Estimation of genetic parameters, identification of genomic regions, and candidate genes for enhancing feeding behavior and feed efficiency in pigs.

• Ano de defesa: 2025
• Autor(a) principal: Gervásio, Izally Carvalho
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Comportamento alimentar; Eficiência alimentar; Feed efficiency; Feeding behavior; Genetic breeding; Genomic identification; Identificação genômica; Melhoramento genético; Pigs; Suínos
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/11/11139/tde-09052025-144254/

Feed efficiency and feeding behavior are key factors in the sustainability of pig production, yet recording individual feed intake remains costly. Therefore, breeding to improve feed efficiency and feeding-related traits is relevant. In this context, we estimated variance components and genetic parameters for eight feed efficiency and feeding behavior traits in Landrace (LA) and Yorkshire (YO) pigs using three data collection strategies: weekly (continuous), bi-weekly, and a combination of both (merged datasets). 9,023 LA and 12,166 YO pigs were recorded using the Feed Intake Recording Equipment (FIRE) system. The traits analyzed include average daily feed intake (ADFI), feed conversion ratio (FCR), residual feed intake (RFI), average daily occupancy time (AOTD), average number of visits to the feeder (ANVD), average feed intake per visit (AFIV), average occupancy time per visit (AOTV), and average feed rate per visit (AFRV). Genetic parameters were estimated using linear mixed models under the AI-REML method. We also calculated theoretical accuracy and assessed selected animals\' reranking to evaluate the selection consistency across data collection scenarios. The heritability estimates for feed efficiency traits ranged from 0.23 to 0.31 for ADFI, 0.29 to 0.39 for FCR, and 0.30 to 0.36 for RFI. Feeding behavior traits were found to be moderately to highly heritable and ranged from 0.44 to 0.17 (AFIV), 0.34 to 0.19 (AFRV), 0.32 to 0.24 (ANVD), 0.24 to 0.16 (AOTD), and 0.47 to 0.22 (AOTV). The combined data scenario yielded heritability and repeatability estimates differently compared to individual scenarios, likely due to the larger amount of information (i.e., more accurate estimates). The theoretical accuracies ranged from 0.59 to 0.65, with the combined scenario providing the most stable estimates. Reranking analyses indicated greater consistency in selection outcomes under the combined (merged datasets) or continuous data recording strategies for genetically improving feed efficiency and feeding behavior in maternal line pig populations. A comprehensive understanding of the genetic factors influencing feeding behavior in pigs is crucial for the design or refinement of genomic selection strategies for enhancing the productive efficiency, welfare, and overall sustainability of pig production systems. Therefore, the identification of quantitative trait loci (QTL) associated with pig feeding behavior and identifying the epigenomic regulatory regions, candidate genes, pathways, and transcription factors within and/or near this associated QTL to a better understanding of its effect on AFIV (amount of feed consumed per visit) and AOTD, visiting time during 24 hours. A genome-wide association analysis was conducted using the singlestep GWAS (ssGWAS) approach. Functional analyses included SNP annotation using the Variant Effect Predictor (VEP), overlap with known QTLs, functional enrichment analysis in DAVID, transcription factor association with SNPs using Enrichr, and data integration and visualization using IGV. Functional analyses highlighted several transcription factors that bind to both promoters and enhancers of active genes, that may regulate feeding behavior traits in pigs, including STAT3, STAT5A, ATAT5B, RARA, SMAD4, MYC, FOXP3, SP1, NOTCHI, RXRA, FOS, NRF1, BACHI1, NR1l2, GLI1, THAP11, PDX1, NFIC, CCNT2, TP53 and ERG1. Integrating SNPs with chromatin state regions uncovered several potential candidate genes, such as SLC22A2, CPAMD8, and NKX2-6. The identification of these candidate genes, pathways, and transcription factors contributes to a better understanding of the regulatory mechanisms behind AFIV and AOTD.