|Title||Genomic regions underlying uniformity of yearling weight in Nellore cattle evaluated under different response variables|
|Author(s)||Souza Iung, Laiza Helena de; Mulder, Herman Arend; Rezende Neves, Haroldo Henrique de; Carvalheiro, Roberto|
|Source||BMC Genomics 19 (2018). - ISSN 1471-2164|
Animal Breeding and Genetics
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Beef cattle - DHGLM - Genetic heterogeneity of residual variance - Growth traits - GWAS - Micro-environmental sensitivity|
Background: In livestock, residual variance has been studied because of the interest to improve uniformity of production. Several studies have provided evidence that residual variance is partially under genetic control; however, few investigations have elucidated genes that control it. The aim of this study was to identify genomic regions associated with within-family residual variance of yearling weight (YW; N=423) in Nellore bulls with high density SNP data, using different response variables. For this, solutions from double hierarchical generalized linear models (DHGLM) were used to provide the response variables, as follows: a DGHLM assuming non-null genetic correlation between mean and residual variance (rmv0) to obtain deregressed EBV for mean (dEBVm) and residual variance (dEBVv); and a DHGLM assuming rmv=0 to obtain two alternative response variables for residual variance, dEBVv_r0 and log-transformed variance of estimated residuals (ln_ σ ě 2 $$ (\\upsigma)_(\\widehat(\\mathrm(e)))^2 $$ ). Results: The dEBVm and dEBVv were highly correlated, resulting in common regions associated with mean and residual variance of YW. However, higher effects on variance than the mean showed that these regions had effects on the variance beyond scale effects. More independent association results between mean and residual variance were obtained when null rmv was assumed. While 13 and 4 single nucleotide polymorphisms (SNPs) showed a strong association (Bayes Factor>20) with dEBVv and ln_ σ ě 2 $$ (\\upsigma)_(\\widehat(\\mathrm(e)))^2 $$ , respectively, only suggestive signals were found for dEBVv_r0. All overlapping 1-Mb windows among top 20 between dEBVm and dEBVv were previously associated with growth traits. The potential candidate genes for uniformity are involved in metabolism, stress, inflammatory and immune responses, mineralization, neuronal activity and bone formation. Conclusions: It is necessary to use a strategy like assuming null rmv to obtain genomic regions associated with uniformity that are not associated with the mean. Genes involved not only in metabolism, but also stress, inflammatory and immune responses, mineralization, neuronal activity and bone formation were the most promising biological candidates for uniformity of YW. Although no clear evidence of using a specific response variable was found, we recommend consider different response variables to study uniformity to increase evidence on candidate regions and biological mechanisms behind it.