Genomic study of the resilience of buffalo cows to a negative energy balance

• Main Author: de Araujo Neto, Francisco Ribeiro
• Publication Date: 2022
• Other Authors: dos Santos, Jessica Cristina Gonçalves [UNESP], da Silva Arce, Cherlynn Daniela [UNESP], Borquis, Rusbel Raul Ascpilcueta, dos Santos, Daniel Jordan Abreu, Guimarães, Katia Cylene, do Nascimento, André Vieira [UNESP], de Oliveira, Henrique Nunes [UNESP], Tonhati, Humberto [UNESP]
• Format: Article
• Language: eng
• Source: Repositório Institucional da UNESP
• Download full: http://dx.doi.org/10.1007/s13353-021-00680-x; http://hdl.handle.net/11449/230273
• topic: Bubalus bubalis; GWAS; Production efficiency; ssGBLUP

The research article was carried out with the objective of studying the genetic variation on the resilience of buffaloes to negative energy balance—NEB (measured by changes in body weight in early lactation)—as well as investigating genomic regions of interest for this trait. A model of reaction norms was used, considering milk production as the trait to be analyzed and solutions of the contemporary groups to weight changes as environmental gradient. In this methodology, the genetic value of the slope represents the measure of resilience of the animals. After the estimation step, a genome-wide association analysis was performed for the slope of the reaction norms model, to obtain a list of windows and associated genes. The heritability estimates for milk production over the resilience gradient ranged from 0.13 to 0.28, with lower values in the intermediate environmental groups. Regarding the productive resilience of dairy buffalo cows to NEB, the genomic windows with the highest contribution to the genetic variance were detected on chromosomes BBU 1, 2, 3, 4, 9, 12, 19, and 21. A functional analysis of the genes described in the selected windows indicated association with metabolic routes related to growth and immunity of the animals, with an emphasis on the STAT6 gene. The results presented indicate that there is for this trait genetic variation to be used as selection criteria, in addition to genomic regions that can increase the precision of the selection.