Identification of candidate genes and genomic prediction for early heifer pregnancy in Nelore beef cattle

This study aimed to assess the influence of differential weighting in genomic regions harboring candidate causal loci on the prediction accuracy and inflation for early heifer pregnancy (heifers that calved up to 30 months of age) in Nellore (Bos indicus) heifers using the single step genomic BLUP model (ssGBLUP). Phenotypic records of 102,294 Nellore heifers born between 2010 and 2017 were used in this study. The pedigree dataset harbored information from 176,107 animals born between 1998 and 2017, including 5,145 sires and 35,705 dams. Seven different models for genomic prediction were defined by combining the SNP weights obtained in the iterations (1st and 2nd) of the weighted single step GWAS (ssw1GBLUP and ssw2GBLUP) or candidate QTLs reported in the literature. Hence, the lambda (λ) values estimated in the WssGWAS were used to weight the SNPs adjacent to the candidate regions or QTL previously reported in the literature. To estimate the genetic parameters and perform the WssGWAS and WssGBLUP for early heifer pregnancy, a single-trait Bayesian analysis considering a threshold animal model was used. Accuracy, bias, and inflation parameters were evaluated in the validation subset based on the linear regression (LR) method. Genomic windows of ten consecutive SNPs that explained >0.5 % of the additive genetic variance were selected to explore and determine possible candidate genes. Among the identified genes, we can highlight the PGRMC2, TENM3, GRIP1, TMEM45A, and KLF3, given their roles in endocrine fertility, expression of contractile proteins, average daily gain, dry matter intake, and fat deposition. Several genomic regions associated with QTL related to early heifer pregnancy were identified. The identification of such regions and the respective candidate genes associated with sexual precocity and fertility would contribute to improve the genetic knowledge regarding early sexual precocity of Nellore cattle. The prediction accuracy increased roughly 25.6 % using the ssGBLUP compared to BLUP models. The prediction accuracy with the WssGBLUP when incorporating weighted SNPs with the λ values obtained in the 1st (ssw1GBLUP) and 2nd iteration (ssw2GBLUP) of the WssGWAS was higher (∼18 %) than that described for the ssGBLUP model. The inflation also increased the weighting of the most relevant SNPs obtained with the GWAS, most likely overestimating the GEBV. The models that weighted SNPs close to QTLs reported in the literature yielded to less biased and deflated predictions compared to ssw1GBLUP and ssw2GBLUP models. Genomic selection is a feasible alternative for genomic evaluation of early heifer pregnancy in Nellore beef cattle by increasing the prediction accuracy of young animals. In addition, the use of information obtained from the WssGWAS is an alternative to increase reliability and reduce genomic prediction bias. Therefore, the results obtained herein indicate that it is possible to improve the prediction accuracy and reduce the bias of genomic prediction by using genomic information and differentially weighted genomic regions harboring candidate QTLs previously reported in the literature for early heifer pregnancy.