Haplotype distribution in the GLI3 gene and their associations with growth traits in cattle

Gene 513 (2013) 141–146

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Methods Paper

Haplotype distribution in the GLI3 gene and their associations with growth traits in cattle Yong-Zhen Huang a, 1, Ke-Yi Wang a, 1, Hua He a, Qing-Wu Shen a, Chu-Zhao Lei a, Xian-Yong Lan a, Chun-Lei Zhang b, Hong Chen a,⁎ a b

College of Animal Science and Technology, Northwest A&F University, Shaanxi Key Laboratory of Molecular Biology for Agriculture, Yangling Shaanxi 712100, China Institute of Cellular and Molecular Biology, Jiangsu Normal University, Xuzhou Jiangsu, 221116, China

a r t i c l e

i n f o

Article history: Accepted 11 October 2012 Available online 9 November 2012 Keywords: Association Cattle Haplotype GLI3 Growth traits

a b s t r a c t The glioma-associated oncogene family zinc finger 3 gene (GLI3) mediates in all vertebrates hedgehog (Hh) signaling that plays an essential role in the induction and patterning of numerous cell types during invertebrate and vertebrate development. In this study, a total of 6 single nucleotide polymorphisms (SNPs: 1–6) were identified by polymerase chain reaction–single stranded conformational polymorphism (PCR–SSCP) and DNA pool sequencing, including all 13 exons and 12 exon–intron boundaries within the bovine GLI3 gene. 16 haplotypes and 13 combined genotypes were revealed and the linkage disequilibrium was assessed in 708 individuals representing three main cattle breeds from China. The statistical analyses indicated that the SNP2, 3 and 4 are associated with the body weight at birth and 6 months in Nanyang cattle population (P b 0.05). No significant association was detected between 11 combined genotypes and body weight at five different ages. Our results provide evidence that polymorphisms in the GLI3 gene are associated with growth traits, and may be used for marker-assisted selection in beef cattle breeding program. © 2012 Published by Elsevier B.V.

1. Introduction The glioma-associated oncogene family zinc finger 3 (GLI3) mediates sonic hedgehog (Shh) signaling in all vertebrates that plays an essential role in the induction and patterning of numerous cell types during invertebrate and vertebrate development (Hooper and Scott, 2005; Ingham and McMahon, 2001). As they are key mediators of hedgehog

Abbreviations: bp, base pair (s); CH, Chinese Holstein; ES, epaxial somite; GLI3, glioma-associated oncogene family zinc finger 3 gene; GLM, general linear models; He, heterozygosity; HWE, Hardy–Weinberg equilibrium; JX, Jiaxian cattle; LD, linkage disequilibrium; LSM, least square means estimates; MDR1, multidrug resistance protein 1; MRFs, myogenic regulatory factors; MYF5, myogenic factor 5; MYF5, myogenic regulatory factors 5; MYF6, myogenic regulatory factors 6; MYOD1, myogenic differentiation 1; Ne, effective allele numbers; NY, Nanyang; PAGE, polyacrylamide gel electrophoresis; PCR–SSCP, polymerase chain reaction-single stranded conformational polymorphism; PIC, polymorphism information content; POU1F1, POU domain, class 1, transcription factor 1; QC, Qinchuan; Shh, sonic hedgehog; SNPs, single nucleotide polymorphisms; SPSS, statistical product and service solutions; TBE, is a buffer solution containing a mixture of Tris base, boric acid and EDTA. ⁎ Corresponding author at: No.22 Xinong Road, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, China. Tel.: +86 29 87092004; fax: +86 29 87092164. E-mail addresses: [email protected] (Y.-Z. Huang), [email protected] (K.-Y. Wang), [email protected] (H. He), [email protected] (Q.-W. Shen), [email protected] (C.-Z. Lei), [email protected] (X.-Y. Lan), [email protected] (C.-L. Zhang), [email protected] (H. Chen). 1 Yong-Zhen Huang and Ke-Yi Wang contributed equally to this work. 0378-1119/$ – see front matter © 2012 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.gene.2012.10.052

morphogenetic signals (Robert et al., 2009), the GLI family members (GLI12 and 3) have been extensively scrutinized by genetic, molecular and biochemical means in the past couple of decades. A great deal of information has been obtained about the functions of GLI proteins in various vertebrate species (Abbasi et al., 2009). The ultimate transcriptional effectors of Hh signaling within the responding cell are the GLI family of zinc finger transcription factors (Lum and Beachy, 2004), although GLI-independent mechanisms have also been described (Krishnan et al., 1997). Mutations in this gene have been associated with several diseases (Gary et al., 2005). The GLI proteins appear to have overlapping as well as distinct functions during development based on the phenotypes of single and double mouse mutants (Mo et al., 1997; Park et al., 2000). The GLI3 gene is of particular interest in the context of limb development, since mutations in the GLI3 gene result in polydactyly in both mice and humans (Hui and Joyner, 1993; Vortkamp et al., 1991). In mice, GLI genes are differentially expressed in somite. Combining genetic studies with the use of a transgenic mouse line expressing a reporter gene under the control of the myogenic factor 5 (MYF5) epaxial somite enhancer, GLI2 or GLI3 is required for MYF5 activation in the epaxial muscle progenitor cells (McDermott et al., 2005). The mutation extra-toes (GLI3Xt-J) on chromosome 13 of the mouse are known to be involved in the development of the skeleton. The analyses have shown an association between the GLI3Xt-J allele and a body weight increase of about 6.5%. This effect is genetically dominant. It would appear that if the gene of interest is not GLI3 itself, it must be

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some other gene very close to this locus (Martin et al., 2007). These suggest that the GLI3 gene possibly regulates muscle growth, and thus influences the performance of livestock. The bovine GLI3 gene has 13 exons, it is located on chromosome 4. The purpose of this study was to identify single nucleotide polymorphisms (SNPs) and to carry out haplotype construction and association analysis so as to contribute to the understanding of the role of GLI3 in variation of growth traits in cattle, which possibly contributed to animal breeding and genetics. 2. Materials and methods 2.1. Animal source, DNA preparation and growth data We investigated the genetic variation in bovine GLI3, an initial investigation of 708 individuals representing four cattle breeds in China: Nanyang (NY, n = 187), Qinchuan (QC, n = 287), Jiaxian cattle (JX, n = 139), and Chinese Holstein (CH, n = 95). Nanyang, Qinchuan, and Jiaxian are three important breeds for beef production in China, while Chinese Holstein is a dairy breed. These four are the main breeds in China and they are reared in the provinces of Henan, and Shaanxi. The Nanyang animals were from the breeding center of Nanyang cattle (Nanyang city, Henan Province, China); the Jiaxian animals were from the breeding farm of Jiaxian Cattle (Jiaxian county, Henan Province, China); the Qinchuan animals were from the reserved farm (Weinan city, Shaanxi Province, China), the breeding farm of Qinchuan cattle and the fineness breeding center of Qinchuan cattle (Fufeng county, Shaanxi Province, China); and the Chinese Holstein animals were from the breeding farm of milk cattle (Xi'an city, Shaanxi province, China). Genomic DNA of 708 cattle were isolated from 2% heparin-treated blood samples and stored at −80 °C, following the standard procedures (Sambrook et al., 2002). The concentrations of DNA were estimated spectrophotometrically, and then the genomic DNA was diluted to 50 ng/μL. All DNA samples were stored at −20 °C for subsequent analysis. The 187 animals of Nanyang breed used for the association study came from 3 to 5 common ancestors, and pedigrees of core breeding population animals were traced back three generations. The animals were weaned at an average of 6 months and raised from weaning to slaughter on a corn–corn silage diet. Body weight was recorded at birth and after 6 (weaning), 12, 18, and 24 months, these traits were measured following the method described in Gilbert et al. (1993). 2.2. Variant detection Primers used to amplify the bovine GLI3 gene were designed from a published gene sequence (GenBank accession number: NW_ 001494928). Primers, restriction enzymes selected, and fragment

sizes are given in Table 1. The detection results of allelic variation at the SNPs were based on the electrophoretic PCR–SSCP pattern of the PCR products. PCR was performed in 25 μL of reaction volume, containing 50– 100 ng genomic DNA, 10 pM of each primer, 1 × buffer (including 1.5 mM MgCl2), 200 μM dNTPs and 1.5 units of Taq DNA polymerase. Six pairs of primers were designed for PCR amplification of the GLI3 gene from cattle genomic DNA (Table 1). PCR products were commercially sequenced for genetic variant discovery. Generally, PCR products amplified from genomic DNA were directly sequenced in both directions. In an effort to discover SNPs in a cost-effective manner, SNP discovery was implemented by sequencing pooled PCR products, which were amplified from DNA of founder animals from each cattle breed. The sequences were imported into the BioXM software (Version 2.6) and were analyzed and searched for SNPs. 2.3. Genotyping PCR primers were redesigned to facilitate genotyping of the six single nucleotide polymorphisms (SNPs) using PCR–SSCP technique in four study populations. Aliquots of 5 μL PCR products were mixed with 5 μL denaturing solution (95% formamide, 25 mM EDTA, 0.025% xylene-cyanole, and 0.025% bromophenol blue), heated at 98 °C for 10 min, and immediately chilled on ice. Denatured DNA was subjected to 10% PAGE (80×73×0.75 mm) in 1× TBE buffer and constant voltage (200 V) for 2.5 h at a constant temperature of 4 °C. The gel was stained with 0.1% silver nitrate (Huang et al., 2010a). 2.4. Data analyses Gene frequencies were determined for each breed by direct counting. Hardy–Weinberg equilibrium (HWE) had been tested based on likelihood ratio for different locus-population combinations and the number of observed and effective alleles by POPGENE software (Version 3.1; Yeh et al., 1999). Population genetic indexes, such as genotypic frequencies, allelic frequencies, gene heterozygosity (He) (Nei, 1973) and effective allele numbers (Ne), were computed by POPGENE software. Polymorphism information content (PIC) was calculated according to Botstein's methods (1980). The linkage disequilibrium (LD) structure as measured by D′ and r 2 were performed with the HAPLOVIEW software (Version 3.32) (Amandine et al., 2010; Barrett et al., 2005; Zhao et al., 2007). Haplotypes were obtained for each animal using the PHASE computer program (Version 2.1) (Stephens et al. 2001).

Table 1 Genetic variants are identified in the bovine GLI3 gene. SNPs

Sequences of primers (F/R)

AT (°C)a

SAF (bp)b

Variant type

Variant location

Mutation type

SNP 1

5′ 5′ 5′ 5′ 5′ 5′ 5′ 5′ 5′ 5′ 5′ 5′

58.5

452

g.8143A>G

Exon 1

Silent

0

58.5

370

g.123600T>C

Exon 4

Silent

115,457

58.5

370

g.123696T>C

Exon 4

Silent

96

57.4

429

g.128688A>G

Exon 5

Silent

4992

50.2

336

g.205649T>C

Exon 10

Silent

76,961

50.2

336

g.205754A>C

Intron 10

–

SNP 2 SNP 3 SNP 4 SNP 5 SNP 6 a b c

CAGTTGCCAAGAATGAG 3′ CTCTTTAAGTGCACATGC 3′ TGAAAGGAATGGCTCTGT 3′ TGCTCTGGAAAGTTAGGTG 3′ TGAAAGGAATGGCTCTGT 3′ TGCTCTGGAAAGTTAGGTG 3′ GCTTCTTATCCTCCAGTTC 3′ TCTCAGGTCAGTCCCAGT 3′ CTTTCATTCACTTGGAGC 3′ ACGACAATGGAACAGGCT 3′ CTTTCATTCACTTGGAGC 3′ ACGACAATGGAACAGGCT 3′

AT: annealing temperature. SAF: size of amplification fragment. DPS: distance from previous SNP (nt).

DPS (nt)c

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The association analyses between single SNP marker genotypes and growth traits were performed by the least squares method as applied in the general linear model (GLM) procedure of SPSS software (Version 16.0) (Derecka et al., 2009; Holzer and Precht 1992; Huang et al., 2010b). Statistical analysis was performed on the basis of records of growth traits in 187 animals of Nanyang breed. Pedigrees of base breeding population animals were traced back three generations to create the numerator relationship matrix. All analyses were done in two steps, first using a full animal model and then using a reduced animal model. The full animal model included fixed effects of marker genotype, age, season of birth (spring vs. fall), age of dam, sire, farm, sex and random effects (permanent environment, animal, and residual). The effect associated with season of birth, age of dam, sire, farm and sex were not matched in the linear model, as the preliminary statistical analyses indicated that these effects did not have a significant influence on variability of traits in the analyzed populations. The reduced model was used in the final analysis (Boldman et al., 1995; Henderson, 1986; Hickford et al., 2009; Huang et al., 2010c). The SPSS software (Version 16.0) was used to analyze the relationship between the genotypes and traits in cattle. The reduced linear model included fixed effects of age and genotype. The linear model: Y ijk ¼ μ þ Ai þ Gj þ Eijk ; where Yijk = the trait measured on each of the ijkth animal; μ = the overall population mean; Ai = fixed effect due to the ith age; Gj = the fixed effect associated with jth genotype; Eijk = the random error. The least square means estimates (LSM) with standard errors for different genotypes and growth trait were used. In this model, age and marker genotypes were considered as fixed effects, and growth traits as the dependent variables. For a more detailed review of the results, we also corrected P values by Bonferroni correction that was used to account for multiple testing to obtain more robust results. Association of combined genotypes for different haplotypes with body weight was tested to explore the possible interaction between the different haplotypes (Huang et al., 2011a,b). The model was similar to that of single marker association analysis; except that the interactions between the different combined genotypes were included as a fixed effect. The software SPSS was used to analyze the association between the combined genotypes and traits in cattle. 3. Results

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genotype and allele were calculated in the four Chinese bovine populations (Table 2). Compared with previously reported sequence, the 123600T and 123696T allele showed two transition 123600T>C and 123696T>C. The two mutations 123600T>C and 123696T>C were silent in exon 4. It is interesting that the SNPs at nt123600C and nt123696 were in complete linkage disequilibrium: with CC genotypes always being together and TT genotypes always together. The frequency of allele C (CH: 0.00–JX: 0.57) has shown a high prevalence in these breeds and TC genotype (CH: 0.00–NY: 0.66) was more frequent than other genotypes. The genotypic frequencies of SNP2/3 locus in all Chinese cattle populations were in agreement with the Hardy–Weinberg equilibrium (P > 0.05) except NY and QC cattle populations (Table 2), which showed that there were not a dynamic equilibrium even in artificial selection, migration, and genetic drift function in the NY and QC cattle populations. At the SNP4 locus, three SSCP genotypes were identified and denominated as SNP4-AA (homozygous), SNP4-AG (heterozygous) and SNP4-GG (homozygous) genotypes. The frequencies of genotype and allele were calculated in the four Chinese bovine populations (Table 2). Compared with previously reported sequence, the 128688A allele showed a transition A>G at position 128688. The mutation 128688A>G was silent in exon 5. The frequency of allele A (QC: 0.43–NY: 0.55) has shown a high prevalence in these breeds and AG genotype (CH: 0.38–NY: 0.51) was more frequent than other genotypes. The genotypic frequencies of SNP4 locus in all Chinese cattle populations were in agreement with the Hardy–Weinberg equilibrium (P > 0.05) except QC cattle population. At the SNP5 and 6 loci, two SSCP genotypes were also identified and denominated as SNP5/6-TT–AA (homozygous), and SNP5/6-CC– CC (homozygous) genotypes. SNP 5 and 6 were in complete linkage disequilibrium; therefore, the genotyping of SNP5 and 6 have the same results. The frequencies of genotype and allele were calculated in the four Chinese bovine populations (Table 2). Compared with previously reported sequence, the 205649T allele and 205754A allele showed one transition T>C and one transversion A>C. The mutations 205649T>C was silent in exon 10 and 205754A>C in intron 10. It is interesting that the SNPs at nt205649 and nt205754 were in complete linkage disequilibrium: with CC genotypes always being together and TT genotypes always together. The frequency of allele C has shown a high prevalence in most of the populations (NY: 0.60, QC: 0.44, JX: 0.46, and CH: 1.00) and CC genotype was more frequent in most of the populations. The genotypic frequencies of SNP5/6 locus in all Chinese cattle populations were in agreement with the Hardy–Weinberg equilibrium (P > 0.05) except NY and JX cattle populations.

3.1. SNPs identified 3.2. Diversity analyses The genotyping of the six SNPs was successfully implemented using DNA pool sequencing, and PCR–SSCP methods. At the SNP1 locus, three SSCP genotypes were identified and denominated as SNP1-AA (homozygous), SNP1-AG (heterozygous) and SNP1-GG (homozygous) genotypes. The frequencies of genotype and allele were calculated in the four Chinese bovine populations (Table 2). Compared with previously reported sequence, the 8143A allele showed a transition A>G at position 8143. The mutation 8143A>G was silent in exon 1. The frequency of allele A (JX: 0.56–CH: 1.00) has shown a high prevalence in these breeds and AA genotype (JX: 0.34–CH: 1.00) was more frequent than other genotypes. The genotypic frequencies of SNP1 locus in all Chinese cattle populations were in agreement with the Hardy–Weinberg equilibrium (P >0.05) (Table 2), which showed that there was a dynamic equilibrium even in artificial selection, migration, and genetic drift function. At the SNP2 and 3 loci, three SSCP genotypes were also identified and denominated as SNP2/3-TT–TT (homozygous), SNP2/3-TC–TC (heterozygous) and SNP2/3-CC–CC (homozygous) genotypes. The SNP 2 and 3 were in complete linkage disequilibrium; therefore, the genotyping of SNP2 and 3 have the same results. The frequencies of

Genetic indices (He, Ne and PIC) in these four Chinese cattle populations were presented in Table 2. The values of the difference between expected and observed He (gene heterozygosity) were approaching 0.5. The values of Ne (effective allele numbers) were approaching 2. The maximum and minimum PIC values were 0.00 and 0.38. According to the PIC classification system (PIC value b 0.25, low polymorphism; 0.25 b PIC valueb 0.5, intermediate polymorphism; and PIC value > 0.5, high polymorphism). These cattle populations belonged to intermediate genetic diversity at the six SNPs loci except CH cattle populations in SNP1, 2–3, and 5–6 loci. This reflected that there was not a very high genetic diversity within Chinese bovine GLI3 gene in the analyzed populations. Genetic diversity is essential for preservation of adaptive potential of species and improvement of production of potentially high selected breeds. 3.3. Linkage disequilibrium and haplotype analysis There were monomorphism in SNP1, 2, 3, 5, and 6 in CH breed, therefore, the disequilibrium between polymorphism pairs and

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Table 2 Genotypic and allelic frequencies (%), value of χ2 test and diversity parameter of bovine GLI3 gene. SNPsa

Breedb (number)

Genotype/number/GFc

SNP 1

NY (187) QC (287) JX (139) CH (95) NY (187) QC (287) JX (139) CH (95) NY (187) QC (287) JX (139) CH (95) NY (187) QC (287) JX (139) CH (95)

AA/73/0.39 133/0.46 47/0.34 95/1.00 TT–TT/29/0.16 52/0.18 27/0.20 95/1.00 AA/55/0.29 66/0.23 47/0.34 24/0.25 TT–AA/75/0.40 162/0.56 75/0.54 0/0.00

SNP 2 (2–3)

SNP 4

SNP 5 (5–6)

AG/85/0.45 123/0.43 62/0.45 0/0.00 TC–TC/124/0.66 166/0.58 66/0.47 0/0.00 AG/95/0.51 114/0.40 59/0.42 36/0.38 TC–AC/0/0.00 0/0.00 0/0.00 0/0.00

Allele/AFd GG/29/0.16 31/0.11 30/0.22 0/0.00 CC–CC/34/0.18 69/0.24 46/0.30 0/0.00 GG/37/0.20 107/0.37 33/0.24 35/0.37 CC–CC/112/0.60 125/0.44 64/0.46 95/1.00

A/0.61 0.68 0.56 1.00 T–T/0.49 0.47 0.43 1.00 A/0.55 0.43 0.55 0.44 T–A/0.40 0.56 0.54 0.00

G/0.39 0.32 0.44 0.00 C–C/0.51 0.53 0.57 0.00 G/0.45 0.57 0.45 0.56 C–C/0.60 0.44 0.46 1.00

χ2 (HWE)e

Hef

Neg

PICh

P > 0.05 P > 0.05 P > 0.05 – P b 0.05 P b 0.05 P > 0.05 – P > 0.05 P b 0.05 P > 0.05 P > 0.05 P b 0.05 P > 0.05 P b 0.05 –

0.48 0.44 0.49 0.00 0.49 0.50 0.49 0.00 0.50 0.49 0.49 0.49 0.48 0.49 0.50 0.00

1.91 1.78 1.97 1.00 1.99 1.99 1.96 1.00 1.98 1.96 1.98 1.97 1.93 1.97 1.99 1.00

0.36 0.34 0.37 0.00 0.37 0.37 0.37 0.00 0.37 0.37 0.37 0.37 0.37 0.38 0.38 0.00

a SNPs: single nucleotide polymorphisms; SNP1: g.8143A>G; SNP2: g.123600T>C; SNP3: g.123696T>C; SNP4: g.128688A>G; SNP5: g.205649T>C; SNP6: g. g.205754A>C. SNP2 (2–3): SNP 2 and 3 are in complete linkage disequilibrium, therefore, SNP2 and SNP3 of the genotyping results is the same. SNP5 (5–6): SNP 5 and 6 are in complete linkage disequilibrium, therefore, SNP5 and SNP6 of the genotyping results is the same. b Breed: NY, Nanyang; QC, Qinchuan; JX, Jiaxian; CH, Chinese Holstein. c GF: genotypic frequency. d AF: allelic frequency. e χ2 (HWE): Hardy–Weinberg equilibrium χ2 value, Hardy–Weinberg equilibrium (P > 0.05), Hardy–Weinberg disequilibrium (P b 0.05). f He: gene heterozygosity. g Ne: effective allele numbers. h PIC: polymorphism information content.

haplotype structure analysis of the GLI3 gene in three cattle populations (NY, QC, and JX), the results are shown in Table S1 and Table 3. The SNP2 and SNP3, SNP5 and SNP6 were in complete linkage disequilibrium; therefore, the linkage disequilibrium between the four SNPs in three cattle populations was estimated, which indicated that the D′ values ranged from 0.029 to 0.673; the r 2 values were from 0.001 to 0.273; the mean r 2 between adjacent SNPs was 0.058, 0.063 and 0.092 for NY, QC, and JX, respectively. These indicated that the four SNPs had little linkage disequilibrium. The possibility is that recombination will be high and LD will be low in genovariation-dense regions. The result of haplotype analysis of four SNPs showed that sixteen different haplotypes were identified in these animals. Hap 1 (−ATAC-) had the highest haplotype frequencies in NY (20.00%), followed by QC (11.46%), and JX (18.49%). Hap 8 (−GTAT-) had the highest haplotype frequencies in NY (22.50%); Hap 5 (−ACGT) had the higher haplotype frequencies in QC (17.37%) and JX (20.85%).

of bovine GLI3 gene. The SNP2, 3, and 4 were significantly associated with body weight at birth, and 6 months. After Bonferroni correction, the associations would still be significant. These suggest that these mutations indirectly contribute to quality characteristics at different times in cattle. The result of haplotype analysis of six SNPs showed that 16 different haplotypes were identified (Table 3). Combination of two of all the haplotypes is used to analyze the correlation between combined genotypes and growth traits, and all the 13 possible GLI3 combined genotypes are detected in the study cattle population. The 13 combined genotype association results for different haplotypes are shown in Table S2. The association analysis suggested that no significant differences were detected between the combined genotypes of the five SNPs and Table 3 Haplotype (Hap) and haplotype frequency within studied population of four SNPs in bovine GLI3 gene. Haplotypes

3.4. Association analysis of single SNP marker, haplotype, and haplotype combinations The results of the association analyses are shown in Table 4. We analyzed the associations of these six SNPs with growth traits (the body weight at birth, 6 months, 12 months, 18 months, and 24 months) in Nanyang cattle. Statistical results showed that the animals with SNP2/3-CC-CC (g.123600T>C, and g.123696T>C) genotypes had significantly greater body weight than those with genotypes SNP2/3-TT–TT (P b 0.05) at 6 months of age, demonstrating that the alleles SNP2/3-C-C might be associated with an increased in body weight at 6 months of age in the population. The animals with SNP4-GG (g.128688A>G) genotypes had significantly greater body weight than those with genotypes SNP4-AA (P b 0.05) at birth, demonstrating that the allele SNP4-G might be associated with an increased body weight at birth in the population. As shown in Table 4, the growth traits values of heterozygotic genotypes were lower than that of homozygotic genotype in four SNPs

Hap 1 Hap 2 Hap 3 Hap 4 Hap 5 Hap 6 Hap 7 Hap 8 Hap 9 Hap 10 Hap 11 Hap 12 Hap 13 Hap 14 Hap 15 Hap 16 Totala

SNPs

Frequency in population

SNP1

SNP2

SNP4

SNP5

NY (n = 187)

QC (n = 287)

JX (n = 139)

A A A A A A G G G G G G G A A A

T T T C C C T T T T C C C T T C

A G G A G G A A G G A G A A G A

C T C T T C T T C T C T C T C C

0.2000 0.0250 0.0250 0.1469 0.0281 0.0750 0.0530 0.2250 0.0720 0.0750 0 0.0750 0 0 0 0 11

0.1146 0 0 0.1126 0.1737 0.1098 0.0319 0.0774 0.1167 0.0582 0.0063 0.0163 0.0089 0.0047 0.1298 0.0391 14

0.1849 0 0.0951 0.0981 0.2085 0.0721 0.0320 0.1138 0.1013 0.0262 0.0232 0.0301 0 0 0 0.0146 12

Note: NY, Nanyang; QC, Qinchuan; JX, Jiaxian. a Total: 11 haplotypes in NY, 14 haplotypes in QC, 12 haplotypes in JX.

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Table 4 Associations between single SNP and haplotype and body weight (kg) in Nanyang population. Mean ± SE. SE: standard error. SNPs

SNP 1

SNP 2 (2–3)

SNP 4

SNP 5 (5–6)

Genotypes

AA AG GG P value1 TT–TT TC–TC CC–CC P value AA AG GG P value TT–AA TC–AC3 CC–CC P value

Number (total = 187) 73 85 29 29 124 34 55 95 37 75 0 112

Traits4 Birth

6

12

18

24

30.02 ± 0.38 29.56 ± 0.31 29.75 ± 0.71 0.4248 29.73 ± 0.53 29.87 ± 0.32 29.50 ± 0.56 0.4633 29.44a ± 0.62 29.79a ± 0.42 31.65b ± 0.78 0.04272 29.83 ± 0.35 – 30.01 ± 0.45 0.7461

165.00 ± 3.60 156.75 ± 2.93 162.25 ± 6.86 0.3947 152.25a ± 4.06 159.30ab ± 2.47 167.61b ± 4.28 0.03492 158.31 ± 5.24 160.12 ± 3.60 168.80 ± 6.63 0.4521 161.17 ± 2.48 – 158.61 ± 3.18 0.5277

228.79 ± 4.24 220.64 ± 3.45 218.00 ± 8.08 0.0867 218.05 ± 4.69 221.28 ± 2.85 228.78 ± 4.94 0.5096 223.88 ± 5.60 223.68 ± 3.84 227.60 ± 7.08 0.9270 223.14 ± 3.04 – 221.56 ± 3.89 0.7497

299.38 ± 5.49 292.46 ± 4.45 303.75 ± 10.45 0.9159 289.50 ± 6.68 299.02 ± 4.07 301.06 ± 7.04 0.2329 295.19 ± 7.33 299.00 ± 5.03 293.20 ± 9.27 0.5210 302.78 ± 4.09 – 293.22 ± 5.24 0.1537

373.83 ± 7.10 357.16 ± 5.76 373.75 ± 13.52 0.5890 359.90 ± 8.73 369.56 ± 5.32 369.94 ± 9.21 0.4803 364.69 ± 9.24 369.62 ± 6.34 353.60 ± 11.69 0.3414 369.51 ± 5.31 – 368.47 ± 6.80 0.9046

Note: Values with different superscripts within the same column differ significantly at P b 0.05 (a, b, ab). Values are given as mean ± SE. 1 Probability of the F-test for genotype effect. 2 Significant effect (P b 0.05) after modified Bonferroni correction for trait-wise multiple tests. 3 Because of the haplotype TC–AC (n = 0) was absent, only one contrast was estimated—the difference between haplotype TT–AA (n = 75) and CC–CC (n = 112). 4 Traits under study were weight at birth (Birth), 6 months (6), 12 months (12), 18 months (18), and 24 months (24).

body weight in Nanyang population (Table S2). The paradox indicates that the genotype effect of one single SNP may be influenced by other SNPs, and the result is a reflection of interactions of multiple SNPs. Consequently, the analysis of haplotype combination is superior to the analysis of a single SNP. This coincides with the conclusion of Fallin et al., who considered that the inheritance of haplotype combinations was more effective than that of one single SNP (Fallin et al., 2001). 4. Discussion

and JX). The most frequent haplotypes are present in all the breeds studied, whereas the rare haplotypes are exclusive to specific breeds. The two haplotypes (Hap 1 and 8) shared by all three populations comprised 42.50, 19.20, and 29.87% of all haplotypes observed in NY, QC and JX, respectively. The high-frequency haplotypes have probably been present in the population for a long time. Consequently, most of the new mutants are derived from common haplotypes, implying that rarer variants represent more recent mutations and are more likely to be related to common haplotypes than to other rare variants (Posada and Crandall 2001).

4.1. SNPs identified 4.3. Association analysis Bovine GLI3 gene maps to the chromosome 4 and its coding region consists of 13 exons, which are similar in size to human GLI3 gene. In the present study, genomic DNA of all four cattle breeds was successfully amplified using primer pairs for the GLI3 gene. We amplified and sequenced all the introns and exons in the regions of the coding sequence (CDS) of the GLI3 gene from 100 animals; 25 DNA samples were selected randomly from each cattle breed. Compared with previously reported sequence, six SNPs include five silent mutations (SNP1-5) in exons 1, 4, 5 and 10; and one mutation (SNP6) in intron 10 was identified in these animals (Table 1). At the SNP5/6 locus, heterozygous (SNP5/6-TC–AC) was not detected in these four study populations; this phenomenon showed that the SNPs at nt205649 and nt205754 are in complete linkage disequilibrium: with CC always together and TA always together. In the PRL locus a comparable population demonstrated a lack of the AA genotype (Lan et al., 2009) and in the NPM1 locus almost no genotype WW was detected in 1035 individuals of 4 cattle breeds (Huang et al., 2010a). The reason why genotype TC–AC was absent in these breeding farms needs further investigation. 4.2. Linkage disequilibrium and haplotype analysis We found 16 haplotypes present in the GLI3 gene locus: 11 haplotypes in NY, 14 haplotypes in QC, 12 haplotypes in JX; with only 9 haplotypes (Hap 1, 4–10, and 12) shared by all three populations (Table 3). Three haplotypes (Hap 13, 14 and 15) were unique to QC, and one haplotype (Hap 2) specific to NY was detected. Three different haplotypes were found only in two breeds (one haplotype: Hap 3, common to NY and JX, two haplotype: Hap 11 and 16, common to QC

The above results suggested that the cattle with genotype SNP2/ 3-CC–CC, and SNP4-GG could be selected to obtain greater body weight. Because the SNP2, 3, and 4 (SNP2: g.123600T>C; SNP3: g.123696T>C; and SNP4: g.128688A>G) were silent mutations, which cannot result in the change of amino acid. But recently there were some reports about the effects of the silent mutations on the gene function and phenotype (Komar 2007). A silent polymorphism in the MDR1 gene resulted in substrate specificity change (Kimchi-Sarfaty et al., 2007). A silent mutation of goat POU1F1 gene had been found to associate with milk yield and birth weight (Lan et al., 2007). So, it's an interesting work to find out the mechanism for the association between these silent mutations and the growth traits in beef cattle. Previous studies have shown that the SHH signaling via an essential Gli-binding site in the MYF5 epaxial somite (ES) enhancer is required for the specification of epaxial muscle progenitor cells. SHH signaling is also required for the normal mediolateral patterning of myogenic cells within the somite. Combining genetic studies with the use of a transgenic mouse line expressing a reporter gene under the control of the MYF5 epaxial somite enhancer, the results show that GLI2 or GLI3 is required for MYF5 activation in the epaxial muscle progenitor cells (McDermott et al., 2005). Genetic studies have established that the myogenic regulatory factor 5 (MYF5) plays a key role in the specification of mesodermal cells to myogenic lineage, because MYF5-deficient mouse embryos display a significant delay in myogenesis until the onset of MYOD1 expression (Tajbakhsh et al., 1997) and in the absence of three of the four MRFs, MYF5, MYOD1 and MYF6 (Mouse) no skeletal muscle progenitor cell is formed (Kassar-Duchossoy et al., 2004; Rudnicki et al.,

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1993). Previous work has shown that SHH is required for MYF5 activation in epaxial, but not in hypaxial muscle progenitor cells (Kruger et al., 2001). Furthermore, Shh signaling appears to directly act on MYF5 transcription in epaxial progenitor cells. The GLI proteins, which mediate Shh signaling, directly control MYF5 expression in epaxial muscle progenitor cells. GLI2 or GlLI3 is required for epaxial muscle progenitor cell specification and that SHH plays an essential role in this process to convert GLI3, but not GLI2 into a transcriptional activator. The results establish that the GLI genes have essential specific and redundant functions during skeletal myogenesis (McDermott et al., 2005). 5. Conclusions In this study, 6 single nucleotide polymorphisms (SNPs) and 16 haplotypes in the coding region and noncoding region of the bovine GLI3 gene exist in four cattle populations, with their frequencies differing among these breeds; we observed significant associations of GLI3 genotypes with growth traits in one cattle population at some of age. However, no significant associations were found between combined genotypes for different haplotypes in the bovine GLI3 gene and growth traits. These data strongly suggest that GLI3 polymorphisms may be used as a genetic marker for the breeding of new breeds of beef cattle. However, we conclude that further research and validation of the various allelic effects, functional mechanisms and the bioactivity are needed in an independent sample prior to claiming that the GLI3 gene SNPs identified or others can be used for marker-assisted selection for the beef cattle. Acknowledgments This study was supported by the National Natural Science Foundation of China (Grant No. 30972080), Agricultural Science and Technology Innovation Projects of Shaanxi Province (No. 2012NKC01-13), Program of National Beef Cattle and Yak Industrial Technology System (CARS-38), Basic and Foreland Technology Study Program of Henan Province (Grant No. 072300430160). Appendix A. Supplementary data Supplementary data to this article can be found online at http:// dx.doi.org/10.1016/j.gene.2012.10.052. References Abbasi, A.A., Goode, D.K., Amir, S., Grzeschik, K.H., 2009. Evolution and functional diversification of the Gli family of transcription factors in vertebrates. Evol. Bioinforma. 5, 5–13. Amandine, M., Yves, A., Bertrand, S., Gilles, R., Hubert, L., Dominique, R., 2010. Genetic variability and linkage disequilibrium patterns in the bovine DNAJA1 gene. Mol. Biotechnol. 44, 190–197. Barrett, J.E., Fry, B., Maller, J., Daly, M.J., 2005. HAPLOVIEW: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265. Boldman, K.G., Kriese, L.A., Van Vleck, L.D., Van Tassell, C.P., Kachman, S.D., 1995. A Manual for Use of MTDFREML: A Set of Programs to Obtain Estimates of Variances and Covariances [DRAFT]. U.S. Department of Agriculture, Agricultural Research Service, Washington, DC. Botstein, D., White, R.L., Skolnick, M., Davis, R.W., 1980. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 32, 314–331. Derecka, K., et al., 2009. Sequence variants in the bovine gonadotrophin releasing hormone receptor gene and their associations with fertility. Anim. Genet. 41, 329–331. Fallin, D., et al., 2001. Genetic analysis of case/control data using estimated haplotype frequencies: application to APOE locus variation and Alzheimer's disease. Genome Res. 11 (1), 143–151. Gary, V.O.V., Tyurina, R.O.K., Anand, C., 2005. GLI function is essential for motor neuron induction in zebrafish. Dev. Biol. 282 (2), 550–570.

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