Meat Science 105 (2015) 57–62

Contents lists available at ScienceDirect

Meat Science journal homepage: www.elsevier.com/locate/meatsci

Effects of genetic variants in the promoter region of the bovine adiponectin (ADIPOQ) gene on marbling of Hanwoo beef cattle Yoonjeong Choi a, Michael E. Davis b, Hoyoung Chung a,⁎ a b

Animal Genomics Bioinformatics Division, National Institute of Animal Science, Suwon, 441–701, Republic of Korea The Ohio State University, Department of Animal Science, Columbus OH 43210, USA

a r t i c l e

i n f o

Article history: Received 20 March 2014 Received in revised form 19 December 2014 Accepted 24 February 2015 Available online 3 March 2015 Keywords: ADIPOQ Hanwoo cattle Polymorphism Carcass trait

a b s t r a c t This study aimed to verify genetic effects of the bovine adiponectin (ADIPOQ) gene on carcass traits of Hanwoo cattle. The measured carcass traits were marbling score (MAR), backfat thickness (BFT), loineye area (LEA), and carcass weight (CAW). Selection of primers was based on the bovine ADIPOQ sequence, and the analysis amplified approximately 267 and 333 bp genomic segments, including 67 bp of insertions in the promoter region. Sequencing analysis confirmed genetic variants (g.81966235C N T, g.81966377 T N C, and g.81966364D N I) that showed significant effects on MAR. The present results suggest that the identified SNPs are useful genetic markers for the improvement of carcass traits in Hanwoo cattle. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction Many studies have tried to find significant associations between quantitative trait loci (QTL) and genetic markers identified from the whole genome and focused on finding causative genetic variants related to production traits of beef cattle (Casas et al., 2000; Davis et al., 1998; Gao et al., 2007; Li et al., 2004). Carcass, fat, and meat quality traits are critical factors for determination of quality grades of meat products in the commercial beef industry. If genetic variants explain phenotypic variations of QTLs, the utilization of genetic markers in marker assisted selection (MAS) programs should be a potential candidate approach. However, a limited number of genetic markers, which often explain a relatively small proportion of the genetic variation for QTLs (Dekkers, 2004), have been developed for carcass and meat quality traits in beef. As a member of the adipocytokine family, ADIPOQ, which is the most abundant protein secreted by white adipose tissue in mammalian species, influences lipogenesis, glucose genesis, insulin sensitivity, inflammatory processes, and cardiovascular functions (Pineiro et al., 2005; Scherer, Williams, Fogliano, Baldini, & Lodish, 1995; Yokota et al., 2002). ADIPOQ, a signaling molecule with 247 amino acids, has either a full-length or a globular form of proteins according to the proteolytic cleavages with disulphide bonds (Yokota et al., 2002). The full-length form tends to decrease glucose, whereas the globular form stimulates oxidation in muscle. Recent studies argued that ADIPOQ, which is also produced in brown adipose tissue (Kadowaki & Yamauchi, 2005; Morsci, Sellner, Schnabel, & Taylor, 2006), enhances fatty acid oxidation ⁎ Corresponding author. Tel.: +82 31 290 1596; fax: +82 31 290 1796. E-mail address: [email protected] (H. Chung).

http://dx.doi.org/10.1016/j.meatsci.2015.02.014 0309-1740/© 2015 Elsevier Ltd. All rights reserved.

and associates with fatty acid binding proteins (Dall’Olio, Roberta, Buttazzoni, Zambonelli, & Vincenzo, 2009; Wei et al., 2013). The functional roles described above are related to fat mechanisms, and a human study suggests that ADIOPQ is associated with obesity and diabetes (Hsueh et al., 2003). Another study also revealed significant functional roles of ADIPOQ, demonstrating that the expression levels are associated with obesity in humans (Kubota et al., 2002). In addition, human medical science tried to show phenotypic associations of obesity, diabetic susceptibility, and metabolic phenotypes with the genetic variations of ADIPOQ (Melistas et al., 2009; Szopa et al., 2009). In contrast to human studies, analyses of animal data focused on finding associations of carcass traits with genetic variants in genomic regions of ADIPOQ. As a result, QTL analyses regarding loin eye area (LEA) and back-fat thickness (BFT) in cattle have shown several genetic variants of the ADIPOQ gene in Angus, Korean, and Chinese cattle (Morsci et al., 2006; Shin & Chung, 2013; Zhang et al., 2009). Recent evidence suggests that secretion of ADIPOQ is negatively correlated with adipose tissue mass (Kadowaki & Yamauchi, 2005) and is detected in skeletal muscle (Morsci et al., 2006), indicating that ADIPOQ is correlated with the regulation of lipid and carbohydrate metabolism. In addition, a study found that ADIPOQ influences yield grade and weight traits in cattle (Morsci et al., 2006). Location of the bovine ADIPOQ gene was revealed to be on bovine chromosome 1 (BTA1) near QTLs for marbling scores (MAR), meat quality grade, and LEA (Cai et al., 2004). Polymorphisms in the ADIPOQ gene in pigs were also reported to be associated with fatty acid binding protein (FABP4), fat deposition, carcass traits (Dai et al., 2006; Dall’Olio et al., 2009; Wei et al., 2013), and reproductive traits (Houde, Murphy, Mathieu, Bordignon, & Palin, 2008).

58

Y. Choi et al. / Meat Science 105 (2015) 57–62

The abundant marbling found in Hanwoo cattle has become an important factor influencing the quality of meat products in commercial beef markets. In general, studies focused on associations between genetic variants and fat traits using well-constructed Hanwoo populations that made it easy to trace allele effects. However, it is necessary to verify marker effects in random beef populations. If marker effects are confirmed, the alleles may be used as standardized molecular markers to maximize production rates of herds. Therefore, the present analysis tried to find significant associations based on genetic variants of ADIPOQ for various carcass traits using a random beef population from the entire nation without considering paternal or maternal genetic effects. 2. Materials and methods 2.1. Animals and Carcass traits The ethics and welfare committee of the National Institute of Animal Science (NIAS) approved this experiment. A total of 1,954 Hanwoo cattle were randomly collected from the entire nation with help of the Nonghyup Hanwoo Cooperation in Korea during 2012 and 2013. Meat samples from Hanwoo cattle that were registered in the national database were randomly collected from 9 packing facilities of Korean Animal Products Evaluation (KAPE), which is an official Korean grader for meat quality grades. The meat quality of Hanwoo cattle was based on a grading system (http://www.ekape.or.kr/view/eng/system/beef.asp) from KAPE. The data collected by KAPE were carcass weight (CAW, kg), loineye area (LEA, cm2), backfat thickness (BFT, cm), and marbling score (MAR, ranged from 1 [poor] to 9). Approximately 5 g of muscle samples were collected from the longissimus thoracis muscle around the 6th rib 24 hours after slaughter, and stored at −70 °C. 2.2. Genomic DNA preparation For the extraction of genomic DNA, approximately 1 gram of muscle sample was used. After chopping the samples briefly, the pieces were placed into a tube with an extraction buffer, and genomic DNAs were extracted using a commercial kit (Wizard DNA extraction kit, Promega) according to the manufacturer’s guidelines. DNA quantity and purity (A260/A280 ratio) for each sample were assessed using the NanoDrop 1000 spectrophotometer (Thermo Scientific, USA), and the genomic DNAs were stored in a −70 °C freezer until genotyping. 2.3. Amplification Primers were based on the bovine sequence (GenBank accession number JQ775868) and designed using the DNAselect program of DNAstar version 6.0. To select appropriate primer sets that can be used for all cattle breeds, alignments were performed with 6 reference sequences from GenBank (accession numbers DQ156119, EU296533, EU313339, EU492456, EU492457, and JQ775868). Eight potential SNPs (g.81965420G N C, g.81965993G N C, g.81966286C N A, g.81966 690A N G, g.81966775 T N C, g.81966798G N A, g.81966940A N G, and g. 81967160 T N C) were found. The present analysis selected a SNP position (g.81966377 T N C) that may have been identified in many cattle breeds according to the literature (Morsci et al., 2006; Shin & Chung, 2013; Zhang et al., 2013). Therefore, the primer set focused on the genomic region (81966163–81966429, approximately 267 bp) that contained the SNP, and the forward and reverse primers were GCAGC TCTAC TTGGC ATCC (nucleotide positions 81966163–81966184) and CTTGA ATCAG TCGTC CTTAC CC (81966410–81966429), respectively. The alignment found a 67 bp insertion inside the priming region, and, therefore, the final amplification products were expected to be 267 bp and 333 bp using the same primer set. Two microliters of 10 X reaction buffer (10 mM Tris, pH 8.3, 50 mM KCl, 0.1% Triton X-100, 1.5 mM MgCl2), 2.5 mM dNTP, 10 pmol of each primer, 50 ng of genomic DNA, and 1 unit of Taq DNA polymerase

(Gibco BRL, Grand Island, NY) in a final volume of 20 ul were used. After heating at 95 °C for 2 min, a total of 35 cycles were adapted for denaturation at 94 °C/1 min, annealing at 59 °C/1 min, and polymerization at 72 °C/1.5 min (MJ Research, PT-200, Watertown, MA). DNA bands in the agarose gels were stained with ethidium bromide and were visualized with UV light. 2.4. Detection of single nucleotide polymorphism After PCR, DNA samples were purified using the PCR purification system (Nucleogen, Korea) to perform direct sequencing analyses for all samples with an ABI3730 XL Genetic Analyzer (Applied Biosystems) at NIAS. The direct sequencing analysis was performed for all samples with both forward and reverse primers to minimize base calling errors. The conditions for sequencing analysis were 94 °C/10 sec and 60 °C/ 4 min for 35 cycles in a total volume of 10 ul (5 ul of sequencing buffer, 1.6 pM primer, and 50 ng of the amplified DNA, 0.5 ul Bigdye terminator, and distilled water). A purification procedure for sequencing products was conducted with 75% Isopropanol and 70% Ethanol for centrifugation at 2,800 rpm for 45 min. After drying the samples at room temperature for 2 hours, deionized formamide (10 ul) was added. The alignment for all individual sequences was conducted with the SeqMan program of DNAstar version 6.0, and genotypes were determined according to the peaks of sequence diagrams. For the genotyping to determine insertion or deletion, 1.5% agarose gel electrophoresis was conducted, and genotypes were determined based on banding patterns of DNA for fast (267 bp, Deletion) and slow (333 bp, Insertion) mobilities. 2.5. Statistical analysis Analysis of variance was conducted using the Statistical Analysis System (SAS, 2012) with general linear model (GLM) procedures to investigate effects of the genotypes on carcass traits. Least squares means were compared using Fisher's least significant difference test with a comparison error rate of 0.05. Additive genetic effects were estimated by the difference between estimates for the two homozygous genotypes, and the dominance deviation was estimated by the difference between the solution for the heterozygous genotype and the average of the solutions for the two homozygous genotypes. Least squares means and standard errors for each trait were estimated using a linear model as follows: Y = μ + G + e, where Y is the observation of traits, μ is the overall mean for each trait, G is the fixed effect of genotype, and e is the residual error. Allele frequencies and Hardy-Weinberg equilibrium were estimated using Arlenquin version 3.0. Linkage disequilibrium in a 333 bp genomic region was estimated using the Haploview software. The haplotypes were constructed with g.81966235C N T, g.81966364D N I, and g.81966377 T N C. 3. Results 3.1. Descriptive summary of carcass traits Table 1 presents a descriptive summary of the carcass traits (MAR, BFT, LEA, and CAW), reporting measurement units, means with Table 1 Descriptive summary of carcass traits for Hanwoo cattle. Item

Marbling (MAR, 1–9)

Backfat thickness (BFT, mm)

Loineye area (LEA, cm2)

Carcass weight (CAW, kg)

Mean SD Minimum Maximum Normality

5.9 1.7 1 9 0.002

11.9 4.0 4 29 0.051

90.1 9.5 61 125 0.285

427.1 47.0 286 579 0.206

1 MAR: marbling scores (1, low to 9, high), BFT: backfat thickness, LEA: loineye area, and CAW: carcass weight. Normality was based on the chi-square distribution.

Y. Choi et al. / Meat Science 105 (2015) 57–62

standard deviations, and normality statistics. To make sure that the collected samples were appropriate for statistical analyses, normality tests using the chi-square distribution were performed. The tests revealed no significant departures from normality. 3.2. Analysis of genetic variants Amplification produced 267 and 333 bp genomic segments, confirming the sizes using agarose gel electrophoresis and sequencing analyses. As shown in Fig. 1, the present analysis determined genotypes according to the mobilities of DNA banding patterns, showing that the fast (267 bp) and slow (333 bp) DNA bands were assigned to alleles D (Deletion) and I (Insertion), respectively, as INDEL. In addition, sequence analysis confirmed a 67 bp insertion (I, allele) that began at nucleotide positions 81966364 to 81966429 according to the reference sequence (Baylor Btau_4.6.1/bos Tau7). The present PCR analysis did not find any animal with more than 2 insertions (134 bp), whereas 195 animals (approximately 10%) in the Hanwoo population possessed I alleles, showing allele frequencies for D (0.844) and I (0.156). The alignment of sequences with a reference from UCSC verified nucleotide substitutions at positions 81966235 (C N T) and 81966377 (T N C) in the promoter region of 5’UTR. The SNP g.81966235C N T presented allele frequencies for C (0.828) and T (0.172), resulting in 68.5% (CC), 28.5% (TC), and 3% (TT). Genotype frequencies for CC, CT, and TT of g.81966377 T N C were 47.1%, 43.1%, and 9.8%, respectively, with allele frequencies of G (0.686) and A (0.314). The INDEL g.81966364D N I presented allele frequencies of D (0.844) and I (0.156), with genotypic frequencies of DD (71.2%), DI (26.4%), and II (2.4%). Significant departures from Hardy-Weinberg equilibrium (HWE) were not detected for the two SNPs or for the insertion. The analysis confirmed 7 haplotypes (TDC, TIT, TDT, TIC, CDC, CDT, and CIC), showing the highest and lowest frequencies for CDC (63.078%) and TDC (0.075%). 3.3. Association analysis The results of association analyses between the ADIPOQ gene SNPs and carcass traits are presented in Table 2. Genotypic effects of g.81966377 T N C explained significant phenotypic variation in MAR and LEA (P b 0.05), and in BFT and CAW (P b 0.01). Animals with genotypes CC (6.054) and TC (5.944) had significantly higher MAR than animals with genotype TT (5.130), with significant additive genetic effects. On the other hand, a reverse pattern was observed in that animals carrying the T allele possessed higher values for BFT, LEA, and CAW than animals

59

having the C allele. The analysis also confirmed both additive and dominance effects for BFT and CAW, whereas LEA showed significant additive effects only. The genotypes of the major alleles (C and D) of g.8196235C N T and g.81966364D N I, respectively, had lower MAR scores than those of the other alleles. The analysis confirmed the existence of dominance (g.8196235C N T) and additive (g.81966364D N I) effects. Other than MAR, significant associations of g.8196235C N T and g.81966364D N I with other traits were not detected. The haplotype analysis ascertained significant linkage disequilibrium between the INDEL (g.81966364D/I) and a SNP (g.81966377 T N C). The haplotypes were significantly associated with MAR, whereas other traits did not show signficances (Table 3). The analysis verified that haplotypes (TDC, TIT, TDT, and TIC) presented higher MAR compared with others (CDC, CDT, and CIC), and the major finding is that animals with T alleles (g.81966235C N T) in the haplotypes presented higher MAR. 4. Discussion 4.1. Genetic variants Previous studies positioned the bovine ADIPOQ gene on BTA1 using a BAC clone (CHORI-240 bovine BAC library, http://bacpac.chori.org/ bovine240.htm) (Morsci et al., 2006) and sequences (NW_003103812. 1) for 7 Chinese cattle breeds (Cai et al., 2004). After precise mapping of ADIPOQ encoding an adipocytokine adiponectin, a study using comparative mapping analysis between human and bovine explored 11 genetic variants located 2 kb upstream of the promoter region of ADIPOQ in cattle (Morsci et al., 2006) and registered the sequences in GenBank (accession numbers DQ156119 and DQ156120). A previous study (Hattori, Suzuki, Hattori, & Kasai, 2003) also used 2 SNPs (at nucleotide positions g.1431C N T and g.1596G N A) from the promoter region (DQ156119) and a SNP (at nucleotide position g.2606 T N C) from exon 2 (DQ156120) for association analyses. The alignments in this analysis revealed that DQ156119 corresponded to sequences (nucleotide positions 81965344 to 81967830) of UCSC that showed the starting position of exon 1 (nucleotide position 81967041) as a reference position of ADIPOQ. Comparative alignment showed that previously identified SNPs (g.1431C N T and g.1596G N A), which correspond to g.81966400C N T and g. 81966235G N A of ADIPOQ, are harbored around the binding sites of transcription factors (TAFII150 and TAFII250), located 300 bp upstream of exon 1 (Morsci et al., 2006). Other studies discovered several SNPs (g.81966400C N T, g.81966395 T N C, g.81966377 T N C, and g.81966235C N T) in the promoter region (Morsci et al., 2006; Shin &

Fig. 1. A map has been constructed with the identified SNPs and insertions for the bovine ADIPOQ gene using Hanwoo and based on reference sequences from nucleotide positions 81966163 to 81966429 in UCSC (http://genome.ucsc.edu/). Two SNPs (g.81966235 T N C and g.81966377 T N C) were identified using sequencing analyses, and the 67 bp INDEL from 81966364 to 81966419 (TTTGG CTTGC CGCCC CAGGG AACCT GGTGC AACCC AATTC GGGCT TGGGA TGCCA AGTAG AGCTG C) was identified using agarose gel electrophoresis after amplifications that produced 267 (Deletion) and 333 bp (Insertion) fragments. Haplotype analysis confirmed significant linkage disequilibrium between the starting point of insertion and g.81966377 T N C. M: a 100 bp ladder.

60

Y. Choi et al. / Meat Science 105 (2015) 57–62

Table 2 Least square means and standard errors of marbling, backfat thickness, loineye area, and carcass weight by bovine ADIPOQ genotype. Trait1

g.81966377 T N C

P

C/C (N = 920) MAR BFT LEA CAW

6.054 11.745 88.378 423.985

± ± ± ±

MAR BFT LEA CAW

g.81966235C N T C/C (N = 1339) 5.235 ± 12.967 ± 89.847 ± 435.923 ±

MAR BFT LEA CAW

g.81966364D N I D/D (N = 1390) 5.235 ± 12.412 ± 89.357 ± 437.212 ±

C/T (N = 842) 0.14 a 0.32 a 0.76 a 3.78 a

5.944 11.881 90.452 425.763

0.07 ab 0.18 0.42 2.24

C/T (N = 556) 5.477 ± 12.575 ± 89.311 ± 436.532 ±

T/T (N = 192)

± ± ± ±

0.14 a 0.32 a 0.77 ab 3.86 a

5.130 14.565 92.478 458.522

0.11 a 0.31 0.69 3.66

T/T (N = 57) 6.226 ± 12.250 ± 92.247 ± 434.678 ±

± ± ± ±

0.37 b 0.85 b 2.01 b 9.99 b

0.31 b 0.77 1.78 9.38

0.07 0.18 0.42 2.22

D/I (N = 515) 5.474 ± 12.617 ± 88.511 ± 432.667 ±

0.11 0.31 0.70 3.71

I/I (N = 47) 6.243 12.846 93.551 437.889

a

± ± ± ±

0.36 0.93 2.13 11.29

b

Dominance

0.034 0.004 0.014 0.002

−0.923 2.821 4.099 34.537

± ± ± ±

P

Effect Additive 0.244 0.437 1.238 −3.579

± ± ± ±

Effect Additive −1.121 −0.297 −4.002 3.846

± ± ± ±

0.001 0.233 0.315 0.826 P

a

Effect Additive

0.003 0.829 0.075 0.529

0.37* 0.86*** 2.02* 10.14***

0.704 −2.547 0.047 −30.980

± ± ± ±

0.42 0.98** 2.32 11.62**

0.16 0.43 0.98 5.21

Dominance 0.545 1.311 2.712 5.316

± ± ± ±

0.24* 0.62 1.43 7.56

0.37** 0.95 2.17 11.51

Dominance −0.616 0.098 −5.889 −5.709

± ± ± ±

0.44 1.13 2.59 13.69

a and b

Different letters denote statistically significant differences between genotypes. *P b 0.05, **P b 0.01, ***P b 0.001. MAR: marbling score (1–9), BFT: backfat thickness (cm), LEA: loineye area (cm2), and CAW: carcass weight (kg). The measurement scales for carcass traits were based on a grading system (http://www.ekape.or.kr/view/eng/system/beef.asp) from Korean Animal Products Evaluation (KAPE). 1

Chung, 2013). The sequencing analysis in the present study confirmed 2 SNPs (g.81966377 T N C and g.81966235C N T), but g.81966400C N T and g.81966395 T N C were not detected. Therefore, the existence of genetic variants in cattle may depend on breed chrarcteristics as well as geographic region sampled. For example, a previous study reported a C allele frequency of 0.524 for g.81966377 T N C (Shin & Chung, 2013), but the present analysis found a frequency of 0.686 for the C allele in Hanwoo cattle. The differences in allele frequencies depend on sampling procedures with constitutional differences of genetic materials. However, the current study provided a unique opportunity to use the confirmed SNPs as genetic markers because the analysis sampled a large random Hanwoo cattle population. From the direct submission of sequences at GenBank (accession number EU492456), a large insertion (67 bp) was found in the promoter region using Angus (Morsci et al., 2006) and Chinese cattle breeds (Zhang et al., 2009). The present analysis also verified the insertion segment (67 bp) in the promoter region. However, a previous study using Korean cattle (Shin & Chung, 2013) did not find the insertion segment, even though their amplification size was 978 bp (nucleotide positions 81965936–81966913), which contained the 267 bp product (81966163–81966429) of this analysis. Therefore, the present results are the first report for Hanwoo cattle regarding the insertion segment in the promoter region of ADIPOQ, which may be used as a future reference for Hanwoo. In addition, previous reports did not use the SNP (g.81966235C N T), which was identified in Hanwoo cattle, for association analyses. This SNP needs to be verified in many local cattle

breeds due to the genetic differences (Yang, 2009; Zhang et al., 2009, 2013). 4.2. Function Candidate genes with the targeted QTLs seem to be a useful procedure to verify genetic effects. Up until now, potential candidate genes have been identified for association analyses based on physiological relationships with QTLs. ADIPOQ is one potential gene to be analyzed due to its functional and biological processes that modulate lipid synthesis, glucose utilization, and fatty acid oxidation (Dall'Olio et al., 2009). In addition, several reports have suggested that white adipose tissues express adiponectin (Chandran, Phillips, Ciaraldi, & Henry, 2003; Jacobi, Ajunwon, Weber, Kuske, & Dyer, 2004). Furthermore, a human study found significant genetic correlations between ADIPOQ and obesity parameters (Shetty, Economides, Horton, Mantzoros, & Veves, 2004). It has also been suggested that ADIPOQ may have benefit in breeding schemes of cattle populations, because the gene is located nearby QTLs of LEA and BFT on BTA1 in Angus (Morsci et al., 2006). Thus, ADIPOQ may be a valuable functional gene for meat quality traits in beef cattle when fat related traits are believed to be important in commercial production. The promoter region of the ADIPOQ gene contains regulatory sequences that control the expression levels, and therefore, variations in this region could be responsible for the metabolic activity of fat. Functional studies showed that ADIPOQ expression was detected in

Table 3 Least square means (LSM) and standard errors (SE) of marbling, backfat thickness, loineye area, and carcass weight by bovine ADIPOQ haplotype. Haplotype 1

TDC TIT TDT TIC CDC CDT CIC a and b

N

2 3 56 265 1233 382 13

Frequency (%)

0.075 0.151 2.880 13.570 63.078 19.560 0.682

MAR (1–9) 2

LEA (cm2)

BFT (cm)

CAW (kg)

LSM

±

SE

LSM

±

SE

LSM

±

SE

LSM

±

SE

P = 0.007 8.001 5.184 5.500 4.955 4.576 4.275 3.888

± ± ± ± ± ± ±

2.12 a 0.34 ab 1.50 ab 0.15 ab 0.07 b 0.13 b 0.70 b

P = 0.075 14.001 11.501 13.289 12.547 11.836 11.201 9.222

± ± ± ± ± ± ±

5.45 3.85 0.88 0.40 0.18 0.33 1.81

P = 0.411 85.001 90.001 85.631 85.234 85.432 83.316 83.001

± ± ± ± ± ± ±

12.79 9.04 2.07 0.95 0.44 0.79 4.26

P = 0.857 386.005 412.004 394.368 379.245 379.992 378.231 370.111

± ± ± ± ± ± ±

67.19 47.51 10.90 5.02 2.32 4.17 22.39

Different letters denote statistically significant differences between genotypes. Haplotype: the haplotypes were constructed with g.81966235C N T, g.81966364D N I, and g.81966377 T N C. 2 MAR: marbling score, BFT: backfat thickness, LEA: loineye area, and CAW: carcass weight. The measurement scales for carcass traits were based on a grading system (http://www.ekape.or.kr/view/eng/system/beef.asp) from Korean Animal Products Evaluation (KAPE). 1

Y. Choi et al. / Meat Science 105 (2015) 57–62

61

skeletal muscle of human, swine, and chicken (Lord, Ledoux, Murphy, Beaudry, & Palin, 2005; Maddineni, Metzger, Hendricks,, & Ramachandran, 2005) and in adipose tissue of human, rodents, and porcine (Dai et al., 2006). Therefore, results suggest that the bovine ADIPOQ gene regulates differentiation and deposition of fat in cattle.

Acknowledgements

4.3. Association

References

To use candidate genetic markers in breeding tasks for markerassisted selection programs, it is necessary to estimate the actual allele effects based on randomly selected animal populations. Therefore, this study used commercial Hanwoo beef cattle as a random population from the entire nation of Korea and tried to increase reliability of estimates of allele effects on quality grades of beef cattle. Results indicated that the SNP g.81966377 T N C and g.81966364D N I have significant additive genetic effects on MAR and BFT. A similar result was reported in that variants in the promoter region of ADIPOQ were associated with growth, BFT, and LEA in Angus even though the authors used different sets of SNPs (Morsci et al., 2006). A study also argued that the ADIPOQ gene has a great impact on growth traits in 5 Chinese local cattle breeds (Zhang et al., 2013). However, the SNP in the promoter region have inconsistent effects on MAR in different cattle breeds. For example, studies using Korean and Chinese native cattle breeds did not confirm significant associations with MAR (Shin & Chung, 2013; Yang, 2009; Zhang et al., 2009). The inconsistent results between studies may be due to the fact that mutations do not always have the same allelic substitution effects in all cattle breeds due to varying genetic and environmental backgrounds (Schennink, Bovenhuis, Le0on-Kloosterziel,, van Arendonk, & Visker, 2009). On the other hand, this study confirmed significant effects of g.81966377 T N C on MAR, BFT, LEA, and CAW. Once allele effects are confirmed for a carcass trait, others traits may also be expected to be affected by the same alleles due to strong relationships among carcass traits. The present study agreed the statement with g.81966377 T N C, whereas g.81966235C N T and g.81966364D N I showed significance for MAR only. However, this is not a mandatory hypothesis, because allele effects may differ between traits, as well as between cattle breeds. As this analysis confirmed a significant association between g.81966364D N I and MAR, the INDEL should be a valuable genetic marker for Hanwoo cattle, even though previous studies did not verify significant associations in other cattle breeds. However, haplotype analysis verified a key allele, which is allele T of g.81966235C N T rather than g.81966364D N I (Table 3). Thus, the present analysis confirms that the identified SNPs should be valuable genetic variants for several carcass traits. Even if haplotypes are more useful in explaining phenotypic variation, the alleles in SNPs still should have merit as genetic markers for MAR. An association analysis also showed that swine ADIPOQ SNP (g.81967079G N A) has significant effects on BFT and LEA (Dai et al., 2006). In addition, a genome wide association study (GWAS) confirmed that the ADIPOQ gene regulates bone development (Berner et al., 2004; Biver et al., 2011; Oshima et al., 2005), as well as yield grade and weight traits (Fox et al., 2007). As previous analyses illustrated a crucial role of ADIPOQ in fat and glucose metabolism, as well as yield grade and weight (Berner et al., 2004; Kissebah et al., 2000; Oshima et al., 2005; Wu et al., 2002), this study explored SNPs in the bovine ADIPOQ gene and analyzed associations with several carcass traits that are mainly related to meat quality. Consequently, results of the present study and future genotypic data for randomly selected Hanwoo beef cattle based on the candidate genetic variation in the ADIPOQ gene will provide critical information for genetic improvement of carcass traits and will have a great impact on commercial markets. Furthermore, this analysis demonstrated that the SNPs can be used to determine genetic effects in any Hanwoo cattle population.

Berner, H. S., Lyngstadaas, S. P., Spahr, A., Monjo, M., Thommesen, L., Drevon, C. A., Syversen, U., & Reseland, J. E. (2004). Adiponectin and its receptors are expressed in bone-forming cells. Bone, 35, 842–849. Biver, E., Salliot, C., Combescure, C., Gossec, L., Hardouin, P., Legroux-Gerot, I., & Cortet, B. (2011). Influence of adipokines and ghrelin on bone mineral density and fracture risk, a systematic review and meta-analysis. Journal Clinical Endocrinology Metabolism, 96, 2703–2713. Cai, L., Taylor, J., Smyth, K., Findeisen, B., Lehn, C., Davis, S., & Davis, S. (2004). Quantitative trait loci and somatostatin. US Patent Application no 20040018511 Casas, E., Shackelford, S. D., Keele, J. W., Stone, R. T., Kappes, S. M., & Koohmaraie, M. (2000). Quantitative trait loci affect growth and carcass composition of cattle segregating alternate forms of myostatin. Journal of Animal Science, 78, 560–569. Chandran, M., Phillips, S. A., Ciaraldi, T., & Henry, R. R. (2003). Adiponectin, more than just another fat cell hormone? Diabetes Care, 26, 2442–2450. Dai, M. H., Xia, T., Zhang, G. D., Chen, X. D., Gan, L., Feng, S. Q., Qiu, H., Peng, Y., & Yang, Z. Q. (2006). Cloning, expression and chromosome localization of porcine adiponectin and adiponectin receptors genes. Domestic Animal Endocrinology, 30, 117–125. Dall’Olio, S., Roberta, D., Buttazzoni, L., Zambonelli, P., & Vincenzo, R. (2009). Study of porcine adiponectin (ADIPOQ) gene and association of a missense mutation with EBVs for production and carcass traits in Italian Duroc heavy pigs. Livestock Science, 125, 101–104. Davis, G. P., Hetzel, D. J. S., Corbet, N. J., Scacheri, S., Lowden, S., Renaud, J., Mayne, C., Stevenson, R., Moore, S. S., & Byrne, K. (1998). The mapping of quantitative trait loci for birth weight in a tropical beef herd. Proceedings of the 6th World Congress on Genetics Applied to Livestock Production. Armidale, 26. (pp. 441–444). Dekkers, J. C. (2004). Commercial application of marker- and gene assisted selection in livestock, strategies and lessons. Journal of Animal Science, 82, E313–E328. Fox, C. S., Heard-Costa, N., Cupples, L. A., Dupuis, J., Vasan, R. S., & Atwood, L. D. (2007). Genome-wide association to body mass index and waist circumference, the Framingham Heart Study 100 K project. BMC Medical Genetics, 8, S18. Gao, J., Pfeifer, D., He, L. J., Qiao, F., Zhang, Z., Arbman, G., Wang, Z. L., Jia, C. R., Carstensen, J. , & Sun, X. F. (2007). Association of NFKBIA polymorphism with colorectal cancer risk and prognosis in Swedish and Chinese populations. Scandinavian Journal of Gastroenterology, 42, 345–350. Hattori, Y., Suzuki, M., Hattori, S., & Kasai, K. (2003). Globular adiponectin upregulates nitric oxide production in vascular endothelial cells. Diabetologia, 46, 1543–1549. Houde, A. A., Murphy, B. D., Mathieu, O., Bordignon, V., & Palin, M. F. (2008). Characterization of swine adiponectin and adiponectin receptor polymorphisms and their association with reproductive traits. Animal Genetics, 39, 249–257. Hsueh, W. C., St. Jean, P. L., Mitchell, B. D., Pollin, T. I., Knowler, W. C., Ehm, M. G., Bell, C. J., Sakul, H., Wagner, M. J., Burns, D. K., & Shuldiner, A. R. (2003). Genome-wide and fine-mapping linkage studies of type 2 diabetes and glucose traits in the old order Amish, evidence for a new diabetes locus on chromosome 14q11 and confirmation of a locus on 1q21-q24. Diabetes, 52, 550–557. Jacobi, S. K., Ajunwon, K. M., Weber, T. E., Kuske, J. L., & Dyer, C. J. (2004). Cloning and expression of porcine adiponectin, and its relationship to adiposity, lipogenesis and the acute phase response. Journal of Endocrinology, 182, 133–144. Kadowaki, T., & Yamauchi, T. (2005). Adiponectin and adiponectin receptors. Endocrine Reviews, 26, 439–451. Kissebah, A. H., Sonnenberg, G. E., Myklebust, J., Goldstein, M., Broman, K., James, R. G., Marks, J. A., Krakower, G. R., Jacob, H. J., Weber, J., Martin, L., Blangero, J., & Comuzzie, A. G. (2000). Quantitative trait loci on chromosomes 3 and 17 influence phenotypes of the metabolic syndrome. Proceedings of the National Academy of Sciences of the United States of America, 97, 14478–14483. Kubota, N., Terauchi, Y., Yamauchi, T., Kubota, T., Moroi, M., Matsui, J., Eto, K., Yamashita, T. , Kamon, J., Satoh, H., Yano, W., Froguel, P., Nagai, R., Kimura, S., Kadowaki, T., & Noda, T. (2002). Disruption of adiponectin cause insulin resistance and neointimal formation. The Journal of Biological Chemistry, 277, 25863–25866. Li, C., Basarab, J., Snelling, W. M., Benkel, B., Kneeland, J., Murdoch, B., Hansen, C., & Moore, S. S. (2004). Identification and fine mapping of quantitative trait loci for backfat on bovine chromosomes 2, 5, 6, 19, 21, and 23 in a commercial line of Bos taurus. Journal of Animal Science, 82, 967–972. Lord, E., Ledoux, S., Murphy, B. D., Beaudry, D., & Palin, M. F. (2005). Expression of adiponectin and its receptors in swine. Journal of Animal Science, 83, 565–578. Maddineni, S., Metzger, S., Hendricks,, G., III, & Ramachandran, R. (2005). Adiponectin gene is expressed in multiple tissues in the chicken, Food deprivation influences adiponectin messenger ribonucleic acid expression146, 4250–4256. Melistas, L., Mantzoros, C. S., Kontogianni, M., Antonopoulou, S., Ordovas, J. M., & Yiannakouris, N. (2009). Association of the + 45 T N G and +276G N T polymorphisms in the adiponectin gene with insulin resistance in nondiabetic Greek women. European Journal Endocrinology, 161, 845–852. Morsci, N. S., Sellner, E. M., Schnabel, R. D., & Taylor, J. F. (2006). Association analysis of adiponectin and somatostatin polymorphisms on BTA1 with growth and carcass traits in Angus cattle. Animal Genetics, 37, 554–562.

This work contributes to the internal project “Cooperative Research Program for Agriculture Science & Technology Development (Number PJ010220012015)” and was supported by the National Institute of Animal Science in Rural Development Administration of Korea.

62

Y. Choi et al. / Meat Science 105 (2015) 57–62

Oshima, K., Nampei, A., Matsuda, M., Iwaki, M., Fukuhara, A., Hashimoto, J., Yoshikawa, H., & Shimomura, I. (2005). Adiponectin increases bone mass by suppressing osteoclast and activating osteoblast. Biochemical and Biophysical Research Communications, 331, 520–526. Pineiro, R., Iglesias, M. J., Gallego, R., Raghay, K., Eiras, S., Rubio, J., Dieguez, C., Gualillo, O., Gonzalez-Juanatey, J. R., & Lago, F. (2005). Adiponectin is synthesized and secreted by human and murine cardiomyocytes. FEBS Letters, 579, 5163–5169. SAS (2012). SAS Procedures Guide (Version 9). Cary, NC: SAS Inst. Inc. Schennink, A., Bovenhuis, H., Le0on-Kloosterziel,, K. M., van Arendonk, J. A. M., & Visker, M. H. P. W. (2009). Effect of polymorphisms in the FASN, OLR1, PPARGC1A, PRL and STAT5A genes on bovine milk-fat composition. Animal Genetics, 40, 909–916. Scherer, P. E., Williams, S., Fogliano, M., Baldini, G., & Lodish, H. F. (1995). A novel serum protein similar to C1q, produced exclusively in adipocytes. The Journal of Biological Chemistry, 270, 26746–26749. Shetty, G. K., Economides, P. A., Horton, E. S., Mantzoros, C. S., & Veves, A. (2004). Circulating adiponectin and resistin levels in relation to metabolic factors, inflammatory markers, and vascular reactivity in diabetic patients and subjects at risk for diabetes. Diabetes Care, 27, 2450–2457. Shin, S., & Chung, E. (2013). Novel SNPs in the bovine ADIPOQ and PPARGC1A genes are associated with carcass traits in Hanwoo (Korean cattle). Molecular Biology Reports, 40, 4651–4660. Szopa, M., Malczewska-Malec, M., Kiec-Wilk, B., Skupien, J., Wolkow, P., Malecki, M. T., & Sieradzki, J. (2009). Variants of the adiponectin gene and type 2 diabetes in a Polish population. Acta Diabetologica, 46, 317–322.

Wei, S., Zan, L. S., Wang, H. B., Cheng, G., Du, M., Jiang, Z., Hausman, G. J., McFarland, D. C., & Dodson, M. V. (2013). Adenovirus-mediated interference of FABP4 regulates mRNA expression of ADIPOQ, LEP and LEPR in bovine adipocytes. Genetics and Molecular Research, 27, 494–505. Wu, X., Cooper, R. S., Borecki, I., Hanis, C., Bray, M., Lewis, C. E., Zhu, X., Kan, D., Luke, A., & Curb, D. (2002). A combined analysis of genomewide linkage scans for body mass index from the National Heart, Lung and Blood Institute Family Blood Pressure Program. American Journal of Human Genetics, 70, 1247–1256. Yang, Y. J. (2009). SNPs detection of adiponectin gene and its relationship with carcass and meat quality traits in Qinchuan cattle. Yi Chuan, 31, 1006–1012. Yokota, T., Meka, C. S., Medina, K. L., Igarashi, H., Comp, P. C., Takahashi, M., Nishida, M., Oritani, K., Miyagawa, J., Funahashi, T., Tomiyama, Y., Matsuzawa, Y., & Kincade, P. W. (2002). Paracrine regulation of fat cell formation in bone marrow cultures via adiponectin and prostaglandins. The Journal of Clinical Investigation, 109, 1303–1310. Zhang, L., Chen, H., Lan, X., Zhang, C., Zhang, L., Zhang, A., Zhang, Q., Lei, C., & Zhang, H. (2009). The novel 5 bp deletion polymorphism in the promoter region of bovine ACRP30 gene. Molecular Biology Reports, 36, 895–899. Zhang, L., Yang, M., Li, C., Xu, Y., Sun, J., Lei, C., Lan, X., Zhang, C., & Chen, H. (2013). Identification and genetic effect of a variable duplication in the promoter region of the cattle ADIPOQ gene. Animal Genetics. http://dx.doi.org/10.1111/age.12112.