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

Author(s)

Lucas Tassoni Andrietta

• Document type: Master's Dissertation
• Press: Pirassununga.
• Institution: Universidade de São Paulo (USP). Faculdade de Medicina Veterinária e Zootecnia (FMVZ/SBD)
• Defense date: 2022-07-22

Examining board members

Ricardo Vieira Ventura; Anderson Antonio Carvalho Alves; Rafael Espigolan

• Advisor: Ricardo Vieira Ventura

Abstract

Mating strategies are considered essential tools on animal breeding programs, playing an important role to achieve genetic progress. The advent of Genomic Selection in the last decade, in addition to the improvements on reproduction techniques, shortened the generation interval, enhanced breeding values prediction reliabilities and selection intensity, which provided an expressive genetic gain across several industries. In order to understand attributes of genotypic information and optimize matings, the objective of this study, through the simulation of a dairy cattle population, was to explore different approaches to extract attributes of genotypic information from individuals in the herd, with the objective of the evaluation of the predictive performance when using such data through two Machine Learning algorithms (Random Forests and K-Nearest Neighbors) in 11 proposed scenarios referring to the inbreeding coefficient (Froh), genetic value, in addition to the proposal of a mating strategy. The use of the proposed feature extraction methods contributed to the reduction of data by up to 98%, implying in most scenarios, in lower costs and better results when compared to the use of raw data. The Random Forests algorithm for the proposed regression scenarios showed the better results, especially in the prediction of Froh values using the genotypes of the sire and dam in comparison with the of the individual and its own information, with the result of r2 of the former being superior by 29%, when using the proposed Euclidean distance method. Also noteworthy is the proposed visual approach, favoring the development of studies in search of individuals to be mated according to the interests related to herd uniformity and potential exponents in reproduction. (AU)

• FAPESP's process: 20/04461-6 - Applications of machine learning and genomic data to improve economic traits in dairy cattle
• Grantee: Lucas Tassoni Andrietta
• Support Opportunities: Scholarships in Brazil - Master