Prediction ability for growth and maternal traits using SNP arrays based on different marker densities in Nellore cattle using the ssGBLUP

This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10,000 informative SNPs obtained from the Illumina BovineHD BeadChip shows accurate and less biased predictions. Low-density customized arrays under ssGBLUP method could be feasible and cost-effective in genomic selection.

Genetic correlation estimates between calving ease in primiparous cows and economically important traits in Nellore cattle

This study aimed to estimate (co)variance components and genetic parameters for calving ease (CE) and their genetic correlations with growth, reproductive, carcass, and feed efficiency traits in Nellore cattle. Phenotypes for CE are scored in two categories: normal calving and assisted calving. The traits considered were probability of precocious calving, age at first calving, stayability, adjusted scrotal circumference at 365 days of age, accumulated cow productivity, age at puberty of males, gestation length, birth weight, adjusted weights at 210 and 450 days of age, adult cow weight, frame score, hip height, rib eye area, subcutaneous backfat thickness, rump fat thickness, intramuscular fat percentage, residual feed intake and dry matter intake. The estimation of genetic parameters was performed using a two-trait threshold-linear animal model, except for CE, stayability, and probability of precocious calving, which were evaluated through a two-trait threshold animal model. The direct (0.27) and maternal (0.19) heritability estimates for CE in heifers primiparous Nellore indicated that selecting for this trait is feasible. The selection to improve the female sexual precocity should consider CE during the selection and mating decisions to reduce calving problems. Genetic correlation estimates between CE and BW suggest that selecting low birth weight to reduce calving problems is not an appropriate strategy to improve calving ease in heifers Nellore. Therefore, adopting a multi-trait selection model with CE and BW in the Nellore breed would reduce calving difficulties, particularly in sexually precocious heifers, without impairing the growth, reproductive, feed efficiency conversion, and carcass indicator traits.

Genetic Parameters and Genomic Prediction for Calving Ease in Primiparous Nellore Heifers

The calving ease (CE) trait has recently been introduced in animal breeding programs, and studies on its genetic variability have proven essential for the genetic advancement of animals. An increase in dystocia rates in primiparous heifers has been observed due to the birth of heavier calves. As this is a new trait, no established model exists for its analysis. Thus, this study developed different statistical models to evaluate CE, aiming to estimate genetic parameters and perform genomic predictions for this trait. A total of 39,664 records of CE from primiparous Nellore heifers born between 2010 and 2017 were collected, belonging to the animal breeding program of the Nellore breed in Brazil, managed by the National Association of Breeders and Researchers (ANCP, Ribeirão Preto, Brazil). The results showed that direct heritability estimates ranged from 0.11 to 0.24, while maternal heritability estimates ranged from 0.09 to 0.11. Despite these low to moderate heritability estimates, the trait has potential for direct selection. Models incorporating the heifer category (HC) (early or traditional) and birth weight (BW), as well as the dam age at calving (DAC) and BW, were more suitable for estimating variance components. On the other hand, the model that considered only the HC and the model that included the DAC excelled in predictive ability, making them more appropriate for genomic predictions.