COL4A1 Is Associated With Arterial Stiffness by Genome-Wide Association ScanCLINICAL PERSPECTIVE
Background— Pulse wave velocity (PWV), a noninvasive index of central arterial stiffness, is a potent predictor of cardiovascular mortality and morbidity. Heritability and linkage studies have pointed toward a genetic component affecting PWV. We conducted a genome-wide association study to identify single-nucleotide polymorphisms (SNPs) associated with PWV.
Methods and Results— The study cohort included participants from the SardiNIA study for whom PWV measures were available. Genotyping was performed in 4221 individuals, using either the Affymetrix 500K or the Affymetrix 10K mapping array sets (with imputation of the missing genotypes). Associations with PWV were evaluated using an additive genetic model that included age, age2, and sex as covariates. The findings were tested for replication in an independent internal Sardinian cohort of 1828 individuals, using a custom chip designed to include the top 43 nonredundant SNPs associated with PWV. Of the loci that were tested for association with PWV, the nonsynonymous SNP rs3742207 in the COL4A1 gene on chromosome 13 and SNP rs1495448 in the MAGI1 gene on chromosome 3 were successfully replicated (P=7.08×10−7 and P=1.06×10−5, respectively, for the combined analyses). The association between rs3742207 and PWV was also successfully replicated (P=0.02) in an independent population, the Old-Order Amish, leading to an overall P=5.16×10−8.
Conclusions— A genome-wide association study identified a SNP in the COL4A1 gene that was significantly associated with PWV in 2 populations. Collagen type 4 is the major structural component of basement membranes, suggesting that previously unrecognized cell-matrix interactions may exert an important role in regulating arterial stiffness.
Received September 22, 2008; accepted January 26, 2009.
Central arterial stiffening is one of the hallmarks of arterial aging. Carotid-femoral pulse wave velocity (PWV) is the preferred noninvasive measure of central arterial stiffness.1 PWV is increased in patients with cardiovascular conditions such as hypertension, diabetes, metabolic syndrome, and atherosclerosis.2 Furthermore, PWV is an independent predictor of hypertension3 and of coronary heart disease and stroke in healthy subjects4 and an independent predictor of mortality in the general population,5 in hypertensive subjects,6 in older community dwelling individuals,7 and in patients with end-stage renal disease.8 Thus, understanding the mechanisms of central arterial stiffening may lead to effective interventions that could improve PWV and favorably impact cardiovascular morbidity and mortality.
Clinical Perspective see p 151
Increasing central arterial stiffness has traditionally been thought to result from the breakdown of elastin in central arterial walls, due to the repeated cycles of distension and recoil of the central aorta, and from the deposition and cross-linking of collagen. There is an increasing recognition that arterial stiffening is influenced by lifestyle (eg, dietary salt consumption, and exercise) and is regulated by several signaling pathways (eg, nitric oxide).2 Furthermore, gene expression studies,9 heritability,10,11 and linkage11 analyses, and a genome-wide association study (GWAS) performed in 644 subjects from the Framingham Heart Study12 are all consistent with the likely involvement of genetic factors in modulating the variability in PWV.
To look for these factors, we performed a GWAS in a large founder population (see online-only Data Supplement) from Sardinia. Furthermore, to confirm the validity of our findings we tested the association of the detected loci with PWV both in a second Sardinian cohort and in a separate founder population, the Old-Older Amish.
The SardiNIA study recruited 6148 men and women aged 14 to 102 years (62.5% of the eligible population)10 from a cluster of 4 towns in the Lanusei valley on the island of Sardinia. The cohort includes 34 469 relative pairs, 4933 sibling pairs, 180 half-sibling pairs, 4014 first cousins, 4256 parent–child pairs, 675 grandparent–grandchild pairs, and 6400 avuncular pairs in addition to other more distant relatives. Most subjects (95%) had all 4 grand-parents born in Sardinia,10 and environmental factors have remained relatively homogeneous. To achieve the accrual goal for the study, the project was advertised through provincial, religious, and municipal authorities; in local television, newspaper, and radio messages; through local physicians and by mailings and phone calls. All subjects underwent extensive phenotyping, which included assessment of traditional cardiovascular risk factors (eg, blood pressure and cholesterol fractions) with standard methodologies, as well as the assessment of arterial stiffness by PWV. Blood draws yielded lymphocytes for subsequent DNA extraction. All subjects provided a written informed consent for participation in the study that was approved by both the Sardinian and the National Institute on Aging’s Institutional Review Boards.
PWV was measured in triplicate using nondirectional transcutaneous Doppler probes (model 810A, 9- to 10-Mhz probes, Parks Medical Electronics Inc, Aloha, Ore), as previously described13: A minimum of 10 arterial flow waves from the right common carotid artery and the right femoral artery were simultaneously recorded and averaged. PWV was calculated as distance divided by time. The distance traveled by the flow wave was measured with an external tape measure over the body surface and was calculated as the distance between the manubrium and the femoral sampling site, minus the distance between the manubrium and the carotid sampling site, and the time traveled by the flow wave was measured as the time delay between the feet of simultaneously recorded carotid and femoral arterial waveforms. The waveforms were simultaneously collected by 2 sonographers, 1 recording at the carotid site and 1 recording at the femoral site. All data were subsequently analyzed by a single investigator (A.S.) who was blinded to the clinical characteristics of the subjects. Details of a reproducibility study for PWV are provided in the Data Supplement. Forty-four individuals did not contribute PWV data to the analysis because of poor quality waveforms (n=21) or because of atrial fibrillation (n=23).
We took advantage of the relatedness among individuals in our sample to substantially reduce study costs.14 Specifically, because our sample includes many large families, we reasoned that genotyping a relatively small number of markers in all individuals would allow us to identify shared haplotype stretches within each family. We could then genotype a subset of the individuals in each family at higher density to characterize the haplotypes in each stretch and impute missing genotypes in other individuals in the family.14
Genotyping was performed with the Affymetrix 10K and 500K Mapping Arrays in 3329 and 1412 individuals, respectively (436 subjects were genotyped with both chips). Individuals typed with the 500K array were specifically selected because they represented the largest families in our sample, not based on their phenotype. Furthermore, for the larger sibships in our cohort, both parents and 1 child were selected, whereas in the smaller sibships, only the 2 parents were selected. The lower density arrays were used to genotype everyone else. Except when parents and offsprings were genotyped in the same family, we tried to ensure that individuals genotyped with the high-density array were only distantly related to one another. In the 2893 individuals typed with only the 10K panel, we took advantage of the overlapping dataset and of the relatedness of the population to estimate the missing genotypes based on stretches of shared haplotypes, using a modified Lander-Green algorithm.15,16 This approach for estimating missing genotypes is implemented in MERLIN (http://www.sph.umich.edu/csg/abecasis/MERLIN/) and is described in detail elsewhere16,17 and in the Data Supplement.
Prior the imputation process, low-quality markers that could affect the accuracy of dosage estimates were removed. In particular, markers that met any of the following criteria were discarded: call rate ≤90%, minor allele frequency ≤5%, excess of Mendelian inconsistencies or departure from Hardy-Weinberg equilibrium. From the 10K and the 500K chips, we were able to analyze 7407 and 356 359 markers, respectively, resulting in a total of 362 129 markers after accounting for overlapping polymorphisms.18 The genotype completeness rates exceeded 98%.
After completing this initial phase, we devised a custom chip to test for internal replication of our major findings and to eliminate possible genotyping errors. This chip consisted of 11 617 single-nucleotide polymorphisms (SNPs) and included, for each of the quantitative traits in the SardiNIA study,10 the top SNPs that were associated with these traits in GWAS. Forty-three unique SNPs associated with PWV were included on this chip. This custom chip was used to type the remaining 1857 subjects recruited into the SardiNIA study who had not been typed with the 500K or 10K chips (denoted as SardiNIA stage 2). These individuals were not related to the individuals typed with the 500K chip (kinship coefficient=0) (see Data Supplement), and thus were a suitable cohort to serve as an internal replication sample. We considered a SNP to be internally replicated if the direction of effect was in the same direction as the initial study with P<0.05.
To ensure adequate control of type I error rates, an inverse normal transformation was applied to PWV before analysis, to reduce the impact of outliers and minimize deviations from normality (supplementary Figure 1). This transformation involves ranking all available PWV values, transforming these ranks into quantiles and finally converting the resulting quantiles into normal deviates. To perform the genome-wide association analysis, a simple regression model was fitted and a variance component approach that modeled background polygenic effects was used to account for correlation between different observed phenotypes within each family.17 The association analysis was performed using an additive genetic model and was adjusted for age, age2, and sex. An initial Q/Q plot suggested that the genomic control parameter was inflated (λ=1.14), likely reflecting residual relatedness in this founder population. Therefore, the probability values were adjusted according to the genomic control method19 (supplementary Figure 2). The probability values derived from the initial GWAS and from the association tests on the individuals genotyped with the custom chip were then combined using the z-scores meta-analysis methods, and taking into account the number of subjects analyzed in each set and the direction and magnitude of the estimated effect.
The top findings from our study were tested in 813 subjects from a genetically distant second-founder population, the Old-Order Amish of Lancaster, Pa. These included healthy Amish subjects enrolled in 2 studies, the Heredity and Phenotype Intervention (HAPI) Heart Study and the Amish Longevity Study (ALS). The HAPI Heart Study was initiated in 2002 to measure the cardiovascular response to 4 short-term interventions affecting cardiovascular risk factors and to identify the genetic and environmental determinants of these responses.20 The ALS was initiated in 2000 to identify the genetic factors associated with living to an old age, and recruited Amish individuals living to age 92 years or older, their offspring, and the offspring spouses.21 PWV was assessed with the CompliorSP device (Artech Medical, Pantin, France) before the interventions were administered, and genotyping was performed with an Affymetrix 500K chip. GWAS in the Old-Order Amish were adjusted for family structure, and the data transformation and statistical analyses were performed in an analogous manner to those in the SardiNIA study.
After excluding subjects with atrial fibrillation or poor quality PWV tracings, a total of 4221 and 1828 Sardinians were considered for the initial GWA analysis and for the internal replication (SardiNIA stage 2), respectively. The demographic and clinical characteristics of the study cohort are shown in Table 1. The mean age was 43.7±17.6 years (range, 14 to 102 years), 58% were women, 20% reported a history of smoking, 29% were hypertensive, 5% were diabetics, and only 1% reported a clinical history of myocardial infarction. As expected,2 PWV increased with advancing age in a quadratic fashion (Figure 1).
We first conducted GWAS to survey the genome for common variants associated with PWV. Figure 2 graphically summarizes the associations of PWV with the >329 129 SNPs with a minor allele frequency >5% that passed quality control checks and transmission disequilibrium testing. The top 100 hits are shown in supplementary Table 1. Promising findings were noted on chromosomes 1, 2, 4, 12 to 14, 17, 20, and 21, encompassing 18 SNPs with P<2×10−5.
To further evaluate these initial results, a secondary custom chip from Affymetrix was devised based on the top findings from the GWAS. For PWV, the top 85 SNPs were considered; of these, 43 were not in strong linkage disequilibrium and were included in the custom chip. The 1828 individuals typed with this custom chip were genetically independent from those genotyped in the initial analysis. Thus, the custom chip helped validate the initial findings, and provided an internal replication within the Sardinian cohort. Of the 43 SNPs that were included in the custom designed chip, 2 SNPs, rs3742207 and rs1495448 in the COL4A1 and MAGI1 genes, respectively, were significantly associated with PWV with the same directionality of effect as in the GWAS (Table 2). Furthermore, when the results of the initial GWAS study group and the stage 2 study group were combined in a meta-analysis (Table 2), the association of the C allele of rs3742207 with increased PWV was strengthened (P=7.08×10−7), whereas the association of the T allele of rs1495448 with increased PWV was weakened (P=1.07×10−5).
We repeated the association analyses using models that adjusted for mean arterial pressure, creatinine, and the use of blood pressure lowering medications, which are important covariates of arterial stiffness, in addition to age, age2, and sex that were adjusted for in the base models. The association of rs3742207 with PWV was slightly strengthened, whereby the probability value decreased from 5.94×10−5 to 1.78×10−5. Next, we excluded subjects on antihypertensive medications (n=544) and subjects on dialysis (n=10), and repeated the analyses adjusting for age, age2, sex, mean arterial pressure, and creatinine. The probability value for the association of rs3742207 with PWV was 2.48×10−5. In this last model, the probability value was 3.19×10−5 when diabetic individuals (n=50) were excluded. However, the genomic control parameters for these 3 additional models were higher than the one for the base model (λ=1.16, 1.17, and 1.17, respectively, compared with 1.14 of the initial model). Conversely, the association of PWV with rs1495448 (in the MAGI1 gene) was weakened when the analyses were repeated using these 3 sets of additional adjustments (P=0.0014, P=0.0038, and P=0.0081, respectively).
The associations of the 2 loci rs3742207 and rs1495448 with PWV were further evaluated in the Amish population, a genetically distant founder population of European ancestry. The HAPI Heart Study and the ALS participants underwent both assessment of PWV and genotyping using the Affymetrix 500K chip. We confirmed that allele C of the SNP rs3742207 (the more frequent allele in the Amish population) was associated with PWV (P=0.02) with a comparable effect size (Table 2), whereas rs1495448 was not (P=0.49; Table 2). Combining SardiNIA, SardiNIA stage 2, and the Old-Order Amish yielded an overall P=5.16×10−8 for the association between rs3742207 and PWV.
The heritability of PWV in the SardiNIA study is 0.226 (adjusted for age, sex, and age-by-sex interaction).10 The proportion of variance in PWV (which is equivalent to the proportion of the heritable fraction) that is explained by rs3742207 is 0.87%. The overall effect size is 18.9 cm/s (calculated as a weighted average of the effect observed in each of the 3 study cohorts, in which the weights correspond to those used in the meta-analysis).
The replicated SNP rs3742207 is a common nonsynonymous coding polymorphism located in exon 45 of the Col4A1 gene. This polymorphism involves a substitution of adenine by cytosine resulting in an amino acid change from glycine to histidine at position 1334, which is located in a central region of the protein that consists of multiple triple-helix repeat domains. Nonetheless, further studies are necessary to determine whether the true causal variant is this SNP or another one, which according to linkage disequilibrium structure (Figure 3),12,22,23 seems to lie in exon 45 or nearby.
We conducted a GWAS in the SardiNIA cohort, and found that SNP rs3742207 in the COL4A1 gene was significantly associated with PWV. Furthermore, this locus was successfully replicated both in an independent sample within the SardiNIA cohort, and in the Old-Order Amish population, an external genetically distant founder population of European ancestry, suggesting that this COL4A1 variant may have an effect in other populations.
Collagen Type 4
There are 6 different types of type 4 collagen-α chains (α1 to α6). Each one is encoded by a different gene and comprises repeating triple-helical domains with a characteristic G-X-Y motif interposed between the amino terminus and a globular C-terminus. These α chains assemble into triple helices to form type 4 collagen. The collagen type 4 α1 molecule consists of 20 triple helical repeats G-X-Y(X), where the first position is always a Glycine residue. Mutations in this Glycine residue cause Mendelian disorders characterized by small vessel,24 or small and large vessels25 angiopathy.
Type 4 collagen had not previously been considered to be involved in regulating arterial stiffness. Unlike collagen types 1 and 3, which are constituents of the extracellular matrix that are found in the medial layer of the arterial walls where they impart the tensile strength to the arteries, type 4 collagen is a structural component of basement membranes. At present, there is no functional evidence implicating COL4A1 as a determinant of PWV. Nonetheless, a speculative discussion of putative mechanisms through which COL4A1 may influence arterial stiffness is provided in the Data Supplement.
Studies of the Genetics of Arterial Stiffness
Heritability studies have consistently concluded that a genetic component likely underlies the variance in arterial stiffness.26–28 Similarly, results of linkage studies for noninvasive indices of arterial stiffness11,27,28 suggested an underlying genetic component, even though the findings among these studies did not necessarily overlap.
Previous association studies that examined the genetic underpinnings of arterial stiffness focused mostly on polymorphisms in candidate genes that are believed to be involved in regulating arterial structure and/or function, such as nitric oxide synthase,29 angiotensin II type 1 receptor,30 collagen 1,26 G-protein β-3 subunit,31 β-adrenergic receptors,32 fibrillin 1,33 and C-reactive protein.34 These candidate gene studies, which were conducted in single populations, did not attempt replication in other populations, and often yielded discrepant results.27,29
The first GWAS evaluating SNPs associated with arterial stiffness was performed in ≈644 participants in the Framingham Heart Study, using a 100K Affymetrix GeneChip array.11 None of the associations with the various markers of arterial stiffness reached genome-wide significance in that study. The 100K chip that was used did not include rs3742207 or any neighboring SNP in the same linkage disequilibrium (LD) block (supplementary Figure 3). It did include 7 other SNPs in the COL4A1 gene region, 3 of which were associated (0.01<P<0.05) with PWV in age-adjusted and sex-adjusted analyses (available at http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=search&db=gap&term=carotid-femoral%20pulse%20wave%20velocity&doptcmdl=SAnalyses). Of note, some of these markers were also associated with PWV in SardiNIA (Figure 3A) but with higher probability values than SNP rs3742207.
The GWAS in the SardiNIA study was performed in a larger cohort and with a much denser SNP map, and showed, for the first time, a link between a polymorphism in the collagen type 4 α1 gene and arterial stiffness. Each copy of the minor allele (C) of rs3742207 is associated with a 21 cm/s higher PWV, such that homozygotes for this minor allele have an ≈42 cm/s (6.3%) higher PWV than homozygotes for the major allele, an effect size comparable with those reported in GWAS of other traits, including ones recently studied in the Sardinian cohort.18,35,36 Importantly, in a cohort of 1678 Danes aged 40 to 70 years, a PWV increase of 34 cm/s was associated with a 2.9% increase in age-adjusted and sex-adjusted cardiovascular mortality.5 Interestingly, rs3742207 was recently found to be associated with the prevalence of myocardial infarction in Japanese individuals.37
This study was conducted primarily in a founder population in SardiNIA, so caution should be exercised in extending these results to other populations. However, the values and distributions of PWV in our study did not significantly differ from those obtained in other out-bred populations. Furthermore, and in contrast to the microisolates where the gene pool is restricted to a few variants, the number of founders of the current day Sardinian population is large enough to encompass most of the existing alleles in the European population, although with some differences in frequency. Importantly, recent studies from the SardiNIA project18,35,36 have demonstrated that findings in SardiNIA are reproducible in other populations; and specifically in this study, we were able to replicate the association of our top finding in an independent and genetically distant founder population of European ancestry. Nonetheless, additional studies in populations of different ethnic origin are needed to further replicate and extend our finding.
Using GWAS, we found that a SNP in the COL4A1 gene is strongly associated with PWV, an established independent predictor of adverse cardiovascular outcomes. Collagen type 4 is the major structural component of basement membranes, suggesting that previously unrecognized cell-matrix interactions may exert an important role in regulating arterial stiffness. Further work is needed to elucidate these mechanisms, and this could potentially lead to the development of novel interventions aimed at delaying or preventing the risks associated with accelerated arterial stiffening. This would help fulfill, in part, the high expectations for breakthroughs in basic science and in clinical medicine that are engendered by modern-era genetics.
We thank Monsignore Piseddu, Bishop of Ogliastra; the Mayors of Lanusei, Ilbono, Arzana, and Elini; the head of the local Public Health Unit ASL4; and the residents of the towns for volunteering and cooperation. In addition, we thank the Mayor and the administration in Lanusei for providing and furnishing the clinic site. We thank the team of physicians and nurses who carried out the physical examinations and the recruitment personnel who enrolled the volunteers.
Sources of Funding
The SardiNIA (“ProgeNIA”) team was supported by contract NO1-AG-1-2109 from the National Institute on Aging. This work was supported, in part, by the Intramural Research Program of the National Institute on Aging, National Institutes of Health.
The Amish studies were supported by a National Institutes of Health Institutional Training Grant in Cardiac and Vascular Cell Biology (T32HL072751), grant U01 HL72515, the University of Maryland General Clinical Research Center (M01 RR 16500), the National Center for Research Resources, the Clinical Nutrition Research Unit of Maryland (P30DK072488), and the Paul Beeson Physician Faculty Scholars in Aging Program of the American Federation of Aging Research.
Laurent S, Cockcroft J, Van Bortel L, Boutouyrie P, Giannattasio C, Hayoz D, Pannier B, Vlachopoulos C, Wilkinson I, Struijker-Boudier H. Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J. 2006; 27: 2588–2605.
Najjar SS, Scuteri A, Lakatta EG. Arterial aging: is it an immutable cardiovascular risk factor? Hypertension. 2005; 46: 454–462.
Najjar SS, Scuteri A, Shetty V, Wright JG, Muller DC, Fleg JL, Spurgeon HP, Ferrucci L, Lakatta EG. Pulse wave velocity is an independent predictor of the longitudinal increase in systolic blood pressure and of incident hypertension in the Baltimore Longitudinal Study of Aging. J Am Coll Cardiol. 2008; 51: 1377–1383.
Mattace-Raso FU, van der Cammen TJ, Hofman A, van Popele NM, Bos ML, Schalekamp MA, Asmar R, Reneman RS, Hoeks AP, Breteler MM, Witteman JC. Arterial stiffness and risk of coronary heart disease and stroke: the Rotterdam Study. Circulation. 2006; 113: 657–663.
Willum-Hansen T, Staessen JA, Torp-Pedersen C, Rasmussen S, Thijs L, Ibsen H, Jeppesen J. Prognostic value of aortic pulse wave velocity as index of arterial stiffness in the general population. Circulation. 2006; 113: 664–670.
Laurent S, Boutouyrie P, Asmar R, Gautier I, Laloux B, Guize L, Ducimetiere P, Benetos A. Aortic stiffness is an independent predictor of all-cause and cardiovascular mortality in hypertensive patients. Hypertension. 2001; 37: 1236–1241.
Sutton-Tyrrell K, Najjar SS, Boudreau RM, Venkitachalam L, Kupelian V, Simonsick EM, Havlik R, Lakatta EG, Spurgeon H, Kritchevsky S, Pahor M, Bauer D, Newman A. Elevated aortic pulse wave velocity, a marker of arterial stiffness, predicts cardiovascular events in well-functioning older adults. Circulation. 2005; 111: 3384–3390.
Blacher J, Guerin AP, Pannier B, Marchais SJ, Safar ME, London GM. Impact of aortic stiffness on survival in end-stage renal disease. Circulation. 1999; 99: 2434–2439.
Durier S, Fassot C, Laurent S, Boutouyrie P, Couetil JP, Fine E, Lacolley P, Dzau VJ, Pratt RE. Physiological genomics of human arteries: quantitative relationship between gene expression and arterial stiffness. Circulation. 2003; 108: 1845–1851.
Pilia G, Chen WM, Scuteri A, Orru M, Albai G, Dei M, Lai S, Usala G, Lai M, Loi P, Mameli C, Vacca L, Deiana M, Olla N, Masala M, Cao A, Najjar SS, Terracciano A, Nedorezov T, Sharov A, Zonderman AB, Abecasis GR, Costa P, Lakatta E, Schlessinger D. Heritability of cardiovascular and personality traits in 6,148 Sardinians. PLoS Genet. 2006; 2: e132.
Mitchell GF, DeStefano AL, Larson MG, Benjamin EJ, Chen MH, Vasan RS, Vita JA, Levy D. Heritability and a genome-wide linkage scan for arterial stiffness, wave reflection, and mean arterial pressure: the Framingham Heart Study. Circulation. 2005; 112: 194–199.
Vaitkevicius PV, Fleg JL, Engel JH, O'Connor FC, Wright JG, Lakatta LE, Yin FC, Lakatta EG. Effects of age and aerobic capacity on arterial stiffness in healthy adults. Circulation. 1993; 88: 1456–1462.
Lander ES, Green P. Construction of multilocus genetic linkage maps in humans. Proc Natl Acad Sci USA. 1987; 84: 2363–2367.
Scuteri A, Sanna S, Chen WM, Uda M, Albai G, Strait J, Najjar S, Nagaraja R, Orru M, Usala G, Dei M, Lai S, Maschio A, Busonero F, Mulas A, Ehret GB, Fink AA, Weder AB, Cooper RS, Galan P, Chakravarti A, Schlessinger D, Cao A, Lakatta E, Abecasis GR. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007; 3: e115.
Mitchell BD, McArdle PF, Shen H, Rampersaud E, Pollin TI, Bielak LF, Jaquish C, Douglas JA, Roy-Gagnon MH, Sack P, Naglieri R, Hines S, Horenstein RB, Chang YP, Post W, Ryan KA, Brereton NH, Pakyz RE, Sorkin J, Damcott CM, O'Connell JR, Mangano C, Corretti M, Vogel R, Herzog W, Weir MR, Peyser PA, Shuldiner AR. The genetic response to short-term interventions affecting cardiovascular function: rationale and design of the Heredity and Phenotype Intervention (HAPI) Heart Study. Am Heart J. 2008; 155: 823–828.
Abecasis GR, Cookson WO. GOLD–graphical overview of linkage disequilibrium. Bioinformatics. 2000; 16: 182–183.
Gould DB, Phalan FC, Breedveld GJ, van Mil SE, Smith RS, Schimenti JC, Aguglia U, van der Knaap MS, Heutink P, John SW. Mutations in Col4a1 cause perinatal cerebral hemorrhage and porencephaly. Science. 2005; 308: 1167–1171.
Plaisier E, Gribouval O, Alamowitch S, Mougenot B, Prost C, Verpont MC, Marro B, Desmettre T, Cohen SY, Roullet E, Dracon M, Fardeau M, Van Agtmael T, Kerjaschki D, Antignac C, Ronco P. COL4A1 mutations and hereditary angiopathy, nephropathy, aneurysms, and muscle cramps. N Engl J Med. 2007; 357: 2687–2695.
Brull DJ, Murray LJ, Boreham CA, Ralston SH, Montgomery HE, Gallagher AM, McGuigan FE, Davey Smith G, Savage M, Humphries SE, Young IS. Effect of a COL1A1 Sp1 binding site polymorphism on arterial pulse wave velocity: an index of compliance. Hypertension. 2001; 38: 444–448.
North KE, MacCluer JW, Devereux RB, Howard BV, Welty TK, Best LG, Lee ET, Fabsitz RR, Roman MJ. Heritability of carotid artery structure and function: the Strong Heart Family Study. Arterioscler Thromb Vasc Biol. 2002; 22: 1698–1703.
Czarnecka D, Kawecka-Jaszcz K, Stolarz K, Olszanecka A, Dembinska-Kiec A, Kiec-Wilk B. Ambulatory blood pressure, left ventricular mass and vascular phenotypes in relation to the endothelial nitric oxide synthase gene Glu298Asp and intron 4 polymorphisms in a population-based family study. J Hum Hypertens. 2005; 19: 413–420.
Benetos A, Gautier S, Ricard S, Topouchian J, Asmar R, Poirier O, Larosa E, Guize L, Safar M, Soubrier F, Cambien F. Influence of angiotensin-converting enzyme and angiotensin II type 1 receptor gene polymorphisms on aortic stiffness in normotensive and hypertensive patients. Circulation. 1996; 94: 698–703.
Chen W, Srinivasan SR, Boerwinkle E, Berenson GS. Beta-adrenergic receptor genes are associated with arterial stiffness in black and white adults: the Bogalusa Heart Study. Am J Hypertens. 2007; 20: 1251–1257.
Medley TL, Cole TJ, Gatzka CD, Wang WY, Dart AM, Kingwell BA. Fibrillin-1 genotype is associated with aortic stiffness and disease severity in patients with coronary artery disease. Circulation. 2002; 105: 810–815.
Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R, Heath SC, Timpson NJ, Najjar SS, Stringham HM, Strait J, Duren WL, Maschio A, Busonero F, Mulas A, Albai G, Swift AJ, Morken MA, Narisu N, Bennett D, Parish S, Shen H, Galan P, Meneton P, Hercberg S, Zelenika D, Chen WM, Li Y, Scott LJ, Scheet PA, Sundvall J, Watanabe RM, Nagaraja R, Ebrahim S, Lawlor DA, Ben-Shlomo Y, Davey-Smith G, Shuldiner AR, Collins R, Bergman RN, Uda M, Tuomilehto J, Cao A, Collins FS, Lakatta E, Lathrop GM, Boehnke M, Schlessinger D, Mohlke KL, Abecasis GR. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008; 40: 161–169.
Li S, Sanna S, Maschio A, Busonero F, Usala G, Mulas A, Lai S, Dei M, Orru M, Albai G, Bandinelli S, Schlessinger D, Lakatta E, Scuteri A, Najjar SS, Guralnik J, Naitza S, Crisponi L, Cao A, Abecasis G, Ferrucci L, Uda M, Chen WM, Nagaraja R. The GLUT9 gene is associated with serum uric acid levels in Sardinia and Chianti cohorts. PLoS Genet. 2007; 3: e194.
Yamada Y, Kato K, Oguri M, Fujimaki T, Yokoi K, Matsuo H, Watanabe S, Metoki N, Yoshida H, Satoh K, Ichihara S, Aoyagi Y, Yasunaga A, Park H, Tanaka M, Nozawa Y. Genetic risk for myocardial infarction determined by polymorphisms of candidate genes in a Japanese population. J Med Genet. 2008; 45: 216–221.
Pulse wave velocity, a noninvasive index of central arterial stiffness, is a potent predictor of cardiovascular mortality and morbidity. Heritability and linkage studies have pointed toward a genetic component affecting pulse wave velocity. We conducted a genome-wide association study to identify single-nucleotide polymorphisms associated with pulse wave velocity. Analyses were performed in 4221 participants in the SardiNIA study, a founder population. We found that the nonsynonymous single-nucleotide polymorphisms rs3742207 in the COL4A1 gene was significantly associated with pulse wave velocity. This locus was successfully replicated both in an independent sample within the SardiNIA cohort and in the Old-Order Amish population, an external genetically distant founder population of European ancestry, with an overall P=5.16×10−8. Collagen type 4 is the major structural component of basement membranes, suggesting that previously unrecognized cell-matrix interactions may exert an important role in regulating arterial stiffness, which is an established independent predictor of adverse cardiovascular outcomes. Further work is needed to elucidate these mechanisms, but this could potentially lead to the development of novel interventions aimed at delaying or preventing the risks associated with accelerated arterial stiffening.
↵*The first 2 authors contributed equally to this work.
The online-only Data Supplement is available at http://circgenetics.ahajournals.org/cgi/content/full/CIRCGENETICS.108.823245/DC1.