Background: Identifying true-positive variants in genome-wide associations (GWA) depends on several factors, including the number of genotyped individuals. The limited dimensionality of the genomic information may give insights into the optimal number of individuals to use in GWA. This study investigated different discovery set sizes in GWA based on the number of largest eigenvalues explaining a certain proportion of variance in the genomic relationship matrix (G). An additional investigation included the change in accuracy by adding variants, selected based on different set sizes, to the regular SNP chips used for genomic prediction. Methods: Sequence data were simulated containing 500k SNP with 200 or 2000 quantitative trait nucleotides (QTN). A regular 50k panel included one every ten simulated SNP. Effective population size (Ne) was 20 and 200. The GWA was performed with the number of genotyped animals equivalent to the number of largest eigenvalues of G (EIG) explaining 50, 60, 70, 80, 90, 95, 98, and 99% of the variance. In addition, the largest discovery set consisted of 30k genotyped animals. Limited or extensive phenotypic information was mimicked by changing the trait heritability. Significant and high effect size SNP were added to the 50k panel and used for single-step GBLUP with and without weights. Results: Using the number of genotyped animals corresponding to at least EIG98 enabled the identification of QTN with the largest effect sizes when Ne was large. Smaller populations required more than EIG98. Furthermore, using genotyped animals with higher reliability (i.e., higher trait heritability) helped better identify the most informative QTN. The greatest prediction accuracy was obtained when the significant or the high effect SNP representing twice the number of simulated QTN were added to the 50k panel. Weighting SNP differently did not increase prediction accuracy, mainly because of the size of the genotyped population. Conclusions: Accurately identifying causative variants from sequence data depends on the effective population size and, therefore, the dimensionality of genomic information. This dimensionality can help identify the suitable sample size for GWA and could be considered for variant selection. Even when variants are accurately identified, their inclusion in prediction models has limited implications.

Objective: This study evaluated the effect of pedigree errors (PEs) on the accuracy of estimated breeding value (EBV) and genetic gain for carcass traits in Korean Hanwoo cattle.Methods: The raw data set was based on the pedigree records of Korean Hanwoo cattle. The animals’ information was obtained using Hanwoo registration records from Korean animal improvement association database. The record comprised of 46,704 animals, where the number of the sires used was 1,298 and the dams were 38,366 animals. The traits considered were carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS). Errors were introduced in the pedigree dataset through randomly assigning sires to all progenies. The error rates substituted were 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%, respectively. A simulation was performed to produce a population of 1,650 animals from the pedigree data. A restricted maximum likelihood based animal model was applied to estimate the EBV, accuracy of the EBV, expected genetic gain, variance components, and heritability (h2) estimates for carcass traits. Correlation of the simulated data under PEs was also estimated using Pearson’s method.Results: The results showed that the carcass traits per slaughter year were not consistent. The average CWT, EMA, BFT, and MS were 342.60 kg, 78.76 cm<sup>2, 8.63 mm, and 3.31, respectively. When errors were introduced in the pedigree, the accuracy of EBV, genetic gain and h2 of carcass traits was reduced in this study. In addition, the correlation of the simulation was slightly affected under PEs.Conclusion: This study reveals the effect of PEs on the accuracy of EBV and genetic parameters for carcass traits, which provides valuable information for further study in Korean Hanwoo cattle.

A spurious negative genetic correlation between direct and maternal effects of weaning weight (WW) in beef cattle has historically been problematic for researchers and industry. Previous research has suggested the covariance between sires and herds may be contributing to this relationship. The objective of this study was to estimate the variance components (VC) for WW in American Angus with and without sire by herd (SxH) interaction effect when genomic information is used or not. Five subsets of approximately 100k animals for each subset were used. When genomic information was included, genotypes were added for 15,637 animals. Five replicates were performed. Four different models were tested, namely, M1: without SxH interaction effect and with covariance between direct and maternal effect (σam) ≠ 0; M2: with SxH interaction effect and σam ≠ 0; M3: without SxH interaction effect and with σam = 0; M4: with SxH interaction effect and σam = 0. VC were estimated using the restricted maximum likelihood (REML) and single-step genomic REML (ssGREML) with the average information algorithm. Breeding values were computed using single-step genomic BLUP (ssGBLUP) for the models above and one additional model, which had the covariance zeroed after the estimation of VC (M5). The ability of each model to predict future breeding values was investigated with the linear regression method. Under REML, when the SxH interaction effect was added to the model, both direct and maternal genetic variances were greatly reduced, and the negative covariance became positive (i.e., when moving from M1 to M2). Similar patterns were observed under ssGREML, but with less reduction in the direct and maternal genetic variances and still a negative covariance. Models with the SxH interaction effect (M2 and M4) had a better fit according to the Akaike Information Criteria (AIC). Breeding values from those models were more accurate and had less bias than the other three models. The rankings and breeding values of Artificial Insemination (AI) sires (N = 1,977) greatly changed when the SxH interaction effect was fit in the model. Although the SxH interaction effect accounted for 3% to 5% of the total phenotypic variance and improved the model fit, this change in the evaluation model will cause severe reranking among animals.

It was hypothesized that single-nucleotide polymorphisms (SNPs) extracted from text-mined genes could be more tightly related to causal variant for each trait and that differentially weighting of this SNP panel in the GBLUP model could improve the performance of genomic prediction in cattle. Fitting two GRMs constructed by text-mined SNPs and SNPs except text-mined SNPs from 777k SNPs set (exp_777K) as different random effects showed better accuracy than fitting one GRM (Im_777K) for six traits (e.g. backfat thickness: + 0.002, eye muscle area: + 0.014, Warner–Bratzler Shear Force of semimembranosus and longissimus dorsi: + 0.024 and + 0.068, intramuscular fat content of semimembranosus and longissimus dorsi: + 0.008 and + 0.018). These results can suggest that attempts to incorporate text mining into genomic predictions seem valuable, and further study using text mining can be expected to present the significant results.

Simple SummaryFor meat tenderness, single nucleotide polymorphisms (SNPs) in the μ-calpain (CAPN1) and calpastatin (CAST) genes have been reported to be associated with Warner-Bratzler shear force (WBSF) in different cattle populations, including Korean Hanwoo cattle. In this study, we validated the association of seven SNPs in CAPN1 and CAST genes with meat tenderness in two different muscle cuts tenderness in the Longissimus thoracis (LT) and Semimembranosus (SM) muscles. Two SNPs in CAPN1 and one SNPs in CAST genes showed association with WBSF of both muscle types. Furthermore, of twelve reconstructed haplotypes, six demonstrated significant associations with WBSF values. These findings may be one of the strong evidences that CAPN1 and CAST gene mutations are strongly associated with WBSF. The information of significantly-associated SNPs and the resulted haplotypes could be utilized in the Hanwoo breeding program for further genetic improvement of tenderness traits.AbstractPrevious studies demonstrated that polymorphisms in the μ-calpain (CAPN1) and calpastatin (CAST) genes had significant effects on meat tenderness in different cattle populations. The aim of this study was to validate the potential association of seven single nucleotide polymorphisms (SNPs) harbored in these two candidate genes with meat tenderness in the Longissimus thoracis (LT) and Semimembranosus (SM) muscles. A total of 1000 animals were genotyped using TaqMan SNP genotyping arrays, and the meat tenderness of two muscle (LT and SM at 7 days post-slaughter) was assessed based on Warner-Bratzler WBSF (WBSF) testing. We observed significant associations of the CAPN1:c.580T>C, CAPN1:c.658T>C and CAST:c.1985G>C polymorphisms (p < 0.05) with the WBSF values in the LT and SM muscles. Additive effects of the C allele in CAPN1:c.580T>C and CAST:c.1985G>C were associated with an increase of 0.16 and 0.15 kg, and 0.08 and 0.26 kg WBSF in the LT and SM, respectively; CAPN1:c.658T>C had negative effects on the WBSFs. Furthermore, six reconstructed haplotypes demonstrated significant associations with WBSF values (p < 0.05). In conclusion, the significant associations identified between the SNPs in CAPN1, CAST and WBSF values could be utilized in marker-assisted selection programs in order to improve the beef tenderness of Hanwoo cattle.