Abstract
Stayability, which can be defined as the probability of a cow calving at a certain age when given the opportunity, is an important reproductive trait in beef cattle because it is directly related to herd profitability. The objective of this study was to estimate genetic parameters and to identify possible genomic regions associated with the phenotypic expression of stayability in Nellore cows. The variance components were estimated by Bayesian inference using a threshold animal model that included the systematic effects of contemporary group and sexual precocity and the random effects of animal and residual. The SNP effects were estimated by the single-step genomic BLUP method using information of 2,838 animals (2,020 females and 930 sires) genotyped with the Illumina High-Density BeadChip Array (San Diego, CA, USA). The variance explained by windows formed by 200 consecutive SNPs was used to identify genomic regions of largest effect on the expression of stayability. The heritability was 0.11 ± 0.01 when A matrix (pedigree) was used and 0.14 ± 0.01 when H matrix (relationship matrix that combines pedigree information and SNP data) was used. A total of 147 candidate genes for stayability were identified on chromosomes 1, 2, 5, 6, 9 and 20 and on the X chromosome. New candidate regions for stayability were detected, most of them related to reproductive, immunological and central nervous system functions.
Highlights
There is growing concern regarding the increase in the world population and the consequent increasing global demand for food
The estimated heritability (h2) for STAY was higher when the genomic information was included in the analyses through matrix H (Table 2)
This result can be explained by the fact that 39% of the animals were offspring of multiple sires. These findings agree with [26] who found that the heritabilities for carcass traits in Nellore cattle estimated with the single-step GBLUP method (ssGBLUP) method were higher than those obtained with BLUP and that the addition of genomic information to A matrix resulted in greater additive genetic variance for the traits with a smaller number of observations
Summary
There is growing concern regarding the increase in the world population and the consequent increasing global demand for food. One solution to mitigate a possible food crisis is to increase animal productivity. According to FAO estimates, by 2050, Brazil will be responsible for providing 40% of the global food demand. The country currently possesses about 219 million cattle [1]. When it comes to increasing productivity in the agricultural sector, especially beef cattle, reproductive rates are the most important factors that need to be considered. In Nellore cattle, reproductive traits have been proved to be 4 to 13 times economically more important than growth traits as reported by [2]. According to [3], cows are the animal category that consumes
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