Skip Navigation


Human Molecular Genetics Advance Access originally published online on September 14, 2004
Human Molecular Genetics 2004 13(21):2647-2657; doi:10.1093/hmg/ddh286
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
13/21/2647    most recent
ddh286v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (20)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Katzov, H.
Right arrow Articles by Prince, J. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Katzov, H.
Right arrow Articles by Prince, J. A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Human Molecular Genetics, Vol. 13, No. 21 © Oxford University Press 2004; all rights reserved

A cladistic model of ACE sequence variation with implications for myocardial infarction, Alzheimer disease and obesity

Hagit Katzov1, Anna M. Bennet2, Patrick Kehoe4, Björn Wiman5, Margaret Gatz3,6, Kaj Blennow7, Boris Lenhard1, Nancy L. Pedersen3,6, Ulf de Faire2 and Jonathan A. Prince1,*

1Center for Genomics and Bioinformatics, 2Division of Cardiovascular Epidemiology, Institute of Environmental Medicine and 3Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden, 4Department of Clinical Sciences, Care of the Elderly, University of Bristol, The John James Building, Frenchay Hospital, Bristol, UK, 5Department of Clinical Chemistry, Karolinska Hospital, Stockholm, Sweden, 6Department of Psychology, University of Southern California, Los Angeles, CA, USA and 7Department of Clinical Neuroscience and Transfusion Medicine, University of Göteborg, Sahlgren's University Hospital, Sweden

Received June 22, 2004; Revised August 25, 2004; Accepted September 4, 2004


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Sequence variation in ACE, which encodes angiotensin I converting enzyme, contributes to a large proportion of variability in plasma ACE levels, but the extent to which this impacts upon human disease is unresolved. Most efforts to associate ACE with other heritable traits have involved a single Alu insertion/deletion polymorphism, despite the probable existence of other functional sequence variants with effects that may not be consistently detectable by solely typing the Alu indel. Here, utilizing single nucleotide polymorphisms (SNPs) that differentiate major ACE clades in European populations, we demonstrate a number of significant phenotype associations across more than 4000 Swedish individuals. In a systematic analysis of metabolic phenotypes, effects were detected upon several traits, including fasting plasma glucose levels, insulin levels and measures of obesity (P-values ranging from 0.046 to 8.4x10–6). Extending cladistic models to the study of myocardial infarction and Alzheimer disease, significant associations were observed with greater effect sizes than those typically obtained in large-scale meta-analyses based on the Alu indel. Population frequencies of ACE genotypes were also found to change with age, congruent with previous data suggesting effects upon longevity. Clade models consistently outperformed those based upon single markers, reinforcing the importance of taking into consideration the possible confounding effects of allelic heterogeneity in this genomic region. Utilizing computational tools, potential functional variants are highlighted that may underlie phenotypic variability, which is discussed along with the broader implications these results may have for studies attempting to link variation in ACE to human disease.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Common variants of ACE, which encodes angiotensin I converting enzyme, contribute to a large proportion of variance in circulating plasma ACE levels, a relationship that represents one of the most compelling examples of a genetic effect upon a complex trait in human populations (1,2). Association of ACE has been claimed with numerous phenotypes, including myocardial infarction (MI), Alzheimer disease (AD), type II diabetes and various quantitative traits related to these disorders (3–5). Typically, these studies have focused on a single Alu insertion/deletion polymorphism in intron 16 of ACE and they have generally been conducted on relatively small populations. As a consequence, results have been inconsistent, leaving open the implicit question of whether altered ACE activity can mediate susceptibility to disease and/or variation in disease-related traits.

Studies on ACE and ACE plasma levels have provided strong evidence that several polymorphic sites contribute to the observed phenotypic variation (2,6,7). These findings of allelic heterogeneity have been facilitated by exhaustive searches for DNA sequence variation across the gene and by haplotype and cladistic analyses to demonstrate associations (8–10). These analytical strategies have highlighted the importance of studying linkage disequilibrium (LD) structure within and around a locus that may harbor multiple variants that influence a trait of interest. In European populations, the ACE locus is marked by strong LD between polymorphic sites and consequently by a small number of common extant haplotypes. Limited haplotype diversity in the vicinity of ACE prohibits the fine-mapping of specific alleles that contribute to variability in the ACE trait (11,12). However, strong LD also implies a relatively simple allelic architecture, whereby common haplotypes can be discerned with a limited number of genetic markers and then used in association analyses (13,14). A simple cladistic structure with three major ancestral clades (A, B and C) was proposed by Farrall et al. (8) for populations of European ancestry. Theoretical and practical aspects of clade-based modeling have been dealt with previously (15,16).

We have utilized the well-documented LD and haplotype structure of the ACE locus to guide the selection of markers that delineate the major ACE clades and used these in a multi-tiered association study. Our primary motivation has been that if allelic heterogeneity is present, the application of a simple clade model might be an appropriate step forward in attempting to replicate and clarify previously claimed associations of ACE variants with metabolic quantitative traits and discrete disease states, in particular MI and AD.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
The previously proposed genealogy of ACE entails three common ancestral clades (A, B and C), whereby clade C was putatively formed by recombination between haplotypes of the A and B clades (8,9). Clade A has been shown to be associated with low plasma ACE activity, clade B with higher activity and C clade with intermediate levels (9,14). Typing only two markers, one on either side of the recombination site, is sufficient to distinguish these three clades, at least in Europeans (9,14). We note that Soubrier et al. (14) selected three single nucleotide polymorphisms (SNPs) to distinguish the effects of four clades (which included a D clade). The D clade was observed to be quite rare, however; and on this basis we considered it imprudent to attempt to delineate its independent effects in the present study.

Following efforts to define LD structure around the ACE locus (see Materials and Methods), three SNPs were selected for genotyping in all studied clinical materials; one occurring in the 3' region of ACE (rs4343) and two that occur in the promoter (rs4291 and rs1800764). All markers were found to be in Hardy–Weinberg equilibrium. The selection of this particular 3' marker was based upon the robustness of its genotyping assay, and since near-perfect LD makes numerous markers in this region equivalent (and thus redundant), including the Alu I/D polymorphism (17). Two 5' markers were selected as LD is slightly weaker for this region. These markers have previously been shown to have local maximum effects upon ACE plasma levels, thus suggesting putative independent functional effects (2,18).

For haplotype construction, combining the rs4343 marker with either of the two 5' SNPs (either rs4343+rs4291 or rs4343+rs1800764) creates haplotypic genotypes that reflect combinations of the major A, B and C clades (14,17). For brevity, we refer to haplotypic genotypes throughout this study as cladetypes. For initial analyses using this strategy, cladetypes were constructed using the rs4343+rs1800764 combination. Posterior probability estimates for individual phase calls using HAPLOTYPER were all in excess of 0.95.

Associations with quantitative phenotypes
We began with the study of quantitative metabolic phenotypes in materials from the Stockholm Heart Epidemiology Program (SHEEP). Of approximately 50 traits available for the majority of individuals from these materials (19,20), the investigation was limited to 17 that are commonly examined in relation to cardiovascular disease (21,22) (Table 1). We acknowledged that many of these would exhibit strong correlations, thus entailing some redundancy, but opted nonetheless to investigate each independently. For example, several measures of obesity were examined owing to uncertainty about which most accurately predicts cardiovascular disease risk (23). The intention was to perform a systematic evaluation of traits that would limit the number of tests in initial exploratory analyses, while at the same time providing sufficient power to detect associations if present. We have presumed that power will decrease as one moves away from primary biological effects (e.g. ACE plasma levels) to intermediate effects (e.g. metabolic traits) to disease risk, and as such justified the order of phenotype analysis in this study. These analyses were performed in three stages.


View this table:
[in this window]
[in a new window]
  		Table 1. Distribution of selected traits in MI cases and controls
 
Stage I.
Cladetypes were tested against all quantitative traits listed in Table 1 in men and women separately. Results for these analyses are shown in Table 2. We noted that with 34 tests (17 traits in both men and women), at least one nominal uncorrected P-value of 0.0015 would be required to declare formal significance from this preliminary phenotype screen (assuming a strict Bonferroni correction with {alpha}=0.05). The number of individuals in the C/C clade category was small (a total of 18 individuals in this sample) and this group was therefore excluded from initial comparisons. Among the tested traits, the strongest evidence of association was obtained for fasting plasma glucose (FPG) levels in men (P=0.000041). There was no evidence for an effect in women (P=0.67). Additional traits that exhibited significant evidence of association include BMI (P=0.012), HOMA (P=0.0053), insulin levels (P=0.011), WC (P=0.0023) and WHR (P=0.022), all evident exclusively in men (Table 2). At this stage we also tested the alternative model involving the rs4343+rs4291 combination and specifically examined the FPG trait. This produced a P-value of 0.0014 in the male sample. On the basis of the weaker evidence for this model, all further work was done using the rs4343+rs1800764 combination.


View this table:
[in this window]
[in a new window]
  		Table 2. Quantitative trait analyses in men and women
 
Stage II.
Cladetype analyses were performed in men after stratification into infarct patients and controls, focusing on the six traits that exhibited evidence of association in stage I. This revealed significant effects upon the FPG trait in both groups (Table 3). However, for the other traits, significant effects were observed only in infarct patients (Table 3). In general, across all six phenotypes, lower values were apparent in A/A, A/C and C/B categories, whereas higher values were evident in A/B and B/B groups (Table 3). We limited post hoc tests for individual cladetype comparisons to the WC trait only (since this trait exhibited the strongest evidence of association). Numerous comparisons were significant, the strongest being between cladetype A/B and B/B (P=0.0001; data for other comparisons not shown). Focusing on the strongest finding (for the WC trait), an additional model was fitted by including smoking (coded as 1, current smoker and 0, non-smoker) and age as covariates (F4,438=7.4, P=0.0000084). The proportion of total variance in WC explained in this model was 6.2%. One final model was also tested including individuals with the C/C cladetype. There were only three individuals with this cladetype in the male infarct sample with an average WC of 95.8±10.0 (mean±SEM) and the significance of the ANOVA omnibus test with this group included was F5,440=6.0; P=0.000023.


View this table:
[in this window]
[in a new window]
  		Table 3. Quantitative trait analyses of selected traits in men
 
Stage III.
Individual markers were tested for association in male MI patients with the six traits that exhibited significance in stages I and II. The results for these analyses are shown in Table 4. A trend was apparent, whereby the rs1800764 marker provided consistent and strong evidence of association, whereas the rs4343 variant exhibited relatively weak effects (Table 4). Again, adjusting the strongest finding (rs1800764 and the WC trait) for age and smoking improved the model fit (F2,482=9.0, P=0.0001). The proportion of total variance in WC explained in this model is 3.5% (contrast to the earlier 6.2% for the clade model). For comparison, variability at rs4343 (which is in nearly perfect LD with the Alu indel) explains only 1.6% of the variance in WC (data not shown).


View this table:
[in this window]
[in a new window]
  		Table 4. Single marker associations in male MI patients
 
Disease associations
Cladetype distributions were compared in MI patients and controls and in an extended set of Swedish AD materials and controls previously described (17). Our intention was to apply the models that had been used to detect quantitative trait associations to explore the possibility that ACE affects these diseases differentially as has been postulated in several previous studies (24–26). The results of this analysis are presented in Table 5. For MI, significant association was observed in the male sample where an enrichment of the AB cladetype was evident in the infarct group (P=0.0077). Importantly however, inclusion of WC as a covariate completely eliminates the significance in this model (final model P=0.39). This could indicate that the MI effect is secondary to obesity, but one caveat in this is that the number of individuals with obesity phenotypes is smaller than the total sample, implying a reduction in sample size and thus power in this obesity adjusted model. For comparisons involving the AD sample, the AA cladetype was found to be enriched in cases (P=0.0011). Effects were similar when analyzing men and women separately (see footnote to Table 5), and thus only a combined sample is presented. Single marker genotypes were also tested for disease association in 3x2 contingency tables. In the male MI population, borderline significance was seen for rs4343 and rs1800764 ({chi}2 P-values of 0.056 and 0.088, respectively). For the complete AD set compared with controls and for these same markers, significance was P=0.055 and P=0.30, respectively.


View this table:
[in this window]
[in a new window]
  		Table 5. Clade distributions in MI and AD samples
 
Clade frequencies across age groups
Contingency tables were constructed for the comparison of cladetype distributions as a function of age. To model these effects, the entire set of control samples (approximately 2200 individuals) was stratified into three parts defined by tertiles. The results of this analysis are shown in Table 6. Similar findings were obtained for men and women analyzed separately (data not shown). As an alternative to this strategy, we also used ANOVA, treating age as a quantitative trait dependent upon genotype, which produced similar results (F4,2218=3.80, P=0.0044) (specific data for each genotype group not shown). Single marker tests examining rs4343 and rs1800764 in 3x3 contingency tables produced {chi}2 P-values of 0.010 and 0.091, respectively.


View this table:
[in this window]
[in a new window]
  		Table 6. Clade frequencies across age groups
 
Genomic contexts and potential regulatory effects of ACE variants
The genomic contexts of bi-allelic polymorphisms extending across ACE were analyzed for potential regulatory effects using RAVEN (see Materials and Methods). We initially examined all validated SNPs (those entries with population frequency data or with multiple independent submissions to dbSNP) spanning the ACE locus for occurrence in evolutionarily conserved regions. This preliminary sweep was done using the UCSC genome browser (http://genome.ucsc.edu/) examining conservation among human/mouse/rat/chimp/chicken sequences. Work to further prioritize markers was primarily guided by a previous study in which a small number of sites were shown to exhibit independent effects upon ACE plasma levels (2). Of the markers chosen for closer analysis, four are located 5' upstream of ACE transcription start site, four in exons resulting in synonymous substitutions and two in introns. These findings are summarized in Table 7. We refer to the standard, longer isoform of ACE (RefSeq accession no. NM_000789). The shorter isoform is observed mostly in testis mRNA libraries, but was reproducibly isolated from at least one other tissue sample (medulla, cDNA library NIH_MGC_119).


View this table:
[in this window]
[in a new window]
  		Table 7. In silico analysis of select sequence variants in ACE
 
Among all variants examined in this analysis, the synonymous marker rs4362 located within the 5' end of exon 23 (of ACE isoform 1) was considered the most likely functional candidate. The first 42 nucleotides of that exon are perfectly conserved between human and mouse sequences, which is observed exceptionally rarely across such an evolutionary distance when the conservation of the protein coding information is the only selection constraint. This strongly suggests that the region serves some additional function, possibly involving the regulation of expression or splicing. The search for known transcription factor sites revealed high potential of the region for binding zinc finger class transcription factors, which are known to bind in clusters to some regulatory regions (27). The program ESEfinder (http://exon.cshl.org/ESE) (28) was used to test the different alleles of this site for potential effects on the binding of serine/arginine-rich splicing factors (SR proteins). The C allele at this site is associated with two adjacent high-scoring SRp55 motifs that are both disrupted by the T allele (high scores dropping from 3.83 to 2.28 and from 3.22 to 0.67, respectively).

Of the four promoter SNPs, marker rs4292 was deemed to be the most likely to have a functional role. This marker is flanked by a sequence highly conserved between human and rodents. Putative transcription factor binding sites detected therein include a cluster of Gflp (zinc finger) binding sites. Thus, rs4292 has the potential to abolish binding to one of the Gflp sites. Of the two alternative alleles, the minor one (T) is present in mouse and rat.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
Knowledge of sequence polymorphism, LD structure and extant haplotype diversity in ACE across world populations is among the most extensive to date (10). This information has been used to establish the influence of variation in ACE upon circulating ACE plasma levels (1,7,12), a relationship that stands as one of the best examples of a human quantitative trait locus (QTL) yet identified. This is unique as it allows the investigation of other phenotype associations with the knowledge that functionally different versions of the gene occur in human populations. Against this background, we have posed the following questions in this study: (1) What measurable phenotypes, in addition to ACE plasma levels, are detectably influenced by variation in ACE? (2) Does this information advance our understanding of any clinically defined disease phenotypes and offer support for claimed genetic associations with those diseases? (3) Given the known genetic architecture of ACE, has the reliance on the Alu indel polymorphism, the most studied variant at this locus, perhaps undermined replication efforts for claimed disease associations? (4) What polymorphic sites in ACE might underlie observed phenotypic effects?

The data we present substantiate the prospect that variants of ACE do have a number of detectable phenotypic effects that extend beyond and possibly as a result of the impact of these variants upon ACE activity itself. These appear primarily to reflect an influence upon glucose/energy metabolism and obesity. In support of this, an effect upon glucose levels has been observed previously (29) and similarly a relatively large study recently demonstrated the plausible involvement of ACE in obesity (30). Given differences between A and B clades, whereby the former is related to low ACE activity, and the latter to high activity, the plausible biological mechanism behind these observations is that elevated ACE activity increases trait levels. This hypothesis is supported by recent findings that treatment with ACE inhibitors leads to a significant decrease in both plasma glucose and insulin levels in a mouse model (31), but also by evidence that chronic ACE inhibition may decrease the risk of type II diabetes (32,33). Thus, together with the genetic data we present, it would appear plausible that the modification of ACE activity could lead to altered glucose and insulin levels, and obesity, as has been suggested (31,34).

Associations with quantitative metabolic traits were observed exclusively in men, which may derive in part from the sample size being considerably larger than for women. However, other studies have also detected associations exclusively in men (35–38) and gender specific effects, especially in relation to metabolic phenotypes, may be a major confounding factor in genetic analyses (39,40). Our strategy has been systematic and hierarchical, leaving women unexamined at later stages. Thus, we cannot exclude the possibility that weak effects exist in women, which might only be detectable in stratified analyses or in larger populations. Of greater potential concern is the evidence that association was more pronounced in the infarct patients. While the observation that glucose levels are influenced by ACE alleles in both infarct patients and controls in an equivalent manner is reassuring, the explanation for the considerably stronger effects upon obesity measures in the infarct group, however, is at present unknown. We speculate that this may reflect causal pathways between ACE alleles and MI or may be due to interaction with medication following infarct events. Importantly, none of the MI patients have taken ACE inhibitors. Changes in post-infarct diet or a unique genetic background common to infarct patients could also contribute to this observation.

One of the most interesting features of these data was the evidence that different cladetypes influence obesity/MI and AD (Table 5). In light of recent epidemiological evidence that obesity may be a risk factor for AD (41), this appeared paradoxical. We believe that an explanation to this can be found in the multitude of biochemical functions of ACE. In addition to its well-defined role in the conversion of angiotensin I (angI) to angiotensin II (angII) and catabolism of bradykinin (42,43), ACE has recently been implicated in ß-amyloid (Aß) degradation, itself one of the principle pathogenic agents in AD (44). Thus, in the presence of low ACE activity (either due to possession of clade A alleles or other causes), levels of angI and bradykinin could be chronically elevated and result in reduced body weight, while at the same time Aß could accumulate to neurotoxic levels. A diagram of this putative relationship is shown in Figure 1. We acknowledge, however, that this model may be oversimplified and not reflect the true complexity of the biochemical systems that underlie the observed data. Nonetheless, the assumption of pleiotropic effects of ACE alleles is affirmed (25,26). Thus, with regard to AD in particular, alleles of both A and B clades could be contributing to disease, either directly via Aß or indirectly via obesity, confounding the detection of genetic association.



View larger version (12K):
[in this window]
[in a new window]
  		Figure 1. Proposed biochemical functions of ACE in relation to MI, obesity and AD. In addition to being involved in the degradation of Aß in the brain, somatic ACE releases angII and inactivates bradykinin (43,44). Higher ACE activity and obesity are associated with clade B (57), whereas alleles in clade A are associated with lower ACE levels and AD (17). Obesity and MI may be risk factors for the development of AD (41); however, the mechanism is currently unknown.

 
The relationships between AD and obesity/MI might also have relevance to the long-standing question of whether ACE exhibits longevity effects (25). If different alleles of ACE can contribute to mortality (via obesity/MI and AD) then strategies for addressing longevity will be dependent upon both the strength of the effects upon those diseases and their prevalences. In the present study, significantly different cladetype frequencies were observed across age groups. In comparing the MI and AD case–control data, clades associated with AD seem to be those diminishing with age, as opposed to those associated with obesity or MI. This loosely conforms to the data previously presented by Schächter et al. (25), where the Alu deletion allele (which occurs on B and C clades) was enriched in centenarians, appearing paradoxical given the assumed association of this allele with MI (45).

The vast majority of studies on ACE have examined a single Alu indel polymorphism in intron 16, although there are a few exceptions where more than one variant has been examined (17,46). Solely focusing on the Alu indel has potentially led to discrepancies that may be reflected in the widespread inability to replicate findings across studies. For the phenotypes examined in this study, clade models consistently outperformed single marker tests. Genotyping a single marker without knowledge of the genetic architecture of a region will stochastically capture the effects of various haplotype combinations. For example, markers in this 3' region primarily differentiate the A clade from the B and C clades, whereas the promoter variants differentiate the B clade from the A and C clades [see specifically Farrall et al. (8)]. The sequence variants that give rise to functionally different versions of ACE and/or variable ACE levels in human populations are still unknown. An in silico analysis of potential functional polymorphisms recommends two possible candidates: rs4292, located in the ACE promoter, and rs4362, located in exon 23. Interestingly, rs4362 was not included in the study of Cox et al. (2), and lies in between two sites exhibiting the greatest independent effects upon the ACE plasma trait. Importantly, for the Alu indel there was no direct evidence in the available cDNA or EST sequences that the Alu insertion could actively change the expression level or structure of the transcript. This would be in line with the findings of Cox et al. (2), suggesting that this particular variant may be inert. While speculative, we envision from these data a possible two-site model, whereby a single promoter (possibly rs4292) and a single intragenic site (possibly rs4362) might explain functional differences among the A, B and C clades in Europeans. We reiterate that the LD relationships between these markers make typing of more than two sites unnecessary to differentiate clades, at least in the European populations for which data are available. Thus, among the markers we have tested rs4921, rs4292 and rs1800764 in the promoter are essentially equivalent and segregate together, as do rs4363, rs4362, rs4343, Alu indel, rs4331 and rs4309 (14). In light of this and depending on the specific ancestries of the populations examined, studies that have already typed the Alu indel may be able to conveniently increase their resolution by simply including a promoter variant.

In summary, efforts to relate genetic variation to human disease continue at an unprecedented rate, despite concerns that the number and complexity of the underlying genetic factors may be intractable. Confronted with this, it is important for disease gene mapping efforts that human populations actually maintain variants that differentially affect gene function and that the genetic architecture of a locus is reasonably well resolved (especially in the specific population being studied) before association strategies are attempted. We believe we have employed this knowledge gainfully to demonstrate significant effects of ACE alleles upon several human phenotypes, principally quantitative traits related to energy metabolism and obesity. We further show that the models employed to demonstrate effects upon obesity also efficiently capture effects upon MI and AD, two diseases that have long been posited to be associated with ACE (45,47). That genotypes associated with MI and AD appear to influence these diseases in an opposite fashion may derive from the different biological functions of ACE involving proteolytic substrate diversity. Substantial research remains to be performed in order to resolve the number and character of biologically relevant alleles of ACE along with functional studies that will provide insight to the mechanism and the role of ACE in disease.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Clinical materials
The principal clinical DNA materials employed in this study derive from individuals participating in SHEEP, a population based case–control study aimed at investigating the effects of various risk factors for MI in men and women (19). The subjects were aged 45–70 and were selected from the Stockholm County population registry. The materials consist of healthy controls and individuals who have suffered a single MI event and been allowed a recovery time of 3 months prior to blood sampling and anthropometric measures (20,48). A detailed description of examined traits for these individuals can be found in Table 1. Many of the AD patients and controls used in this study have been reported previously (17). We greatly expanded the number of AD samples and controls for the present study. This included an additional Swedish AD sample consisting of 158 individuals recruited as part of a longitudinal geriatric population study in Piteå, Sweden (49), and an additional sample of 196 AD patients and 545 normal elderly controls from the Swedish Twin Registry HARMONY study which is described in detail elsewhere (50). The complete AD case sample consisted of 270 males and 466 females, with an average age-at-examination (AAE) of 77.0±7.4 (SD) years. The total set of AD controls consisted of 439 males and 573 females, with an AAE of 77.2±6.6 (SD) years.

SNP selection and verification
We initially developed genotyping assays for 13 markers extending across ACE, which were tested in 93 Swedish control samples. These were (from 5'–3') rs1800764, rs4291, rs4303, rs4309, rs4314, rs4331, Alu I/D, rs4343, rs4348, rs4976, rs4362, rs4363 and rs4364. Markers rs4303, rs4314, rs4348, rs4976 and rs4364 were monomorphic in this sample. Oligonucleotide sequences for dynamic allele specific hybridization (DASH) assays and PCR reactions as well as LD structure have been reported previously (17). Three SNPs were selected (see Results) to be genotyped in the entire sample in the present study (rs1800764, rs4291 and rs4343). Details on all markers may be found in the dbSNP database (http://www.ncbi.nlm.nih.gov/SNP/) under their respective IDs. Surrounding 50 bp sequences in each direction were examined for repeats and duplicated sequences using Repeat-Masker (http://repeatmasker.genome.washington.edu/cgi-bin/RepeatMasker/) and BLAST (http://www.ncbi.nlm.nih.gov/blast).

Genotyping
Genotyping of SNPs was performed using an induced fluorescence resonance energy transfer modification of iFRET-DASH (51). All PCR reactions were run in 10–20 µl volumes with 1.5 mM MgCl2 and using 5–20 ng genomic DNA.

Computational detection of potential regulatory effect of sequence variation
Marker loci were analyzed for their putative effects on the binding specificity of transcription factors from the JASPAR database (52). We extracted human/mouse/rat alignments of the regions around the SNPs from the UCSC Genome Browser (53) and searched for potential regulatory variation using a nascent web-based tool named RAVEN (Lenhard et al., manuscript in preparation; write to boris.lenhard@cgb.ki.se for early access). RAVEN combines phylogenetic footprinting (54) with scanning of all sequence variants for transcription factor binding sites that may be differentially affected by the variation.

Statistical analysis
Deviation from Hardy–Weinberg equilibrium for genotypes at individual loci as well as differences in genotype and cladetype distributions between discrete groups were assessed using the Pearson chi square statistic. Deviation from normality for trait distributions was assessed using the Kolgomorov–Smirnov test. Correlations between traits were established using Spearman's rho statistic. Tests for association between genotypes and quantitative traits were performed using Kruskal–Wallis analysis of ranks or the Mann–Whitney U test for traits with non-normal distributions, or alternatively, ANOVA for normally distributed traits. For MI and AD, adjusted odds ratios were estimated using logistic regression. These statistical analyses were performed using StatView version 5.0 (Abacus Concepts). Haplotype frequencies were estimated using the HAPLOTYPER program (55). LD between marker pairs within ACE was estimated using the r2 metric (56).


   ACKNOWLEDGEMENTS
 
Generous financial support was provided by Pfizer Inc., NIH grant no. R01 AG08724, The Swedish Medical Research Council, Loo and Hans Ostermans Foundation, The Knut and Alice Wallenberg Foundation, The Swedish Old Servants Foundation (Gamla Tjänarinnor), Åke Wibergs Foundation, Torsten and Ragnar Söderbergs Foundation, Fredrik and Ingrid Thurings Foundation, King Gustav the Vth and Queen Victoria's Foundation and The Swedish Heart and Lung Foundation.


   FOOTNOTES
 
* To whom correspondence should be addressed at: Center for Genomics and Bioinformatics, Karolinska Institute, Berzelius väg 35 171 77, Stockholm, Sweden. Tel: +46 852486274; Fax: +46 8324826; Email: jonathan.prince{at}cgb.ki.se


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

  1. Rigat, B., Hubert, C., Alhenc-Gelas, F., Cambien, F., Corvol, P. and Soubrier, F. (1990) An insertion/deletion polymorphism in the angiotensin I-converting enzyme gene accounting for half the variance of serum enzyme levels. J. Clin. Invest., 86, 1343–1346.[Web of Science][Medline]

  2. Cox, R., Bouzekri, N., Martin, S., Southam, L., Hugill, A., Golamaully, M., Cooper, R., Adeyemo, A., Soubrier, F., Ward, R. et al. (2002) Angiotensin-1-converting enzyme (ACE) plasma concentration is influenced by multiple ACE-linked quantitative trait nucleotides. Hum. Mol. Genet., 11, 2969–2977.[Abstract/Free Full Text]

  3. Bengtsson, K., Orho-Melander, M., Lindblad, U., Melander, O., Bog-Hansen, E., Ranstam, J., Rastam, L. and Groop, L. (1999) Polymorphism in the angiotensin converting enzyme but not in the angiotensinogen gene is associated with hypertension and type 2 diabetes: the Skaraborg hypertension and diabetes project. J. Hypertens., 17, 1569–1575.[Web of Science][Medline]

  4. Narain, Y., Yip, A., Murphy, T., Brayne, C., Easton, D., Evans, J.G., Xuereb, J., Cairns, N., Esiri, M.M., Furlong, R.A. et al. (2000) The ACE gene and Alzheimer's disease susceptibility. J. Med. Genet., 37, 695–697.[Abstract/Free Full Text]

  5. Butler, R., Morris, A.D. and Struthers, A.D. (1997) Angiotensin-converting enzyme gene polymorphism and cardiovascular disease. Clin. Sci. (Lond.), 93, 391–400.[Medline]

  6. Tiret, L., Rigat, B., Visvikis, S., Breda, C., Corvol, P., Cambien, F. and Soubrier, F. (1992) Evidence, from combined segregation and linkage analysis, that a variant of the angiotensin I-converting enzyme (ACE) gene controls plasma ACE levels. Am. J. Hum. Genet., 51, 197–205.[Web of Science][Medline]

  7. McKenzie, C.A., Abecasis, G.R., Keavney, B., Forrester, T., Ratcliffe, P.J., Julier, C., Connell, J.M., Bennett, F., McFarlane-Anderson, N., Lathrop, G.M. et al. (2001) Trans-ethnic fine mapping of a quantitative trait locus for circulating angiotensin I-converting enzyme (ACE). Hum. Mol. Genet., 10, 1077–1084.[Abstract/Free Full Text]

  8. Farrall, M., Keavney, B., McKenzie, C., Delepine, M., Matsuda, F. and Lathrop, G.M. (1999) Fine-mapping of an ancestral recombination breakpoint in DCP1. Nat. Genet., 23, 270–271.[CrossRef][Web of Science][Medline]

  9. Keavney, B., McKenzie, C.A., Connell, J.M., Julier, C., Ratcliffe, P.J., Sobel, E., Lathrop, M. and Farrall, M. (1998) Measured haplotype analysis of the angiotensin-I converting enzyme gene. Hum. Mol. Genet., 7, 1745–1751.[Abstract/Free Full Text]

  10. Rieder, M.J., Taylor, S.L., Clark, A.G. and Nickerson, D.A. (1999) Sequence variation in the human angiotensin converting enzyme. Nat. Genet., 22, 59–62.[CrossRef][Web of Science][Medline]

  11. Villard, E., Tiret, L., Visvikis, S., Rakotovao, R., Cambien, F. and Soubrier, F. (1996) Identification of new polymorphisms of the angiotensin I-converting enzyme (ACE) gene, and study of their relationship to plasma ACE levels by two-QTL segregation-linkage analysis. Am. J. Hum. Genet., 58, 1268–1278.[Web of Science][Medline]

  12. Zhu, X., McKenzie, C.A., Forrester, T., Nickerson, D.A., Broeckel, U., Schunkert, H., Doering, A., Jacob, H.J., Cooper, R.S. and Rieder, M.J. (2000) Localization of a small genomic region associated with elevated ACE. Am. J. Hum. Genet., 67, 1144–1153.[Web of Science][Medline]

  13. Johnson, G.C., Esposito, L., Barratt, B.J., Smith, A.N., Heward, J., Di Genova, G., Ueda, H., Cordell, H.J., Eaves, I.A., Dudbridge, F. et al. (2001) Haplotype tagging for the identification of common disease genes. Nat. Genet., 29, 233–237.[CrossRef][Web of Science][Medline]

  14. Soubrier, F., Martin, S., Alonso, A., Visvikis, S., Tiret, L., Matsuda, F., Lathrop, G.M. and Farrall, M. (2002) High-resolution genetic mapping of the ACE-linked QTL influencing circulating ACE activity. Eur. J. Hum. Genet., 10, 553–561.[CrossRef][Web of Science][Medline]

  15. Templeton, A.R. (1995) A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping or DNA sequencing. V. Analysis of case/control sampling designs: Alzheimer's disease and the apoprotein E locus. Genetics, 140, 403–409.[Abstract]

  16. Durrant, C., Zondervan, K.T., Cardon, L.R., Hunt, S., Deloukas, P. and Morris, A.P. (2004) Linkage disequilibrium mapping via cladistic analysis of single-nucleotide polymorphism haplotypes. Am. J. Hum. Genet., 75, 35–43.[CrossRef][Web of Science][Medline]

  17. Kehoe, P.G., Katzov, H., Feuk, L., Bennet, A.M., Johansson, B., Wiman, B., de Faire, U., Cairns, N.J., Wilcock, G.K., Brookes, A.J. et al. (2003) Haplotypes extending across ACE are associated with Alzheimer's disease. Hum. Mol. Genet., 12, 859–867.[Abstract/Free Full Text]

  18. Zhu, X., Bouzekri, N., Southam, L., Cooper, R.S., Adeyemo, A., McKenzie, C.A., Luke, A., Chen, G., Elston, R.C. and Ward, R. (2001) Linkage and association analysis of angiotensin I-converting enzyme (ACE)-gene polymorphisms with ACE concentration and blood pressure. Am. J. Hum. Genet., 68, 1139–1148.[CrossRef][Web of Science][Medline]

  19. Reuterwall, C., Hallqvist, J., Ahlbom, A., De Faire, U., Diderichsen, F., Hogstedt, C., Pershagen, G., Theorell, T., Wiman, B. and Wolk, A. (1999) Higher relative, but lower absolute risks of myocardial infarction in women than in men: analysis of some major risk factors in the SHEEP study. The SHEEP Study Group. J. Intern. Med., 246, 161–174.[CrossRef][Web of Science][Medline]

  20. Leander, K., Wiman, B., Hallqvist, J., Sten-Linder, M. and de Faire, U. (2003) PAI-1 level and the PAI-1 4G/5G polymorphism in relation to risk of non-fatal myocardial infarction: results from the Stockholm Heart Epidemiology Program (SHEEP). Thromb. Haemost., 89, 1064–1071.[Web of Science][Medline]

  21. North, K., Howard, B. and Welty, T. (2003) Genetic and environmental contributions to cardiovascular disease risk in American Indians: the strong heart family study. Am. J. Epidemiol., 157, 303–314.[Abstract/Free Full Text]

  22. Broeckel, U., Hengstenberg, C., Mayer, B., Holmer, S., Martin, L.J., Comuzzie, A.G., Blangero, J., Nurnberg, P., Reis, A., Riegger, G.A. et al. (2002) A comprehensive linkage analysis for myocardial infarction and its related risk factors. Nat. Genet., 30, 210–214.[CrossRef][Web of Science][Medline]

  23. Dobbelsteyn, C.J., Joffres, M.R., MacLean, D.R. and Flowerdew, G. (2001) A comparative evaluation of waist circumference, waist-to-hip ratio and body mass index as indicators of cardiovascular risk factors. The Canadian Heart Health Surveys. Int. J. Obestr. Relat. Metab. Disord., 25, 652–661.

  24. Kehoe, P.G. (2003) The renin–angiotensin–aldosterone system and Alzheimer's disease? J. Renin Angiotensin Aldosterone Syst., 4, 80–93.[Abstract/Free Full Text]

  25. Schachter, F., Faure-Delanef, L., Guenot, F., Rouger, H., Froguel, P., Lesueur-Ginot, L. and Cohen, D. (1994) Genetic associations with human longevity at the APOE and ACE loci. Nat. Genet., 6, 29–32.[CrossRef][Web of Science][Medline]

  26. Galinsky, D., Tysoe, C., Brayne, C.E., Easton, D.F., Huppert, F.A., Dening, T.R., Paykel, E.S. and Rubinsztein, D.C. (1997) Analysis of the apo E/apo C-I, angiotensin converting enzyme and methylenetetrahydrofolate reductase genes as candidates affecting human longevity. Atherosclerosis, 129, 177–183.[CrossRef][Web of Science][Medline]

  27. Matthews, J.M. and Sunde, M. (2002) Zinc fingers–folds for many occasions. IUBMB Life, 54, 351–355.[Web of Science][Medline]

  28. Cartegni, L., Wang, J., Zhu, Z., Zhang, M.Q. and Krainer, A.R. (2003) ESEfinder: A web resource to identify exonic splicing enhancers. Nucl. Acids Res., 31, 3568–3571.[Abstract/Free Full Text]

  29. Zingone, A., Dominijanni, A., Mele, E., Marasco, O., Melina, F., Minchella, P., Quaresima, B., Tiano, M.T., Gnasso, A., Pujia, A. et al. (1994) Deletion polymorphism in the gene for angiotensin converting enzyme is associated with elevated fasting blood glucose levels. Hum. Genet., 94, 207–209.[Web of Science][Medline]

  30. Strazzullo, P., Iacone, R., Iacoviello, L., Russo, O., Barba, G., Russo, P., D'Orazio, A., Barbato, A., Cappuccio, F.P., Farinaro, E. et al. (2003) Genetic variation in the renin-angiotensin system and abdominal adiposity in men: the Olivetti Prospective Heart Study. Ann. Intern. Med., 138, 17–23.[Abstract/Free Full Text]

  31. Shiuchi, T., Cui, T.X., Wu, L., Nakagami, H., Takeda-Matsubara, Y., Iwai, M. and Horiuchi, M. (2002) ACE inhibitor improves insulin resistance in diabetic mouse via bradykinin and NO. Hypertension, 40, 329–334.[Abstract/Free Full Text]

  32. Alkharouf, J., Nalinikumari, K., Corry, D. and Tuck, M. (1993) Long-term effects of the angiotensin converting enzyme inhibitor captopril on metabolic control in non-insulin-dependent diabetes mellitus. Am. J. Hypertens., 6, 337–343.[Web of Science][Medline]

  33. McFarlane, S.I., Kumar, A. and Sowers, J.R. (2003) Mechanisms by which angiotensin-converting enzyme inhibitors prevent diabetes and cardiovascular disease. Am. J. Cardiol., 91, 30H–37H.[Web of Science][Medline]

  34. Henriksen, E.J. and Jacob, S. (2003) Modulation of metabolic control by angiotensin converting enzyme (ACE) inhibition. J. Cell Physiol., 196, 171–179.[CrossRef][Web of Science][Medline]

  35. O'Donnell, C.J., Lindpaintner, K., Larson, M.G., Rao, V.S., Ordovas, J.M., Schaefer, E.J., Myers, R.H. and Levy, D. (1998) Evidence for association and genetic linkage of the angiotensin-converting enzyme locus with hypertension and blood pressure in men but not women in the Framingham Heart Study. Circulation, 97, 1766–1772.[Abstract/Free Full Text]

  36. Wittrup, H.H., Tybjaerg-Hansen, A., Steffensen, R., Deeb, S.S., Brunzell, J.D., Jensen, G. and Nordestgaard, B.G. (1999) Mutations in the lipoprotein lipase gene associated with ischemic heart disease in men. The Copenhagen city heart study. Arterioscler. Thromb. Vasc. Biol., 19, 1535–1540.[Abstract/Free Full Text]

  37. Higaki, J., Baba, S., Katsuya, T., Sato, N., Ishikawa, K., Mannami, T., Ogata, J. and Ogihara, T. (2000) Deletion allele of angiotensin-converting enzyme gene increases risk of essential hypertension in Japanese men: the Suita Study. Circulation, 101, 2060–2065.[Abstract/Free Full Text]

  38. Karamohamed, S., Demissie, S., Volcjak, J., Liu, C., Heard-Costa, N., Liu, J., Shoemaker, C.M., Panhuysen, C.I., Meigs, J.B., Wilson, P. et al. (2003) Polymorphisms in the insulin-degrading enzyme gene are associated with type 2 diabetes in men from the NHLBI Framingham Heart Study. Diabetes, 52, 1562–1567.[Abstract/Free Full Text]

  39. McCarthy, J.J., Meyer, J., Moliterno, D.J., Newby, L.K., Rogers, W.J. and Topol, E.J. (2003) Evidence for substantial effect modification by gender in a large-scale genetic association study of the metabolic syndrome among coronary heart disease patients. Hum. Genet., 114, 87–98.[CrossRef][Web of Science][Medline]

  40. Sagnella, G.A., Rothwell, M.J., Onipinla, A.K., Wicks, P.D., Cook, D.G. and Cappuccio, F.P. (1999) A population study of ethnic variations in the angiotensin-converting enzyme I/D polymorphism: relationships with gender, hypertension and impaired glucose metabolism. J. Hypertens., 17, 657–664.[CrossRef][Web of Science][Medline]

  41. Gustafson, D., Rothenberg, E., Blennow, K., Steen, B. and Skoog, I. (2003) An 18-year follow-up of overweight and risk of Alzheimer disease. Arch. Intern. Med., 163, 1524–1528.[Abstract/Free Full Text]

  42. Riordan, J.F. (2003) Angiotensin-I-converting enzyme and its relatives. Genome Biol., 4, 225.[CrossRef][Medline]

  43. Georgiadis, D., Beau, F., Czarny, B., Cotton, J., Yiotakis, A. and Dive, V. (2003) Roles of the two active sites of somatic angiotensin-converting enzyme in the cleavage of angiotensin I and bradykinin: insights from selective inhibitors. Circ. Res., 93, 148–154.[Abstract/Free Full Text]

  44. Hu, J., Igarashi, A., Kamata, M. and Nakagawa, H. (2001) Angiotensin-converting enzyme degrades Alzheimer amyloid beta-peptide (Abeta); retards Abeta aggregation, deposition, fibril formation; and inhibits cytotoxicity. J. Biol. Chem., 276, 47863–47868.[Abstract/Free Full Text]

  45. Cambien, F., Poirier, O., Lecerf, L., Evans, A., Cambou, J.P., Arveiler, D., Luc, G., Bard, J.M., Bara, L., Ricard, S. et al. (1992) Deletion polymorphism in the gene for angiotensin-converting enzyme is a potent risk factor for myocardial infarction. Nature, 359, 641–644.[CrossRef][Medline]

  46. Haiman, C.A., Henderson, S.O., Bretsky, P., Kolonel, L.N. and Henderson, B.E. (2003) Genetic variation in angiotensin I-converting enzyme (ACE) and breast cancer risk: the multiethnic cohort. Cancer Res., 63, 6984–6987.[Abstract/Free Full Text]

  47. Kehoe, P.G., Russ, C., McIlory, S., Williams, H., Holmans, P., Holmes, C., Liolitsa, D., Vahidassr, D., Powell, J., McGleenon, B. et al. (1999) Variation in DCP1, encoding ACE, is associated with susceptibility to Alzheimer disease. Nat. Genet., 21, 71–72.[CrossRef][Web of Science][Medline]

  48. Bennet, A.M., Prince, J.A., Fei, G.Z., Lyrenas, L., Huang, Y., Wiman, B., Frostegard, J. and Faire, U. (2003) Interleukin-6 serum levels and genotypes influence the risk for myocardial infarction. Atherosclerosis, 171, 359–367.[CrossRef][Web of Science][Medline]

  49. Andreasen, N., Hesse, C., Davidsson, P., Minthon, L., Wallin, A., Winblad, B., Vanderstichele, H., Vanmechelen, E. and Blennow, K. (1999) Cerebrospinal fluid beta-amyloid(1–42) in Alzheimer disease: differences between early- and late-onset Alzheimer disease and stability during the course of disease. Arch. Neurol., 56, 673–680.[Abstract/Free Full Text]

  50. Lichtenstein, P., De Faire, U., Floderus, B., Svartengren, M., Svedberg, P. and Pedersen, N.L. (2002) The Swedish Twin Registry: a unique resource for clinical, epidemiological and genetic studies. J. Intern. Med., 252, 184–205.[CrossRef][Web of Science][Medline]

  51. Prince, J.A., Feuk, L., Howell, W.M., Jobs, M., Emahazion, T., Blennow, K. and Brookes, A.J. (2001) Robust and accurate single nucleotide polymorphism genotyping by dynamic allele-specific hybridization (DASH): design criteria and assay validation. Genome Res., 11, 152–162.[Abstract/Free Full Text]

  52. Sandelin, A., Alkema, W., Engstrom, P., Wasserman, W.W. and Lenhard, B. (2004) JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucl. Acids Res., 32 (Database issue), D91–D94.[Abstract/Free Full Text]

  53. Karolchik, D., Baertsch, R., Diekhans, M., Furey, T.S., Hinrichs, A., Lu, Y.T., Roskin, K.M., Schwartz, M., Sugnet, C.W., Thomas, D.J. et al. (2003) The UCSC Genome Browser Database. Nucl. Acids Res., 31, 51–54.[Abstract/Free Full Text]

  54. Lenhard, B., Sandelin, A., Mendoza, L., Engstrom, P., Jareborg, N. and Wasserman, W.W. (2003) Identification of conserved regulatory elements by comparative genome analysis. J. Biol., 2, 13.[CrossRef][Medline]

  55. Niu, T., Qin, Z.S., Xu, X. and Liu, J.S. (2002) Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms. Am. J. Hum. Genet., 70, 157–169.[CrossRef][Web of Science][Medline]

  56. Hill, W. (1974) Estimation of linkage disequilibrium in randomly mating populations. Heredity, 33, 229–239.[Web of Science][Medline]

  57. Barton, M., Carmona, R., Morawietz, H., d'Uscio, L.V., Goettsch, W., Hillen, H., Haudenschild, C.C., Krieger, J.E., Munter, K., Lattmann, T. et al. (2000) Obesity is associated with tissue-specific activation of renal angiotensin-converting enzyme in vivo: evidence for a regulatory role of endothelin. Hypertension, 35, 329–336.[Abstract/Free Full Text]


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Journal of Renin-Angiotensin-Aldosterone SystemHome page
R. Angunsri, T. Sritharathikhun, S. Suttirat, and T. Tencomnao
Association of angiotensin-converting enzyme gene promoter single nucleotide polymorphisms and haplotype with major depression in a northeastern Thai population
Journal of Renin-Angiotensin-Aldosterone System, September 1, 2009; 10(3): 179 - 184.
[Abstract] [PDF]

Home page
 Physiol. GenomicsHome page
S. Lacchini, A. S. Heimann, F. S. Evangelista, L. Cardoso, G. J. J. Silva, and J. E. Krieger
Cuff-induced vascular intima thickening is influenced by titration of the Ace gene in mice
Physiol Genomics, May 13, 2009; 37(3): 225 - 230.
[Abstract] [Full Text] [PDF]

Home page
 Circ. Res.Home page
F.A. Sayed-Tabatabaei, B.A. Oostra, A. Isaacs, C.M. van Duijn, and J.C.M. Witteman
ACE Polymorphisms
Circ. Res., May 12, 2006; 98(9): 1123 - 1133.
[Abstract] [Full Text] [PDF]

Home page
 J. Biol. Chem.Home page
M. L. Hemming and D. J. Selkoe
Amyloid {beta}-Protein Is Degraded by Cellular Angiotensin-converting Enzyme (ACE) and Elevated by an ACE Inhibitor
J. Biol. Chem., November 11, 2005; 280(45): 37644 - 37650.
[Abstract] [Full Text] [PDF]

Home page
 Am J EpidemiolHome page
D. J. Lehmann, M. Cortina-Borja, D. R. Warden, A. D. Smith, K. Sleegers, J. A. Prince, C. M. van Duijn, and P. G. Kehoe
Large Meta-Analysis Establishes the ACE Insertion-Deletion Polymorphism as a Marker of Alzheimer's Disease
Am. J. Epidemiol., August 15, 2005; 162(4): 305 - 317.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
H. Lei, I. N. M. Day, and I. Vorechovsky
Exonization of AluYa5 in the human ACE gene requires mutations in both 3' and 5' splice sites and is facilitated by a conserved splicing enhancer
Nucleic Acids Res., July 14, 2005; 33(12): 3897 - 3906.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
13/21/2647    most recent
ddh286v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (20)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Katzov, H.
Right arrow Articles by Prince, J. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Katzov, H.
Right arrow Articles by Prince, J. A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?