual concentration of fatty acids, it will be calculated the proportion of saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, the ratio of polyunsaturated fatty acids and saturated fatty acids, fatty acids of n-6 and n-3 series and the n-6/n-3 ratio. In addition, the desaturation index (ID) (adding a double bond) and elongation index (IE) (conversion from 16 to 18 carbon chains atoms) will be calculated. The genetic parameter and (co)variance estimates for these traits will be estimated by restricted maximum likelihood method. The Bovine HD SNP BeadChip, with more than 777,000 SNP, will be utilized to genotyped the animals. To perform the genome wide association analyses, single and multiple regression analyses, will be done. These analyses will allow a genome scan in seeking areas of interest in the genome. Then, SNPs with significant effects obtained in the multiple regression analysis, it will be analyzed by an animal model including the effects of SNPs in the model. The genomic breeding values for meat fatty acid profile will be predicted using different a prior distributions for variances and SNPs effects. The estimation of SNP effects will be performed with BLUP, BayesB and BayesLasso models. The results of this project will establish new elements to differentiate, develop and promote the attributes of Brazilian beef meat on the basis of a solid scientific and technical support. In addition, this project will allow the formation, training and qualification of students and teachers that will be important for future research and for teaching in undergraduate and graduate courses. (AU)

Scientific publications (9)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MUELLER, LENISE FREITAS; CARVALHO BALIEIRO, JULIO CESAR; FERRINHO, ADRIELLE MATIAS; MARTINS, TAIANE DA SILVA; DA SILVA CORTE, ROSANA RUEGGER PEREIRA; DE AMORIM, TAMYRES RODRIGUES; MANGINI FURLAN, JOYCE DE JESUS; BALDI, FERNANDO; CRAVO PEREIRA, ANGELICA SIMONE. Gender status effect on carcass and meat quality traits of feedlot Angus x Nellore cattle. ANIMAL SCIENCE JOURNAL, v. 90, n. 8, p. 1078-1089, AUG 2019. Web of Science Citations: 0.
JUSTINO CHIAIA, HERMENEGILDO LUCAS; PERIPOLLI, ELISA; DE OLIVEIRA SILVA, RAFAEL MEDEIROS; BRAGA FEITOSA, FABIELE LOISE; ANTUNES DE LEMOS, MARCOS VINICIUS; BERTON, MARIANA PIATTO; OLIVIERI, BIANCA FERREIRA; ESPIGOLAN, RAFAEL; TONUSSI, RAFAEL LARA; MANSAN GORDO, DANIEL GUSTAVO; DE ALBUQUERQUE, LUCIA GALVAO; DE OLIVEIRA, HENRIQUE NUNES; FERRINHO, ADRIELLE MATHIAS; MUELLER, LENISE FREITAS; KLUSKA, SABRINA; TONHATI, HUMBERTO; CRAVO PEREIRA, ANGELICA SIMONE; AGUILAR, IGNACIO; BALDI, FERNANDO. Genomic prediction ability for beef fatty acid profile in Nelore cattle using different pseudo-phenotypes. JOURNAL OF APPLIED GENETICS, v. 59, n. 4, p. 493-501, NOV 2018. Web of Science Citations: 0.
PERIPOLLI, ELISA; METZGER, JULIA; ANTUNES DE LEMOS, MARCOS VINICIUS; STAFUZZA, NEDENIA BONVINO; KLUSKA, SABRINA; OLIVIERI, BIANCA FERREIRA; BRAGA FEITOSA, FABIELI LOUISE; BERTON, MARIANA PIATTO; LOPES, FERNANDO BRITO; MUNARI, DANISIO PRADO; LOBO, RAYSILDO BARBOSA; MAGNABOSCO, CLAUDIO DE ULHOA; DI CROCE, FERNANDO; OSTERSTOCK, JASON; DENISE, SUE; CRAVO PEREIRA, ANGELICA SIMONE; BALDI, FERNANDO. Autozygosity islands and ROH patterns in Nellore lineages: evidence of selection for functionally important traits. BMC Genomics, v. 19, SEP 17 2018. Web of Science Citations: 2.
ANTUNES DE LEMOS, MARCOS VINICIUS; BERTON, MARIANA PIATTO; FERREIRA DE CAMARGO, GREGORIO MIGUEL; PERIPOLLI, ELISA; DE OLIVEIRA SILVA, RAFAEL MEDEIROS; OLIVIERI, BIANCA FERREIRA; CESAR, ALINE S. M.; CRAVO PEREIRA, ANGELICA SIMONE; DE ALBUQUERQUE, LUCIA GALVAO; DE OLIVEIRA, HENRIQUE NUNES; TONHATI, HUMBERTO; BALDI, FERNANDO. Copy number variation regions in Nellore cattle: Evidences of environment adaptation. LIVESTOCK SCIENCE, v. 207, p. 51-58, JAN 2018. Web of Science Citations: 7.
ABOUJAOUDE, CAROLYN; CRAVO PEREIRA, ANGELICA SIMONE; BRAGA FEITOSA, FABIELI LOUISE; ANTUNES DE LEMOS, MARCOS VINICIUS; JUSTINO CHIAIA, HERMENEGILDO LUCAS; BERTON, MARIANA PIATTO; PERIPOLLI, ELISA; DE OLIVEIRA SILVA, RAFAEL MEDEIROS; FERRINHO, ADRIELLE MATHIAS; MUELLER, LENISE FREITAS; OLIVIERI, BIANCA FERREIRA; DE ALBUQUERQUE, LUCIA GALVAO; DE OLIVEIRA, HENRIQUE NUNES; TONHATI, HUMBERTO; ESPIGOLAN, RAFAEL; TONUSSI, RAFAEL; GORDO, DANIEL MANSAN; BRAGA MAGALHAES, ANA FABRICIA; BALDI, FERNANDO. Genetic parameters for fatty acids in intramuscular fat from feedlot-finished Nelore carcasses. ANIMAL PRODUCTION SCIENCE, v. 58, n. 2, p. 234-243, 2018. Web of Science Citations: 2.
TONUSSI, RAFAEL LARA; DE OLIVEIRA SILVA, RAFAEL MEDEIROS; BRAGA MAGALHAES, ANA FABRICIA; ESPIGOLAN, RAFAEL; PERIPOLLI, ELISA; OLIVIERI, BIANCA FERREIRA; BRAGA FEITOSAL, FABIELI LOISE; ANTUNES LEMOS, MARCOS VINICFUS; BERTON, MARIANA PIATTO; JUSTINO CHIAIA, HERMENEGILDO LUCAS; CRAVO PEREIRA, ANGELICA SIMONE; LOBO, RAYSILDO BARBOSA; FRAMARTINO BEZERRA, LUIZ ANTONIO; MAGNABOSCO, CLAUDIO DE ULHOA; LOURENCO, DANIELA ANDRESSA LINO; AGUILAR, IGNACIO; BALDI, FERNANDO. Application of single step genomic BLUP under different uncertain paternity scenarios using simulated data. PLoS One, v. 12, n. 9 SEP 28 2017. Web of Science Citations: 5.
DIAS, M. M.; CANOVAS, A.; MANTILLA-ROJAS, C.; RILEY, D. G.; LUNA-NEVAREZ, P.; COLEMAN, S. J.; SPEIDEL, S. E.; ENNS, R. M.; ISLAS-TREJO, A.; MEDRANO, J. F.; MOORE, S. S.; FORTES, M. R. S.; NGUYEN, L. T.; VENUS, B.; DIAZ, I. S. D. P.; SOUZA, F. R. P.; FONSECA, L. F. S.; BALDI, F.; ALBUQUERQUE, L. G.; THOMAS, M. G.; OLIVEIRA, H. N. SNP detection using RNA-sequences of candidate genes associated with puberty in cattle. Genetics and Molecular Research, v. 16, n. 1 MAR 24 2017. Web of Science Citations: 6.
ABOUJAOUDE, CAROLYN; BERTON, MARIANA PIATTO; DE OLIVEIRA, HENRIQUE NUNES; ESPIGOLAN, RAFAEL; TONUSSI, RAFAEL LARA; DE OLIVEIRA SILVA, RAFAEL MEDEIROS; BALDI, FERNANDO. . JOURNAL OF APPLIED GENETICS, v. 58, n. 1, p. 123-132, FEB 2017. Web of Science Citations: 4.
LEMOS, MARCOS V. A.; JUSTINO CHIAIA, HERMENEGILDO LUCAS; BERTON, MARIANA PIATTO; FEITOSA, FABIELI L. B.; ABOUJAOUD, CAROLYN; CAMARGO, GREGORIO M. F.; PEREIRA, ANGELICA S. C.; ALBUQUERQUE, LUCIA G.; FERRINHO, ADRIELLE M.; MUELLER, LENISE F.; MAZALLI, MONICA R.; FURLAN, JOYCE J. M.; CARVALHEIRO, ROBERTO; GORDO, DANIEL M.; TONUSSI, RAFAEL; ESPIGOLAN, RAFAEL; DE OLIVEIRA SILVA, RAFAEL MEDEIROS; DE OLIVEIRA, HENRIQUE NUNES; DUCKETT, SUSAN; AGUILAR, IGNACIO; BALDI, FERNANDO. Genome-wide association between single nucleotide polymorphisms with beef fatty acid profile in Nellore cattle using the single step procedure. BMC Genomics, v. 17, MAR 9 2016. Web of Science Citations: 23.

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