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Three statistical models (an admixture model, linear regression, and ridge-regression BLUP) and two strategies for selecting SNP panels (uniformly spaced vs. maximum Euclidean distance of SNP allele frequencies between ancestral breeds) were compared for estimating genomic-estimated breed composition (GBC) in Brangus and Santa Gertrudis cattle, respectively. Animals were genotyped with a GeneSeek Genomic Profiler bovine low-density version 4 SNP chip. The estimated GBC was consistent among the uniformly spaced SNP panels, and values were similar between the three models. However, estimated GBC varied considerably between the three methods when using fewer than 10 000 SNPs that maximized the Euclidean distance of allele frequencies between the ancestral breeds. The admixture model performed most consistently across various SNP panel sizes. For the other two models, stabilized estimates were obtained with an SNP panel size of 20 000 SNPs or more. Based on the uniformly spaced 20K SNP panel, the estimated GBC was 69.8-70.5% Angus and 29.5-30.2% Brahman for Brangus, and 63.9-65.3% Shorthorn and 34.7-36.1% Brahman in Santa Gertrudis. The estimated GBC of ancestries for Santa Gertrudis roughly agreed with the pedigree-expected values. However, the estimated GBC in Brangus showed a considerably larger Angus composition than the pedigree-expected value (62.5%). The elevated Angus composition in the Brangus could be due to the mixture of some 1/2 Ultrablack animals (Brangus × Angus). Another reason could be the consequences of selection in Brangus cattle for phenotypes where the Angus breed has advantages., (© 2020 Stichting International Foundation for Animal Genetics.)

SNP arrays are widely used in genetic research and agricultural genomics applications, and the quality of SNP genotyping data is of paramount importance. In the present study, SNP genotyping concordance and discordance were evaluated for commercial bovine SNP arrays based on two types of quality assurance (QA) samples provided by Neogen GeneSeek. The genotyping discordance rates (GDRs) between chips were on average between 0.06% and 0.37% based on the QA type I data and between 0.05% and 0.15% based on the QA type II data. The average genotyping error rate (GER) pertaining to single SNP chips, based on the QA type II data, varied between 0.02% and 0.08% per SNP and between 0.01% and 0.06% per sample. These results indicate that genotyping concordance rate was high (i.e. from 99.63% to 99.99%). Nevertheless, mitochondrial and Y chromosome SNPs had considerably elevated GDRs and GERs compared to the SNPs on the 29 autosomes and X chromosome. The majority of genotyping errors resulted from single allotyping errors, which also included the opposite instances for allele 'dropout' (i.e. from AB to AA or BB). Simultaneous allotyping errors on both alleles (e.g. mistaking AA for BB or vice versa) were relatively rare. Finally, a list of SNPs with a GER greater than 1% is provided. Interpretation of association effects of these SNPs, for example in genome-wide association studies, needs to be taken with caution. The genotyping concordance information needs to be considered in the optimal design of future bovine SNP arrays., (© 2019 Stichting International Foundation for Animal Genetics.)

Single nucleotide polymorphism (SNP) chips have been widely used in genetic studies and breeding applications in animal and plant species. The quality of SNP genotypes is of paramount importance. More often than not, there are situations in which a number of genotypes may fail, requiring them to be imputed. There are also situations in which ungenotyped loci need to be imputed between different chips, or high-density genotypes need to be imputed based on low-density genotypes. Under these circumstances, the validity and reliability of subsequent data analyses is subject to the accuracy of these imputed genotypes. For justifying a better understanding of factors affecting imputation accuracy, in the present study, the impacts of SNP genotyping call rate and SNP genotyping error rate on the accuracy of genotype imputation were investigated under two scenarios in 20 116 U.S. Holstein cattle, each genotyped with a GGP 50K SNP chip. When the two factors were not correlated in scenario 1, simulated genotyping call rate varied from 50% to 100% and simulated genotyping error rate changed from 0% to 50%, with both factors being independent of each other. In scenario 2, genotyping error rates were correlated with genotyping call rate, and the relationship was set up by fitting a linear regression model between the two variables on a real dataset. That is, the simulated SNP call rate varied from 100% to 50% whereas the SNP genotyping rate changed from 0% to 13.55%. Finally, a 5-fold cross-validation was used to assess the subsequent imputation accuracy. The results showed that when original SNP genotyping call rate were independent of SNP genotyping error rate, the imputation accuracy did not change significantly with the original genotyping call rate (P>0.05), but it decreased significantly as the genotyping error rate increased (P<0.01). However, when original genotyping call rate was negatively correlated with genotyping error rate, the imputation error increased with elevated original genotyping error rate. In both scenarios, genotyping call rate needs to be no less than 0.90 in order to obtain 98% or higher genotype imputation accuracy. The present results can provide guidance for establishing quality assurance criteria for SNP genotyping in practice.

Natural and artificial selection, geographical segregation and genetic drift can result in differentiation of allelic frequencies of single nucleotide polymorphism (SNP) at many loci in the animal genome. For individuals whose ancestors originated from different populations, their genetic compositions exhibit multiple components correlated with the genotypes or allele frequencies of these breeds or populations. Therefore, by using an appropriate statistical method, one can estimate the genomic contribution of each breed (ancestor) to the genome of each individual animal, which is referred to as the genomic breed composition (GBC). This paper reviews the principles, statistical methods and steps for estimating GBC of individual animals using SNP genotype data. Based on a linear regression model and an admixture model respectively, the protocols were demonstrated by the breed characterization of 198 purported Akaushi cattle, which included selection of reference SNPs and reference individual animals, and computing GBC for animals to be evaluated. The reference populations consist of 36 574 cattle from five cattle breeds (Akaushi, Angus, Hereford, Holstein and Jersey), each genotyped on either a 40K or 50K SNP chip. Four common SNP panels scanned from commercial chips for estimating GBC of individual animals are optimally selected, thereby expanding the functionalities of the currently available commercial SNP chips. It remains to be explored in future studies as to how estimated GBC can be incorporated to improve the accuracies on genomic prediction in purebred animals and crossbreds as well.

We previously have shown that plasma concentrations of endocannabinoids (EC) are positively correlated with feed efficiency and leaner carcasses in finishing steers. However, whether the animal growth during the finishing period affects the concentration of EC is unknown. The objective of this study was to quantify anandamide (AEA) and 2-arachidonyl glycerol (2-AG) in plasma during different stages of the finishing period and identify possible associations with production traits and carcass composition in beef calves. Individual DMI and BW gain were measured on 236 calves ( = 127 steers and = 109 heifers) for 84 d on a finishing ration. Blood samples were collected on d 0 (early), 42 (mid), and 83 (late) of days on study (DOS). Cattle were slaughtered 44 d after the feeding study. Plasma concentration of AEA at 0 DOS was indirectly associated with the G:F ( < 0.01) and directly associated with residual feed intake (RFI; < 0.05) in steers. In contrast, plasma concentration of AEA at 83 DOS was directly associated with the G:F and indirectly associated RFI in heifers and steers ( < 0.01). In addition, AEA concentration at 42 and 83 DOS was positively associated with ADG and DMI ( < 0.01) in heifers and steers. Furthermore, 2-AG concentration at 42 DOS was positively associated with ADG in steers ( < 0.01) and heifers ( < 0.10). Plasma concentration of AEA was positively associated ( < 0.05) with HCW, USDA-calculated yield grade, and 12th-rib fat thickness in heifers, whereas no associations were found in steers. In contrast, 2-AG concentration was not associated with any carcass traits. These results provide evidence that circulating EC change during animal growth and that AEA concentration may be a useful predictor of growth and feed efficiency and, in females, of carcass attributes.

The objective of this experiment was to determine the association of circulating plasma leptin concentrations with production and body composition measures of finishing beef steers and heifers and to determine if multiple sampling time points improve the associations of plasma leptin concentrations with production and body composition traits. Individual dry matter intake (DMI) and ADG were determined for 84 d using steers and heifers (n = 127 steers and n = 109 heifers). Blood was collected on day 0, day 42, and day 83 for determination of plasma leptin concentrations. Leptin concentrations were greater in heifers than those in steers on day 0 (P < 0.001 for sex by day interaction), and leptin concentrations increased in both sexes but were not different from each other on day 83. Leptin concentrations at all 3 time points and the mean were shown to be positively associated with DMI (P ≤ 0.006), whereas the mean leptin concentration explaining 8.3% of the variance of DMI. Concentrations of leptin at day 42, day 83, and the mean of all 3 time points were positively associated with ADG (P ≤ 0.011). Mean leptin concentration was negatively associated with gain:feed ratio and positively associated with residual feed intake (RFI), indicating that more efficient cattle had lower leptin concentrations. However, leptin concentrations explained very little of the variation in residual feed intake (≤ 3.2% of the variance). Leptin concentrations were positively associated with body fat measured by ultrasonography at the 12th rib and over the rump (P < 0.001), with the mean leptin concentration explaining 21.9% and 12.7% of the variance in 12th rib and rump fat thickness, respectively. The same trend was observed with carcass composition where leptin concentrations were positively associated with 12th rib fat thickness, USDA-calculated yield grade (YG), and marbling score (P ≤ 0.006) and mean leptin concentration explained 16.8, 18.2, and 4.6% of the variance for 12th rib fat thickness, yield grade, and marbling score, respectively. Given these and previous results, it appears that leptin physiology could be a candidate for mechanisms that contribute to feed intake and feed efficiency variation in beef cattle., (Published by Elsevier Inc.)

Genetic marker effects and type of inheritance are estimated with poor precision when minor marker allele frequencies are low. A stable composite population (MARC II) was subjected to marker assisted selection for 2 yr to equalize and genetic marker frequencies to evaluate the epistatic and pleiotropic effects of these markers on BW, reproduction, and first calf performance traits in replacement beef females ( = 171) managed under 2 postweaning development protocols. Traits evaluated on the heifers were birth BW, weaning BW, 11-mo BW, 12-mo BW, 13-mo BW, first breeding season pregnancy evaluation BW, first calving season BW, 11-mo puberty, 12-mo puberty, 13-mo puberty, first breeding season pregnancy, and first calf weaning rate. Additionally, heifer's first calf performance traits of ordinal calving date, first calf birth BW, and first calf weaning BW (with and without age adjustment) were analyzed. Selection to increase minor allele frequencies and balanced sampling across genotype classes enhanced the ability to detect all genetic effects except dominance × dominance epistasis. The × genotype effect was significant ( < 0.05) for 11-mo BW and 12-mo BW and tended to be significant ( = 0.08) for 13-mo BW. Consistently, for all 3 traits, the most significant effect among epistatic × genotype effects was the additive effect, with the G allele decreasing BW. There were no associations between × genotype and fertility related traits ( ≥ 0.46) in this study. Additionally, there were no × genotype associations with first progeny performance traits ( ≥ 0.14). The large effect of the additive × additive interaction on first calf weaning BW was imprecisely estimated, which may warrant further investigation.

Genetic marker effects and type of inheritance are estimated with poor precision when minor marker allele frequencies are low. A stable composite population (MARC III) was subjected to marker-assisted selection for multiple years to equalize specific marker frequencies to 1) estimate effect size and mode of inheritance for previously reported SNP on targeted beef carcass quality traits (n=254), 2) estimate pleiotropic effects of previously reported SNP on nontarget performance traits (n=542 or 254), and 3) evaluate tenderness SNP specific residual variance for LM tenderness. Three haplotypes within μ-calpain (CAPN1), a SNP in calpastatin (CAST), and a dinucleotide substitution in diacylglycerol O-acyltransferase 1 (DGAT1) were successfully selected to equalize their frequencies. Traits evaluated were birth BW, weaning BW, yearling BW, final BW, dressing percent, HCW, fat thickness, LM area, USDA marbling score, yield grade, LM slice shear force (SSF), and visible and near-infrared (VISNIR)-predicted SSF. While the CAPN1 genotype effect on SSF was not significant (P=0.12), the direction and size of CAPN1 contrasts were consistent with previous research. Effects on SSF between divergent CAPN1 haplotypes (1.153 kg) and the additive effect of CAST (0.902 kg) were large, and animals homozygous for tender alleles at both CAPN1 and CAST would have 4.11 kg lower SSF (27.5% of the mean) than animals homozygous tough for both markers. Furthermore, the interaction between CAPN1 and CAST for SSF was not significant (P=0.40). There were significant effects for DGAT1 on adjusted fat thickness (P=0.02) and VISNIR-predicted SSF (P<0.001) with additive and dominance modes of inheritance (P<0.05) for both traits. Furthermore, CAST genotype specific residual variance models fit significantly better (P<0.001) than single residual variance models for SSF, with the tougher genotypes having progressively larger residual (and hence phenotypic) variances. Therefore, risk of a tough steak from the undesired CAST genotype is increased through both an increase in mean and an increase in variation. This work is supportive of the importance of CAPN1 and CAST for mean tenderness in beef, confirms an effect of CAST on beef LM tenderness variation, and identifies an effect of DGAT1 on subcutaneous fat thickness.

The experiment was designed to validate the use of ultrasound to evaluate body composition in mature beef cows. Both precision and accuracy of measurement were assessed. Cull cows (n = 87) selected for highly variable fatness were used. Two experienced ultrasound technicians scanned and assigned BCS to each cow on 2 consecutive days. Ultrasound traits were backfat thickness (UBFT), LM area (ULMA), body wall thickness (UBWT), rump fat depth (URFD), rump muscle depth (URMD), and intramuscular fat (UIMF; %). Cows were then harvested. Carcass traits were HCW, backfat thickness (CBFT), LM area (CLMA), body wall thickness (CBWT), and marbling score (CMS). Correlations between consecutive live measurements were greatest for subcutaneous fat (r > 0.94) and lower for BCS (r > 0.74) and URMD (r > 0.66). Repeatability bias differed from 0 for only 1 technician for URMD and UIMF (P < 0.01). Technicians differed in repeatability SE for only ULMA (P < 0.05). Correlations between live and carcass measurements were high for backfat and body wall thickness (r > 0.90) and slightly less for intramuscular fat and LM area (r = 0.74 to 0.79). Both technicians underestimated all carcass traits with ultrasound, but only CBFT and CBWT prediction bias differed from 0 (P < 0.05). Technicians had similar prediction SE for all traits (P > 0.05). Technician effects generally explained <1% of the total variation in precision. After accounting for technician, animal effects explained 50.4% of remaining variation in differences between repeated BCS (P < 0.0001) but were minimal for scan differences. When cows with mean BCS <4 or >7 were removed, the portion of remaining variation between repeated measurements defined by animal effects increased for most traits and was significant for UBFT and URFD (P = 0.03). Technician effects explained trivial variation in accuracy (P > 0.24). Animal effects explained 87.2, 75.2, and 81.7% (P < 0.0001) of variation remaining for CBFT, CLMA, and CBWT prediction error, respectively, and remained large and highly important (P < 0.0001) when only considering cows with BCS from 4 to 7. We conclude that experienced ultrasound technicians can precisely and accurately measure traits indicative of composition in mature beef cows. However, animal differences define substantial variation in scan differences and, especially, prediction errors. Implications for technician certification, carcass pricing, and genetic evaluation are discussed.

Genetic marker effects and interactions are estimated with poor precision when minor marker allele frequencies are low. An Angus population was subjected to marker assisted selection for multiple years to increase divergent haplotype and minor marker allele frequencies to 1) estimate effect size and mode of inheritance for previously reported SNP on targeted beef carcass quality traits; 2) estimate effects of previously reported SNP on nontarget performance traits; and 3) evaluate tenderness SNP specific residual variance models compared to a single residual variance model for tenderness. Divergent haplotypes within µ-calpain (CAPN1), and SNP within calpastatin (CAST) and growth hormone receptor (GHR) were successfully selected to increase their frequencies. Traits evaluated were birth BW, weaning BW, final BW, fat thickness, LM area, USDA marbling score, yield grade, slice shear force (SSF), and visible and near infrared predicted slice shear force. Both CAPN1 and CAST exhibited additive (P < 0.001) modes of inheritance for SSF and neither exhibited dominance (P ≥ 0.19). Furthermore, the interaction between CAPN1 and CAST for SSF was not significant (P = 0.55). Estimated additive effects of CAPN1 (1.049 kg) and CAST (1.257 kg) on SSF were large in this study. Animals homozygous for tender alleles at both CAPN1 and CAST would have 4.61 kg lower SSF (38.6% of the mean) than animals homozygous tough for both markers. There was also an effect of CAST on yield grade (P < 0.02). The tender CAST allele was associated with more red meat yield and less trimmable fat. There were no significant effects (P ≥ 0.23) for GHR on any of the traits evaluated in this study. Furthermore, CAST specific residual variance models were found to fit significantly better (P < 0.001) than single residual variance models for SSF, with the tougher genotypes having larger residual variance. Thus, the risk of a tough steak from the undesired CAST genotype is increased through both an increase in mean and an increase in variation. This work confirms the importance of CAPN1 and CAST for tenderness in beef, provides a new effect of CAST on beef tenderness, and questions the utility of GHR as a selection marker for beef quality.

The objective of this study was to characterize variation and identify SNP and chromosomal regions associated with mineral concentrations in LM of Angus beef cattle. Samples of LM from 2,285 Angus cattle were obtained, and concentrations of 7 minerals, including iron, magnesium, manganese, phosphorus, potassium, sodium, and zinc, were quantified. Genomic DNA extracted from the ground beef sample used for mineral composition was genotyped with the Bovine SNP50 Infinium II BeadChip, and effects of SNP on each trait were estimated using the Bayes-Cπ module of GenSel software. Pedigree-based estimates of heritabilities and corresponding genetic variances indicate iron was the only mineral concentration that could be considered a good candidate for manipulation by genomic selection. The amount of variation that could be accounted for by SNP genotypes was concordant with pedigree-based heritabilities and varied from very low for potassium and sodium (<0.09) to medium high (0.37) for iron. Although significant chromosomal regions were identified for all minerals analyzed in this study, further study focused on iron. Seven regions on 6 chromosomes (1, 2, 7, 10, 15, and 28) were identified to have a major effect on the iron content of LM in Angus cattle. The accuracy of direct genomic values (DGV) for iron concentration was estimated using a 5-fold cross-validation strategy. The accuracy of DGV estimated as the genetic correlation between DGV and the phenotype (iron concentration) adjusted for contemporary groups was 0.59. A bivariate animal model was used to estimate genetic correlations between iron concentrations and a reduced set of economically important carcass traits: HCW, rib eye area, calculated USDA yield grade, percent KPH, and marbling score. The genetic correlations between iron concentration and HCW, percentage KPH, marbling score, and rib eye area were small (-0.19 to 0.15) and nonsignificant. Although still weak (0.22), a positive significant genetic correlation was identified between iron content and USDA calculated yield grade. Beef is a major contributor of iron and zinc in the human diet, and this study found that iron content might be effectively manipulated through marker-assisted selection programs, without compromising other carcass and palatability traits.

The objective of this study was to estimate genetic parameters for concentrations of minerals in LM and to evaluate their associations with beef palatability traits. Samples of LM from 2,285 Angus cattle were obtained and fabricated into steaks for analysis of mineral concentrations and for trained sensory panel assessments. Nine minerals, including calcium, copper, iron, magnesium, manganese, phosphorus, potassium, sodium, and zinc, were quantified. Restricted maximum likelihood procedures were used to obtain estimates of variance and covariance components under a multiple-trait animal model. Estimates of heritability for mineral concentrations in LM varied from 0.01 to 0.54. Iron and sodium were highly and moderately heritable, respectively, whereas the other minerals were lowly heritable except for calcium, copper, and manganese, which exhibited no genetic variation. Strong positive genetic correlations existed between iron and zinc (0.49, P < 0.05), between magnesium and phosphorus (0.88, P < 0.05), between magnesium and sodium (0.68, P < 0.05), and between phosphorus and potassium (0.69, P < 0.05). Overall tenderness assessed by trained sensory panelists was positively associated with manganese, potassium, and sodium and negatively associated with phosphorus and zinc concentrations (P < 0.05). Juiciness assessed by trained sensory panelists was negatively associated with magnesium and positively associated with manganese and sodium concentrations (P < 0.05). Livery or metallic flavor was not associated with any of the minerals (P > 0.05). Beefy flavor was positively associated with calcium, iron, and zinc and negatively associated with sodium concentration, whereas a painty or fishy flavor was positively associated with sodium and negatively associated with calcium and potassium concentrations (P < 0.05). Beef is a major contributor of iron and zinc in the human diet, and these results demonstrate sufficient genetic variation for these traits to be improved through marker-assisted selection programs without compromising beef palatability.

Beef is considered to be an excellent source of dietary iron. However, little is known about the genetic control of beef iron content. We hypothesized that genetic polymorphisms in transferrin receptor 2 (TFR2) and solute carrier family 40 (iron-regulated transporter), member 1 (SLC40A1) could influence skeletal muscle iron content. The objective of this study was to use Angus cattle to identify single-nucleotide polymorphisms (SNPs) in the exons and flanking regions of the bovine TFR2 and SLC40A1 genes and to evaluate the extent to which genetic variation in them was associated with bovine longissimus dorsi muscle iron content. Ten novel SNPs were identified in TFR2, of which one SNP tended to be associated (P < 0.013) with skeletal muscle iron content. Nine novel SNPs in SLC40A1, NC007300: rs133108154, rs137140497, rs135205621, rs136600836, rs134388440, rs136347850, rs134186279, rs134621419 and rs137555693, were identified, of which SNPs rs134388440, rs136347850 and rs137555693 were significantly associated (P < 0.007) with skeletal muscle iron content. High linkage disequilibrium was observed among SLC40A1 SNPs rs134388440, rs136347850 and rs137555693 (R(2)  > 0.99), from which two haplotypes, TGC and CAT, were defined. Beef from individuals that were homozygous for the TGC haplotype had significantly (P < 0.001) higher iron content than did beef from CAT homozygous or heterozygous individuals. The estimated size of effect of the identified haplotypes was 0.3% of the phenotypic variance. In conclusion, our study provides evidence for genetic control of beef iron concentration. Moreover, SNPs identified in SLC40A1, rs134388440, rs136347850 and rs137555693 might be useful markers for the selection of Angus cattle for altered iron content., (© 2011 The Authors, Animal Genetics © 2011 Stichting International Foundation for Animal Genetics.)

The most widely used system to predict percentage of retail product from the four primal cuts of beef is USDA yield grade. The purpose of this study was to determine whether routine ultrasound measurements and additional rump measurements could be used in place of the carcass measurements used in the USDA yield grade equation to more accurately predict the percentage of saleable product from the four primals. This study used market cattle (n = 466) consisting of Angus bulls, Angus steers, and crossbred steers. Live animal ultrasound measures collected within 7 d of slaughter were 1) scan weight (SCANWT); 2) 12th- to 13th-rib s.c. fat thickness (UFAT); 3) 12th- to 13th-rib LM area (ULMA); 4) s.c. fat thickness over the termination of the biceps femoris in the rump (URFAT; reference point); 5) depth of gluteus medius under the reference point (URDEPTH); and 6) area of gluteus medius anterior to the reference point (URAREA). Traditional carcass measures collected included 1) HCW; 2) 12th-to 13th-rib s.c. fat thickness (CFAT); 3) 12th- to 13th-rib LM area (CLMA); and 4) estimated percentage of kidney, pelvic, and heart fat (CKPH). Right sides of carcasses were fabricated into subprimal cuts, lean trim, fat, and bone. Weights of each component were recorded, and percentage of retail product from the four primals was expressed as a percentage of side weight. A stepwise regression was performed using data from cattle (n = 328) to develop models to predict percentage of retail product from the four primals based on carcass measures or ultrasound measures, and comparisons were made between the models. The most accurate carcass prediction equation included CFAT, CKPH, and CLMA (R2 = 0.308), whereas the most accurate live prediction equation included UFAT, ULMA, SCANWT, and URAREA (R2 = 0.454). When these equations were applied to a validation set of cattle (n = 138), the carcass equation showed R2 = 0.350, whereas the ultrasound data showed R2 = 0.460. Ultrasound measures in the live animal were potentially more accurate predictors of retail product than measures collected on the carcass.

Steers from research crossbreeding projects (n = 406) were serially scanned using real-time ultrasound at 35-d intervals from reimplant time until slaughter. Cattle were evaluated for rump fat depth, longissimus muscle area (ULMA), 12th-rib fat thickness (UFAT), and percentage of intramuscular fat (IMF) to determine the ability of ultrasound to predict carcass composition at extended periods before slaughter. Additional background information on the cattle, such as live weight, ADG, breed of sire, breed of dam, implant, and frame score was also used. Carcass data were collected by trained personnel at "chain speed," and samples of the 12th-rib LM were taken for ether extract analysis. Simple correlation coefficients showed positive relationships (P < 0.01) between ultrasound measures taken less than 7 d before slaughter and carcass measures: ULMA and carcass LM area (CLMA, r = 0.66); UFAT and carcass 12th-rib fat thickness (CFAT, r = 0.74); and IMF and carcass numeric marbling score (r = 0.61). The same correlation coefficients for ultrasound measures taken 96 to 105 d before slaughter and carcass values (P < 0.01) were 0.52, 0.58, and 0.63, respectively. Steers were divided into source-verified and nonsource-verified groups based on the level of background information for each individual. Regression equations were developed for the carcass measurements; 46% of the variation could be explained for CLMA and 44% of CFAT at reimplant time, 46% of the variation in quality grade and 42% of the variation in yield grade could be explained. Significant predictors of quality grade were IMF (P < 0.001), natural log of 12th-rib fat thickness (LUFAT, P < 0.001), and ADG (P < 0.01), whereas LUFAT (P < 0.001), ULMA (P < 0.01), live weight (P < 0.001), hip height (P < 0.001), and frame score (P < 0.001) were significant predictors of yield grade. Regressions using ultrasound data taken 61 to 69 d before slaughter showed increasing R2. Live ultrasound measures at reimplant time are a viable tool for making decisions regarding future carcass composition.

The objective of the study was to estimate variance components, heritability, and repeatability of ultrasound longissimus muscle area (ULMA) measures. Data included 4,653 serial ULMA measures from 882 purebred Angus bulls and heifers. Animals were born over a 4-yr period from 1998 to 2001. Each year, bulls and heifers were ultrasonically scanned four to eight times, with a 4- to 6-wk interval between scans. Initially, data were subdivided by scan session across years and were analyzed in a multitrait model (MTM). Data pooled across years and scan session were then analyzed using random regression models (RRM) to estimate trends in genetic parameter estimates. Additive direct genetic variance increased with advancing scan session ranging from 8.67 cm4 at the first scan (mean age = 35 wk) to a maximum of 19.48 cm4 at the sixth scan (mean age = 56 wk). Heritability of ULMA increased from 0.35 at first scan to a maximum of 0.48 at the fourth scan (mean age = 50 wk). Additive direct genetic variance and heritability values at about 1 yr of age (fifth scan) were 18.24 cm4 and 0.45, respectively. Estimates from RRM also showed an increase in sigma(a)2 and h2 with age. Trends in sigma(pe)2 estimates, although tending to fluctuate, also increased with age. Additive direct genetic variance at 1 yr of age ranged from 15.8 cm4 to 17.0 cm4 for the different models. Heritability of yearling ULMA measures ranged from 0.40 to 0.42 and repeatabilities ranged from 0.80 to 0.84. For the range of ages used in the current study, both MTM and RRM showed close to maximum heritability values at around 1 yr of age. Therefore, phenotypic differences in yearling ULMA between Angus cattle are better indicators of genetic differences than earlier measurements. Angus breeders could, therefore, use ULMA measures made at around 1 yr of age to select next generation parents.

Basal hormone/metabolite concentrations and responses to intravenous challenges of glucose, insulin and epinephrine were examined in Friesian cows from selection lines of low or high genetic merit treated with recombinantly-derived bovine somatotropin (bST) or control formulation in a 2 x 2 factorial design. Cows from the low genetic merit (low breeding index, LBI) line had previously been shown to be more responsive to the galactopoietic effects of bST (50 mg/day) than those from the high breeding index (HBI) line. Despite this, comparisons of metabolic differences were not confounded by differences in energy balance because bST treatment had also caused an increase in voluntary intake of cut pasture. Circulating levels of somatotropin, insulin-like growth factor-I (IGF-I) and insulin were greater in bST-treated than control cows but neither bST treatment nor selection line influenced basal concentrations of glucose, non-esterified fatty acids (NEFA), beta-hydroxybutyrate, urea or creatinine. Treatment with bST produced a small increase in sensitivity of cows to the lipolytic effects of epinephrine and this effect was similar in both selection lines. HBI cows had greater circulating insulin levels following the glucose challenge than LBI cows but bST treatment did not affect the insulin response to exogenous glucose. Whereas bST treatment retarded the glycogenolytic response to epinephrine and the clearance of blood glucose in response to insulin in LBI cows, it had no effect on epinephrine-stimulated glycogenolysis, and caused enhanced glucose clearance in response to insulin, in HBI cows. Results are consistent with bST altering the homeorhetic control of metabolism but do not adequately explain the greater responsiveness of LBI cows to the galactopoietic effects of bST.

Summary: Over the years, ad‐hoc procedures were used for designing SNP arrays, but the procedures and strategies varied considerably case by case. Recently, a multiple‐objective, local optimization (MOLO) algorithm was proposed to select SNPs for SNP arrays, which maximizes the adjusted SNP information (E score) under multiple constraints, e.g. on MAF, uniformness of SNP locations (U score), the inclusion of obligatory SNPs and the number and size of gaps. In the MOLO, each chromosome is split into equally spaced segments and local optima are selected as the SNPs having the highest adjusted E score within each segment, conditional on the presence of obligatory SNPs. The computation of the adjusted E score, however, is empirical, and it does not scale well between the uniformness of SNP locations and SNP informativeness. In addition, the MOLO objective function does not accommodate the selection of uniformly distributed SNPs. In the present study, we proposed a unified local function for optimally selecting SNPs, as an amendment to the MOLO algorithm. This new local function takes scalable weights between the uniformness and informativeness of SNPs, which allows the selection of SNPs under varied scenarios. The results showed that the weighting between the U and the E scores led to a higher imputation concordance rate than the U score or E score alone. The results from the evaluation of six commercial bovine SNP chips further confirmed this conclusion. [ABSTRACT FROM AUTHOR]

Genetic marker effects and type of inheritance are estimated with poor precision when minor marker allele frequencies are low. A stable composite population (MARC II) was subjected to marker assisted selection for 2 yr to equalize CSN1S1 and TG genetic marker frequencies to evaluate the epistatic and pleiotropic effects of these markers on BW, reproduction, and first calf performance traits in replacement beef females (n = 171) managed under 2 postweaning development protocols. Traits evaluated on the heifers were birth BW, weaning BW, 11-mo BW, 12-mo BW, 13-mo BW, first breeding season pregnancy evaluation BW, first calving season BW, 11-mo puberty, 12-mo puberty, 13-mo puberty, first breeding season pregnancy, and first calf weaning rate. Additionally, heifer's first calf performance traits of ordinal calving date, first calf birth BW, and first calf weaning BW (with and without age adjustment) were analyzed. Selection to increase minor allele frequencies and balanced sampling across genotype classes enhanced the ability to detect all genetic effects except dominance dominance epistasis. The CSN1S1 TG genotype effect was significant (P < 0.05) for 11-mo BW and 12-mo BW and tended to be significant (P = 0.08) for 13-mo BW. Consistently, for all 3 traits, the most significant effect among epistatic CSN1S1 TG genotype effects was the CSN1S1 additive effect, with the G allele decreasing BW. There were no associations between CSN1S1 TG genotype and fertility related traits (P = 0.46) in this study. Additionally, there were no CSN1S1 TG genotype associations with first progeny performance traits (P = 0.14). The large effect of the CSN1S1 additive TG additive interaction on first calf weaning BW was imprecisely estimated, which may warrant further investigation. [ABSTRACT FROM AUTHOR]

Context: Genomic selection has been of increasing interest in the genetic improvement of Zebu cattle, particularly for quantitative traits that are difficult or expensive to measure, such as carcass traits and meat tenderness. The success of genomic selection depends on several factors, and at its core is the availability of single-nucleotide polymorphism (SNP) chips that are appropriately designed for Bos indicus cattle. However, the currently available commercial bovine SNP chips are mostly designed for Bos taurus cattle. There are two commercial Bos indicus SNP chips; namely, GeneSeek genomic profiler high-density Bos indicus (GGP-HD i) SNP chip and a low-density (LD) Bos indicus SNP chip (Z chip), but these two Bos indicus SNP chips were built with mixed contents of SNPs for Bos indicus and Bos taurus cattle, due to limited availability of genotype data from Bos indicus cattle. Aims: To develop a new GGP indicus 35 000 SNP chip specifically for Bos indicus cattle, which has a low cost, but high accuracy of imputation to Illumina BovineHD chips. Methods: The design of the chip consisted of 34 000 optimally selected SNPs, plus 1000 SNPs pre-reserved for those on the Y chromosome, 'causative' mutations for a variety of economically relevant traits, genetic health conditions and International Society for Animal Genetics globally recognised parentage markers for those breeds of cattle. Key results: The present results showed that this new indicus LD SNP chip had considerably increased minor allele frequencies in indicus breeds than the previous Z-chip. It demonstrated with high imputation accuracy to HD SNP genotypes in five indicus breeds, and with considerable predictability on 14 growth and reproduction traits in Nellore cattle. Conclusions: This new indicus LD chip represented a successful effort to leverage existing knowledge and genotype resources towards the public release of a cost-effective LD SNP chip specifically for Bos indicus cattle, which is expected to replace the previous GGP indicus LD chip and to supplement the existing GGP-HD i 80 000 SNP chip. Implications: A new SNP chip specifically designed for Bos indicus , with high power of imputation to Illumina BovineHD technology and with excellent coverage of the whole genome, is now available on the market for Bos indicus cattle, and Bos indicus and Bos taurus crosses. This work reports the development of a low-density DNA genetic markers (single-nucleotide polymorphism) panel specifically for Bos indicus cattle and their crosses, applied to identify the association of these markers with economically relevant traits and to the genomic selection, with high accuracy of genotype imputation to high-density single-nucleotide polymorphism chips. [ABSTRACT FROM AUTHOR]