low birth weight in American Simmental beef cattle

Data on calving ease (CE) and birth, weaning weight (WW), and yearling weight (YW) were obtained from the American Simmental Association (ASA) and included pedigree and performance information on 11,640,735 animals. Our objective was to quantify differential response from selection for high CE vs. low birth weight (LBW) in first-calf Simmental heifers. We hypothesized that direct selection for CE should be used as the primary approach to reduce dystocia and mitigate losses in growth-related traits. WW and YW were adjusted to 205 and 365 d of age, respectively. Sire and maternal grandsire (co)variance components for CE, birth weight (BW), and 205-d weaning weight (205-d WW), and sire covariance components for 160-d postweaning gain (160-d gain) were estimated using a sire-maternal grandsire model. Direct and maternal expected progeny differences (EPD) for CE, BW, and 205-d WW and direct EPD for 160-d gain and 365-d yearling weight (365-d YW) for first-calf Simmental heifers population (465,710 animals) were estimated using a threshold-linear multivariate maternal animal model. This population was used to estimate genetic trends and as a selection pool (control) for various selection scenarios. Selection scenarios were high CE (HCE), LBW, the all-purpose selection index (API = -1.8 BW + 1.3 CE + 0.10 WW + 0.20 YW) of the ASA and its two derived subindices: (API1 = 1.3 CE + 0.20 YW) and (API2 = -1.8 BW + 0.20 YW), and lastly Dickerson's selection index (DSI = -3.2 BW + YW). Data for each selection scenario were created by selecting sires with EPD greater than or equal to the average along with the top 75% of dams. Comparison between selection scenarios involved evaluating the direct and maternal genetic trends from these scenarios. Direct heritabilities for CE, BW, 205-d WW, 160-d gain, and 365-d YW of Simmental cattle were 0.23, 0.52, 0.28, 0.21, and 0.33, respectively. The single trait, HCE, selection scenario, as opposed to LBW, increased the intercept for CE by 57.7% and the slopes (P < 0.001) for BW, 205-d WW, 160-d gain, and 365-d YW by 27.9%, 37.5%, 16%, and 28%, respectively. Comparisons of various selection scenarios revealed that the CE-based selection scenarios (HCE, API, and API1) had a greater response for CE and growth traits.

Multiple association analysis of loci and candidate genes that regulate body size at three growth stages in Simmental beef cattle

Background

Body size traits as one of the main breeding selection criteria was widely used to monitor cattle growth and to evaluate the selection response. In this study, body size was defined as body height (BH), body length (BL), hip height (HH), heart size (HS), abdominal size (AS), and cannon bone size (CS). We performed genome-wide association studies (GWAS) of these traits over the course of three growth stages (6, 12 and 18 months after birth) using three statistical models, single-trait GWAS, multi-trait GWAS and LONG-GWAS. The Illumina Bovine HD 770 K BeadChip was used to identify genomic single nucleotide polymorphisms (SNPs) in 1217 individuals.

Results

In total, 19, 29, and 10 significant SNPs were identified by the three models, respectively. Among these, 21 genes were promising candidate genes, including SOX2, SNRPD1, RASGEF1B, EFNA5, PTBP1, SNX9, SV2C, PKDCC, SYNDIG1, AKR1E2, and PRIM2 identified by single-trait analysis; SLC37A1, LAP3, PCDH7, MANEA, and LHCGR identified by multi-trait analysis; and P2RY1, MPZL1, LINGO2, CMIP, and WSCD1 identified by LONG-GWAS.

Conclusions

Multiple association analysis was performed for six growth traits at each growth stage. These findings offer valuable insights for the further investigation of potential genetic mechanism of growth traits in Simmental beef cattle.

Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

Simple Summary

Dominance effects play important roles in determining genetic changes with regard to complex traits. We conducted genomic predictions and genome-wide association studies in order to investigate the effects of dominance on carcass weight, dressing percentage, meat percentage, average daily gain, and chuck roll in 1233 Simmental beef cattle. Using dominance models, we improved the predictive abilities and found several candidate single-nucleotide polymorphisms (SNPs) and genes associated with these traits. Our studies helped us to understand causal mutation mapping and genomic selection models with dominance effects in Chinese Simmental beef cattle.

Abstract

Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.