Evaluation of the productive and reproductive performance of sexual precocity at different ages in Nellore heifers.

This study aimed to investigate the differences between productive and reproductive performance traits of sexually precocious and non-sexually precocious Nellore heifers and to evaluate the genetic correlation of sexual precocity with traits of economic importance. For this purpose, 300,000 Nellore heifers were evaluated for reproductive traits: heifer pregnancy (HP) at 14 (HP), 18 (HP), and 24 (HP) months; heifer rebreeding (HR); number of progenies up to 53 months (NP); and probability of the cow remaining in the herd until 76 months with at least 3 progenies (Stay). The growth-related traits evaluated included female yearling weight (YW); average daily gain from weaning to yearling (ADG); weight at maturity (MW); weaning weight of first progeny (WW); and female visual scores at yearling for conformation (Conf), precocity (Prec) and muscling (Musc).

Genome-wide association study considering genotype-by-environment interaction for productive and reproductive traits using whole-genome sequencing in Nellore cattle.

Background: The genotype-by-environment interaction (GxE) in beef cattle can be investigated using reaction norm models to assess environmental sensitivity and, combined with genome-wide association studies (GWAS), to map genomic regions related to animal adaptation. Including genetic markers from whole-genome sequencing in reaction norm (RN) models allows us to identify high-resolution candidate genes across environmental gradients through GWAS. Hence, we performed a GWAS via the RN approach using whole-genome sequencing data, focusing on mapping candidate genes associated with the expression of reproductive and growth traits in Nellore cattle.

Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle.

Genomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control.