Development of a genomic database for Nellore cattle and computational tools for implementing large-scale studies

• Grant number: 16/19514-2
• Support Opportunities: Research Grants - Young Investigators Grants
• Start date: August 01, 2017
• End date: July 31, 2022
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Ricardo Vieira Ventura
• Grantee: Ricardo Vieira Ventura
• Host Institution: Faculdade de Medicina Veterinária e Zootecnia (FMVZ). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated researchers: Danísio Prado Munari ; Francisco Palma Rennó ; Heidge Fukumasu ; Joanir Pereira Eler ; José Bento Sterman Ferraz ; Júlio Cesar de Carvalho Balieiro ; Marcos Veiga dos Santos ; Minos Esperândio Carvalho ; Rachel Santos Bueno Carvalho
• Associated scholarship(s): 18/14035-4 - Cluster analysis and breed prediction using genomic data in a multibreed beef cattle population, BP.IC; 17/14987-2 - Development of a genomic database for Nellore cattle and computational tools for implementing large-scale studies, BP.JP

Abstract

Recent Brazilian projects have shown promising results regarding the incorporation of molecular data as a fundamental part of the genetic evaluation processes, and several of these initiatives have already accumulated SNPs in abundance, originated mostly from commercial genotyping platforms. Current research shows that the vast majority of these commercial panels do not include causal mutations as part of its set of markers, suggesting the incorporation of sequencing techniques for including such mutations, which will likely increase the accuracy of genomic predictions, as well as the persistence of the accuracies over generations, even between breeds. This project proposes the full sequencing of 200 Nellore animals at different coverage levels, given that the animals are carefully selected via graph theory and by studying the haplotype frequencies of candidate animals for sequencing, which will minimize the re-sequencing of chromosome segments. These sequences will be used to develop an on-line database that will serve as a basis for the imputation to the sequencing level of thousands of animals already genotyped in different research projects using the Illumina HD panel (~ 800,000 SNPs). All HD genotypes will be also used as a reference population during the imputation process from lower density panels to the HD level, enabling researchers to genotype a much higher number of animals in each project, even without financial resources to compose the own reference population. This project will also investigate: i) the development of computational tools for analysis and conversion of molecular data to different genomic analyzes software, available via pipeline; ii) prediction of imputation accuracy through artificial neural networks before imputing; iii) imputation quality from HD panels to the sequence level according to different post-alignment quality controls; iv) diversity of genomic segments (CNVs, haplotypes, ROH and SNPs) within and between groups of HD genotypes submitted to the database; v) comparison of Illumina and Affymetrix HD platforms and vi) mating system incorporating genomic data. (AU)

Scientific publications

(8)

(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)

SOUZA, ANDRE MOREIRA; SANTOS WEIGERT, RODRIGO DE ANDRADE; MACHADO DE SOUSA, ELAINE PARROS; ANDRIETTA, LUCAS TASSONI; VENTURA, RICARDO VIEIRA. Practical implications of using non-relational databases to store large genomic data files and novel phenotypes. JOURNAL OF ANIMAL BREEDING AND GENETICS, v. 139, n. 1, AUG 2021. (20/04461-6, 16/19514-2)

VENTURA, RICARDO VIEIRA; FONSECA E SILVA, FABYANO; YANEZ, JOSE MANUEL; BRITO, LUIZ F.. Opportunities and challenges of phenomics applied to livestock and aquaculture breeding in South America. ANIMAL FRONTIERS, v. 10, n. 2, p. 8-pg., 2020-04-01. (16/19514-2)

PAULA DE FREITAS CURTI; ALANA SELLI; DIÓGENES LODI PINTO; ALEXANDRE MERLOS-RUIZ; JULIO CESAR DE CARVALHO BALIEIRO; RICARDO VIEIRA VENTURA. Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview. Animal Reproduction, v. 20, n. 2, 2023-08-28. (21/11156-8, 16/19514-2, 21/03101-9)

VENTURA, RICARDO V.; BRITO, LUIZ F.; OLIVEIRA, JR., GERSON A.; DAETWYLER, HANS D.; SCHENKEL, FLAVIO S.; SARGOLZAEI, MEHDI; VANDERVOORT, GORDON; FONSECA E SILVA, FABYANO; MILLER, STEPHEN P.; CARVALHO, MINOS E.; et al. A comprehensive comparison of high-density SNP panels and an alternative ultra-high-density panel for genomic analyses in Nellore cattle. ANIMAL PRODUCTION SCIENCE, v. 60, n. 3, p. 333-346, 2020. (16/19514-2, 12/50533-2)

SELLI, ALANA; VENTURA, RICARDO V.; FONSECA, PABLO A. S.; BUZANSKAS, MARCOS E.; ANDRIETTA, LUCAS T.; BALIEIRO, JULIO C. C.; BRITO, LUIZ F.. Detection and Visualization of Heterozygosity-Rich Regions and Runs of Homozygosity in Worldwide Sheep Populations. ANIMALS, v. 11, n. 9, SEP 2021. (16/19514-2, 20/04461-6)

PINTO, DIOGENES LODI; SELLI, ALANA; TULPAN, DAN; ANDRIETTA, LUCAS TASSONI; GARBOSSA, POLLYANA LEITE MATIOLI; VANDER VOORT, GORDON; MUNRO, JASPER; MCMORRIS, MIKE; ALVES, ANDERSON ANTONIO CARVALHO; CARVALHEIRO, ROBERTO; et al. Image feature extraction via local binary patterns for marbling score classification in beef cattle using tree-based algorithms. LIVESTOCK SCIENCE, v. 267, p. 10-pg., 2023-01-06. (20/04461-6, 16/19514-2, 21/11156-8)

PEREZ, BRUNO C.; BALIEIRO, JULIO C. C.; CARVALHEIRO, ROBERTO; TIRELO, FABIO; OLIVEIRA JUNIOR, GERSON A.; DEMENTSHUK, JULIANA M.; ELER, JOANIR P.; FERRAZ, JOSE B. S.; VENTURA, RICARDO V.. Accounting for population structure in selective cow genotyping strategies. JOURNAL OF ANIMAL BREEDING AND GENETICS, v. 136, n. 1, p. 23-39, JAN 2019. (16/19514-2)

ALVES, ANDERSON ANTONIO CARVALHO; ANDRIETTA, LUCAS TASSONI; LOPES, RAFAEL ZINNI; BUSSIMAN, FERNANDO OLIVEIRA; FONSECA E SILVA, FABYANO; CARVALHEIRO, ROBERTO; BRITO, LUIZ FERNANDO; BALIEIRO, JULIO CESAR DE CARVALHO; ALBUQUERQUE, LUCIA GALVAO; VENTURA, RICARDO VIEIRA. Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses. FRONTIERS IN ANIMAL SCIENCE, v. 2, p. 19-pg., 2021-08-20. (16/19514-2, 20/04461-6)