DEGs associated with the gain main effect in the individual cohort and meta analyses. Genes are ordered by adjusted meta-P-value. The individual cohort cells for DEGs identified in the meta-analysis are colored according to the sign of their log2 fold change, where green indicates up-regulation and red indicates down-regulation in high gain. Genes with all gray cell indicate those that were excluded because they were also significant for the gain by intake interaction term. (XLSX 1224 kb)
The identification of genetic markers associated with complex traits that are expensive to record such as feed intake or feed efficiency would allow these traits to be included in selection programs. To identify large-effect QTL, we performed a series of genome-wide association studies and functional analyses using 50 K and 770 K SNP genotypes scored in 5,133 animals from 4 independent beef cattle populations (Cycle VII, Angus, Hereford and Simmental × Angus) with phenotypes for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake. A total of 5, 6, 11 and 10 significant QTL (defined as 1-Mb genome windows with Bonferroni-corrected P-value <0.05) were identified for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake, respectively. The identified QTL were population-specific and had little overlap across the 4 populations. The pleiotropic or closely linked QTL on BTA 7 at 23 Mb identified in the Angus population harbours a promising candidate gene ACSL6 (acyl-CoA synthetase long-chain family member 6), and was the largest effect QTL associated with dry matter intake and mid-test body weight explaining 10.39% and 14.25% of the additive genetic variance, respectively. Pleiotropic or closely linked QTL associated with average daily gain and mid-test body weight were detected on BTA 6 at 38 Mb and BTA 7 at 93 Mb confirming previous reports. No QTL for residual feed intake explained more than 2.5% of the additive genetic variance in any population. Marker-based estimates of heritability ranged from 0.21 to 0.49 for residual feed intake across the 4 populations. This GWAS study, which is the largest performed for feed efficiency and its component traits in beef cattle to date, identified several large-effect QTL that cumulatively explained a significant percentage of additive genetic variance within each population. Differences in the QTL identified among the different populations may be due to differences in power to detect QTL, environmental variation, or differences in the genetic architecture of trait variation among breeds. These results enhance our understanding of the biology of growth, feed intake and utilisation in beef cattle.
Twenty-five wether lambs (34 ± 0.9 kg) fitted with ruminal and abomasal infusion catheters were used in a completely randomized design to determine the effects of differing proportions of ruminal and abomasal casein infusion on N balance in lambs fed a high-concentrate diet (85% corn grain, 1.6% N; DM basis) for ad libitum intake. Wethers were infused with 0 (control) or 10.4 g/d of N from casein with ruminal:abomasal infusion ratios of 100:0, 67:33, 33:67, or 0:100% over a 14-d period. Feed, orts, feces, and urine were collected over the last 5 d. Total N intake and excretion were greater (P < 0.01) in lambs infused with casein than in controls; however, N retention did not differ in lambs infused with casein compared with controls, suggesting that N requirements were met without casein supplementation. Total N intake and total N excretion did not differ among casein infusion treatments. Urinary N excretion decreased linearly (P = 0.07) with decreasing ruminal infusion of casein. Site of casein infusion quadratically (P = 0.06) influenced N retained (g/d), with the greatest retention observed in the 33:67 ruminal:abomasal infusion treatment. Dry matter intake from feed decreased from 1,183 to 945 g/d (P = 0.02) in lambs infused with casein compared with controls, but apparently digested DM did not differ among treatments. These data indicate that decreasing the ruminal degradability of supplemental protein above that required to maximize N retention results in decreased urinary excretion of N without greatly affecting apparent diet digestion.
(A) Cohort and age of animals selected for this study. (B) Breed percentages of phenotypic quadrants for each cohort (HH = high gain, high intake; LH = low gain, high intake; LL = low gain, low intake; HL = high gain, low intake). (C) Rationale for exclusion of animals within each cohort. (D) Sequencing statistics. (XLSX 27 kb)
Feed costs comprise the majority of variable expenses in beef cattle systems making feed efficiency an important economic consideration within the beef industry. Due to the expense of recording individual feed-intake phenotypes, a genomic-enabled approach could be advantageous toward improving this economically relevant trait complex. A genome-wide association study (GWAS) was performed using 748 crossbred steers and heifers representing seven sire breeds with phenotypes for ADG and ADFI. Animals were genotyped with the BovineSNP50v2 BeadChip containing approximately 54,000 SNP. Both traits were analyzed using univariate SNP-based (BayesC) and haplotype-based (BayesIM) models and jointly using BayesIM to perform a bivariate GWAS. For BayesIM, a hidden Markov model (HMM) of haplotype segments of variable length was built where haplotypes were mapped to clusters based on local similarity. The estimated HMM was then used to assign haplotype cluster genotypes, instead of SNP genotypes, as latent covariates in a Bayesian mixture model. The number of haplotype clusters at each location was assumed to be either 8 (BayesIM8) or 16 (BayesIM16). A total of three univariate analyses for each trait and two bivariate analyses were performed. Posterior SD (PSD) for ADG were 0.28 (0.08), 0.37 (0.11), 0.37 (0.11), 0.35 (0.11), and 0.35 (0.12) for BayesC, BayesIM8, BayesIM16, BayesIM8 bivariate, and BayesIM16 bivariate, respectively. ADFI PSD were 0.30 (0.07), 0.44 (0.13), 0.42 (0.12), 0.38 (0.10), and 0.38 (0.10) for the same models. The top 1% of 1-Mb windows that explained the largest fraction of genetic variation in common between univariate SNP and haplotype models ranged from 24% to 40% and from 20% to 32% for ADG and ADFI, respectively. Spearmen rank correlations between molecular breeding values from SNP and haplotype-based models in the training data were similar for both traits (>0.96) suggesting that either model would lead to similar rankings of animals, although resolution of potential QTL appeared to be greater for BayesIM.
This study assessed the influence of cattle genotype and diet on the carriage and shedding of zoonotic bacterial pathogens and levels of generic Escherichia coli in feces and ruminal contents of beef cattle during the growing and finishing periods. Fifty-one steers of varying proportions of Brahman and MARC III [0 (15), ¼ (20), ½ (7), and ¾ Brahman (9)] genotypes were divided among 8 pens, such that each breed type was represented in each pen. Four pens each were assigned to 1 of 2 diets [100% chopped bromegrass hay or a diet composed primarily of corn silage (87%)] that were individually fed for a 119-d growing period, at which time the steers were switched to the same high-concentrate, corn-based finishing diet and fed to a target weight of 560 kg. Feces or ruminal fluid were collected and analyzed at alternating intervals of 14 d or less. Generic E. coli concentrations in feces or ruminal fluid did not differ (P > 0.10) by genotype or by growing diet in the growing or finishing periods. However, the concentrations in both feces and ruminal fluid increased in all cattle when switched to the same high-corn diet in the finishing period. There was no effect (P > 0.25) of diet or genotype during either period on E. coli O157 shedding in feces. Forty-one percent of the steers were positive for Campylobacter spp. at least once during the study, and repeated isolations of Campylobacter spp. from the same steer were common. These repeated isolations from the same animals may be responsible for the apparent diet (P = 0.05) and genotype effects (P = 0.02) on Campylobacter in feces in the finishing period. Cells bearing stx genes were detected frequently in both feces (22.5%) and ruminal fluid (19.6%). The number of stx-positive fecal samples was greater (P < 0.05) for ½ Brahman steers (42.9%) than for ¼ Brahman (25.0%) or ¾ Brahman steers (22.2%), but were not different compared with MARC III steers (38.3%). The greater feed consumption of ½ Brahman and MARC III steers may have resulted in greater starch passage into the colon, accompanied by an increase in fecal bacterial populations, which may have further improved the ability to detect stx genes in these cattle. There was no correlation between either ADG or daily DMI and the number of positive samples of E. coli O157, Campylobacter spp., or stx genes, which agrees with our current understanding that these microorganisms occur commonly in, and with no measurable detriment to, healthy cattle.
Within the past decade, the development and use of novel nutritional, genomic, and genetic improvement technologies has promoted and lead to great increases in the genetic propensity of beef cattle for a variety of traits including growth, carcass composition, and specifically feed efficiency. However, the optimization of feed efficiency has primarily focused on host genetics, management, and diet. The rumen and lower gastrointestinal tract (GIT) contain diverse microbial ecosystems that are essential for the host to digest plant material and regulate nutrient uptake and utilization, necessitating their examination to fully understand the microbial-associated interactions throughout the gut with production parameters. To assess the association of the microbial community with variation in feed efficiency, ADG, and average daily DMI (ADFI), we examined the microbial community of the GIT from steers differing in feed efficiency using deep 16S rRNA gene-based community profiling. Steers were selected from 2 contemporary groups and were ranked based on their standardized distance from the bivariate mean (ADG and ADFI), assuming a bivariate normal distribution with a calculated correlation between ADG and ADFI. Four steers with the greatest deviation within each Cartesian quadrant were sampled (n = 16/group; 2 groups). Bacterial 16S rRNA gene amplicons were sequenced from the GIT content using next-generation sequencing technology. Bacterial diversity and richness metrics revealed no differences among the quadrants. However, finer changes in the relative abundance of microbial populations and operational taxonomic units did reveal differences between feed efficiency groups (P < 0.05), including shifts in dominant phyla and functionally significant genera in several segments of the GIT such as Proteobacteria (P = 0.030) and Butyrivibrio (P = 0.019) in the jejunum. These studies suggest the GIT microbial community differs at the 16S level in cattle that vary in ADG, ADFI, and feed efficiency; however, it is not clear whether host factors are driving changes in the microbiome or changes in the microbiome are contributing to differences in feed efficiency.
Ghrelin is a gut peptide that when acylated is thought to stimulate appetite. Circulating ghrelin concentrations could potentially be used as a predictor of DMI in cattle. The objective of this experiment was to determine the association of circulating ghrelin concentrations with DMI and other production traits. Steers and heifers were fed a finishing diet, and individual intake was recorded for 84 d. Blood samples were collected via jugular venipuncture following the DMI and ADG measurement period. Plasma active ghrelin and total ghrelin were quantified using commercial RIA. Active ghrelin was not correlated to DMI (P=0.36), but when DMI was modeled using a multivariate analysis including plasma metabolites and sex, active ghrelin was shown to be positively associated with DMI (P<0.01) and accounted for 6.2% of the variation accounted for by the regression model (R2=0.33). Total ghrelin was negatively correlated to DMI (P<0.01), but was not significant in a multivariate regression analysis (P=0.13). The ratio of active:total ghrelin was positively associated with DMI (P<0.01) and accounted for 10.2% of the variation in the model (R2=0.35). Active ghrelin was positively associated with ADG (P<0.05), while total ghrelin was negatively associated with ADG (P<0.01), and the ratio of active:total ghrelin was positively associated with ADG (P<0.01). Active ghrelin was not associated with G:F (P=0.88), but total ghrelin concentrations were negatively associated with G:F (P<0.01) and accounted for 10.24% of the variation (R2=0.25). Heifers consumed less feed than steers (P<0.01), tended to have greater active ghrelin concentrations (P=0.06), and had greater total ghrelin concentrations than steers (P=0.04). Total ghrelin concentrations were not different between sire breeds (P=0.80), but active ghrelin concentrations and the ratio of active:total ghrelin differed between breeds (P<0.01), indicating that genetics have an effect on the amount and form of circulating ghrelin. Total ghrelin concentrations tended (P=0.08) to be correlated with HCW, but no other carcass characteristics were correlated with active or total ghrelin concentrations (P>0.10). Results indicated that ghrelin concentrations are associated with DMI in beef cattle and that there is genetic variation that leads to differences in the amount and form of circulating ghrelin which could contribute to variation observed in DMI of beef cattle.
