| Full text | |
| Author(s): |
Mota, Lucio F. M.
;
Arikawa, Leonardo M.
;
Valente, Julia P. S.
;
Fonseca, Larissa F. S.
;
Mercadante, Maria E. Z.
;
Cyrillo, Joslaine N. S. G.
;
Oliveira, Henrique N.
;
Albuquerque, Lucia G.
Total Authors: 8
|
| Document type: | Journal article |
| Source: | SCIENTIFIC REPORTS; v. 15, n. 1, p. 15-pg., 2025-07-01. |
| Abstract | |
Genomic selection (GS) has become a widely used tool in breeding programs, enhancing selection accuracy and leading to faster genetic progress. However, in small populations, GS faces challenges due to limited data and a large number of markers potentially leading to biased predictions. Implementing feature selection strategies is essential to improve prediction accuracy and avoid overfitting. Hence, we compared the predictive ability of genomic best linear unbiased prediction (GBLUP), Bayesian B (BayesB), and elastic net (ENet) models, using all markers and feature selection via GWAS and fixation index (FST) to reduce marker numbers, for growth and ultrasound carcass traits in three Nellore cattle populations differentially selected for yearling body weight (YBW). The populations evaluated included: Nellore Control (NeC), selected for YBW; Nellore Selection (NeS), selected for maximum YBW; and Nellore Traditional (NeT), selected for maximum YBW and lower residual feed intake (RFI) since 2013. Comparing the statistical approaches using GBLUP as the reference, ENet improved prediction accuracy by 10% for growth traits and 12% for carcass traits, while BayesB showed no improvement for growth traits but achieved a 3% gain for carcass traits. When comparing models using all markers to those with variable selection, both GWAS and FST improved prediction accuracy across models, with FST outperforming GWAS in stratified populations. A stricter GWAS threshold (> 1.0% explained variance), compared to a less conservative criterion (> 0.5%), reduced BayesB prediction accuracy (6.8%), while slightly increasing accuracy for GBLUP (1.3%) and ENet (2.4%). Similarly, a more restrictive FST threshold (> 0.2) against a less conservative (> 0.1) resulted in smaller gains for GBLUP (4%) and ENet (5%), but reduced BayesB accuracy (- 4%). Overall, selecting markers through GWAS and FST improves prediction accuracy for both growth and carcass traits, particularly in stratified populations. However, stricter thresholds can negatively impact accuracy, highlighting the need for optimized marker selection strategies. (AU) | |
| FAPESP's process: | 17/50339-5 - Institutional research development plan of the Animal Science Institute (PDIp) |
| Grantee: | Lenira El Faro Zadra |
| Support Opportunities: | Research Grants - State Research Institutes Modernization Program |
| FAPESP's process: | 09/16118-5 - Genomic tools to genetic improvement of direct economic important traits in Nelore cattle |
| Grantee: | Lucia Galvão de Albuquerque |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/20026-8 - Multi-user equipment approved in grant 2017/10630-2: server |
| Grantee: | Lucia Galvão de Albuquerque |
| Support Opportunities: | Multi-user Equipment Program |
| FAPESP's process: | 17/10630-2 - Genetic aspects of meat production quality, efficiency and sustainability in Nelore breed animals |
| Grantee: | Lucia Galvão de Albuquerque |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 06/58092-4 - Estudo de caracteristicas de carcaca de animais nelore e caracu, de um ano ate a idade adulta, utilizando ultra-sonografia. |
| Grantee: | Maria Eugênia Zerlotti Mercadante |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 22/11852-7 - Structural equation modeling as a tool to interpret genotype-environment interaction and genome-wide association (GWAS) for growth and reproductive traits in Nellore heifers using whole-genome sequence data |
| Grantee: | Lúcio Flávio Macedo Mota |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |