Applications of machine learning and genomic data to improve economic traits in dairy cattle

• Grant number: 20/04461-6
• Support Opportunities: Scholarships in Brazil - Master
• Start date: September 01, 2020
• End date: March 31, 2022
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Ricardo Vieira Ventura
• Grantee: Lucas Tassoni Andrietta
• Host Institution: Faculdade de Medicina Veterinária e Zootecnia (FMVZ). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

Mating strategies are considered essential tools on herds under animal breeding programs,playing an important role to achieve genetic progress. The advent of Genomic Selection on the last decade, in addition to the improvements on reproduction techniques, shortened the generation interval, enhanced prediction reliabilities and selection intensity, respectively, which provided an expressive genetic gain across several industries. Those conditions revolutionized the way to select sires and dams, reflecting on the improvement of many lactational and non-lactational traits. Some negative facets, as the decrease of genetic diversity and increase of endogamy, could be observed on breeds under selection pressure. In order to optimize matings,and also evaluating the genetic diversity associated with genetic progress, artificial intelligence tools could be an alternative to the traditional linear methods. The usage of non-linear Machine Learning algorithms, and its non standard detection patterns capacity, must be applied tomatings schemes enhancement, specially its impact on economically important traits in the dairy industry. (AU)

Scientific publications

(4)

(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)

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)

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)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

ANDRIETTA, Lucas Tassoni. Applications of machine learning and genomic data to improve economic traits in dairy cattle. 2022. Master's Dissertation - Universidade de São Paulo (USP). Faculdade de Medicina Veterinária e Zootecnia (FMVZ/SBD) Pirassununga.