J. Anim Sci. 2008. 86:1-16. doi:10.2527/jas.2006-799
© 2008 American Society of Animal Science
Polymorphisms and haplotypes in the bovine neuropeptide Y, growth hormone receptor, ghrelin, insulin-like growth factor 2, and uncoupling proteins 2 and 3 genes and their associations with measures of growth, performance, feed efficiency, and carcass merit in beef cattle1
E. L. Sherman*,
J. D. Nkrumah*,
,
B. M. Murdoch*,
C. Li*,
,
Z. Wang*,
A. Fu* and
S. S. Moore*,2
* Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada;
and
Igenity Livestock Production Business Unit, Merial Ltd.; and and
Agriculture and Agri-Food Canada Research Centre, Lacombe, Alberta, T4L 1W1 Canada
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Abstract
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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.
Key Words: candidate gene • cattle • feed efficiency • carcass merit • haplotype • single nucleotide polymorphism
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INTRODUCTION
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The physiological regulation of intake, growth, and energy partitioning in animals is under the control of multiple genes, which may be important candidates for unraveling the genetic variation in economically relevant traits (ERT) in farm animals. Polymorphisms in these candidate genes that show association with specific ERT are useful quantitative trait nucleotides for marker-assisted selection. In the current study, we evaluated associations between SNP in several bovine genes with ERT.
The genes evaluated were 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), melano-cortin-4 receptor (MC4R), proopiomelanocortin (POMC), and GH. The NPY peptide functions as a central appetite stimulator and is important in feed intake and energy-balance control (Wynne et al., 2005
). The GHR protein is bound by GH resulting in the activation of hormonal systems involved in growth promotion (Breier, 1995; Phillips, 1995). Some SNP in the bovine GHR gene have been previously tested for associations with growth traits and meat production but were only found associated with IGF1 serum levels, carcass weight, lean cuts, carcass dressing percentage weights, and drip loss (Ge et al., 2003; Maj et al., 2004, 2006a, Di Stasio et al., 2005). Ghrelin is a GH-releasing peptide as well as an appetite stimulant (Kojima et al., 1999; Dickin et al., 2004; Wynne et al., 2005). The IGF2 gene is implicated in growth, metabolism, and BW regulation (DeChiara et al., 1990), and 1 SNP is associated with rib-eye area in beef cattle (Schmutz and Goodall, 2005). The UCP2 and UCP3 genes have also been implicated in metabolic efficiency (Erlanson-Albertsson, 2003).
The objectives of this study were to examine SNP or haplotypes in these genes for associations with growth, feed efficiency measures, and carcass merit in an experimental population of beef cattle.
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MATERIALS AND METHODS
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Animals and Data Collection
The animals used in the study were cared for according to the guideline of the Canadian Council on Animal Care (CCAC, 1993).
Performance, feed intake, and ultrasound measurements were available for 464 animals, and carcass measurements were available on 381 animals. These experimental animals were Continental x British hybrid beef steers sired by Angus, Charolais, or University of Alberta Hybrid bulls and have been previously described (Nkrumah et al., 2004, 2006). Briefly, the dams from the University of Alberta Kinsella ranch were composed of 3 composite lines: Beef synthetic 1, which is composed of approximately 33% Angus and Charolais, 20% Galloway, and the rest other beef breeds; Beef Synthetic 2, composed of approximately 60% Hereford and the rest other beef breeds; and Dairy x Beef synthetic, which was composed of 60% Holstein, Brown Swiss or Simmental, and 40% beef breed (mostly Angus and Charolais; Goonewardene et al., 2003). The sires include those from the Onefour Ranch at the Lethbridge Research Center of Agriculture and Agri-Food Canada and additional hybrid sires from the same lines as the dams. The sires of the animals used were determined using a panel of bovine microsatellite markers.
The data collection and test procedures have also been previously described (Nkrumah et al., 2004, 2006). Feed intake for each animal was measured using the GrowSafe automated feeding system (GrowSafe Systems Ltd., Airdrie, Alberta, Canada), which has been previously tested by Basarab et al. (2003). Animals were from 6 test groups in the GrowSafe system, 2 groups per year over 3 yr. During the test the animals had ad libitum access to feed and drinking water. The test diet is as described (Nkrumah et al., 2004, 2006). Raw GrowSafe data was recorded, and each animal’s daily feed intake was calculated using custom software (Basarab et al., 2003). Body weight measurements were taken every week throughout the testing time, and the final BW was taken on the final day of the efficiency testing.
Procedures for obtaining the carcass merit measurements have also been previously described (Nkrumah et al., 2004). Briefly, ultrasound measurements of back-fat at the 12th- to 13th-ribs, LM area (ULMA), and marbling score were taken every 28 d using an Aloka 500V real-time ultrasound with a 17-cm, 3.5-MHz linear array transducer (Overseas Monitor Corp. Ltd., Richmond, British Columbia, Canada) using procedures as previously described (Brethour, 1992). The final ultrasound measurements were predicted using a regression analysis and were used in the study. Standard industry carcass data was collected after a 24-h chill (–4°C) following slaughter.
Traits Studied
In total, 19 traits were studied. The ADG for each animal during the test was calculated as the linear regression coefficient of BW (kg) on time (d) using PROC REG of SAS (1999). Metabolic midpoint BW was calculated as the midpoint BW0.75. The average daily DMI over the 70-d test period was calculated from the total DMI multiplied by the ME content of the diet and divided by 10 to standardize it to 10 MJ of ME/kg of DMI. This standardized DMI for each animal was divided by the testing time to obtain average daily DMI. Feed conversion ratio (FCR) was calculated as the ratio of daily DMI to ADG. Partial efficiency of growth above maintenance (PEG) was calculated as the ratio of ADG to the difference of expected DMI for maintenance from average daily DMI. Expected DMI for maintenance was calculated according to NRC (1996; Brody, 1935; Arthur et al., 2001; Nkrumah et al., 2004). Residual feed intake (RFI) was calculated from the difference between actual DMI of the animal and the expected DMI (Arthur et al., 2001; Nkrumah et al., 2004). Final BW was the final BW of the animal during the feed intake test, and the slaughter BW was the BW at slaughter.
The Canadian beef carcass grading system was used for all the carcass trait evaluations (Agriculture Canada, 1992). Carcass weight was measured as the weight of both the left and right halves of each carcass. Carcass LM area was also measured. Carcass grade fat was measured at the 12th- to 13th-rib. Marbling score is a measure of the i.m. fat with a score of 1 to <2 units = trace marbling (Canada A quality grade), 2 to <3 units = slight marbling (Canada AA quality grade), 3 to <4 units = small to moderate marbling (Canada AAA quality grade), and 
4 units = slightly abundant marbling (Canada Prime). Lean meat yield (LMY) is an estimate of the saleable meat (Jones et al., 1984), and yield grade is the proportion of lean meat and is classified as follows: 1 = 59%; 2 = <59%; and 3 = 
54%. The overall means and SD of the traits analyzed in this study are listed in Table 1 and show there is ample variation within the population to assess the difference in the traits between the different genotype groups. Correlations between these traits have been previously evaluated (Nkrumah et al., 2004).
SNP Discovery and Genotyping
In total, 24 SNP were evaluated in this study. The SNP locations in each gene, the GenBank accession number, and the positions are listed in Table 2. Eighteen of the SNP were compiled and chosen from sequences of the selected candidate genes published in GenBank. Five of the SNP have not been previously reported in these sequences and were found through sequence analysis of our experimental animals. Briefly, sequences of the selected genes were obtained from GenBank and primers were constructed using Primer Express software (Applied Biosystems, Foster City, CA).. Genomic DNA (10 ng) extracted from blood samples using a standard high salt phenol/chloroform extraction method was amplified using standard PCR techniques. The product of the PCR was verified by agarose gel electrophoresis and cleaned with ExoSAP PCR clean up system (Amersham #US70996, Amersham Biosciences, Piscataway, NJ). Sequencing was performed on a Beckman CEQ 8000 Genetic Analysis System (Beckman Coulter, Fullerton, CA), and SNP were discovered by comparing sequences of a 12-animal panel.
The SNP genotyping was carried out using the Illumina GoldenGate assay (Oliphant et al., 2002) on the BeadStation system (Illumina Inc., San Diego CA). The GoldenGate assay allows genotyping of 1,536 SNP using 250 ng of genomic DNA. Briefly, the DNA was bound to 2 allele specific oligonucletides and 1 locus-specific oligonucleotide. After extension and ligation reactions, the allele and locus specific oligonucleotides were joined and washed. These loci specific products were then amplified by PCR using universal primers that were labeled with Cy3 and Cy5. This was followed by the hybridization to the Bead Chip and posthybridization washes. The Cy3 and Cy5 fluorescent signals emitted from the matrix were then read using Illumina BeadScan software by the Illumina system. The genotypes were then called using the Illumina GenCall software. The IGF2 SNP was also genotyped using standard RFLP techniques, as previously described (Goodall and Schmutz, 2003). These genotypes confirmed the results from the Illumina GoldenGate assay.
Statistical Analysis
Single nucleotide polymorphism allele frequency (Table 3) and Hardy-Weinberg equilibrium (data not shown) were estimated and tested using PROC ALLELE (SAS Inst. Inc., Cary, NC). All SNP were found to be in Hardy-Weinberg equilibrium except for NPY SNP3 (P < 0.05), a T to C substitution (accession No. AY491054). Associations between the SNP genotypes and traits were analyzed using a linear mixed model in SAS. The statistical analyses model included fixed effects of SNP genotype or haplotype, test year (3 levels), test group nested within year (6 levels), and breed of sire (Angus, Charolais, or hybrid); a linear covariate of age of animal on test; and random effects of sire and dam of animal. The additive and nonadditive genetic effects were also estimated in SAS according to Falconer and Mackay (1996). The average allele substitution effect was estimated by regressing the phenotype on the number of copies of 1 allele of a SNP using a mixed model in SAS.
False discovery rate (FDR) was calculated for the 24 SNP for each trait under the fixed genotype and allele substitution models using the formula FDR = mP(i)/, where m is the total number of tests and P(i) is the P-value at rank i when the P-values are ranked from least to greatest (Benjamini and Hochberg, 1995; Weller et al., 1998).
Haplotypes were constructed for the genes that contained more than 1 SNP with associations using PROC HAPLOTYPE in SAS Genetics. This program uses the maximum likelihood estimates from an EM algorithm to generate haplotype frequencies. Based on these frequencies, probabilities that an animal contains 0, 1, or 2 copies of a haplotype are assigned to individuals. The haplotypes obtained from SAS were very similar (<1% difference) to haplotypes obtained from the program Haplore (Zhang et al., 2005) that uses parental genotypic information to create haplotypes. Because we did not have dam genotypes and 13% of our animals did not have sire genotypes either, SAS was chosen because it can estimate haplotypes for animals without parental genotypes. Haplotypes were constructed only from SNP that showed associations for the NPY, GHR, and both UCP genes. The UCP SNP were included in 1 haplotype across both genes because UCP2 and UCP3 are separated by <200 kb, with UCP3 being upstream of UCP2 (Stone et al., 1999). Haplotypes with a frequency <0.01 were not included in the analysis because of the small number of animals with those haplotypes. Haplotypes in a single gene were analyzed in a single test using a regression of the traits against the haplotype probabilities, and the estimates are based on the deviation from one of the haplotypes (Appendix Tables 2, 4, and 8).
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RESULTS AND DISCUSSION
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In the current study, several genes were chosen based on their biological functions to be evaluated for association between polymorphisms and growth, performance, and carcass traits. These genes were chosen because they have been shown to be involved in the regulation of appetite, metabolism, or growth (Renaville et al., 2002; Erlanson-Albertsson, 2003; Wynne et al., 2005). These functions seemed the most likely to have an effect on the BW, gain, efficiency, and carcass merit of the animals. Single nucleotide polymorphisms within these genes were examined for effects on these traits in beef cattle. Thirteen of the SNP did not show associations with the traits (P < 0.05) and will not be further discussed. These SNP are GHR SNP3, GHR SNP4, GHR SNP5, CRH SNP1, CRH SNP2, POMC SNP1, POMC SNP2, GH SNP, MC4R SNP, CART SNP, UCP3 SNP1, UCP3 SNP3, and UCP2 SNP2. The allele substitution effects and fixed effects for each gene are shown in Table 4 to Table 9 along with haplotype associations for NPY, GHR, and UCP, which had more than 1 SNP with associations. The estimates of the LS means and haplotypes are shown in the Appendix.
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Table 4. Allele substitution effect estimates and fixed effect estimates of additive and dominance effects of SNP in neuropeptide Y (NPY) gene in beef steers
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Table 9. False discovery rate calculated for 24-SNP test for the fixed genotyped and allele substitution model within each trait at the P < 0.05 level
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Neuropeptide Y
Three SNP in the bovine NPY gene, 2 A/G SNP, and 1 T/C SNP (Table 2
) that were previously identified (Thue and Buchanan, 2004) were evaluated for associations with growth, performance, and feed efficiency in beef steers. These SNP were first analyzed for single marker associations with the traits (Table 4). The 3 SNP showed significant effects on growth and BW (P < 0.05) with heavier animals containing the A allele of SNP1 and SNP2 and the C allele of SNP3. The strongest effect on ADG was seen for SNP3 (P = 0.021), but the haplotypes of all 3 SNP improved the association (P = 0.019). A similar trend was also seen for the BW measurements, with the haplotype improving the association. The 3 SNP in NPY also showed a trend to affect feed efficiency measured as FCR, but only SNP2 showed allele substitution effects of P < 0.05 for RFI and PEG. None of the SNP showed associations with DMI. The effects of SNP2 on BW and feed efficiency measures were all additive with no significant dominance effects. These effects could not be measured for SNP1 and SNP3 due to the low minor allele frequency and therefore a low number of homozygous animals for that allele. These SNP also affect carcass merit. The strongest effect was seen for marbling with SNP 1 (P = 0.007), which was also seen in the haplotypes. There was also a trend for these SNP to affect backfat (P < 0.1). The NPY SNP2 also showed allele substitution effects for LM area (P < 0.1).
Injections of NPY have been shown to lower the levels of growth hormone, supporting a role in growth (Pierroz et al., 1996). This is consistent with animals containing the G-G-T haplotype being smaller and having a lower ADG (Appendix Table 2). As well, NPY SNP2 showed consistent effects on feed efficiency, with the most efficient animals being GG homozygous animals. This is consistent with the role of NPY in energy expenditure (Woods et al., 1998) but not with the role as an appetite stimulant as there was no association with DMI. The NPY SNP also had constant effects on marbling, which is consistent with these results. A study in humans also concluded that a polymorphism in NPY affected obesity in males and was associated with body mass index and increased leptin levels (van Rossum et al., 2006). Injections of NPY in sheep also cause an increase in leptin mRNA (Dyer et al., 1997) supporting a role for NPY in fatness in mammals. These SNP are in intron 2 of the NPY gene, so it is likely they are only linked to a different causative polymorphism. There have been QTL identified on BTA4 in the region of NPY for marbling and LM area (Mizoshita et al., 2004), and further up the chromosome regions are suggestive QTL for ADG and carcass weight (Casas et al., 2001). It is possible that these SNP are linked to these QTL.
Growth Hormone Receptor
The effects of all the GHR SNP genotypes were evaluated but only GHR SNP2 (A/G SNP accession No. AY643087, position 300, Maj et al., 2006b) and GHR SNP1 (A/G SNP accession No. AF126288, position 149, Ge et al., 1999b) showed associations with the traits studied (Table 5). Body weight, ADG, DMI, and FCR showed associations with SNP2 (P < 0.05) and also had a strong dominance effect of the A allele over the G allele. These effects of GHR SNP2 on growth are consistent with the physiological role of the GHR in the regulation of growth through its activation by growth hormone (Breier, 1995; Phillips, 1995). This SNP also had an allele substitution effect on RFI (0.14 kg/d) and PEG but not the dominance effect seen for BW and growth. The GG animals that were larger and ate more also had the highest RFI and therefore were the least efficient. This SNP mostly affects growth, but because the GH has many other functions besides growth promotion including the regulation of overall protein turnover, fat synthesis, fatty acid oxidation, and stimulation of fatty acid mobilization from body adipose tissues (Istasse et al., 1990; Hayden et al., 1993; Breier, 1995; Renaville et al., 2002), it is possible this SNP is also affecting the efficiency of growth. It is not clear how this mutation causes these effects because the SNP is intronic and therefore does not cause any amino acid changes to the GHR protein.
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Table 5. Allele substitution effect estimates and fixed effect estimates of additive and dominance effects of 2 growth hormone receptor SNP in beef steers
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The GHR SNP1 also showed similar associations as SNP2, but they were not as strong and the haplotypes between SNP1 and SNP2 further reduced the associations. This SNP was previously identified in the promoter region (Ge et al., 1999b) and was analyzed for associations with growth traits and IGF1 blood serum levels (Ge et al., 2003). Although Ge et al. (2003) did not report associations with growth traits, associations were found with IGF1 concentration, indicating an influence on growth. This SNP has also been tested for associations with meat production traits (Maj et al., 2004, 2006a). They saw associations with DMI, carcass weight and percentage of lean in valuable cuts which we did not see. They also saw associations with other carcass merit traits which we did not evaluate in the present study. Because the associations in the current study were not very strong, and this SNP has been shown to have no effect on promoter activity in vitro (Zhou and Jiang, 2005), it is probable that the weakened associations with this SNP are due the linkage with GHR SNP2.
Ghrelin
The A/G SNP found in GHRL (accession No. AY455980) was also evaluated for its affects on growth, feed efficiency, and carcass merit. The GHRL SNP showed minor associations with the feed efficiency traits (P < 0.10) but not with the BW of the animals (Table 6). The frequency of the G allele was low in the population (0.12), and so there were only 8 animals with the GG genotype. It was not possible to examine dominance effects of the alleles, but there was an allele substitution effect of –0.18 kg/d on RFI (P = 0.083). A role for GHRL in feed efficiency is very likely because it has been shown to play roles in determining if fat or carbohydrates are the metabolic substrate used for maintenance of energy balance as seen in GHRL knockout mice (Wortley et al., 2004). This SNP also showed minor associations with carcass merit including positive effects on LMY, yield grade, and quality grade in animals that were AA homozygous (Appendix Table 5). This effect on yield of the animals is consistent with the role of GHRL in promoting the release of GH and therefore to initiate growth (Kojima et al., 1999). In addition to the role of GHRL in GH release, it has also been shown to play important roles in the stimulation of appetite and feeding activity through interactions with peptides such as NPY (Jarkovska et al., 2004). Although this SNP is in intron 3 of the GHRL gene and is therefore not amino acid changing, it could be in linkage disequilibrium with another SNP in the GHRL gene with greater effects on the traits that could explain why the effects seen for this SNP are only suggestive.
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Table 6. Allele substitution effect estimates and fixed effect estimates of additive and dominance effects of a SNP in the ghrelin gene in beef steers
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Insulin-like Growth Factor 2
In this study, a SNP in IGF2 was analyzed for effects on growth and performance. This SNP has been previously reported (Goodall and Schmutz, 2003), and an association study has shown that animals with the C allele have larger rib-eye area but have lower birth weights (Schmutz and Goodall, 2005). In our study, marginal effects on BW measurements were found (allele substitution effect of 6.67 kg; Table 7). This agrees with a study in humans where several allelic variants were associated with BW and body mass index (Gaunt et al., 2001) and a SNP in pigs in intron 3 that has a major effect on muscle growth (Van Laere et al., 2003). We also found that animals with the TT genotype had a greater ADG and lower FCR and that there was a significant dominance effect for FCR and ADG (P < 0.01). These results are consistent with a study in humans that found IGF2 levels associated with BW gain (Sandhu et al., 2003). Because these TT animals were not significantly different for RFI or PEG from the CC or CT animals, it is doubtful that there is an effect on feed efficiency from the IGF2 SNP and that the lower FCR value is probably only due to the higher ADG in these animals. In our study, we also saw associations with rib-eye area (ULMA) but found that TT homozygous animals had larger ULMA, which is not consistent with the previous study (Schmutz and Goodall, 2005) where the C allele has been shown to positively affect rib-eye area. This inconsistency could be due to the low number of animals with the TT genotype in our study (n = 11) although Schmutz and Goodall (2005) were only comparing CC and CT animals in 1 of 2 groups of cattle in the study. It is also possible that this is not the causative SNP and that the phase of the SNP with the causative mutation in the 2 different studies is opposite. Schmutz and Goodall (2005) also reported a decrease in fatness and marbling in CC animals over CT animals, which is consistent with our results. It is not clear how the effects of this SNP are produced because exon 2 of IGF2 is not translated into the final protein. It is possible that it may affect other properties of the transcript (Schmutz and Goodall, 2005) or that the SNP is linked to a different causative SNP. However, there have been QTL identified on BTA 29 close to IGF2 for HCW and marbling (MacNeil and Grosz, 2002) as well as weaning weight and HCW (Casas et al., 2004), and therefore it is likely that this SNP is linked to these QTL.
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Table 7. Allele substitution effect estimates and fixed effect estimates of additive and dominance effects of a SNP in insulin-like growth factor 2 gene in beef steers
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Uncoupling Proteins
Seven SNP in total were analyzed across UCP2 and UCP3. These SNP and their locations are listed in Table 2. Three of the SNP in UCP2 (UCP2 SNP1, SNP3, and SNP4) were in almost complete LD and showed very similar associations so only the results from UCP2 SNP3 are shown. In UCP3 only SNP2, which was previously reported (Stone et al., 1999), showed associations with traits studied. It is an A/G substitution and was associated with ADG (P = 0.025), FCR, and PEG (P < 0.01), although a trend was seen for RFI that is consistent with PEG and FCR although not statistically significant (Table 8). These effects on feed efficiency are consistent with the functions of the gene. The UCP3, which is expressed in skeletal muscle (Boss et al., 1998), has been shown to have a role in the efficiency of ATP production as UCP3 knock-out mice have higher ATP production (Vidal-Puig et al., 2000). The UCP3 could also have a role as a fatty acid cycler which would control excess energy accumulation as fat (Kontani et al., 2002). The UCP2 SNP3 SNP did not show the same effects on feed efficiency but did show a small effect on BW (P < 0.10) with a dominance effect of the A allele (P < 0.05). These associations with BW are in agreement with the associations found between microsatellite markers near UCP2/UCP3 in humans with body mass (Bouchard et al., 1997). The UCP2 SNP3 also showed effects on yield grade, LMY, and backfat with the A allele decreasing the LMY (P = 0.037) and increasing backfat (0.66 cm2, P = 0.44). This was consistent across the other backfat measures (ultrasound backfat at the 12th- to 13th-ribs, grade fat) and a similar pattern was seen for the G allele of UCP3 SNP2 although it was not as strong except for marbling (P = 0.043). These results are in the agreement with the possible roles of these genes. Uncoupling protein 2 has been shown to regulate insulin secretion, and it is upregulated by a high-fat diet, suggesting UCP2 to be important for determining basal metabolic rate and possibly resistance to obesity (Matsuda et al., 1997; Chan et al., 1999; Erlanson-Albertsson, 2003). Also, markers encompassing the location of UCP2/UCP3 in humans have been associated with resting metabolic rate, body mass, body fatness, and fat mass (Bouchard et al., 1997). Haplotypes between these 2 SNP did not reveal any further associations and did not improve above the single SNP associations. Even though these SNP do not cause amino acid changes (UCP3 SNP2 is intronic and UCP2 SNP3 is a silent mutation) the functions show that UCP could be affecting fatness in these animals and there could be more SNP within these genes that are not yet identified.
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Table 8. Allele substitution effect estimates and fixed effect estimates of additive and dominance effects of 2 uncoupling protein 2 and uncoupling protein 3 SNP in beef steers
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Overall, this study evaluated 24 SNP across 19 traits and detected 25 significant marker-by-trait combinations at P < 0.05 based on the allele substitution model and 30 significant marker-by-trait combinations at P < 0.05 based on the fixed genotype effects model. Each trait was treated as a separate experiment and the average FDR was calculated at P < 0.05 based on 24 SNP tests performed and are shown in Table 9. The average FDR across the traits were 26% for the fixed genotype model and 52% for the allele substitution model. The relatively low number of animals with phenotypic information used in the current study may have a considerable impact on the P-values, and thus the results of the FDR calculation based on the approach by Benjamini and Hochberg (1995) as used in this study. As well, the candidate genes were specifically chosen for their known functions related to the performance of the traits, which provides additional support to the findings in this study. The true biological significance of these SNP can only be verified through subsequent validation in an independent animal resource population.
In summary, associations between genetic polymorphisms and carcass merit, growth, and feed efficiency traits in beef cattle are an important step into understanding the genetics of complex traits that are commercially important. Although the results of this study suggest that these genes influence economically important traits, the mechanisms of these genetic changes remain unclear because most of them do not cause amino acid changes. It is possible that they affect the transcription of the genes, splicing, mRNA stability, or even translation. As more knowledge is available on how noncoding sequences affect gene function, it may become apparent how these SNP are contributing to variation in these traits. It is also possible that they are linked to other causative mutations that have not been found yet. In the cases of IGF2 and NPY where QTL have been identified near the gene, it is likely that these SNP are linked to the QTL. It will be very important for these SNP to be verified in other cattle populations to confirm these associations before they can be applied to marker-assisted selection. If the effects are verified, application of these results in marker-assisted selection will improve breeding decisions and have the potential to increase the rate of genetic change in beef cattle, especially in traits such as feed efficiency that are difficult to measure.
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APPENDIX
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Footnotes
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1 This research is a part of the Bovine Genome Project supported by the Alberta Agriculture Research Institute (2002L030R), Alberta Beef Producers (2000AB364), and Beef Cattle Research Council (2002L030R) and by support to Laura Sherman from Alberta Ingenuity and the Natural Sciences and Engineering Research Council. 
2 Corresponding author: stephen.moore{at}ualberta.ca
Received for publication December 5, 2006.
Accepted for publication August 27, 2007.
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Abstract
INTRODUCTION
