Genes that regulate metabolism and energy partitioning have the potential to influence economically important traits in farm animals, as do polymorphisms within these genes. In the current study, SNP in the bovine neuropeptide Y (NPY), growth hormone receptor (GHR), ghrelin (GHRL), uncoupling proteins 2 and 3 (UCP2 and UCP3), IGF2, corticotrophin-releasing hormone (CRH), cocaine and amphetamine regulated transcript (CART), melanocortin-4 receptor (MC4R), proopiomelanocortin (POMC), and GH genes were evaluated for associations with growth, feed efficiency, and carcass merit in beef steers. In total, 24 SNP were evaluated for associations with these traits and haplotypes were constructed within each gene when 2 or more SNP showed significant associations. An A/G SNP located in intron 4 of the GHR gene had the largest effects on BW of the animals (dominance effect P < 0.01) and feed efficiency (allele substitution effect P < 0.05). Another A/G SNP located in the promoter region of GHR had similar effects but the haplotypes of these 2 SNP reduced the effects of the SNP located in intron 4. Three SNP in the NPY gene showed associations to marbling (P < 0.001) as well as with ADG, BW, and feed conversion ratio (FCR; P < 0.05). The combination of these 3 SNP into haplotypes generally improved the association or had a similar scale of association as each single SNP. Only 1 SNP in UCP3, an A/G SNP in intron 3, was associated with ADG (P = 0.025), partial efficiency of growth, and FCR (P < 0.01). Three SNP in UCP2 gene were in almost complete linkage disequilibrium and showed associations with lean meat yield, yield grade, DMI, and BW (P < 0.05). Haplo-types between the SNP in UCP3 and UCP2 generally reduced the associations seen individually in each SNP. An A/G SNP in the GHRL gene tended to show effects on residual feed intake, FCR, and partial efficiency of growth (P < 0.10). The IGF2 SNP most strongly affected LM area (P < 0.01), back fat, ADG, and FCR (P < 0.05). The SNP in the CART, MC4R, POMC, GH, and CRH genes did not show associations at P < 0.05 with any of the traits. Although most of the SNP that showed associations do not cause amino acid changes, these SNP could be linked to other yet to be detected causative mutations or nearby QTL. It will be very important to verify these results in other cattle populations.

Feed intake and feed efficiency of beef cattle are economically relevant traits. The study was conducted to identify QTL for feed intake and feed efficiency of beef cattle by using genotype information from 100 microsatellite markers and 355 SNP genotyped across 400 progeny of 20 Angus, Charolais, or Alberta Hybrid bulls. Traits analyzed include feedlot ADG, daily DMI, feed-to-gain ratio [F:G, which is the reciprocal of the efficiency of gain (G:F)], and residual feed intake (RFI). A mixed model with sire as random and QTL effects as fixed was used to generate an F-statistic profile across and within families for each trait along each chromosome, followed by empirical permutation tests to determine significance thresholds for QTL detection. Putative QTL for ADG (chromosome-wise P < 0.05) were detected across families on chromosomes 5 (130 cM), 6 (42 cM), 7 (84 cM), 11 (20 cM), 14 (74 cM), 16 (22 cM), 17 (9 cM), 18 (46 cM), 19 (53 cM), and 28 (23 cM). For DMI, putative QTL that exceeded the chromosome-wise P < 0.05 threshold were detected on chromosomes 1 (93 cM), 3 (123 cM), 15 (31 cM), 17 (81 cM), 18 (49 cM), 20 (56 cM), and 26 (69 cM) in the across-family analyses. Putative across-family QTL influencing F:G that exceeded the chromosome-wise P < 0.05 threshold were detected on chromosomes 3 (62 cM), 5 (129 cM), 7 (27 cM), 11 (16 cM), 16 (30 cM), 17 (81 cM), 22 (72 cM), 24 (55 cM), and 28 (24 cM). Putative QTL influencing RFI that exceeded the chromosome-wise P < 0.05 threshold were detected on chromosomes 1 (90 cM), 5 (129 cM), 7 (22 cM), 8 (80 cM), 12 (89 cM), 16 (41 cM), 17 (19 cM), and 26 (48 cM) in the across-family analyses. In addition, a total of 4, 6, 1, and 8 chromosomes showed suggestive evidence (chromosome-wise, P < 0.10) for putative ADG, DMI, F:G, and RFI QTL, respectively. Most of the QTL detected across families were also detected within families, although the locations across families were not necessarily the locations within families, which is likely because of differences among families in marker informativeness for the different linkage groups. The locations and direction of some of the QTL effects reported in this study suggest potentially favorable pleiotropic effects for the underlying genes. Further studies will be required to confirm these QTL in other populations so that they can be fine-mapped for potential applications in marker-assisted selection and management of beef cattle.

Feed intake and feed efficiency are economically important traits in beef cattle because feed is the greatest variable cost in production. Feed efficiency can be measured as feed conversion ratio (FCR, intake per unit gain) or residual feed intake (RFI, measured as DMI corrected for BW and growth rate, and sometimes a measure of body composition, usually carcass fatness, RFI(bf)). The goal of this study was to fine map QTL for these traits in beef cattle using 2,194 markers on 24 autosomes. The animals used were from 20 half-sib families originating from Angus, Charolais, and University of Alberta Hybrid bulls. A mixed model with random sire and fixed QTL effect nested within sire was used to test each location (cM) along the chromosomes. Threshold levels were determined at the chromosome and genome levels using 20,000 permutations. In total, 4 QTL exceeded the genome-wise threshold of P < 0.001, 3 exceeded at P < 0.01, 17 at P < 0.05, and 30 achieved significance at the chromosome-wise threshold level (at least P < 0.05). No QTL were detected on BTA 8, 16, and 27 above the 5% chromosome-wise significance threshold for any of the traits. Nineteen chromosomes contained RFI QTL significant at the chromosome-wise level. The RFI(bf) QTL results were generally similar to those of RFI, the positions being similar, but occasionally differing in the level of significance. Compared with RFI, fewer QTL were detected for both FCR and DMI, 12 and 4 QTL, respectively, at the genome-wise thresholds. Some chromosomes contained FCR QTL, but not RFI QTL, but all DMI QTL were on chromosomes where RFI QTL were detected. The most significant QTL for RFI was located on BTA 3 at 82 cM (P = 7.60 x 10(-5)), for FCR on BTA 24 at 59 cM (P = 0.0002), and for DMI on BTA 7 at 54 cM (P = 1.38 x 10(-5)). The RFI QTL that showed the most consistent results with previous RFI QTL mapping studies were on BTA 1, 7, 18, and 19. The identification of these QTL provides a starting point to identify genes affecting feed intake and efficiency for use in marker-assisted selection and management.

Feed provision is one of the greatest costs of beef production and, with the increasing costs of feed, will remain so for the foreseeable future. Improvement in efficiency has the potential to not only increase profits for cattle producers, but also to decrease the environmental footprint of beef cattle production. Both are important in addressing the challenges of increasing feed costs and land pressure. Residual feed intake (RFI) has increasingly become the measure of choice when evaluating feed efficiency in beef cattle, especially because it is independent of growth and BW. The main inhibitor to adoption of RFI remains the cost and technical difficulty in measuring the trait. This makes RFI a prime candidate for marker-assisted selection because the trait is moderately heritable and DNA or other predictive markers could be used in selection schemes. Although multiple markers have been described over several studies, no major gene affecting RFI has been found. However, a combination of genetic markers, when examined jointly, can explain a large proportion of the genetic variation. Two main barriers remain before full adoption of markers for genetic evaluation and marker-assisted selection can be implemented. First, the genetic interaction of genes affecting RFI on other traits is, as yet, not fully understood. Second the numbers of animals with high quality estimates of RFI remains small. However, current developments indicate that these challenges will soon be overcome.

Feed intake and efficiency are economically important traits because feed is the greatest variable cost in beef production. Feed efficiency can be measured as residual feed intake (RFI), which is the difference between actual DMI of an animal and the expected DMI based on its BW and growth rate. Feed conversion ratio (FCR) is the inverse of gross feed efficiency and is the ratio of DMI to ADG. A total of 2,633 SNP across the 29 bovine autosomes were analyzed in 464 steers sired by Angus, Charolais, or Alberta Hybrid bulls for associations with RFI. A total of 150 SNP were associated with RFI at P < 0.05 of which 23 were significant at P < 0.01. Nine of the SNP pairs show high linkage disequilibrium (r(2) > 0.80), so only 1 of the SNP pairs was used in further multiple-marker analyses. Two methods were used to create a panel of SNP that were maximally informative for RFI based on the data. In the first method, 141 unique SNP were combined in a single multivariate model and a backward elimination model was used to drop SNP until all SNP left in the model were significant at P < 0.05. The SNP had greater effects when combined in the multivariate model than when tested individually. In the second method, the estimates from the 141 SNP were used to create a sequential molecular breeding value (MBV) according to the compound covariate prediction (CCP) procedure. The sequential MBV was built by adding the estimated effects one at a time, but only keeping SNP effects in the sequential MBV if the test statistic and the proportion of variance explained were improved. Predictabilities of the 2 methods were compared by regressing RFI on a final MBV created from SNP that remained in each analytical model. The MBV from the compound covariate prediction model produced an r(2) of 0.497, whereas the multivariate model MBV had a decreased r(2) of 0.416. The significant SNP were also tested for associations with DMI and FCR. The SNP showed different combinations of associations with the 4 traits, including some that were only associated with RFI. About 9.5% of the SNP from the 2 models were within 5 cM of previously identified RFI QTL and pinpoint areas to further explore for positional candidate genes. In conclusion, this study has identified a panel of SNP with significant effects on RFI that need to be validated in an independent population and provides continued progress toward selecting markers for use in marker-assisted selection for feed efficiency in beef cattle.