Body weight and body measurements are commonly used to represent growth and measured at several growth stages in beef cattle. Those economically important traits should be genetically improved. To achieve breeding programs, genetic parameters are prerequisite, as they are needed for designing and predicting outcomes of breeding programs, as well as estimating of breeding values. (Co)variance components were estimated for BW and body measurements on Brahman cattle born between 1990 and 2016 from 17 research herds across Thailand. The traits measured were BW, heart girth (GR), hip height (HH) and body length (BL) and were measured at birth, 200 days, 400 days and 600 days of age. The number of records varied between traits from 18 890 for birth BW to 876 for GR at 600 days. Estimation of variance components was performed using restricted maximum likelihood using univariate and multivariate animal models. Pre-weaning traits were influenced by genetic and/or permanent environmental effects of the dam, except for BL. Heritability estimates from birth to 600 days of age ranged from 0.28±0.01 to 0.50±0.06 for BW, 0.27±0.01 to 0.43±0.09 for GR, 0.28±0.01 to 0.58±0.08 for HH and 0.34±0.01 to 0.51±0.08 for BL using univariate analysis. Heritability estimates for the traits studied increased with age. A similar trend was observed for the phenotypic and genetic correlations between subsequent BW and body measurements. A positive correlation was observed between different traits measured at a similar age, ranging from 0.22±0.01 to 0.72±0.01 for the phenotypic correlation and 0.25±0.04 to 0.97±0.11 for the genetic correlation. Also, a positive correlation was observed for similar traits across different age classes ranging from 0.07±0.03 to 0.76±0.02 for the phenotypic correlation and 0.24±0.11 to 0.92±0.05 for the genetic correlation. Therefore, all correlations between body measurements at the same age and across age classes were positive. The results show the potential improvement of growth traits in Brahman cattle, and those traits can be improved simultaneously under the same breeding program.

The objective of this study was to compare the accuracies of genomic prediction for milk yield, fat yield, and protein yield from Philippine dairy buffaloes using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with the accuracies based on pedigree BLUP (pBLUP). To also assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted breeding values (BVs) was also calculated. Two data sets were analyzed. The GENO data consisting of all female buffaloes that have both phenotypes and genotypes (n = 904 with 1,773,305-days lactation records) were analyzed using pBLUP and GBLUP. The ALL data, consisting of the GENO data plus females with phenotypes but not genotyped (n = 1,975 with 3,821,305-days lactation records), were analyzed using pBLUP and ssGBLUP. Animals were genotyped with the Affymetrix 90k buffalo genotyping array. After quality control, 60,827 single-nucleotide polymorphisms were used for downward analysis. A pedigree file containing 2,642 animals was used for pBLUP and ssGBLUP. Accuracy of prediction was calculated as the correlation between the predicted BVs of the test set and adjusted phenotypes, which were corrected for fixed effects, divided by the square root of the heritability of the trait, corrected for the number of lactations used in the test set. To assess the bias of the prediction, the regression coefficient (slope) of the adjusted phenotypes on the predicted BVs was also calculated. Results showed that genomic methods (GBLUP and ssGBLUP) provide more accurate predictions compared to pBLUP. Average GBLUP and ssGBLUP accuracies were 0.24 and 0.29, respectively, whereas average pBLUP accuracies (for GENO and ALL data) were 0.21 and 0.22, respectively. Slopes of the two genomic methods were also closer to one, indicating lesser bias, compared to pBLUP. Average GBLUP and ssGBLUP slopes were 0.89 and 0.84, respectively, whereas the average pBLUP (for GENO and ALL data) slopes were 0.80 and 0.54, respectively.

The aim of this study was to identify quantitative trait loci (QTL) associated with residual feed intake (RFI) and genes whose expression varied significantly with phenotypic differences in RFI. We used data from 2,190 Angus steers with phenotypic records for RFI, all with imputed high density array (770k) genotypes. We used RNA-seq in a multi-tissue experiment from 126 Angus steers divergently selected for RFI for approximately three generations, to analyze the expression of genes significantly associated (GSA) with RFI, with special attention to the genes close by significant QTLs.

Background:

Imputation to whole-genome sequence is now possible in large sheep populations. It is therefore of interest to use this data in genome-wide association studies (GWAS) to investigate putative causal variants and genes that underpin economically important traits. Merino wool is globally sought after for luxury fabrics, but some key wool quality attributes are unfavourably correlated with the characteristic skin wrinkle of Merinos. In turn, skin wrinkle is strongly linked to susceptibility to "fly strike" (Cutaneous myiasis), which is a major welfare issue. Here, we use whole-genome sequence data in a multi-trait GWAS to identify pleiotropic putative causal variants and genes associated with changes in key wool traits and skin wrinkle.

Results:

A stepwise conditional multi-trait GWAS (CM-GWAS) identified putative causal variants and related genes from 178 independent quantitative trait loci (QTL) of 16 wool and skin wrinkle traits, measured on up to 7218 Merino sheep with 31 million imputed whole-genome sequence (WGS) genotypes. Novel candidate gene findings included the MAT1A gene that encodes an enzyme involved in the sulphur metabolism pathway critical to production of wool proteins, and the ESRP1 gene. We also discovered a significant wrinkle variant upstream of the HAS2 gene, which in dogs is associated with the exaggerated skin folds in the Shar-Pei breed.

Conclusions:

The wool and skin wrinkle traits studied here appear to be highly polygenic with many putative candidate variants showing considerable pleiotropy. Our CM-GWAS identified many highly plausible candidate genes for wool traits as well as breech wrinkle and breech area wool cover.