Identification of single nucleotide polymorphisms in the tumor necrosis factor (TNF) and TNF receptor superfamily in the Korean population

Identification of Single Nucleotide Polymorphisms in the Tumor Necrosis Factor (TNF) and TNF Receptor Superfamily in the Korean Population Sung-Mi Cho, JuYoung Kim, Ha-Jung Ryu, Jae-Jung Kim, Hyun-Hee Kim, Joo-Hyun Park, Hung-Tae Kim, Kyung-Hee Kim, Hye-Young Cho, Bermseok Oh, Chan Park, Kuchan Kimm, Inho Jo, Jong-Eun Lee, Hyoung Doo Shin, and Jong-Keuk Lee ABSTRACT: The tumor necrosis factor (TNF) and TNF receptor (TNF-TNFR) superfamily plays crucial roles in immune regulation and host immune responses. The superfamily has been also associated with many immunemediated diseases such as asthma, rheumatoid arthritis, inflammatory bowel disease, and diabetes. In order to investigate genetic variants of the TNF-TNFR superfamily, a total of 63 known single nucleotide polymorphisms (SNPs) in the coding region (cSNPs) of the TNF-TNFR superfamily genes were selected from the public SNP database. Among 63 cSNPs tested in this study, only 24 SNPs (38%) were validated to be polymorphic in the Korean population by primer extension-based SNP genotyping. By means of the new enhanced single strand conformational polymorphism (SSCP) method, we also identified a total of 78 SNPs, including 48 known SNPs and 30 novel SNPs, in the 44 human TNF-TNFR superABBREVIATIONS cSNP SNP in the coding region EtBr ethidium bromide MDE mutation detection enhancement PCR polymerase chain reaction

family genes. The newly discovered SNPs in the TNFTNFR superfamily genes revealed that the Korean population had very different patterns of allele frequency compared with African or white populations, whereas Korean allele frequencies were highly similar to those of Asian (correlation coefficient r ⫽ 0.88, p ⬍ 0.046). A higher similarity of allele frequency was observed between Korean and Japanese populations (r ⫽ 0.90, p ⬍ 0.001). The validated SNPs in the TNF-TNFR superfamily would be valuable for association studies with several immune-mediated human diseases. Human Immunology 65, 710 –718 (2004). © American Society for Histocompatibility and Immunogenetics, 2004. Published by Elsevier Inc. KEYWORDS: SNP; SSCP; TNF-TNFR superfamily; Korean

SNP SSCP TNF

single nucleotide polymorphism single strand conformational polymorphism tumor necrosis factor

INTRODUCTION Genetic variations of human genes have been considered as very useful genetic markers for searching human disease genes. In particular, single nucleotide polymorphisms (SNPs), the most abundant type of variation in

the human genome, can be used to identify causative or susceptibility genes of human complex diseases [1]. The detection of SNPs in the candidate genes is a crucial step before disease association studies. The tumor necrosis

From the National Genome Research Institute (S.-M.C., JY.K., H.-J.R., J.-J.K., H.-H.K., J.-H.P., H.-T.K., H.-H.K., H.Y.C., B.O., C.P., K.K., J.-K.L.) and Department of Biomedical Sciences (I.J.), National Institute of Health, DNA Link Inc. (J.-E.L.), and SNP-Genetics Inc. (H.D.S.), Seoul, Korea. Address reprint requests to: Dr. Jong-Keuk Lee, National Genome Re-

search Institute, National Institute of Health, 5 Nokbun-dong, Eunpyungku, Seoul 122-701, Korea; Tel: 82-2-380-1523; Fax: 82-2-354-1063; E-mail: [email protected]. Received February 3, 2004; revised April 22, 2004; accepted April 26, 2004. S.-M.C., JY.K., and H.-J.R. contributed equally to this work.

Human Immunology 65, 710 –718 (2004) © American Society for Histocompatibility and Immunogenetics, 2004 Published by Elsevier Inc.

0198-8859/04/$–see front matter doi:10.1016/j.humimm.2004.04.005

Polymorphisms of the TNF-TNFR Superfamily

factor (TNF)–TNF receptor (TNFR) superfamily plays a very important role in host immune responses by regulating cell proliferation, survival, differentiation, and apoptosis [2]. Therefore, among many disease candidate genes, the TNF and TNF receptor (TNF-TNFR) superfamily have been targeted as major candidate genes of many immune-mediated diseases. Previously identified dysfunctions of this superfamily have been demonstrated to cause severe immune-mediated disorders such as hyper IgM syndrome (CD40L), autoimmune lymphoproliferative syndrome (Fas/FasL), TNF-R1–associated periodic fever syndrome (TNF-R1), hypohidrotic ectodermal dysplasia (EDA/EDAR) and familial expansile osteolysis (RANK) [2]. Furthermore, the polymorphisms of the superfamily genes have been associated with other human diseases, for example, lymphotoxin-␣ with myocardial infarction [3], CD40 with Graves disease [4], Fas with Alzheimer disease [5], and TNF-R2 with Crohn disease [6]. Interestingly, TNF has been associated with diseases such as malaria [7], asthma [8], schizophrenia [9], and ulcerative colitis [6], indicating that TNF may have pleiotropic effects in the pathogenesis of many immune-mediated diseases. Further studies may reveal additional associations between the superfamily genes and immune-mediated diseases in the future. In order to facilitate disease association studies by using the polymorphisms of these superfamily genes, we screened for the presence of novel SNPs, as well as already known SNPs of the TNF-TNFR superfamily genes, in the Korean population. MATERIALS AND METHODS Genomic DNA Isolation and Samples Unrelated, healthy Koreans, aged 34 – 62 years, were randomly selected in Seoul metropolitan areas for SNP genotyping and SNP detection. A total of 24 DNA samples were used to identify SNPs in the TNF-TNFR superfamily genes in the Korean population by the polymerase chain reaction (PCR)–single strand conformational polymorphism (SSCP) method, and a total of 43 other healthy Korean subjects were used for SNP genotyping analysis by the SNP-IT technique. DNA samples were obtained from these subjects by the standard DNA extraction method. SNP Genotyping for Allele Frequency SNP genotyping was performed by SNP-IT assays with the SNPstream 25K System (Orchid Biosciences, Princeton, New Jersey) as previously described [10]. Briefly, the genomic DNA region spanning the polymorphic site was PCR-amplified with one phosphothiolated primer and one regular PCR primer. The amplified PCR products were then digested with exonuclease. The 5⬘ phos-

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phothiolates were used in this study to protect one strand of the PCR-product from exonuclease digestion. The single-stranded PCR template generated from exonuclease digestion was overlaid onto a 384-well plate to which the SNP-IT primer extension primers were covalently attached. These SNP-IT primers were designed to hybridize immediately adjacent to the polymorphic site. After hybridization of template strands, SNP-IT primers were then extended by a single base with DNA polymerase at the polymorphic site of interest. The extension mixtures contained two labeled terminating nucleotides (one FITC, one biotin) and two unlabeled terminating nucleotides. The final single base incorporated was identified with serial colorimetric reactions with anti– FITC-AP and streptavidin– horseradish peroxidase, respectively. The results of blue and/or yellow color developments were analyzed with an enzyme-linked immunoabsorbent assay reader, and the final genotyping (allele) calls were made with the QCReview program. PCR-SSCP Analysis and Direct Sequencing PCR primers for SSCP analysis were designed from the sequences of 44 TNF-TNFR superfamily genes (http://www. gene.ucl.ac.uk/nomenclature/genefamily/tnftop.html) to amplify each exon of the selected genes (the information for PCR primers used in this study is available on request). A total of 311 primer sets were used for SSCP analysis. The average size of the amplicons was 250.8 ⫾ 62.19 bp (mean ⫾ standard deviation; minimum ⫽ 120 bp, median ⫽ 251 bp, maximum ⫽ 421 bp). Depending on the size of the exon, a minimum of 50 bp intronic regions were included to amplify the exon. In order to perform PCR-SSCP analysis, genomic DNA (20 ng) was used for PCR amplification. Each 20 ␮1 PCR reaction contained 20 ng genomic DNA, 0.25 ␮M of each primer, 200 ␮M of each dNTP, 1 unit of AmpliTaq Gold (Applied Biosystems, Foster City, CA) and reaction buffer with 1.5 mM MgCl2. PCR was performed at 94°C for 2 minutes, followed by 40 cycles at 94°C for 45 seconds, 55°C for 30 seconds, 72°C for 45 seconds, and final extension at 72°C for 5 minutes. The PCR products were mixed (1:1) with loading buffer (95% formamide, 10 mM NaOH, 0.025% bromophenol blue, 0.025% xylene cyanol), denatured at 95°C for 5 minutes, chilled on ice, and loaded on a medium-size (18 cm ⫻ 16 cm ⫻ 2.7 mm) Hoefer SE 600 Ruby gel electrophoresis system (Amersham Pharmacia Biotech Korea, Seoul, Korea) with 0.5⫻ mutation detection enhancement (MDE) gels (FMC BioProducts, Rockland, ME). Electrophoresis was performed in 0.5⫻ TBE buffer and run at 20 W for approximately 2 hours. During electrophoresis, the temperature of the gel was kept constant by setting MultiTemp III Thermostatic Circulator (Amersham Pharmacia Biotech Korea) at 4°C. After gel electrophoresis, the single-stranded

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TABLE 1 Summary of cSNP genotyping in the TNF-TNFR superfamily genes selected from the publicly available SNP databasea Gene (alias) TNFSF1 (LTA)

TNFRSFIA (TNF-R1) TNFRSF1B (TNF-R2) TNFRSF4 (OX40) TNFRSF6B (DcR3) TNFSF7 (CD70) TNFRSF7 (CD27) TNFRSF8 (CD30) TNFSF10 (TRAIL) TNFRSF10A (DR4) TNFRSF10B (DR5) TNFRSF11A (RANK) TNFRSF11B (OPG) TNFSF14 (LIGHT) TNFRSF14 (HVEM) TNFRSF16 (NGFR) EDAR

SNP (rs or new)

Amino acid change

Allele

Frequency (Korean: n ⫽ 43)

rs2071589 rs2229092 rs2229093 rs2239704 rs1139417 rs945439 rs17568 rs2738787 rs1862511 rs25680 rs476488 rs2230622 rs2230625 rs1131532 rs2230229 rs1047266 rs1805034 rs1804853 rs2291668 rs344560 rs4870 rs2234163 rs2072446 new

Pro52Pro His51Pro Thr60Asn 5⬘-UTR Pro12Pro Lys56Lys Glu178Glu Arg1388Gly Cys115Cys Ala59Thr Ser449Ser Asp90Asp Ser412Gly Phe275Phe Arg441Lys Ala67Val Ala192Val Leu331Phe Ala49Ala Lys214Glu Lys17Arg Ala117Thr Ser205Leu Cys352Cys

C:T A:C A:C A:C A:G A:G A:G A:G C:T A:G C:T C:T A:G C:T A:G C:T C:T G:T C:T A:G A:G A:G C:T C:T

0.99:0.01 0.99:0.01 0.36:0.64 0.56:0.44 0.85:0.15 0.80:0.20 0.67:0.33 0.01:0.99 0.29:0.71 0.06:0.94 0.51:0.49 0.70:0.30 0.81:0.19 0.52:0.48 0.96:0.04 0.93:0.07 0.31:0.69 0.95:0.05 0.70:0.30 0.08:0.92 0.52:0.48 0.96:0.04 0.88:0.12 0.09:0.91

a The following SNPs were monomorphic among 43 Korean individuals: rs1800618, rs2228088, rs2229698, rs1062460, rs1042318, rs1804534, rs1804854, rs1050927, rs2291667, rs2234152, rs2234153, rs20577, rs17620, rs2230230, rs2071336, rs2230623, rs2230627, s2234180, rs2234181, rs433272, rs372024, rs430991, rs1059334, rs1064590, rs1059333, rs1059332.

DNA was stained with ethidium bromide (EtBr; 10 ␮g/ml) for 10 minutes and visualized on an ultraviolet transilluminator. The gel picture image was inverted with the Scion Image computer program (http://www.scioncorp.com). In order to identify the position and the type of SNP, individual PCR products that displayed a band shift on the SSCP gel were purified with QIAquick PCR purification kit (Qiagen, Hilden, Germany), following the manufacturer’s instructions. The samples were sequenced with BigDye terminator v2.0 cycle sequencing kit (Applied Biosystems) on an ABI 3100 DNA sequencer, according to the manufacturer’s instructions. Sequences were analyzed by Lasergene software (DNAStar, Madison, WI). Statistical Analysis Statistical analysis was performed by SAS statistical software (SAS Institute, Cary, NC). The correlations of minor allele frequencies between genotyping methods or ethnic groups were determined by the Pearson correlation coefficient. The level of statistical significance was set at p ⫽ 0.05.

RESULTS Confirmation of Publicly Available SNPs in the Coding Regions of the TNF-TNFR Superfamily in the Korean Population In order to initially test the presence of the previously known SNPs of the TNF-TNFR superfamily genes in the Korean population, a total of 63 coding SNPs of the TNF-TNFR superfamily genes were selected from the publicly available SNP database (http://www.ncbi.nlm. nih.gov/SNP/) and tested by the SNP-IT genotyping method by using 43 DNA samples of unrelated Korean individuals. Among 63 SNPs in the coding regions (cSNP) tested in this study, 24 SNPs (38%) were polymorphic and 26 SNPs (41%) were monomorphic in the Korean population (Table 1). The remaining 13 SNPs (20%) failed assay development. These results indicate that many of the SNPs available in the public SNP database are not appropriate genetic markers for mapping disease genes in the Korean population. Therefore, this suggests that prescreening of publicly available SNPs is necessary before use of the SNP markers for genetic studies of a specific population.

Polymorphisms of the TNF-TNFR Superfamily

Polymorphisms of the TNF-TNFR Superfamily Detected by the Enhanced PCR-SSCP in the Korean Population As revealed in Table 1, screening of previously known SNPs in the Korean population suggests that more SNP markers should be identified in the Korean population for association studies of human complex diseases. In order to identify more new SNPs in the TNF-TNFR superfamily genes in the Korean population, the PCRSSCP method was used for SNP discovery. For large-scale SNP detection, we improved previously available PCRSSCP protocols [11–13]. An enhanced PCR-SSCP method was established by using hot-start DNA polymerase and high-resolution MDE gels with an efficient temperature-controlled gel electrophoresis system, as described in the Materials and Methods. In order to validate the new enhanced PCR-SSCP technique and to identify the genetic variants of the TNF-TNFR superfamily in the Korean population, all 44 TNF-TNFR superfamily genes were screened in 24 unrelated Korean individuals by the new PCR-SSCP protocol. Because most of functional genetic variants were found in the coding regions of genes [14], all exons were targeted for SNP detection by designing PCR primers for SSCP analysis. We screened a total of 287 exons of the 44 TNF-TNFR superfamily genes with an enhanced PCRSSCP protocol and identified 78 SNPs, including 48 known SNPs and 30 novel SNPs (Table 2). Among the 78 SNPs detected by PCR-SSCP, 48 SNPs (61.5%) were detected in the coding regions of the gene. Additionally, 15 of the 48 cSNPs, created nonsynonymous amino acid changes that can potentially affect biological function. We also identified 27 (34.6%) SNPs and 3 (3.8%) SNPs in the introns and untranslated regions of the gene, respectively. Although 36% of the SNPs had rare allele frequencies (⬍ 10%), the remainder had allele frequencies ⬎10%, making them useful SNP markers for association studies. Our results indicate that a strategy to discover new SNPs in the coding regions of a gene by PCR-SSCP is an appropriate approach to identify many new functional cSNPs. Ethnic Differences of SNP Allele Frequency in the TNF-TNFR Superfamily In order to determine the ethnic differences of the SNP allele frequency generated either by PCR-SSCP or SNP-IT genotyping in unrelated Korean individuals, we searched the publicly available SNP database and compared our data with those of other ethnic groups. There were significant differences of allele frequency among different ethnic groups (Table 3). The Korean population had very different patterns of allele frequency compared with African or white populations, whereas Korean samples were very similar in allele frequency to Asian sam-

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ples (correlation coefficient r ⫽ 0.88, p ⬍ 0.046) (Table 3). The highest degree of similarity in allele frequency was observed between Korean and Japanese populations (r ⫽ 0.90, p ⬍ 0.001) (Table 4). These data indicate that there are significant differences in the distribution of SNP allele frequency among diverse ethnic groups. However, the high degree of similarity of allele frequencies between Korean and Japanese groups suggests that both populations may share a common origin of ancestry, as expected from their very close geographical location. DISCUSSION SNPs are very useful genetic markers for locating genes responsible for complex diseases through screening of a population sample of unaffected and affected individuals (case-control study). Several technical methods can be used for SNP detection, for example, DNA sequencing, SSCP, DDGE, dHPLC, RFLP, and the protein truncation test (PTT), among others [16]. Among these methods, the single strand conformational polymorphism (SSCP) technique is a relatively simple and sensitive method that can detect sequence changes and small deletions and insertions. SSCP is based on the detection of the altered mobility of a single stranded DNA as a result of conformational alteration caused by sequence changes [17]. The method had been used in mutation detection for many human genetic diseases including hemophilia B, ataxia-telangiectasia, familial hypercholesterolemia and cancers [18 –21], but, in its usual form, an optimization process with respect to gel parameters is necessary for each sequence [22]. In addition, radioisotope or silver staining is frequently used to overcome low resolution of single stranded DNA [22–24]. Therefore, SSCP is not an appropriate system for large-scale polymorphism detection. However, in this study, in order to use the PCR-SSCP technique for large-scale SNP screening, we established a new enhanced PCR-SSCP protocol by the use of hot-start DNA polymerase, a high resolution MDE gel solution, a thermostatically controlled circulator, and EtBr for single stranded DNA staining. The use of hot start DNA polymerase produced a large amount of PCR products by increasing PCR cycles without any loss of specificity. The greater yield of PCR products allowed us to easily detect the SSCP bands by conventional EtBr staining. The effectiveness of the improved PCR-SSCP method for SNP detection was validated by screening a large number of TNF and TNF receptor (TNF-TNFR) superfamily genes. We identified a total of 78 SNPs. An SNP was detected in every 970 bp screened by the SSCP method. Because it has been estimated that one SNP exists per 1 kb in the human genome, the detection of 1 SNP per 970 bp by SSCP method therefore suggests that

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TABLE 2 Summary of 78 SNPs in the TNF-TNFR superfamily genes detected by an enhanced PCR-SSCP in the Korean populationa Gene (alias) TNFSF1(LTA) TNFSF2 (TNF) TNFSF3 (LTB) TNFRSF1A (TNF-R1)

TNFRSF1B (TNF-R2) LTBR (TNF-R3) TNFSF4 (OX40L) TNFRSF4 (OX40) TNFSF5 (CD40L) TNFRSF5 (CD40) TNFSF6 (FasL) TNFRSF6 (Fas) TNFRSF6B (DcR3) TNFSF7 (CD70) TNFRSF7 (CD27) TNFSF8 (CD30L) TNFRSF8 (CD30)

TNFSF9 (4-1BBL) TNFRSF9 (4-1BB) TNFSF10 (TRAIL) TNFRSF10A (DR4) TNFRSF10B (DR5) TNFRSF10C (DcR1) TNFRSF10D (DcR2)

TNFSF11 (OPGL) TNFRSF11A (RANK) TNFRSF11B (OPG) TNFSF12 (TWEAK) TNFRSF12 (DR3) TNFSF13 (APRIL) TNFSF13B (Blys)

SNP (rs or new) rs1041981 N/D N/D rs767455 rs1800692 rs1800693 new rs945439 rs945438 rs653667 rs2364480 new rs12354 new new new rs17568 rs1126535 rs1883832 rs3765456 N/D rs3218612 rs2296601 rs2234978 rs2258056 rs909341 rs1291205 rs1862511 rs758738 rs1059501 rs3181195 new new new new rs3737959 rs2297729 new N/D N/D rs1131532 rs4871857 rs4872077 rs1047266 new new new rs1133782 new new rs1805034 rs2073618 rs3803798 new rs3803800 new new

Amino acid change

SNP

Frequencyc

Exon 3 (80)

Asn60Thr

GCA(A/C)CCT

0.49:0.51

Exon 1 (307) Intron 4 (187) Intron 6 (10) Intron 7 (294) Exon 2 (90) Intron 2 (13) Intron 3 (666) Exon 5 (44) Intron 6 (26) Exon 10 (280) Intron 2 (25) Intron 2 (49) Exon 1 (40) Exon 5 (97) Exon 1 (187) Exon 1 (77) Intron 7 (28)

Pro12Pro

GCC(A/G)CTG GCT(C/T)CTC AGA(A/G)GTG CTT(C/T)TTC CAA(A/G)TGC GCC(A/G)CGG CGT(G/T)TGC GGC(A/C)GGG CAC(INS:A)GGG GAC(G/T)GAG ATT(G/T)ATT CTT (C/T)TCT GGC(C/T)GTG GGA(A/G)ACC AGG(C/T)TGG CGC(C/T)ATG TGG(A/G)GGA

0.87:0.13 0.57:0.43 0.84:0.16 0.44:0.56 0.88:0.12 0.88:0.12 0.40:0.60 0.85:0.15 0.15:0.85 0.83:0.17 0.98:0.02 0.98:0.02 0.98:0.03 0.79:0.21 0.06:0.94 0.48:0.52 0.35:0.65

CAC(A/G)TCA TTT(C/T)ATG AAC(C/T)TTA CCG(C/T)GCC CAG(C/T)TCC CAC(C/G)CTG CTG(C/T)TCC AAG(A/G)CTG CGG(G/T)AGC AAG(A/G)GCT TGT(C/T)GAT TGA(A/T)GAC ACC(C/T)TCT TGT(C/T)CTC AGA(C/T)GAC GTC(A/G)TCT TCC(A/G)GCG

0.99:0.01 0.02:0.98 0.98:0.02 0.76:0.24 0.31:0.69 0.31:0.69 0.25:0.75 0.04:0.96 0.43:0.57 0.33:0.67 0.99:0.01 0.17:0.83 0.83:0.17 0.04:0.96 0.69:0.31 0.10:0.90 0.83:0.17

TTT(C/T)GGG GC (C/G)AGG CCC(A/G)CTG GAG(C/T)GGC CCC(C/G)GCC GAC(C/T)CCA CCT(A/G)GCG AGT(C/T)GCC CAG(A/G)GTC CTT(C/T)GGG ATG(C/T)GGT CAA(C/G)TTG GGC(C/G)AGT GCT(A/C)TCT AGA(A/G)TGG TTC(AA/TT)CAT CCT(C/G)GCC

0.58:0.42 0.97:0.03 0.94:0.06 0.84:0.16 0.71:0.29 0.35:0.65 0.02:0.98 0.94:0.06 0.13:0.88 0.05:0.95 0.33:0.67 0.25:0.75 0.63:0.37 0.99:0.01 0.54:0.46 0.82:0.18 0.85:0.15

Position (nt)b

Exon 3 (26) Intron 3 (46) Exon 7 (74) Exon 1 (432) Exon 2 (62) Exon 2 (149) Exon 3 (149) Exon 2 (39) Exon 6 (140) Exon 3 (38) Exon 4 (158) Intron 2 (54) Intron 2 (59) Intron 3 (14) Exon 4 (2) Intron 6 (27) Exon 11 (51) Exon 5 (407) Exon 4 (109) Intron 5 (14) Exon 2 (57) Intron 1 (10) Exon 1 (185) Intron 1 (43) Exon 7 (160) Intron 8 (62) Exon 5 (392) Exon 6 (54) Exon 1 (260) Exon 7 (102) Intron 9 (213) Exon 2 (29) Intron 4 (162-3) Intron 1 (61)

Lys56Lys Ala172Ala 3⬘-UTR Pro12Leu Glu178Glu Leu50Leu 5⬘-UTR Thr74Thr Thr214Thr Arg111Arg Ser162Ser Thr191Thr Cys115Cys Ala59Thr 3⬘-UTR Arg92Arg Val156Val

Asp90Asp Ser412Gly Phe275Phe Arg209Thr Ala67Val Pro35Ser Leu310Ser Phe308Phe Ala192Val Asn3Lys Ala200Ala Asn96Ser

Polymorphisms of the TNF-TNFR Superfamily

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TABLE 2 Continued

Gene (alias) TNFRSF13B (TACI) TNFRSF13C (BCMA) TNFSF14 (LIGHT) TNFRSF14 (HVEM) TNFSF15 (VEGI) TNFRSF16 (NGFR) TNFSF18 (GITRL) TNFRSF18 (GITR) TNFRSF19 (TROY)

EDA EDAR

XEDAR TNFRSF21 (DR6)

SNP (rs or new) new new rs2017662 rs2071336 rs2291667 rs2291668 rs2234161 rs3810936 new new new new new rs2298213 new rs3751364 rs3751363 rs3751362 rs2296765 rs3749098 rs3749099 new rs3827760 new new N/D N/D

Position (nt)b Exon 5 (15) Exon 5 (121) Exon 3 (200) Exon 3 (248) Exon 2 (184) Exon 2 (236) Intron 3 (1298) Exon 4 (302) Exon 3 (125) Exon 4 (227) Exon 3 (6) Exon 4 (52) Intron 4 (50) Intron 5 (22) Intron 2 (3089) Exon 7 (14) Exon 9 (370) Exon 9 (374) Intron 5 (920) Exon 10 (10) Exon 10 (67) Exon 12 (32) Exon 12 (85) Exon 12 (107) Exon 12 (114)

Amino acid change Asp203Asp Pro251Leu Thr159Thr Thr175Thr Ser32Leu Ala49Ala Val201Val Tyr111Tyr Gly265Gly Leu65Leu Asn150Asn

Ser208Ser Gly403Gly Val405Ile Asp271Asp Pro290Pro Cys352Cys Val370Ala Leu377Leu Ser380Arg

SNP

Frequencyc

TGA(C/T)AAG CCC(C/T)CGA CAC(A/G)AAA TAC(A/G)GAG AGT(C/T)GTG GGC(C/T)GGG GCC(C/T)GTG TGT(A/G)TGC CTA(C/T)GGC GGG(C/T)CTT CCA(C/T)TAC CAA(C/T)GCT GTG(C/T)GGG GCC(A/G)CCG TTT(C/T)CAG GTC(A/G)CAG TGG(C/T)GCT GCT(A/G)TCA TCT(C/T)ATC CGA(C/T)GCC GCC(C/T)GCC TTG(C/T)CTC CTG(C/T)TGT CCT(C/T)GCC GAG(A/C)GCT

0.04:0.96 0.68:0.32 0.17:0.83 0.17:0.83 0.75:0.25 0.25:0.75 0.40:0.60 0.92:0.08 0.93:0.07 0.03:0.97 0.87:0.13 0.93:0.07 0.84:0.16 0.18:0.82 0.39:0.61 0.83:0.17 0.25:0.75 0.92:0.08 0.08:0.92 0.92:0.08 0.08:0.92 0.85:0.15 0.98:0.02 0.98:0.02

a

SNPs, single nucleotide polymorphisms; N/D, not detected; nt, nucleotide; UTR, untranslated region; INS, insertion. Nucleotide numbering is according to the mutation nomenclature [15]. c Allele frequency was determined on the basis of the distinct SSCP banding patterns followed by direct sequencing of the samples. b

the new improved PCR-SSCP protocol is a good method for large-scale SNP discovery and/or detection. In order to indirectly examine the accuracy of PCR-SSCP method, we compared the allele frequencies of SNPs (rs945439,

rs17568, rs1862511, rs1131532, rs1047266, rs1805034, rs2071336, rs2291668) detected by both PCR-SSCP and SNP-IT genotyping methods (Tables 1 and 2). Although we used two independent sets of DNA

TABLE 3 Ethnic comparison of SNP allele frequencies of the TNF-TNFR superfamilya Gene (alias) TNFSF1 (LTA) TNFRSF1A (TNF-R1) TNFRSF6B (DcR3) TNFSF7 (CD70) TNFSF8 (CD30L) TNFSF14 (LIGHT) a

SNP (rs)

Allele

Koreanb

Asian

African

White

Ethnic differencec

rs1041981 rs2239704 rs767455 rs1800692 rs1800693 rs2258056 rs1862511 rs476488 rs344560

A:C A:C A:G C:T A:G C:T C:T C:T A:G

0.49:0.51 0.56:0.44 0.87:0.13 0.57:0.43 0.84:0.16 0.76:0.24 0.27:0.73 0.51:0.49 0.08:0.92

N/A N/A 0.64:0.36 N/A N/A 0.71:0.29 0.33:0.67 0.42:0.58 0.12:0.88

0.52:0.48 0.21:0.79 0.46:0.54 0.92:0.08 0.64:0.36 0.83:0.17 0.88:0.13 0.20:0.80 0.06:0.94

0.36:0.64 0.40:0.60 0.50:0.50 0.65:0.35 0.45:0.55 0.83:0.17 0.79:0.21 0.21:0.79 0.02:0.98

0.16 0.35 0.41 0.35 0.39 0.12 0.61 0.31 0.10

N/A, not available. Allele frequency of the Korean population was determined either by the SSCP or SNP-IT methods. c Ethnic differences in allele frequency were calculated by subtracting the lowest allele frequency from the highest allele frequency of the minor alleles among ethnic groups (White, n ⫽ 42 individuals; African, n ⫽ 42 individuals; Asian, n ⫽ 42 individuals) at each SNP site. b

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TABLE 4 Comparison of SNP allele frequencies between Korean and Japanese populations Gene (alias) TNFSF1 (LTA) TNFRSF1A (TNF-R1) TNFRSF1B (TNF-R2) TNFRSF6B (DcR3) TNFRSF7 (CD27) TNFSF8 (CD30L) TNFSF10 (TRAIL) TNFRSF11B (OPG) TFNRSF13C (BCMA) TNFSF14 (LIGHT) TNFRSF14 (HVEM) TNFSF15 (VEGI) TNFRSF16 (NGFR) EDAR

SNP (rs or new)

Allele

rs1041981 rs2071589 rs2239704 rs1800692 rs653667 rs1291205 rs1059501 rs2297729 rs2230625 rs1131532 rs2073618 rs2071336 rs2291667 rs2291668 rs344560 rs2234161 rs3810936 rs2072446 rs3751364 rs3749098 rs3749099 rs3827760 rs2298213

A:C C:T A:C C:T G:T C:G G:T A:G A:G C:T C:G A:G C:T C:T A:G C:T A:G C:T A:G C:T C:T C:T A:G

Korean

Japaneseb (JSNP)

Ethnic differencec

0.49:0.51 0.99:0.01 0.56:0.44 0.57:0.43 0.40:0.60 0.31:0.69 0.43:0.57 0.10:0.90 0.81:0.19 0.55:0.45 0.26:0.74 0.08:0.92 1:0 0.73:0.27 0.08:0.92 0.25:0.75 0.40:0.60 0.88:0.12 0.39:0.61 0.08:0.92 0.92:0.08 0.85:0.15 0.84:0.16

0.36:0.64 0.98:0.03 0.47:0.53 0.61:0.39 0.31:0.69 0.39:0.61 0.49:0.51 0.12:0.88 0.85:0.15 0.58:0.42 0.29:0.71 0.09:0.91 0.99:0.01 0.63:0.37 0.07:0.93 0.48:0.52 0.39:0.61 0.90:0.10 0.47:0.53 0.03:0.97 0.97:0.03 0.78:0.22 0.81:0.09

0.13 0.01 0.09 0.04 0.09 0.08 0.06 0.02 0.04 0.03 0.03 0.01 0.01 0.10 0.01 0.23 0.01 0.02 0.08 0.05 0.05 0.07 0.03

a

a

Allele frequency of the Korean population was determined by either the SSCP or SNP-IT methods. Allele frequencies were determined from data obtained by searching the JSNP database (http://snp.ims.u-tokyo.ac.jp/) [29]. c Ethnic difference in allele frequency was calculated by subtracting the lowest allele frequency from the highest allele frequency of the minor alleles between Korean (n ⫽ 43 or 24 individuals) and Japanese (n ⫽ 752 individuals) populations. b

samples (43 and 24 samples for SNP-IT and SSCP, respectively), an average difference in allele frequency of only 7% was observed between the PCR-SSCP and SNPIT methods. The 7% difference is very small and can be commonly observed in genotyping of small numbers of different samples. Furthermore, a high correlation of allele frequencies was observed between PCR-SSCP method and SNP-IT method (correlation coefficient r ⫽ 0.82, p ⫽ 0.007). In the SNP detection that used 24 individuals (a total of 48 independent chromosomes), SNPs with allele frequencies of ⬎1%, 5%, and 10% can be detected with a probability of 76%, 95%, and 99%, respectively. Because rare SNPs are not necessary for statistical analysis in association studies, we only targeted SNPs that had allele frequencies of ⬎5% in the Korean population. Therefore, 24 or 43 unrelated Korean individuals for SNP detection are reasonably sufficient numbers of samples to detect most of the SNPs (⬎ 5% allele frequency) present in the Korean population. The TNF-TNFR superfamily plays a crucial role in many immune responses [2]. The number of recognized members of the TNF-TNFR superfamily has expanded substantially in the last several years. To date, 44 TNFTNFR superfamily proteins have been identified. More importantly, the biological function of this group of

proteins has been closely associated with the pathogenesis of many autoimmune diseases [3, 6, 25]. A total of 78 SNPs were identified in the 44 TNF-TNFR superfamily genes in the Korean population by the enhanced PCR-SSCP. The newly discovered SNPs of the TNFTNFR superfamily genes will be extremely valuable for association studies with many immune-mediated diseases. Because significant differences of allele frequency and genomic structure were observed in different ethnic populations [26, 27], it will be of interest to test whether there is any ethnic difference in disease occurrences due to different allele frequencies of functional SNP frequencies. There were significant differences in allele frequency of TNF-TNFR superfamily genes among diverse ethnic groups (Table 3). As expected, a high degree of similarity in SNP allele frequencies was observed between Korean and Asian populations (r ⫽ 0.88, p ⫽ 0.047). However, there was no similarity in the SNP allele frequencies between the Korean group and other ethnic groups (r ⫽ 0.40, p ⫽ 0.283 between Korean and African populations; r ⫽ 0.39, p ⫽ 0.292 between Korean and white populations) (Table 3). Meanwhile, the highest degree of similarity in allele frequencies was observed between Korean and Japanese populations (r ⫽ 0.90, p ⬍ 0.001) (Table 4). The average ethnic difference of allele frequen-

Polymorphisms of the TNF-TNFR Superfamily

cies was 0.31 among diverse ethnic groups (Korean, Asian, African and white), whereas the average ethnic difference of allele frequencies was 0.06 between the Korean and Japanese groups (Tables 3 and 4). These data suggest that genetic origins of the Korean population are far from other ethnic populations (African and white), but very close to the Japanese. In addition, the ethnically different SNP markers of the TNF-TNFR superfamily identified in this study will be also useful for mapping by admixture linkage disequilibrium analysis as mentioned previously [26]. In addition, TNF-TNFR superfamily also has been used as a marker of human ancestry [28]. Therefore, the differences in the SNP allele frequencies of the TNF-TNFR superfamily among different ethnic groups may be of potential importance for disease association studies, as well as population studies of human origin. The broad diversity of SNP allele frequencies among different ethnic groups suggests that ethnic differences of allele frequencies of SNPs must be considered for the association studies of human complex diseases. ACKNOWLEDGMENTS

This work was supported by an intramural grant of the National Institute of Health, Korea. I.J. was supported in part by an IMT-2000 research grant (01-PJ11-PG9-01BT05-0003) from the Ministry of Health and Welfare and the Ministry of Science and Technology, Korea.

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