Published online before print January 22, 2007, doi: 10.1161/01.HYP.0000257256.77680.02
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(Hypertension. 2007;49:446.)
© 2007 American Heart Association, Inc.
Original Articles |
High-Resolution Mapping for Essential Hypertension Using Microsatellite Markers
From the Departments of Medical Science and Cardiorenal Medicine (K.Y., N.H., M.O., T.S., S.U.) and Ophthalmology (N.M., N.I., T.Y., S.O.), Yokohama City University School of Medicine, Yokohama, Japan; Department of Molecular Life Science (K.Y., A.O., J.K.K., H.I.), Course of Basic Medical Science and Molecular Medicine, Tokai University School of Medicine, Isehara, Japan; Department of Geriatric Medicine (Y.T., T.M.), School of Medicine, Ehime University, Ehime, Japan; Second Department of Internal Medicine (M.S.), Nihon University School of Medicine, Tokyo, Japan; Department of Public Health (A.H.), Chiba University Graduate School of Medicine, Chiba, Japan; Department of Medicine and Clinical Science (K.N.), Kyoto University Graduate School of Medicine, Kyoto, Japan; Department of Health Science (H.U.), Shiga University of Medical Science, Shiga, Japan; Department of Geriatric Medicine (T.O.), Osaka University Graduate School of Medicine, Osaka, Japan; National Cardiovascular Center (H.T.), Osaka, Japan; Department of Molecular Pathogenesis (A.K.), Division of Pathophysiology, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan; Institute of National Sciences (S.M.), Nagoya City University, Nagoya, Japan; and the Centre for Bioinformatics and Biological Computing (J.K.K.), School of Information Technology, Murdoch University, Murdoch, Western Australia, Austaralia.
Correspondence to Satoshi Umemura, Department of Medical Science and Cardiorenal Medicine, Yokohama City University School of Medicine, 3–9, Fukuura, Kanazawaku, Yokohama 236-0004, Japan. E-mail umemuras{at}med.yokohama-cu.ac.jp
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Abstract |
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During the past decade, considerable efforts and resources have been devoted to elucidating the multiple genetic and environmental determinants responsible for hypertension and its associated cardiovascular diseases. The success of positional cloning, fine mapping, and linkage analysis based on whole-genome screening, however, has been limited in identifying multiple genetic determinants affecting diseases, suggesting that new research strategies for genome-wide typing may be helpful. Disease association (case–control) studies using microsatellite markers, distributed every 150 kb across the human genome, may have some advantages over linkage, candidate, and single nucleotide polymorphism typing methods in terms of statistical power and linkage disequilibrium for finding genomic regions harboring candidate disease genes, although it is not proven. We have carried out genome-wide mapping using 18 977 microsatellite markers in a Japanese population composed of 385 hypertensive patients and 385 normotensive control subjects. Pooled sample analysis was conducted in a 3-stage genomic screen of 3 independent case–control populations, and 54 markers were extracted from the original 18 977 microsatellite markers. As a final step, each single positive marker was confirmed by individual typing, and only 19 markers passed this test. We identified 19 allelic loci that were significantly different between the cases of essential hypertension and the controls.
Key Words: essential hypertension • genome-wide • association study • Japanese • new candidate regions
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Introduction |
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Hypertension is a leading risk factor for cerebrovascular disease, coronary heart disease, and renal failure.1 It is the major cause of morbidity and mortality and also the third highest risk factor for lifetime burden worldwide.2,3 Kearney et al4 reported that there were 972 million hypertension patients in the world, accounting for 26.4% of the adult population in 2000, and predicted that this figure will increase to 1.56 billion patients (29.2%) by 2025. The present pandemic of cardiovascular diseases has been attributed largely to the high prevalence of hypertension, suggesting that more emphasis should be placed on the prevention, detection, and treatment of hypertension.
Elucidation of the genetic etiology of hypertension has been increasingly emphasized as important for a better understanding of the pathogenesis of this disease and for ultimately improving the prevention strategies, diagnostic tools, and therapy in the new millennium.5 Hypertension is one of the risk factors for coronary heart disease, which is a common complex human genetic disease, and its genetic variance accounts for 30% to 70% of the trait variance.6,7 The sibling recurrent risk ratio of hypertension is reportedly to be 2 to 3.8 Each of the hypertension-causing gene recurrent risk ratios is less than the aggregate sibling recurrent risk ratio. There are now many reports describing the results of genome-wide screens for genes controlling blood pressure (BP). The National Heart, Lung, and Blood Institute Genelink project website (https://genelink.nhlbi.nih.gov) lists the National Heart, Lung, and Blood Institute–supported genome scans for BP. The majority of these reports have described numerous chromosomal regions with suggestive evidence of linkage.9 However, the application of linkage analysis to hypertension, with the exception of obvious Mendelian inheritance, has achieved only limited success thus far.10,11 Since 2000, 
6 large genome scans have been reported,12 namely, an admixture mapping study,13 a Medical Research Council Program-funded British Genetics of Hypertension Study,14 the US National Institutes of Health-funded Family Blood Pressure Program studies,15–18 the Victorian Family Heart Study,19 the San Antonio Heart Study,20 and the Quebec Family Study.21 Except for the admixture mapping study, all of these studies were based on linkage analysis.
In many cases, complex diseases are complicated by genetic heterogeneity and small effects of each gene. In 1996, Risch and Merikangas reported22 that numerous genetic effects in complex diseases were too weak to be identified by linkage analysis and could be better detected by genomic association studies. Thus, the new challenges to identify disease-predisposing variants in human genome research have resulted in approaches, such as the Hapmap project,23 high-density single nucleotide polymorphism (SNP) analysis,24 and microsatellite (MS) association analysis.25 Disease association studies using MS markers distributed across the human genome every 100 to 150 kb have distinct advantages over linkage analysis, the candidate approach, and SNP typing in terms of linkage disequilibrium (LD).25 The MS markers are highly polymorphic, showing a high degree of heterozygosity (on average, 
70%) and LD lengths in the 100- to 200-kb range.26–31 As compared with MS markers, SNPs have a low degree of genetic polymorphism (biallelic) and have a shorter, by 30 kb, LD range, probably because of their older age. Varilo et al32 reported that highly polymorphic MS markers can provide much greater power for detection of intermarker LD than can either single SNPs or SNP haplotypes on chromosomes 1q and 5q. Therefore, it is possible to carry out substantial whole genome association analysis using a smaller number of MS markers than SNPs (eg, tens of thousands of MS markers versus hundreds of thousands or millions of SNPs). Recently, the usefulness of the haplotype approach and haplotype-tagging SNPs from the HapMap project has been questioned.33
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Methods |
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Subjects for MS Typing
A total of 425 (stage 1: 95; stage 2: 131; stage 3: 199) patients with essential hypertension and 467 (stage 1: 103; stage 2: 132; stage 3: 232) normotensive healthy individuals participated in this study. The number of subjects for pooled DNA typing was 95 versus 95 for stage 1, 120 versus 120 for stage 2, and 170 versus 170 for stage 3. After pooled DNA typing, individual typing for the same samples was performed. The difference in number in each stage was derived from the time of sample collection. It was aimed to collect 100 volunteers for each stage each in case and control subjects, but many more subjects were collected beyond our expectations. So, we made the most of all of the subjects to increase the statistical power. The subjects were of Japanese origin from Hokkaido, Tokyo, Kanagawa, Shiga, Osaka, Kyoto, and Ehime. The subjects for the stage 1 and stage 2 screens were recruited from the Millennium Genome Project, and the subjects for the stage 3 screen were recruited from Yokohama City University School of Medicine. The diagnosis of essential hypertension was made according to the guidelines of the Japanese Society of Hypertension (declared in 2000), which include a sitting systolic BP of >140 mm Hg and/or diastolic BP of >90 mm Hg on
2 occasions after the first medical examination. Furthermore, subjects in this study were selected as follows, as shown in Table 1. Our criteria were classification as moderate or severe hypertension. We obtained informed consent from all of the patients and healthy individuals whose DNA samples were used in the analyses. Our experimental procedures were approved by the relevant ethical committee in each participating university and center. All of the personal identities associated with medical information and blood samples were carefully eliminated and replaced with anonymous identities in each recruiting institution.
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Pooled DNA and Genotyping
Ninety-five subjects in stage 1, 120 in stage 2, and 170 in stage 3 were selected from each group (case and control subjects) based on the DNA quality and quantity for DNA pooling analysis. The DNA pooling method was adopted to bring down the cost and the technical burden linked to genotyping thousands of MSs without losing any significant amount of data.
The DNA pooling method for MS typing was carried out by making slight modifications34 of the protocol of Collins et al.35 The key factor in this methodology is the absolute equality of individual DNA quantities, so we used a highly accurate quantitative procedure to construct a pooled DNA template for PCR amplification.35 This pool was composed of strictly measured DNA concentrations, extracted from 95 stage 1, 120 stage 2, and 170 stage 3 Japanese individuals. We checked each DNA concentration 3 times and equalized each DNA concentration by dilution. Multiple peak patterns in the pooled DNA showed the distribution of allele frequencies in the subjects.35 The DNA pooling method enabled us to obtain the allele frequencies of MSs in pooled Japanese individuals by measuring the heights of multiple peaks and to apply this approach to an association study. The quality of the pooled DNA was confirmed by comparing the allelic distributions between individual and pooled typing results using 23 MS markers, unless there was the absence of any significant difference (P
0.05) in allele frequencies between pooled DNA typing and individual. This comparison of allele frequencies for the same allele was performed by Fisher’s exact test.
DNA was extracted using a QIAamp DNA blood kit (Qiagen) under standardized conditions to prevent variation in DNA quality. This was followed by 0.8% agarose gel electrophoresis to check for DNA degradation and RNA contamination. After measurement of the optical density to check for protein contamination, the DNA concentration was determined through 3 successive measurements using the PicoGreen fluorescence assay (Molecular Probes). Standardized pipetting and aliquoting of the DNA samples were robotically performed using a Biomek 2000 and Multimek 96 (Beckman). The pooled DNA template for typing with 2x18 977 MS markers (first set: case subjects; second set: control subjects) was prepared immediately after DNA quantification. After the initial tests, the 18 977 PCR reaction mixtures containing all of the components except primers were prepared and then aliquoted into 96-well reaction plates and stored until use. The MS pooled typing and individual genotyping procedures after the PCR reaction were carried out according to standard protocols using ABI3700 and 3730 DNA analyzers (Applied Biosystems). The standardized preparations allowed the reproducibility and accuracy to be maintained for the pooled DNA typing throughout the experiment. Various kinds of information, such as the peak positions and heights, were manually extracted by the PickPeak and MultiPeaks programs, developed by Applied Biosystems Japan, from the multipeak pattern in the chromatogram ABI fsa files.
In the first stage, 95 case and 95 control subjects were subjected to association analysis using all of the 18 977 markers. Among them, markers showing statistical significance of P<0.05 were subjected to the second stage with another 120 case subjects and 120 control subjects. The markers showing statistical significance of P<0.05 in the second screening were subjected to a third stage with another 170 case subjects and 170 control subjects. All of the positive markers that remained statistically significant (P<0.05) in the stage 3 screening were confirmed by individual genotyping using the same set of 385 case subjects and 385 control subjects as the final step.
Marker Information
MS sequences were computationally detected from all of the chromosomes except for the Y chromosome (in 4 versions of the human genome draft sequence: Golden Path Jun 2004 to the National Center for Biotechnology Information build 35). At present, our laboratory has built 27 037 markers as a full set.25
In this study, we used 18 977 markers with an average spacing of 145.9 kb (Tables I and II, available online at http://hyper.ahajournals. org.) from 19 654 markers in the first built set. The other 678 markers were excluded because of problems with the PCR reaction and marker quality.
The MS markers were investigated for repeat polymorphisms in 200 healthy Japanese by using the DNA pooling method. Our criteria for selection of MS markers for the hypertension association study were dinucleotide repeats with >10 repeats; tri-, tetra-, and pentanucleotide repeats with >5 repeats; and polymorphic MS markers with heterozygosity of >30% but not those with heterozygosity of >85% to eliminate any unstable and highly mutated MS markers. We chose PCR primers that contained no SNPs in the sequences to prevent differential amplification. Seven PCR primer pairs of 54 MS markers for individual typing were redesigned to improve the efficiency of PCR (Table III). Detailed information on the 27 039 MS markers is available on the Japan Biological Information Research Center homepage (http://www.jbirc.aist.go.jp/gdbs/).
Statistical Analysis
The measurements of the heights of multiple peaks in the pooled DNA were applied to association analysis. To calculate P values, we used 2 types of Fisher’s exact test for 2x2 contingency tables for each individual allele and 2xm contingency tables for each locus, where m refers to the number of marker alleles observed in a population. The Markov chain/Monte Carlo simulation method was used to execute Fisher’s exact test for the 2xm contingency table. The simple "allelic" but not "genotype" association was presented for the 2x2 contingency tables for MSs. These analyses were executed using the software package, AStat. The method of Pritchard and Rosenberg36 was used for the detection of stratification in case and control populations using 23 MS markers. The Hardy–Weinberg test for allele frequency distributions at the MS loci was performed by P test for differentiation, as determined by GenePop 3.4. Other basic analyses were carried out using Microsoft Excel.
The authors had full access to the data and take responsibility for its integrity.
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Results |
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Three-Stage Screening: Pooled DNA Typing
Before the 3-stage screenings, we verified spurious associations through the method of Pritchard and Rosenberg36 using 23 randomly selected MS markers from each of chromosomes 1 to 22 and X, with an absence of any significant stratification in either the case or control populations (data not shown). This test is important to prevent spurious associations by population stratifications, especially for late-onset diseases, such as essential hypertension.
We initially identified 54 markers as potential hypertension susceptibility loci by 3-stage screening of 3 independent case–control populations (stage 1: 95; stage 2: 120; stage 3: 170 patients with essential hypertension and normotensive healthy individuals; Table 2). Three-stage screenings were intended to sequentially replicate the results in the 3 independent sample populations and eliminate pseudopositive markers resulting from type I errors.37,38
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The number of markers decreased from 18 977 to 1160 markers in the first screening, then to 284 markers in the second screening, and finally to 54 markers in the third screening. The significance (P<0.05) of the association of positive markers was assessed by the Fisher’s exact test, using either 2x2 or 2xm (m=number of alleles) contingency tables. Both 2x2 and 2xm analysis were performed together at each marker. If either of the 2 had P<0.05, the marker was judged as a positive marker. Finally, 54 markers significant in all of the screening sets were significant in the 2x2 test, and some of the markers were significant in the 2xm test. The concordance between the 2x2 and 2xm tests was relatively low (stage 1: 60%; stage 2: 64%; stage 3: 81%). All of the positive markers were checked by the Hardy–Weinberg test for allele frequency distributions at the MS loci, and then significant markers (P0.05) in the Hardy–Weinberg test were excluded. The positive rates in the second and third screenings were higher than that in the first. This might be partially because of experimental artifacts of the pooled DNA method as reported by Sham et al39 and Shaw et al.40
Individual Typing
The results of pooled typing were presumptive, so we genotyped a total of 770 individuals (385 case subjects versus 385 control subjects) and reanalyzed the 54 markers in the 3-stage screening procedure. These individuals are the same individuals as used in pooled typing and were not from a new cohort. Ultimately, we reduced the number of positive markers from 54 to 19 loci by using individual genotyping in the genome-wide association study for hypertension (Table 2). All 19 of the markers were significant (P<0.05) by 2x2 analysis, but only 3 markers were also significant (P<0.05) by 2xm analysis. In addition, the odds ratios ranged from 0.13 to 1.8 (Table 2).
The 19 genomic loci were observed on chromosome 2, 3, 4, 6, 10, 13, 17, 18, 19, and 20 (Table 3). The observed and expected frequencies of each genotype for the 19 markers in the case and control subjects were in Hardy–Weinberg equilibrium (data not shown). In considering the LD range, the susceptibility genes for hypertension were estimated to reside in a 100- to 150-kb region from each marker. We have also provided a list of the genes that are known to be positioned closest to the centromeric and telomeric side of each marker (Table 3) to highlight the locations of the 19 positive markers. The chromosomal location of the 19 markers in our study (Table 3) was compared with those identified in previous studies (Table 4). Essentially, 3 chromosomal locations were found to overlap in comparison with other studies: chromosome locations 2p11.1-q12.3, 2p25.1, and 6q27.
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Most of the 35 markers that were eliminated by individual typing after the 3-stage screening procedure may have been experimental artifacts or pseudopositive markers because of the DNA pooling method, PCR assay conditions, faulty peak heights during electrophoresis, PCR ghost peaks because of dissociation of labeled fluorescent reagents from a primer oligonucleotide, complications resulting from stutter, and additional nucleotide bands inherent to a particular MS.
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Discussion |
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High-Density MS Markers
We conducted a 3-stage genome-wide scan of 3 independent case–control populations by an association test using 18 977 MS markers to identify susceptible genes for essential hypertension. In this study, we used 18 977 markers with an average spacing of 145.9 kb as the first built set (Tables I and II). Based on recent knowledge, the average length of LD between the disease-susceptible SNPs and the nearby MS alleles is
100 kb.26–31 In other words, if the disease-susceptible SNPs are harbored between 2 neighboring MS markers at an interval 200 kb, LD between the disease-susceptible SNPs and either of the nearby MS will be proved. The use of average spacing of genetic markers across 100 to 200 kb of the entire genome is the best practical solution in genome-wide association analysis before availability of a genome-wide LD map, because the LD pattern varies between different regions of the human genome depending on several factors, such as allele frequency, mutation, and recombination.33 Therefore, our first step for genome-wide analysis was to collect enough MS markers (>18 000 MS, 1 MS at every 150 kb) to cover the euchromatic area (90%) of the human genome (3 giga base; 3x109 kb x0.9/150 kb=18 000). The remaining part of the genome was mostly heterochromatin restricted mainly to centromeres and telomeres, rich in repetitive sequences and believed to lack expressed genes. This 150-kb spacing of MS markers would enable us to assure an average 75-kb LD interval, which was presumed to detect the presence of disease-susceptible loci flanked by 2 neighboring MS markers across the whole genome. Although 54 MS markers were found significant in all 3 stages of the pooling experiments, only 19 (35%) of them were confirmed to be significant when individual typing was performed. This indicates the importance of performing individual typing after all of the pooling experiments to validate the pooled frequency estimates.
Essential Hypertension Susceptibility Genes
We have identified 19 MS loci associated with essential hypertension and compared our findings with those of 6 previous large-scale genome-wide studies that are summarized in Table 4. The loci of linkage analysis in the previous 9 reports were too wide (5 megabases) to identify and speculate about disease susceptibility genes. Three of the 19 identified regions in our study overlapped with a region identified in other races (Table 4). The studies in Table 4, except for the admixture mapping study,13 were linkage studies and suggest a much broader region than our results. For example, we found that the positive MS locus D2S0949i is located on cytoband 2p25.1, and this finding is in accordance with the admixture mapping results obtained by Zhu et al,13 who found evidence for linkage with a marker on chromosome 2p25.1. This concordance between 2 different studies suggests that chromosome 2p25.1 contains an unknown candidate gene for essential hypertension. Interestingly, our MS marker is located within the LPIN1 gene sequence (NM 145693.1), and this is a candidate gene for human lipodystrophy, a disease characterized by loss of body fat, fatty liver, hypertriglyceridemia, and insulin resistance. There have been no reports to indicate that LPIN1 is a candidate gene for essential hypertension, but lipin expression is important for metabolic homeostasis.40 In consideration that hypertension is an associated factor in the metabolic syndrome characterized by obesity, hypertriglyceridemia, and insulin resistance,41,42 LPIN1 deserves to be studied as a new candidate gene.
Another significant MS marker, HUMUT617 on 6q27, is in the same cytoband position reported by Caulfield et al14 Our marker was located within the SMOC2 gene sequence (NM_022138) that codes for a modular extracellular calcium-binding protein43 and a smooth muscle–associated protein upregulated during neointima formation.44 There have been no previous reports suggesting any connection between SMOC2 and BP, but this gene may be involved in the progression of atherosclerosis in the aorta.44
Perspectives
We performed an association analysis of essential hypertension using a high-density set of polymorphic MS markers with original, multistep methodology. The outcome was a rapid and efficient path to detect genomic susceptibility loci for a highly complex disorder. MS markers basically play a role as location markers for regions containing susceptibility and protective genes. Rarely, MS markers may be the causative variance themselves. The next step is to identify susceptibility and protective genes in the 19 narrow regions by SNP, LD block, and haplotype analysis. It is also important to replicate these results in different subjects, ethnic groups, and a larger number of samples. The future successful accomplishment of such analysis will also open the door to investigating the etiology of other multifactorial disorders, including common diseases such as bronchial asthma, type 2 diabetes mellitus, obesity, arteriosclerosis, schizophrenia, and psoriasis.
BP is influenced by nongenetic factors, such as salt intake. In the present study, because we did not focus on salt-induced hypertension, the amounts of urinary excretion of sodium were not examined. It might be noteworthy to perform studies specializing in genes related to salt-induced hypertension.
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Acknowledgments |
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We thank Tomoko Shiota, Ritsuko Nishizaki, Eriko Tokubo, Mikiko Hirayama, Asumi Takaki, and Takashi Shinomiya for their technical assistance. We also thank all of the staff and doctors who contributed to blood sample collection from the subjects.
Source of Funding
This work was supported in part by a Grant-in-Aid for Scientific Research on Priority Areas and "Medical Genome Science" from the Ministry of Education, Culture, Sports, Science and Technology.
Disclosures
None.
Received July 17, 2006; first decision August 14, 2006; accepted December 20, 2006.
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References |
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1. Whelton PK. Epidemiology of hypertension. Lancet. 1994; 344: 101–106.[CrossRef][Medline] [Order article via Infotrieve]
2. Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJ. Selected major risk factors and global and regional burden of disease. Lancet. 2002; 360: 1347–1360.[CrossRef][Medline] [Order article via Infotrieve]
3. Whelton PK, He J, Appel LJ, Cutler JA, Havas S, Kotchen TA, Roccella EJ, Stout R, Vallbona C, Winston MC, Karimbakas J. National High Blood Pressure Education Program Coordinating Committee. Primary prevention of hypertension. Clinical and public health advisory from the National High Blood Pressure Education Program. JAMA. 2002; 288: 1882–1888.
4. Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J. Global burden of hypertension: analysis of worldwide data. Lancet. 2005; 365: 217–223.[Medline] [Order article via Infotrieve]
5. Risch N. Searching for genetic determinants in the new millennium. Nature. 2000; 405: 847–856.[CrossRef][Medline] [Order article via Infotrieve]
6. Cheng LS, Carmelli D, Hunt SC, Williams RR. Evidence for a major gene influencing 7-year increases in diastolic blood pressure with age. Am J Hum Genet. 1995; 57: 1169–1177.[Medline] [Order article via Infotrieve]
7. Levy D, DeStefano AL, Larson MG, O’Donnell CJ, Lifton RP, Gavras H, Cupples LA, Myers RH. Evidence for a gene influencing blood pressure on chromosome 17. Genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the Framingham Heart Study. Hypertension. 2000; 36: 477–483.
8. Niu T, Xu X, Rogus J, Zhou Y, Chen C, Yang J, Fang Z, Schmitz C, Zhao J, Rao VS, Lindpaintner K. Angiotensinogen gene and hypertension in Chinese. J Clin Invest. 1998; 101: 188–194.[Medline] [Order article via Infotrieve]
9. Lander E, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995; 11: 241–247.[CrossRef][Medline] [Order article via Infotrieve]
10. Lifton RP, Gharavi AG, Geller DS. Molecular mechanisms of human hypertension. Cell. 2001; 104: 545–556.[CrossRef][Medline] [Order article via Infotrieve]
11. Altmuller J, Palmer LJ, Fischer G, Scherb H, Wjst M. Genome-wide scans of complex human diseases: True linkage is hard to find. Am J Hum Genet. 2001; 69: 936–950.[CrossRef][Medline] [Order article via Infotrieve]
12. Mein CA, Caulfield MJ, Dobson RJ, Munroe PB. Genetics of essential hypertension. Hum Mol Genet. 2004; 13: R169–R175.
13. Zhu X, Luke A, Cooper RS, Quertermous T, Hanis C, Mosley T, Gu CC, Tang H, Rao DC, Risch N, Weder A. Admixture mapping for hypertension loci with genome-scan markers. Nat Genet. 2005; 37: 177–181.[CrossRef][Medline] [Order article via Infotrieve]
14. Caulfield M, Munroe P, Pembroke J, Samani N, Dominiczak A, Brown M, Benjamin N, Webster J, Ratcliffe P, O’Shea S, Papp J, Taylor E, Dobson R, Knight J, Newhouse S, Hooper J, Lee W, Brain N, Clayton D, Lathrop GM, Farrall M, Connell J, MRC British Genetics of Hypertension Study. Genome-wide mapping of human loci for essential hypertension. Lancet. 2003; 361: 2118–2123.[CrossRef][Medline] [Order article via Infotrieve]
15. Rao DC, Province MA, Leppert MF, Oberman A, Heiss G, Ellison RC, Arnett DK, Eckfeldt JH, Schwander K, Mockrin SC, Hunt SC, HyperGEN Network. A genome-wide affected sibpair linkage analysis of hypertension: the HyperGEN network (1). Am J Hypertens. 2003; 16: 148–150.[CrossRef][Medline] [Order article via Infotrieve]
16. Kardia SL, Rozek LS, Krushkal J, Ferrell RE, Turner ST, Hutchinson R, Brown A, Sing CF, Boerwinkle E. Genome-wide linkage analyses for hypertension genes in two ethnically and geographically diverse populations. Am J Hypertens. 2003; 16: 154–157.[CrossRef][Medline] [Order article via Infotrieve]
17. Thiel BA, Chakravarti A, Cooper RS, Luke A, Lewis S, Lynn A, Tiwari H, Schork NJ, Weder AB. A genome-wide linkage analysis investigating the determinants of blood pressure in whites and African Americans. Am J Hypertens. 2003; 16: 151–153.[CrossRef][Medline] [Order article via Infotrieve]
18. Ranade K, Hinds D, Hsiung CA, Chuang LM, Chang MS, Chen YT, Pesich R, Hebert J, Chen YD, Dzau V, Olshen R, Curb D, Botstein D, Cox DR, Risch N. A genome scan for hypertension susceptibility loci in population of Chinese and Japanese origins. Am J Hypertens. 2003; 16: 158–162.[CrossRef][Medline] [Order article via Infotrieve]
19. Harrap SB, Wong ZY, Stebbing M, Lamantia A, Bahlo M. Blood pressure QTLs identified by genome-wide linkage analysis and dependence on associated phenotypes. Physiol Genomics. 2002; 8: 99–105.
20. Atwood LD, Samollow PB, Hixson JE, Stern MP, MacCluer JW. Genome-wide linkage analysis of blood pressure in Mexican Americans. Genet Epidemiol. 2001; 20: 373–382.[CrossRef][Medline] [Order article via Infotrieve]
21. Rice T, Rankinen T, Province MA, Chagnon YC, Perusse L, Borecki IB, Bouchard C, Rao DC. Genome-wide linkage analysis of systolic and diastolic blood pressure: the Quebec Family study. Circulation. 2000; 102: 1956–1963.
22. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996; 273: 1516–1517.
23. The International HapMap Consortium. The international hapmap project. Nature. 2003; 426: 789–796.[CrossRef][Medline] [Order article via Infotrieve]
24. Ozaki K, Ohnishi Y, Iida A, Sekine A, Yamada R, Tsunoda T, Sato H, Sato H, Hori M, Nakamura Y, Tanaka T. Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat Genet. 2002; 32: 650–654.[CrossRef][Medline] [Order article via Infotrieve]
25. Tamiya G, Shinya M, Imanishi T, Ikuta T, Makino S, Okamoto K, Furugaki K, Matsumoto T, Mano S, Ando S, Nozaki Y, Yukawa W, Nakashige R, Yamaguchi D, Ishibashi H, Yonekura M, Nakami Y, Takayama S, Endo T, Saruwatari T, Yagura M, Yoshikawa Y, Fujimoto K, Oka A, Chiku S, Linsen SE, Giphart MJ, Kulski JK, Fukazawa T, Hashimoto H, Kimura M, Hosina Y, Suzuki Y, Hotta T, Mochida J, Minezaki T, Komai K, Shiozawa S, Taniguchi A, Yamanaka H, Kamatani N, Gojobori T, Bahram S, Inoko H. Whole genome association study of rheumatoid arthritis using 27,039 microsatellites. Hum Mol Genet. 2005; 14: 2305–2321.
26. Oka A, Tamiya G, Tomizawa M, Ota M, Katsuyama Y, Makino S, Shiina T, Yoshitome M, Iizuka M, Sasao Y, Iwashita K, Kawakubo Y, Sugai J, Ozawa A, Ohkido M, Kimura M, Bahram S, Inoko H. Association analysis using refined microsatellite markers localize a susceptible locus for psoriasis vulgaris within a 111-kb segment telomeric of the HLA-C gene. Hum Mol Genet. 1999; 8: 2165–2170.
27. Ota M, Mizuki N, Katsuyama Y, Tamiya G, Shiina T, Oka A, Ando H, Kimura M, Goto K, Ohno S, Inoko H. The critical region for Behcet disease in the human major histocompatibility complex is reduced to a 46-kb segment centromeric of HLA-B by association analysis using refined microsatellite mapping. Am J Hum Genet. 1999; 64: 1406–1410.[CrossRef][Medline] [Order article via Infotrieve]
28. Keicho N, Ohashi J, Tamiya G, Tamiya G, Nakata K, Taguchi Y, Azuma A, Ohishi N, Emi M, Park MH, Inoko H, Tokunaga K, Kudoh S. Fine localization of a major disease-susceptibility locus for diffuse panbronchiolitis. Am J Hum Genet. 2000; 66: 501–507.[CrossRef][Medline] [Order article via Infotrieve]
29. Mizuki N, Ota M, Yabuki K, Katsuyama Y, Ando H, Palimeris GD, Kaklamani E, Accorinti M, Pivetti-Pezzi P, Ohno S, Inoko H. Localization of the pathogenic gene of Behcet’s diseases by microsatellite analysis of three different populations. Invest Ophthalmol Vis Sci. 2000; 41: 3702–3708.
30. Zhang Y, Leaves NI, Anderson GG, Ponting CP, Broxholme J, Holt R, Edser P, Bhattacharyya S, Dunham A, Adcock IM, Pulleyn L, Barnes PJ, Harper JI, Abecasis G, Cardon L, White M, Burton J, Matthews L, Mott R, Ross M, Cox R, Moffatt MF, Cookson WO. Positional cloning of a quantitative trait locus on chromosome 13q14 that influences immunoglobulin E levels and asthma. Nat Genet. 2003; 34: 181–186.[Medline] [Order article via Infotrieve]
31. Ota M, Katsuyama Y, Kimura A, Tsuchiya K, Kondo M, Naruse T, Mizuki N, Itoh K, Sasazuki T, Inoko H. A second susceptibility gene for developing rheumatoid arthritis in the human MHC is localized within a 70-kb interval telomeric of the tumornecrosisfactor (TNF) genes in the HLA class III region. Genomics. 2001; 71: 263–270.[CrossRef][Medline] [Order article via Infotrieve]
32. Varilo T, Paunio T, Parker A, Perola M, Meyer J, Terwilliger JD, Peltonen L. The interval of linkage disequilibrium (LD) detected with microsatellite and SNP markers in chromosomes of Finnish populations with different histories. Hum Mol Genet. 2003; 12: 51–59.
33. Nothnagel M, Rohde K. The effect of single-nucleotid polymorphism marker selection on patterns of haplotype blocks and haplotype frequency estimates. Am J Hum Genet. 2005; 77: 988–998.[CrossRef][Medline] [Order article via Infotrieve]
34. Oka A, Hayashi H, Tomizawa M, Okamoto K, Suyun L, Hui J, Kulski JK, Beilby J, Tamiya G, Inoko H. Localization of a non-melanoma skin cancer susceptibility region within the major histocompatibility complex by association analysis using microsatellite markers. Tissue Antigens. 2003; 61: 203–210.[CrossRef][Medline] [Order article via Infotrieve]
35. Collins HE, Li H, Inda SE, Anderson J, Laiho K, Tuomilehto J, Seldin MF. A simple and accurate method for determination of microsatellite total allele content differences between DNA pools. Hum Genet. 2000; 106: 218–226.[CrossRef][Medline] [Order article via Infotrieve]
36. Pritchard JK, Rosenberg NA. Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet. 1999; 65: 220–228.[CrossRef][Medline] [Order article via Infotrieve]
37. Barcellos LF, Klitz W, Field LL, Tobias R, Bowcock AM, Wilson R, Nelson MP, Nagatomi J, Thomson G. Association mapping of disease loci, by use of a pooled DNA genomic screen. Am J Hum Genet. 1997; 61: 734–747.[Medline] [Order article via Infotrieve]
38. Saito A, Kamatani N. Strategies for genome-wide association studies: optimization of study designs by the stepwise focusing method. J Hum Genet. 2002; 47: 360–365.[CrossRef][Medline] [Order article via Infotrieve]
39. Sham P, Bader JS, Craig I, O’Donovan M, Owen M. DNA pooling: a tool for large-scale association studies. Nat Rev Genet. 2002; 3: 862–871.[CrossRef][Medline] [Order article via Infotrieve]
40. Shaw SH, Carrasquillo MM, Kashuk C, Puffenberger EG, Chakravarti A. Allele frequency distributions in pooled DNA samples: applications to mapping complex disease genes. Genome Res. 1998; 8: 111–123.
41. Phan J, Peterfy M, Reue K. Lipin expression preceding peroxisome proliferators-activated receptor-gamma is critical for adipogenesis in vivo and vitro. J Bio Chem. 2004; 279: 29558–29564.
42. Suviolahti E, Reue K, Cantor RM, Phan J, Gentile M, Naukkarinen J, Soro-Paavonen A, Oksanen L, Kaprio J, Rissanen A, Salomaa V, Kontula K, Taskinen M, Pajukanta P, Peltonen L. Cross-species analyses implicate Lipin 1 involvement in human glucose metabolism. Hum Mol Genet. 2006; 15: 377–386.
43. Vannahme C, Gosling S, Paulsson M, Maurer P, Hartmann U. Characterization of SMOC-2, a modular extracellular calcium-binding protein. Biochem J. 2003; 373: 805–814.[CrossRef][Medline] [Order article via Infotrieve]
44. Nishimoto S, Hamajima Y, Toda Y, Toyoda H, Kitamura K, Komurasaki T. Identification of a novel smooth muscle associated protein, smap2, upregulated during neointima formation in a rat carotid endarterectomy model. Biochim Biophys Acta. 2002; 1576: 225–230.[Medline] [Order article via Infotrieve]
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