Human Molecular Genetics, 2000, Vol. 9, No. 13 1943-1949
© 2000 Oxford University Press
Linkage and association of tumor necrosis factor receptor 2 locus with hypertension, hypercholesterolemia and plasma shed receptor
Basic & Clinical Genomics Laboratory, Department of Physiology and Institute for Biomedical Research, Building F13, The University of Sydney, Sydney, New South Wales 2006, Australia
Received 13 March 2000; Revised and Acepted 20 June 2000.
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ABSTRACT |
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Tumor necrosis factor (TNF) receptor 2 (TNF-R2) has been implicated in insulin resistance and metabolic syndrome disorders, one of which is hypertension (HT). We therefore decided to test markers in and near the TNF-R2 gene (TNFRSF1B) for linkage and association with HT, as well as hypercholesterolemia, and plasma levels of the shed soluble receptor (sTNF-R2). The linkage study, which involved 200 HT Anglo-Celtic Caucasian sibpairs, indicated a sharp, significant linkage peak centered at TNFRSF1B (multipoint maximum LOD score = 2.6 and 3.1 by weighted and unweighted MAPMAKER/SIBS, respectively; two-point LOD scores = 2.9 and 3.9 by weighted and unweighted SPLINK, respectively; P = 10–4 by identical-by-state
2). The case–control study in 134 unrelated HTs who were the offspring of two HT parents and 197 normotensives (NTs) whose parents were both NTs, indicated possible association of TNFRSF1B with HT by haplotype analysis (P = 0.008). Plasma sTNF-R2 was elevated in HTs (P < 0.0001) and showed a correlation with systolic and diastolic blood pressure (BP) (P < 0.0002). A genotypic effect of TNFRSF1B on plasma sTNF-R2, as well as total, low and high density lipoprotein cholesterol, and diastolic BP was observed. These observations are consistent with a scheme leading to raised BP and hypercholesterolemia. In conclusion, TNFRSF1B may be a candidate gene for HT and other metabolic syndrome abnormalities. |
INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Tumor necrosis factor (TNF) receptors, TNF-R1 (alias p55 or p60) and TNF-R2 (p75 or p80), are both involved in eliciting the multitude of effects of the cytokine TNF-
. Such effects include increased lipolysis, insulin resistance, endothelial function, vascular remodeling, cardiac hypertrophy and ventricular contraction (1,2). It could be that insulin resistance is the price paid when TNF acts to reduce weight gain (3). TNF-R2 has a similar ligand-binding domain to that of TNF-R1, but each differs markedly in cytoplasmic domains, indicating usage of distinct signal transduction pathways. TNF-R2 synergizes with TNF-R1, possibly by sequestering TNF- due to its higher affinity and faster dissociation, and ‘passing’ it to TNF-R1 (4). Each receptor is expressed by most cells, but at different densities, and can be regulated independently. TNF- can induce marked upregulation of TNF-R2 mRNA with little or no change in TNF-R1 (5,6). This is followed by rapid shedding of the extracellular domain to give plasma soluble TNF-R2 (sTNF-R2) which neutralizes TNF at high concentrations but, when low, preserves TNF activity and increases long-term effects (7). Enhancement by TNF-R2 of TNF-R1 promotes NF-
B activation and apoptosis (8). TNF-R2 also has independent effects (9) that occur later, are of a long-term nature (5), and include cell proliferation (9). In addition, it is TNF-R2 that mediates strong stimulation by the transmembrane (pro) form of TNF (10).
In patients with essential hypertension (HT) and in genetically HT rats, alterations in both humoral and cellular immunity, including decreased T cells and increased immunoglobulin, have been described, leading to the suggestion that abnormalities in immune system function and inflammatory mediators may be responsible for the onset of HT (11,12). Moreover, there is evidence that the TNF system could contribute to such defects. Essential HT patients display increased TNF- secretion from monocytes (13) and TNF- causes greater monocyte adhesion to vascular endothelial cells of genetically HT compared with control normotensive (NT) rats (14). Vascular damage, caused by HT, might contribute to the increased plasma TNF- seen in this condition. Raised TNF- could also be a response to infection by Chlamidia pneumoniae (15) or Helicobacter pylori (16) antibodies to which are elevated in HT patients. Higher TNF- is associated with increased sTNF-R2 shedding. Moreover, stimulation by TNF- of inducible nitric oxide synthase would increase nitric oxide production, leading to vasodilation and apoptosis of vascular smooth muscle (17). In the kidney, TNF is produced by renal tubules where it affects ion transport (18) and opposes angiotensin II (19). Thus, overall, the effects of the TNF system serve to lower blood pressure (BP). A deficiency in this system might therefore contribute to HT.
When taken together, all of the above observations suggested to us that the TNF-R2 gene (TNFRSF1B; 1p36.2) might be of particular interest in studying the genetic basis of HT. Moreover, since (i) HTs have elevated cholesterol (20); (ii) TNF increases plasma lipids (21,22), involving stimulation of hepatic lipid synthesis and secretion (23) and inhibition of lipoprotein lipase (24); and (iii) adipose tissue of obese subjects exhibits increased expression of TNF and TNF-R2, but not TNF-R1, mRNA, which is accompanied by elevation in plasma sTNF-R2 and insulin resistance (25,26), it also seemed prudent to examine the possibility of a contribution of TNFRSF1B genotype to plasma cholesterol, as well as shed sTNF-R2 in HT patients.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Linkage study
Multipoint analysis using MAPMAKER/SIBS gave peak LOD scores of 3.1 (unweighted) and 2.6 (weighted) (Fig. 1a). Moreover, an exclusion map (Fig. 1a) did not support HT linkage for markers telomomeric of D1S228 and centromeric to D1S436. Two-point linkage scores indicated a peak in which D1S2834 and TNFRSF1B (heterozygosity 69 and 58%, respectively) formed the apex, with LOD scores of 2.9 and 3.9 and P-value of 10–4 by different tests (Fig. 1b). Allele sharing for D1S2834 and TNFRSF1B (64.2 and 63.8%, respectively) significantly exceeded 50%. From identical-by-descent (IBD) estimates using SPLINK, the TNFRSF1B locus contributed to 37% of familial involvement in HT using a non-additive model and 27% using an additive model.
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Association study
Genotype frequencies for TNFRSF1B intron 4 marker (five alleles: CA13–CA17) and an exon 6 bi-allelic variant were in accord with Hardy–Weinberg equilibrium. Moreover, linkage disequilibrium (LD) was observed, the G allele of the exon 6 variant being associated with alleles CA13 and CA14 of the intron 4 variant (D' = 88%, P < 0.001). Allele frequencies of the latter in the NT group (Table 1) were similar to values reported for 78 unrelated probands from Centre d’Etude du Polymorphisme Humaine pedigrees [i.e. 0.19, 0.02, 0.54, 0.24 and 0.01, respectively (27)]. Values in NTs and HTs (Table 1) differed (
2 = 11.7, P = 0.02). In the exon 6 TT genotype group there was an excess of the CA16/T haplotype in HTs (P = 0.008; Table 1). This result was supported, at the P < 0.05 level, by using data from all genotypes with the haplotype frequencies estimated as described by Hill (28), in which doubly heterozygous NTs and HTs had haplotype frequencies estimated by the haplotype frequencies calculated for the homozygote groups, and the total allele frequency.
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Plasma sTNF-R2 (in ng/ml) correlated with systolic and diastolic BP (r > 0.2, P < 0.0004; using pretreatment BP values in the case of HTs) (Fig. 2). sTNF-R2 (mean ± SE) was 3.9 ± 0.2 in HTs and 3.2 ± 0.07 in NTs (P < 0.0001; n = 67 and 194, respectively). The elevation in sTNF-R2 appeared to consist almost entirely of CA15 homozygotes (P < 0.0001 for HT versus NT with this genotype; Fig. 3). The effect of the two major alleles on sTNF-R2 in HTs and NTs is shown in Figure 3. In sharp contrast to CA15, sTNF-R2 levels were similar in CA16 HTs versus NTs. Moreover, CA16 carriers had higher pretreatment diastolic BP (113 ± 18 versus 106 ± 16 mmHg; n = 43 and 61, respectively; P = 0.023).
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Hypercholesterolemia is a characteristic feature of HT. It was therefore of interest that the CA15 allele tracked with elevation in total, LDL and HDL cholesterol in the HT group (Fig. 4). In patients with the HT-associated allele (CA16), LDL showed a negative correlation with systolic BP (–r = 0.25; P = 0.030) and diastolic BP (–r = 0.35; P = 0.0017). This could reflect an effect of elevated mortality of those with higher LDL. In HTs who were overweight (BMI > 25 kg/m2) tracking of the CA15 allele with elevation in LDL was more marked than in the group as a whole: 4.6 ± 0.3, 3.6 ± 0.2 and 1.7 ± 1.1 mmol/l for CA15/CA15, CA15/– and CA15–/–, respectively (P = 0.0029; n = 10, 16 and 2, respectively).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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The present study has found linkage and association at the TNFRSF1B locus with essential HT. The various affected sib pair (ASP) linkage analysis methods have their own particular advantages and disadvantages, so that consistent indications from several tests, as used here, provide some reassurance about the validity of the findings (29). Despite the absence of any requirement for information concerning penetrance, phenocopy, genetic heterogeneity or disease prevalence, allele sharing methods nevertheless depend on accurate estimates of allele frequencies. Reasonable estimates of allele frequencies can be obtained from a sample of ~200 ASPs by way of gene counting or maximum likelihood data (30). Because allele frequency estimates are necessary for reconstruction of parental haplotypes in order to determine inheritance of alleles, the ASPs themselves are preferred for allele frequency estimation. A recent simulation study involving a marker with four equally frequent alleles demonstrated that SPLINK gives the lowest false positive rate and greatest power to detect linkage for ASP data in the absence of parental genotypes (29). The higher LOD scores that we observed for unweighted SPLINK would suggest that allele sharing is increased in larger affected families. Moreover, we obtained consistent findings by SPLINK, identical-by-state (IBS)
2 and MAPMAKER/SIBS in demonstrating linkage for the TNFRSF1B marker, as well as the adjacent marker, D1S2834. Significant probability values for adjacent loci at the LOD 
3, P = 10–4 level and values ascending to these for nearby markers, together with a significant association finding in a separate HT population, genotype/phenotype observations, support from physiological data, a BP quantitative trait locus (QTL) spanning the matching locus (on chromosome 5) in genetically HT rats (SHR) (31), a QTL for stroke latency (31) and considerations for complex traits (32,33), point to the possibility that TNFRSF1B could be a candidate HT gene. Compared with a linkage result, the LD described by a significant association finding is well known to apply to a much narrower genomic region (34), being only 3 kb in a recent simulation (35), but from actual data may extend over 100 kb in Homo sapiens, reflecting establishment of new disequilibria after the cyclical expansions that follow population bottlenecks, as occurred in the Neolithic age (36). Other loci in the vicinity that may influence BP are the chloride channel genes CLCNKA, CLCNKB and CLCN6, and natriuretic peptide genes NPPA and NPPB (Fig. 1b). However, these are remote from the TNFRSF1B linkage peak and have yielded negative SNP association results in the present study groups, at least for NPPA (37) and CLCNKA (38). Moreover, it is of interest that linkage to a 7 mmHg difference in diastolic BP has been reported for the 6-phosphogluconate dehydrogenase locus (PGD; Fig. 1b) for genotypes inferred from plasma PGD activity in a study of four large Caucasian families (923 subjects aged >8 years) ascertained by a HT proband (39).
Further evidence might be sought by use of the family-based transmission disequilibrium test (TDT). However, it is unfortunately difficult to perform robust TDTs for late onset diseases such as HT, since these require a heterozygous parent to be available for testing. The use of sib-TDTs would require the recruitment of much larger sibships. Moreover, the TDT is less powerful than case–control analysis (40), and much less powerful than the type of case–control approach that we use of selecting patients with two affected parents.
Confirmation in other settings will naturally be invaluable before concluding that TNFRSF1B is involved in HT etiology. Our finding of an association with elevated cholesterol, suggests a more general role in metabolic syndrome disorders, a contention further supported by recent unpublished data in which we find an association of the CA16 allele with coronary artery disease (41).
Our study also noted an effect of TNFRSF1B genotype on plasma sTNF-R2. Raised TNF would be expected to increase TNF-R2 shedding, so accounting for the correlation we saw with BP in Figure 2. We also saw a correlation of sTNF-R2 with age (r = 0.24, P < 0.0001), consistent with increasing vascular damage over the lifetime. The physiological consequences of elevated TNF, which would raise sTNF-R2, would be to oppose BP increases. The latter effect would apply to the major allele (CA15), which was associated with increased sTNF-R2. In contrast, HTs with the CA16 allele seemed resistant to elevation in sTNF-R2, and had higher BP, i.e. a reduced vasodilatory response appeared to apply to this genotype. Furthermore, haplotypes with the CA16 allele were 30% more frequent in the HT group. Thus, when taken together, our observations could be viewed as preliminary evidence in favor of a role for TNFRSF1B in HT etiology.
The association that we found for the CA15 allele and elevation in plasma cholesterol extends previous information implicating TNF-R2 in lipid metabolism. Indices of insulin resistance correlate with plasma sTNF-R2, but not sTNF-R1 (25,26). Moroever, TNF-R2 mRNA, but not TNF-R1 mRNA, is elevated in adipose tissue, as is sTNF-R2, but not sTNF-R1, in the circulation (25,26). Furthermore, sTNF-R2 correlates with both BMI, hyperinsulinemia and insulin resistance (21,25,26). Our results therefore suggest that effects of TNF on lipid metabolism may be influenced by TNFRSF1B genotype. Although we saw a correlation of sTNF-R2 with BMI in our subjects, this was much weaker (P = 0.07) than that with BP. Correction for an effect of BMI on BP was therefore not considered necessary.
A hypothetical scheme that relates the present findings for TNFRSF1B in HT onset and hypercholesterolemia is provided in Figure 5.
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In conclusion, our findings of linkage and association with HT and association of a TNFRSF1B marker with relevant phenotypic variables, may provide an impetus for further research exploring the possibility of a role for TNFRSF1B in the genetic basis of HT and other metabolic syndrome disorders.
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MATERIALS AND METHODS |
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Human subjects
Subjects were recruited from eastern Australia, mainly Sydney, by public advertising. Criteria were systolic/diastolic BP of >140/95 mmHg prior to treatment, lack of diabetes, heart or renal disease, and Anglo-Celtic ancestry. The study had ethical approval and all subjects gave informed consent.
The linkage study involved 200 ASPs [from 120 sibships that included 18 trios, 7 quartets and 1 quintet, and which were weighted as described (42)]. This number has been deemed sufficient for 90% power to show linkage at the LOD 2 [P 
0.001 (43)] level in a complex, polygenic disease with multiple weak contributing loci (e.g. relative risk to a sibling, 
s, values of ~1.6) (44). Characteristics (mean ± SD) were: pretreatment systolic/diastolic BP, 171 ± 24/103 ± 10 mmHg; age, 61 ± 10 years; age at disease onset, 43 ± 13 years; BMI, 27 ± 5 kg/m2; male:female ratio, 0.35:0.65.
The association analyses involved a well studied cohort of 134 HT patients whose parents both had HT and 197 NT controls whose parents had normal BP past the age of 50 years (45–48). Since only 1 in 10 HT patients have two affected parents (46), this group was, in effect, a subgroup of a general HT population that would have been 10 times larger than the number that we tested. Demographic parameters for each group resembled those described previously (45–48). For such genetically selected HTs, pretreatment systolic/diastolic BP, 175 ± 24/109 ± 18 mmHg, mean age, 52 ± 12 years, and age at disease onset, 32 ± 10 years, demonstrated moderate to severe, early onset HT. The HT and NT groups had similar BMI (27 ± 5 versus 26 ± 4 kg/m2, respectively), but plasma lipids were elevated (48), as is commonly observed in HT (20). Male:female ratio was 0.46:0.54.
Plasma assays
Concentration of sTNF-R2 in plasma was measured using a MEDGENIX enzyme amplified sensitivity immunoassay (BioSource Europe, Fleurus, Belgium). Plasma lipids were determined by a Reflotron Plus (Boehringer Ingelheim, Germany).
Genotyping
Genomic DNA of ASPs was extracted from leukocytes and genotyped by PCR. The TNFRSF1B microsatellite marker in intron 4 was detected as described previously (27). Five adjacent markers, D1S2667, D1S434, D1S2834, D1S2728 and D1S436, were selected from the Genome Database (http://gdbwww.gdb.org/gdb/gdbtop.html ) and one, D1S228, was from the Applied Biosystems (ABI; Foster City, CA) PRISM Linkage Mapping Set. Other ABI panel 1 and 2 chromosome 1 markers were also tested since those located further away still contribute information in multipoint analysis. TNFRSF1B is 0.1–3.1 cM centromeric of D1S2834 (Cedar Genetics database: http://cedar.genetics.soton.ac.uk/pub/chrom1/map.html ) and 150–440 kb from D1S434 (27). Map intervals for the other markers were: D1S2667–3.8 cM–D1S434–2.7 cM–D1S228–2.3 cM–D1S2834–4.7 cM–D1S2728–4.6 cM–D1S436 (Cedar Genetics database). The primers that we chose were synthesized by Bresatec (Adelaide, South Australia). The forward primer of each pair was labeled at the 5'-end with 6-carboxyfluorescein or its tetrachlorinated or hexachlorinated analogs (FAM, TET or HEX). After PCR, the products were electrophoresed on an ABI 377 automated sequencer and genotypes were assigned using ABI Genotyper software.
For the case–control studies, as well as testing the TNFRSF1B intron 4 microsatellite polymorphism (alleles CA13, CA14, CA15, CA16, CA17), we also developed genotyping methods for putative variants at –353 (49), –1120, in intron 1, and in exon 6 (50) of TNFRSF1B. Screening of 70 chromosomes revealed only the exon 6 variant (T685G: Met198Arg) and a tetranucleotide repeat in intron 1 (the others being sequencing errors in the literature or rare variants). The latter exhibited 25 alleles making it unhelpful for association analysis. However, it had a complex pattern of LD with other markers, thus confirming the existence of population haplotypes in TNFRSF1B. The exon 6 variant was detected by PCR–RFLP analysis using primers 5'-CCG TGA ATG AGC CCA G-3' and 5'-CAG AAG GAG TGA ATG AAT GAG-3', 1.5 mmol/l MgCl2, and 36 PCR cycles. The G allele (344 bp) was not cut by Hsp92II, but the T allele was (yielding 109 and 235 bp bands).
Statistical analyses
Evidence for linkage was assessed by non-parametric ASP methods. Since the relative power and behavior of different standard non-parametric test statistics used for complex traits are known to vary (29), we applied several of the better methods (29) to test for discordant allele sharing by descent (IBD) or by state (IBS). One was SPLINK (Unix version 1.08), which generates IBD estimates using all sibs in a sibship under the constraints imposed by linkage (possible triangle restriction: z[1] < 0.5 and z[0] < 0.5 x z[1], where z[1] and z[0] are sharing of 1 and 0 alleles IBD, respectively) and compares the IBD distribution to that expected under no linkage (51). Both weighted and unweighted SPLINK were applied, where number of weighted sibpairs in a family = number of unweighted sibpairs/(2 x number of affected siblings). Two-point linkage was also performed by IBS 2, which compares observed alleles shared IBS at a marker with what would be observed randomly using contingency table analysis (52). In addition, MAPMAKER/SIBS, which restricts maximization of the likelihood ratio of Risch (53) to within the possible triangle (54), was used to generate multipoint maximum LOD score (MLS) values at 1 cM intervals, as well as to produce information content and exclusion map. For MAPMAKER/SIBS, allele frequencies were estimated using the affected families weighted by number of founders in each pedigree. From significance thresholds set for acceptance of linkage (32,33,43), we considered LOD = 3.2 (33) reasonable, but sought to confirm this by the scores obtained for surrounding markers.
For the association study, StatView (Abacus Concepts, Berkeley, CA) was used for various statistical tests. Determination of linkage disequilibrium between polymorphisms involved analysis of population haplotype frequencies in our largest group of unrelated subjects (the NTs), as described (28).
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ACKNOWLEDGEMENTS |
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This work was supported by a grant from the National Health and Medical Research Council of Australia. We thank Judith O’Neill for help in collection of patient samples.
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FOOTNOTES |
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+ These authors contributed equally to this work
§ To whom correspondence should be addressed. Tel: +61 2 9351 3688; Fax: +61 2 9351 2227; Email: brianm@physiol.usyd.edu.au
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