Comparison of methods for predicting genomic breeding values for growth traits in Nellore cattle

Genetic parameters, genomic prediction, and identification of regulatory regions located on chromosome 14 for weight traits in Nellore cattle

This study aimed to investigate functional variants in chromosome 14 (BTA14) and its impact in genomic selection for birth weight (BW), weaning weight (WW), and yearling weight (YW) in Nellore cattle. Genetic parameter estimation and the weighted single-step genomic best linear unbiased prediction (WssGBLUP) analyses were performed. Direct additive heritability estimates were high for WW and YW, and moderate for BW. Trait-associated variants distributed across multiple regions on BTA14 were observed in the weighted single-step genome-wide association studies (WssGWAS) results, implying a polygenic genetic architecture for weight in different ages. Several genes have been found in association with the weight traits, including the CUB And Sushi multiple domains 3 (CSMD3), thyroglobulin (TG), and diacylglycerol O-acyltransferase 1 (DGAT1) genes. The variance explained per SNP was higher in six functional classes of gene regulatory regions (5UTR, CpG islands, downstream, upstream, long non-coding RNA, and transcription factor binding sites (TFBS)), highlighting their importance for weight traits in Nellore cattle. A marginal increase in accuracy was observed when the selected functional variants (SV) information was considered in the WssGBLUP method, probably because of the small number of SV available on BTA14. The identified genes, pathways, and functions contribute to a better understanding of the genetic and physiological mechanisms regulating weight traits in the Nellore breed.

Use of birth weight as a predictor of genetic merit of subsequent growth traits in Duroc and Landrace pigs

Analysis of correlation between the early testable phenotypes of piglets and the final performance of pigs can serve the early selection for breeding. The objectives of this study were to estimate the genetic parameters for birth weight (BtW), age (AGE) and backfat thickness (BF) up to 115 kg BW and to analyse the relationships among these three traits, and to estimate the accuracy of using BtW to predict estimated breeding values (EBVs) of AGE and BF in Landrace and Duroc pigs. Data on 26 614 Landrace and 19 984 Duroc pigs, born between 2001 and 2018, were collected from the core breeding group of a farm. All pigs were recorded for phenotypes including BtW, AGE and BF. The factors affecting these three traits were analysed using R v4.2.0 Software. The population genetic parameters and breeding values of three traits were estimated by using a multitrait animal model based on AI plate of DMU software. Heritabilities for BtW, AGE and BF were moderate to high for Landrace (0.437, 0.282and 0.137, respectively) and Duroc breeds (0.369, 0.279 and 0.148). BtW was genetically correlated with AGE and BF in Landrace (-0.213, 0.037) and Duroc (-0.214, 0.025). AGE was negatively genetically correlated with BF in both Landrace (-0.036) and Duroc (-0.057) pigs. The heritability of BtW, AGE and BF of Landrace pigs and Duroc pigs were 0.148, 0.182 and 0.075 and 0.168, 0.159 and 0.120, respectively, by taking into account of the litter effect. BtW was genetically correlated with AGE and BF in Landrace (-0.094, 0.002) and Duroc (-0.199, -0.052). AGE was negatively genetically correlated with BF in both Landrace (-0.034) and Duroc (-0.153) pigs. The variances between total individual BtW and AGE and BF were then used to predict the EBV of AGE and BF for individuals with AGE or BF phenotypes missing under 10-fold cross-validation. Prediction accuracy was calculated as the Kendall tau-b correlation coefficient between EBVs and EBVs via 10-fold cross-validation. Prediction accuracy for AGE and BF was 0.655 and 0.611 in Landrace, 0.665 and 0.617 in Duroc. After incorporation of the litter effect, the prediction accuracy for AGE and BF increased to 0.690 and 0.665 in Landrace and to 0.705 and 0.649 in Duroc. So, the EBV of AGE and BF phenotypes missing individuals could be predicted by using the available phenotypic data and the easily measured BtW, and litter effect could boost the accuracy of prediction.