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Svati H Shah,
Jie-Lena Sun,
Robert D Stevens,
James R Bain,
Michael J Muehlbauer,
Karen S Pieper,
Carol Haynes, Elizabeth R Hauser,
William E Kraus,
Christopher B Granger,
Christopher B Newgard,
Robert M Califf,
L Kristin Newby
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ABSTRACT: Cardiovascular risk models remain incomplete. Small-molecule metabolites may reflect underlying disease and, as such, serve as novel biomarkers of cardiovascular risk.
We studied 2,023 consecutive patients undergoing cardiac catheterization. Mass spectrometry profiling of 69 metabolites and lipid assessments were performed in fasting plasma. Principal component analysis reduced metabolites to a smaller number of uncorrelated factors. Independent relationships between factors and time-to-clinical events were assessed using Cox modeling. Clinical and metabolomic models were compared using log-likelihood and reclassification analyses.
At median follow-up of 3.1 years, there were 232 deaths and 294 death/myocardial infarction (MI) events. Five of 13 metabolite factors were independently associated with mortality: factor 1 (medium-chain acylcarnitines: hazard ratio [HR] 1.12 [95% CI, 1.04-1.21], P = .005), factor 2 (short-chain dicarboxylacylcarnitines: HR 1.17 [1.05-1.31], P = .005), factor 3 (long-chain dicarboxylacylcarnitines: HR 1.14 [1.05-1.25], P = .002); factor 6 (branched-chain amino acids: HR 0.86 [0.75-0.99], P = .03), and factor 12 (fatty acids: HR 1.19 [1.06-1.35], P = .004). Three factors independently predicted death/MI: factor 2 (HR 1.11 [1.01-1.23], P = .04), factor 3 (HR 1.13 [1.04-1.22], P = .005), and factor 12 (HR 1.18 [1.05-1.32], P = .004). For mortality, 27% of intermediate-risk patients were correctly reclassified (net reclassification improvement 8.8%, integrated discrimination index 0.017); for death/MI model, 11% were correctly reclassified (net reclassification improvement 3.9%, integrated discrimination index 0.012).
Metabolic profiles predict cardiovascular events independently of standard predictors.
American heart journal 05/2012; 163(5):844-850.e1. · 4.65 Impact Factor
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Daniel K Nolan,
Beth Sutton,
Carol Haynes,
Jessica Johnson,
Jacqueline Sebek,
Elaine Dowdy,
David Crosslin,
David Crossman,
Michael H Sketch,
Christopher B Granger,
David Seo,
Pascal Goldschmidt-Clermont,
William E Kraus,
Simon G Gregory, Elizabeth R Hauser,
Svati H Shah
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ABSTRACT: Coronary artery disease (CAD), and one of its intermediate risk factors, dyslipidemia, possess a demonstrable genetic component, although the genetic architecture is incompletely defined. We previously reported a linkage peak on chromosome 5q31-33 for early-onset CAD where the strength of evidence for linkage was increased in families with higher mean low density lipoprotein-cholesterol (LDL-C). Therefore, we sought to fine-map the peak using association mapping of LDL-C as an intermediate disease-related trait to further define the etiology of this linkage peak. The study populations consisted of 1908 individuals from the CATHGEN biorepository of patients undergoing cardiac catheterization; 254 families (N = 827 individuals) from the GENECARD familial study of early-onset CAD; and 162 aorta samples harvested from deceased donors. Linkage disequilibrium-tagged SNPs were selected with an average of one SNP per 20 kb for 126.6-160.2 MB (region of highest linkage) and less dense spacing (one SNP per 50 kb) for the flanking regions (117.7-126.6 and 160.2-167.5 MB) and genotyped on all samples using a custom Illumina array. Association analysis of each SNP with LDL-C was performed using multivariable linear regression (CATHGEN) and the quantitative trait transmission disequilibrium test (QTDT; GENECARD). SNPs associated with the intermediate quantitative trait, LDL-C, were then assessed for association with CAD (i.e., a qualitative phenotype) using linkage and association in the presence of linkage (APL; GENECARD) and logistic regression (CATHGEN and aortas).
We identified four genes with SNPs that showed the strongest and most consistent associations with LDL-C and CAD: EBF1, PPP2R2B, SPOCK1, and PRELID2. The most significant results for association of SNPs with LDL-C were: EBF1, rs6865969, p = 0.01; PPP2R2B, rs2125443, p = 0.005; SPOCK1, rs17600115, p = 0.003; and PRELID2, rs10074645, p = 0.0002). The most significant results for CAD were EBF1, rs6865969, p = 0.007; PPP2R2B, rs7736604, p = 0.0003; SPOCK1, rs17170899, p = 0.004; and PRELID2, rs7713855, p = 0.003.
Using an intermediate disease-related quantitative trait of LDL-C we have identified four novel CAD genes, EBF1, PRELID2, SPOCK1, and PPP2R2B. These four genes should be further examined in future functional studies as candidate susceptibility loci for cardiovascular disease mediated through LDL-cholesterol pathways.
BMC Genetics 02/2012; 13:12. · 2.47 Impact Factor
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Asad A Shah,
Damian M Craig,
Jacqueline K Sebek,
Carol Haynes,
Robert C Stevens,
Michael J Muehlbauer,
Christopher B Granger, Elizabeth R Hauser,
L Kristin Newby,
Christopher B Newgard,
William E Kraus,
G Chad Hughes,
Svati H Shah
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ABSTRACT: Clinical models incompletely predict the outcomes after coronary artery bypass grafting. Novel molecular technologies can identify biomarkers to improve risk stratification. We examined whether metabolic profiles can predict adverse events in patients undergoing coronary artery bypass grafting.
The study population comprised 478 subjects from the CATHGEN biorepository of patients referred for cardiac catheterization who underwent coronary artery bypass grafting after enrollment. Targeted mass spectrometry-based profiling of 69 metabolites was performed in frozen, fasting plasma samples collected before surgery. Principal components analysis and Cox proportional hazards regression modeling were used to assess the relation between the metabolite factor levels and a composite outcome of postcoronary artery bypass grafting myocardial infarction, the need for percutaneous coronary intervention, repeat coronary artery bypass grafting, and death.
During a mean follow-up period of 4.3 ± 2.4 years, 126 subjects (26.4%) experienced an adverse event. Three principal components analysis-derived factors were significantly associated with an adverse outcome on univariate analysis: short-chain dicarboxylacylcarnitines (factor 2, P = .001); ketone-related metabolites (factor 5, P = .02); and short-chain acylcarnitines (factor 6, P = .004). These 3 factors remained independently predictive of an adverse outcome after multivariate adjustment: factor 2 (adjusted hazard ratio, 1.23; 95% confidence interval, 1.10-1.38; P < .001), factor 5 (odds ratio, 1.17; 95% confidence interval, 1.01-1.37; P = .04), and factor 6 (odds ratio, 1.14; 95% confidence interval, 1.02-1.27; P = .03).
Metabolic profiles are independently associated with adverse outcomes after coronary artery bypass grafting. These profiles might represent novel biomarkers of risk that can augment existing tools for risk stratification of coronary artery bypass grafting patients and might elucidate novel biochemical pathways that mediate risk.
The Journal of thoracic and cardiovascular surgery 02/2012; 143(4):873-8. · 3.41 Impact Factor
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ABSTRACT: Single variant or single gene analyses generally account for only a small proportion of the phenotypic variation in complex traits. Alternatively, gene set or pathway association analyses are playing an increasingly important role in uncovering genetic architectures of complex traits through the identification of systematic genetic interactions. Two dominant paradigms for gene set analyses are association analyses based on SNP genotypes and those based on gene expression profiles. However, gene-disease association can manifest in many ways, such as alterations of gene expression, genotype, and copy number; thus, an integrative approach combining multiple forms of evidence can more accurately and comprehensively capture pathway associations. We have developed a single statistical framework, Gene Set Association Analysis (GSAA), that simultaneously measures genome-wide patterns of genetic variation and gene expression variation to identify sets of genes enriched for differential expression and/or trait-associated genetic markers. Simulation studies illustrate that joint analyses of genomic data increase the power to detect real associations when compared with gene set methods that use only one genomic data type. The analysis of two human diseases, glioblastoma and Crohn's disease, detected abnormalities in previously identified disease-associated pathways, such as pathways related to PI3K signaling, DNA damage response, and the activation of NFKB. In addition, GSAA predicted novel pathway associations, for example, differential genetic and expression characteristics in genes from the ABC transporter family in glioblastoma and from the HLA system in Crohn's disease. These demonstrate that GSAA can help uncover biological pathways underlying human diseases and complex traits.
Genome Research 09/2011; 22(2):386-97. · 13.61 Impact Factor
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Mollie A Minear,
David R Crosslin,
Beth S Sutton,
Jessica J Connelly,
Sarah C Nelson,
Shera Gadson-Watson,
Tianyuan Wang,
David Seo,
Jeffrey M Vance,
Michael H Sketch,
Carol Haynes,
Pascal J Goldschmidt-Clermont,
Svati H Shah,
William E Kraus, Elizabeth R Hauser,
Simon G Gregory
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ABSTRACT: Tenascin-C (TNC) is an extracellular matrix protein implicated in biological processes important for atherosclerotic plaque development and progression, including smooth muscle cell migration and proliferation. Previously, we observed differential expression of TNC in atherosclerotic aortas compared with healthy aortas. The goal of this study was to investigate whether common genetic variation within TNC is associated with risk of atherosclerosis and coronary artery disease (CAD) in three independent datasets. We genotyped 35 single nucleotide polymorphisms (SNPs), including 21 haplotype tagging SNPs, in two of these datasets: human aorta tissue samples (n = 205) and the CATHGEN cardiovascular study (n = 1,325). Eleven of these 35 SNPs were then genotyped in a third dataset, the GENECARD family study of early-onset CAD (n = 879 families). Three SNPs representing a block of linkage disequilibrium, rs3789875, rs12347433, and rs4552883, were significantly associated with atherosclerosis in multiple datasets and demonstrated consistent, but suggestive, genetic effects in all analyses. In combined analysis rs3789875 and rs12347433 were statistically significant after Bonferroni correction for 35 comparisons, p = 2 × 10(-6) and 5 × 10(-6), respectively. The SNP rs12347433 is a synonymous coding SNP and may be biologically relevant to the mechanism by which tenascin-C influences the pathophysiology of CAD and atherosclerosis. This is the first report of genetic association between polymorphisms in TNC and atherosclerosis or CAD.
Human Genetics 02/2011; 129(6):641-54. · 5.07 Impact Factor
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Brian G Kral,
Rasika A Mathias,
Bhoom Suktitipat,
Ingo Ruczinski,
Dhananjay Vaidya,
Lisa R Yanek,
Arshed A Quyyumi,
Riyaz S Patel,
A Maziar Zafari,
Viola Vaccarino, Elizabeth R Hauser,
William E Kraus,
Lewis C Becker,
Diane M Becker
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ABSTRACT: A 58 kb region on chromosome 9p21.3 has consistently shown strong association with coronary artery disease (CAD) in multiple genome-wide association studies in populations of European and East Asian ancestry. In this study, we sought to further characterize the role of genetic variants in 9p21.3 in African American individuals. Apparently healthy African American siblings (n = 548) of patients with documented CAD < 60 years of age were genotyped and followed for incident CAD for up to 17 years. Tests of association for 86 single-nucleotide polymorphisms (SNPs) across the 9p21.3 region in a generalized estimating equation logistic framework under an additive model adjusting for traditional risk factors, family, follow-up time and population stratification were performed. A single SNP within the CDKN2B gene met stringent criteria for statistical significance, including permutation-based evaluations. This variant, rs3217989, was common (minor allele (G) frequency 0.242), conveyed protection against CAD (odds ratio (OR) = 0.19, 95% confidence interval (CI): 0.07 to 0.50, P = 0.0008) and was replicated in a combined analysis of two additional case/control studies of prevalent CAD/MI in African Americans (n = 990, P = 0.024, OR = 0.779, 95% CI: 0.626-0.968). This is the first report of a CAD association signal in a population of African ancestry with a common variant within the CDKN2B gene, independent from previous findings in European and East Asian ancestry populations. The findings demonstrate a significant protective effect against incident CAD in African American siblings of persons with premature CAD, with replication in a combination of two additional African American cohorts.
Journal of Human Genetics 01/2011; 56(3):224-9. · 2.57 Impact Factor
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ABSTRACT: The CDC's Family History Public Health Initiative encourages adoption and increase awareness of family health history. To meet these goals and develop a personalized medicine implementation science research agenda, the Genomedical Connection is using an implementation research (T3 research) framework to develop and integrate a self-administered computerized family history system with built-in decision support into 2 primary care clinics in North Carolina.
The family health history system collects a three generation family history on 48 conditions and provides decision support (pedigree and tabular family history, provider recommendation report and patient summary report) for 4 pilot conditions: breast cancer, ovarian cancer, colon cancer, and thrombosis. All adult English-speaking, non-adopted, patients scheduled for well-visits are invited to complete the family health system prior to their appointment. Decision support documents are entered into the medical record and available to provider's prior to the appointment. In order to optimize integration, components were piloted by stakeholders prior to and during implementation. Primary outcomes are change in appropriate testing for hereditary thrombophilia and screening for breast cancer, colon cancer, and ovarian cancer one year after study enrollment. Secondary outcomes include implementation measures related to the benefits and burdens of the family health system and its impact on clinic workflow, patients' risk perception, and intention to change health related behaviors. Outcomes are assessed through chart review, patient surveys at baseline and follow-up, and provider surveys. Clinical validity of the decision support is calculated by comparing its recommendations to those made by a genetic counselor reviewing the same pedigree; and clinical utility is demonstrated through reclassification rates and changes in appropriate screening (the primary outcome).
This study integrates a computerized family health history system within the context of a routine well-visit appointment to overcome many of the existing barriers to collection and use of family history information by primary care providers. Results of the implementation process, its acceptability to patients and providers, modifications necessary to optimize the system, and impact on clinical care can serve to guide future implementation projects for both family history and other tools of personalized medicine, such as health risk assessments.
BMC Health Services Research 01/2011; 11:264. · 1.66 Impact Factor
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ABSTRACT: Clinical predictive models leave gaps in our ability to stratify cardiovascular risk. High-throughput molecular profiling promises to improve risk classification.
Horizon 1 of the Measurement to Understand the Reclassification of Disease of Cabarrus and Kannapolis (MURDOCK) Study was conceived to apply emerging molecular techniques to existing data sets to characterize mechanistic diversity underlying complex human diseases, response to therapy, and prognosis. No previous studies have applied multiple, complementary molecular techniques in combination with well-developed clinical risk models to refine cardiovascular risk prediction. The MURDOCK Cardiovascular Disease Study will assess molecular profiles integrated with clinical data in "clinomic" profiles for cardiovascular risk classification.
Herein, we describe the design of and rationale for the MURDOCK Cardiovascular Disease Study.
American heart journal 09/2010; 160(3):371-379.e2. · 4.65 Impact Factor
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Lisheng Zhang,
Jessica J Connelly,
Karsten Peppel,
Leigh Brian,
Svati H Shah,
Sarah Nelson,
David R Crosslin,
Tianyuan Wang,
Andrew Allen,
William E Kraus,
Simon G Gregory, Elizabeth R Hauser,
Neil J Freedman
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ABSTRACT: Aging is believed to be among the most important contributors to atherosclerosis, through mechanisms that remain largely obscure. Serum levels of tumor necrosis factor (TNF) rise with aging and have been correlated with the incidence of myocardial infarction. We therefore sought to determine whether genetic variation in the TNF receptor-1 gene (TNFR1) contributes to aging-related atherosclerosis in humans and whether Tnfr1 expression aggravates aging-related atherosclerosis in mice. With 1330 subjects from a coronary angiography database, we performed a case-control association study of coronary artery disease (CAD) with 16 TNFR1 single-nucleotide polymorphisms (SNPs). Two TNFR1 SNPs significantly associated with CAD in subjects >55 years old, and this association was supported by analysis of a set of 759 independent CAD cases. In multiple linear regression analysis, accounting for TNFR1 SNP rs4149573 significantly altered the relationship between aging and CAD index among 1811 subjects from the coronary angiography database. To confirm that TNFR1 contributes to aging-dependent atherosclerosis, we grafted carotid arteries from 18- and 2-month-old wild-type (WT) and Tnfr1(-/-) mice into congenic apolipoprotein E-deficient (Apoe(-/-)) mice and harvested grafts from 1 to 7 weeks post-operatively. Aged WT arteries developed accelerated atherosclerosis associated with enhanced TNFR1 expression, enhanced macrophage recruitment, reduced smooth muscle cell proliferation and collagen content, augmented apoptosis and plaque hemorrhage. In contrast, aged Tnfr1(-/-) arteries developed atherosclerosis that was indistinguishable from that in young Tnfr1(-/-) arteries and significantly less than that observed in aged WT arteries. We conclude that TNFR1 polymorphisms associate with aging-related CAD in humans, and TNFR1 contributes to aging-dependent atherosclerosis in mice.
Human Molecular Genetics 07/2010; 19(14):2754-66. · 7.64 Impact Factor
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ABSTRACT: Genetic heterogeneity, which may manifest on a population level as different frequencies of a specific disease susceptibility allele in different subsets of patients, is a common problem for candidate gene and genome-wide association studies of complex human diseases. The ordered subset analysis (OSA) was originally developed as a method to reduce genetic heterogeneity in the context of family-based linkage studies. Here, we have extended a previously proposed method (OSACC) for applying the OSA methodology to case-control datasets. We have evaluated the type I error and power of different OSACC permutation tests with an extensive simulation study. Case-control datasets were generated under two different models by which continuous clinical or environmental covariates may influence the relationship between susceptibility genotypes and disease risk. Our results demonstrate that OSACC is more powerful under some disease models than the commonly used trend test and a previously proposed joint test of main genetic and gene-environment interaction effects. An additional unique benefit of OSACC is its ability to identify a more informative subset of cases that may be subjected to more detailed molecular analysis, such as DNA sequencing of selected genomic regions to detect functional variants in linkage disequilibrium with the associated polymorphism. The OSACC-identified covariate threshold may also improve the power of an additional dataset to replicate previously reported associations that may only be detectable in a fraction of the original and replication datasets. In summary, we have demonstrated that OSACC is a useful method for improving SNP association signals in genetically heterogeneous datasets.
Genetic Epidemiology 07/2010; 34(5):407-17. · 3.44 Impact Factor
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Svati H Shah,
James R Bain,
Michael J Muehlbauer,
Robert D Stevens,
David R Crosslin,
Carol Haynes,
Jennifer Dungan,
L Kristin Newby, Elizabeth R Hauser,
Geoffrey S Ginsburg,
Christopher B Newgard,
William E Kraus
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ABSTRACT: Molecular tools may provide insight into cardiovascular risk. We assessed whether metabolites discriminate coronary artery disease (CAD) and predict risk of cardiovascular events.
We performed mass-spectrometry-based profiling of 69 metabolites in subjects from the CATHGEN biorepository. To evaluate discriminative capabilities of metabolites for CAD, 2 groups were profiled: 174 CAD cases and 174 sex/race-matched controls ("initial"), and 140 CAD cases and 140 controls ("replication"). To evaluate the capability of metabolites to predict cardiovascular events, cases were combined ("event" group); of these, 74 experienced death/myocardial infarction during follow-up. A third independent group was profiled ("event-replication" group; n=63 cases with cardiovascular events, 66 controls). Analysis included principal-components analysis, linear regression, and Cox proportional hazards. Two principal components analysis-derived factors were associated with CAD: 1 comprising branched-chain amino acid metabolites (factor 4, initial P=0.002, replication P=0.01), and 1 comprising urea cycle metabolites (factor 9, initial P=0.0004, replication P=0.01). In multivariable regression, these factors were independently associated with CAD in initial (factor 4, odds ratio [OR], 1.36; 95% CI, 1.06 to 1.74; P=0.02; factor 9, OR, 0.67; 95% CI, 0.52 to 0.87; P=0.003) and replication (factor 4, OR, 1.43; 95% CI, 1.07 to 1.91; P=0.02; factor 9, OR, 0.66; 95% CI, 0.48 to 0.91; P=0.01) groups. A factor composed of dicarboxylacylcarnitines predicted death/myocardial infarction (event group hazard ratio 2.17; 95% CI, 1.23 to 3.84; P=0.007) and was associated with cardiovascular events in the event-replication group (OR, 1.52; 95% CI, 1.08 to 2.14; P=0.01).
Metabolite profiles are associated with CAD and subsequent cardiovascular events.
Circulation Cardiovascular Genetics 02/2010; 3(2):207-14. · 6.11 Impact Factor
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Hsiang-Cheng Chen,
Virginia Byers Kraus,
Yi-Ju Li,
Sarah Nelson,
Carol Haynes,
Jessica Johnson,
Thomas Stabler, Elizabeth R Hauser,
Simon G Gregory,
William E Kraus,
Svati H Shah
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ABSTRACT: The genetic contributions to the multifactorial disorder osteoarthritis (OA) have been increasingly recognized. The goal of the current study was to use OA-related biomarkers of severity and disease burden as quantitative traits to identify genetic susceptibility loci for OA.
In a large multigenerational extended family (n = 350), we measured 5 OA-related biomarkers: hyaluronan (HA), cartilage oligomeric matrix protein (COMP), N-propeptide of type IIA collagen (PIIANP), C-propeptide of type II procollagen (CPII), and type II collagen neoepitope (C2C). Single-nucleotide polymorphism markers (n = 6,090) covering the whole genome were genotyped using the Illumina HumanLinkage-12 BeadChip. Variance components analysis, as implemented in the Sequential Oligogenic Linkage Analysis Routines, was used to estimate heritabilities of the quantitative traits and to calculate 2-point and multipoint logarithm of odds (LOD) scores using a polygenic model.
After adjusting for age and sex, we found that 4 of the 5 biomarkers exhibited significant heritability (PIIANP 0.57, HA 0.49, COMP 0.43, C2C 0.30; P < or = 0.01 for all). Fourteen of the 19 loci that had multipoint LOD scores of >1.5 were near to or overlapped with previously reported OA susceptibility loci. Four of these loci were identified by more than 1 biomarker. The maximum multipoint LOD scores for the heritable quantitative biomarker traits were 4.3 for PIIANP (chromosome 8p23.2), 3.2 for COMP (chromosome 8q11.1), 2.0 for HA (chromosome 6q16.3), and 2.0 for C2C (chromosome 5q31.2).
Herein, we report the first evidence of genetic susceptibility loci identified by OA-related biomarkers in an extended family. Our results demonstrate that serum concentrations of PIIANP, HA, COMP, and C2C have substantial heritable components, and using these biomarkers, several genetic loci potentially contributing to the genetic diversity of OA were identified.
Arthritis & Rheumatism 02/2010; 62(3):781-90. · 7.87 Impact Factor
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ABSTRACT: Complex diseases will have multiple functional sites, and it will be invaluable to understand the cross-locus interaction in terms of linkage disequilibrium (LD) between those sites (epistasis) in addition to the haplotype-LD effects. We investigated the statistical properties of a class of matrix-based statistics to assess this epistasis. These statistical methods include two LD contrast tests (Zaykin et al., 2006) and partial least squares regression (Wang et al., 2008). To estimate Type 1 error rates and power, we simulated multiple two-variant disease models using the SIMLA software package. SIMLA allows for the joint action of up to two disease genes in the simulated data with all possible multiplicative interaction effects between them. Our goal was to detect an interaction between multiple disease-causing variants by means of their linkage disequilibrium (LD) patterns with other markers. We measured the effects of marginal disease effect size, haplotype LD, disease prevalence and minor allele frequency have on cross-locus interaction (epistasis). In the setting of strong allele effects and strong interaction, the correlation between the two disease genes was weak (r=0.2). In a complex system with multiple correlations (both marginal and interaction), it was difficult to determine the source of a significant result. Despite these complications, the partial least squares and modified LD contrast methods maintained adequate power to detect the epistatic effects; however, for many of the analyses we often could not separate interaction from a strong marginal effect. While we did not exhaust the entire parameter space of possible models, we do provide guidance on the effects that population parameters have on cross-locus interaction.
Statistical Applications in Genetics and Molecular Biology 01/2010; 9(1):Article35. · 1.52 Impact Factor
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ABSTRACT: The CATHGEN study reported associations of chromosome 3q13-21 genes (KALRN, MYLK, CDGAP, and GATA2) with early-onset coronary artery disease (CAD). This study attempted to independently validate those associations. Eleven single nucleotide polymorphisms (SNPs) were examined (rs10934490, rs16834817, rs6810298, rs9289231, rs12637456, rs1444768, rs1444754, rs4234218, rs2335052, rs3803, rs2713604) in patients (N = 1618) from the Intermountain Heart Collaborative Study (IHCS). Given the higher smoking prevalence in CATHGEN than IHCS (41% vs. 11% in controls, 74% vs. 29% in cases), smoking stratification and genotype-smoking interactions were evaluated. Suggestive association was found for GATA2 (rs2713604, p = 0.057, OR = 1.2). Among smokers, associations were found in CDGAP (rs10934490, p = 0.019, OR = 1.6) and KALRN (rs12637456, p = 0.011, OR = 2.0) and suggestive association was found in MYLK (rs16834871, p = 0.051, OR = 1.8, adjusting for gender). No SNP association was found among non-smokers, but smoking/SNP interactions were detected for CDGAP (rs10934491, p = 0.017) and KALRN (rs12637456, p = 0.010). Similar differences in SNP effects by smoking status were observed on re-analysis of CATHGEN. CAD associations were suggestive for GATA2 and among smokers significant post hoc associations were found in KALRN, MYLK, and CDGAP. Genetic risk conferred by some of these genes may be modified by smoking. Future CAD association studies of these and other genes should evaluate effect modification by smoking.
Annals of Human Genetics 09/2009; 73(Pt 6):551-8. · 2.57 Impact Factor
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ABSTRACT: Transcription factors are key mediators of human complex disease processes. Identifying the target genes of transcription factors will increase our understanding of the biological network leading to disease risk. The prediction of transcription factor binding sites (TFBSs) is one method to identify these target genes; however, current prediction methods need improvement. We chose the transcription factor upstream stimulatory factor 1 ( USF1 ) to evaluate the performance of our novel TFBS prediction method because of its known genetic association with coronary artery disease (CAD) and the recent availability of USF1 chromatin immunoprecipitation microarray (ChIP-chip) results. The specific goals of our study were to develop a novel and accurate genome-scale method for predicting USF1 binding sites and associated target genes to aid in the study of CAD. Previously published USF1 ChIP-chip data for 1 per cent of the genome were used to develop and evaluate several kernel logistic regression prediction models. A combination of genomic features (phylogenetic conservation, regulatory potential, presence of a CpG island and DNaseI hypersensitivity), as well as position weight matrix (PWM) scores, were used as variables for these models. Our most accurate predictor achieved an area under the receiver operator characteristic curve of 0.827 during cross-validation experiments, significantly outperforming standard PWM-based prediction methods. When applied to the whole human genome, we predicted 24,010 USF1 binding sites within 5 kilobases upstream of the transcription start site of 9,721 genes. These predictions included 16 of 20 genes with strong evidence of USF1 regulation. Finally, in the spirit of genomic convergence, we integrated independent experimental CAD data with these USF1 binding site prediction results to develop a prioritised set of candidate genes for future CAD studies. We have shown that our novel prediction method, which employs genomic features related to the presence of regulatory elements, enables more accurate and efficient prediction of USF1 binding sites. This method can be extended to other transcription factors identified in human disease studies to help further our understanding of the biology of complex disease.
Human genomics 05/2009; 3(3):221-35.
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Svati H Shah,
Neil J Freedman,
Lisheng Zhang,
David R Crosslin,
David H Stone,
Carol Haynes,
Jessica Johnson,
Sarah Nelson,
Liyong Wang,
Jessica J Connelly, [......],
David C Crossman,
Christopher J H Jones,
Jeffery Vance,
Michael H Sketch,
Christopher B Granger,
Christopher B Newgard,
Simon G Gregory,
Pascal J Goldschmidt-Clermont,
William E Kraus, Elizabeth R Hauser
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ABSTRACT: Neuropeptide Y (NPY) is a strong candidate gene for coronary artery disease (CAD). We have previously identified genetic linkage to familial CAD in the genomic region of NPY. We performed follow-up genetic, biostatistical, and functional analysis of NPY in early-onset CAD. In familial CAD (GENECARD, N = 420 families), we found increased microsatellite linkage to chromosome 7p14 (OSA LOD = 4.2, p = 0.004) in 97 earliest age-of-onset families. Tagged NPY SNPs demonstrated linkage to CAD of a 6-SNP block (LOD = 1.58-2.72), family-based association of this block with CAD (p = 0.02), and stronger linkage to CAD in the earliest age-of-onset families. Association of this 6-SNP block with CAD was validated in: (a) 556 non-familial early-onset CAD cases and 256 controls (OR 1.46-1.65, p = 0.01-0.05), showing stronger association in youngest cases (OR 1.84-2.20, p = 0.0004-0.09); and (b) GENECARD probands versus non-familial controls (OR 1.79-2.06, p = 0.003-0.02). A promoter SNP (rs16147) within this 6-SNP block was associated with higher plasma NPY levels (p = 0.04). To assess a causal role of NPY in atherosclerosis, we applied the NPY1-receptor-antagonist BIBP-3226 adventitially to endothelium-denuded carotid arteries of apolipoprotein E-deficient mice; treatment reduced atherosclerotic neointimal area by 50% (p = 0.03). Thus, NPY variants associate with atherosclerosis in two independent datasets (with strong age-of-onset effects) and show allele-specific expression with NPY levels, while NPY receptor antagonism reduces atherosclerosis in mice. We conclude that NPY contributes to atherosclerosis pathogenesis.
PLoS Genetics 02/2009; 5(1):e1000318. · 8.69 Impact Factor
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Svati H Shah, Elizabeth R Hauser,
James R Bain,
Michael J Muehlbauer,
Carol Haynes,
Robert D Stevens,
Brett R Wenner,
Z Elaine Dowdy,
Christopher B Granger,
Geoffrey S Ginsburg,
Christopher B Newgard,
William E Kraus
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ABSTRACT: Integration of genetic and metabolic profiling holds promise for providing insight into human disease. Coronary artery disease (CAD) is strongly heritable, but the heritability of metabolomic profiles has not been evaluated in humans. We performed quantitative mass spectrometry-based metabolic profiling in 117 individuals within eight multiplex families from the GENECARD study of premature CAD. Heritabilities were calculated using variance components. We found high heritabilities for amino acids (arginine, ornithine, alanine, proline, leucine/isoleucine, valine, glutamate/glutamine, phenylalanine and glycine; h(2)=0.33-0.80, P=0.005-1.9 x 10(-16)), free fatty acids (arachidonic, palmitic, linoleic; h(2)=0.48-0.59, P=0.002-0.00005) and acylcarnitines (h(2)=0.23-0.79, P=0.05-0.0000002). Principal components analysis was used to identify metabolite clusters. Reflecting individual metabolites, several components were heritable, including components comprised of ketones, beta-hydroxybutyrate and C2-acylcarnitine (h(2)=0.61); short- and medium-chain acylcarnitines (h(2)=0.39); amino acids (h(2)=0.44); long-chain acylcarnitines (h(2)=0.39) and branched-chain amino acids (h(2)=0.27). We report a novel finding of high heritabilities of metabolites in premature CAD, establishing a possible genetic basis for these profiles. These results have implications for understanding CAD pathophysiology and genetics.
Molecular Systems Biology 02/2009; 5:258. · 8.63 Impact Factor
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L Adrienne Cupples,
Joseph Beyene,
Heike Bickeböller,
E Warwick Daw,
M Daniele Fallin,
W James Gauderman,
Saurabh Ghosh,
Ellen L Goode, Elizabeth R Hauser,
Anthony Hinrichs,
Jack W Kent,
Lisa J Martin,
Maria Martinez,
Rosalind J Neuman,
Michael Province,
Silke Szymczak,
Marsha A Wilcox,
Andreas Ziegler,
Jean W Maccluer,
Laura Almasy
BMC proceedings 01/2009; 3 Suppl 7:S1.
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ABSTRACT: Association analysis provides a powerful tool for complex disease gene mapping. However, in the presence of genetic heterogeneity, the power for association analysis can be low since only a fraction of the collected families may carry a specific disease susceptibility allele. Ordered-subset analysis (OSA) is a linkage test that can be powerful in the presence of genetic heterogeneity. OSA uses trait-related covariates to identify a subset of families that provide the most evidence for linkage. A similar strategy applied to genetic association analysis would likely result in increased power to detect association. Association in the presence of linkage (APL) is a family-based association test (FBAT) for nuclear families with multiple affected siblings that properly infers missing parental genotypes when linkage is present. We propose here APL-OSA, which applies the OSA method to the APL statistic to identify a subset of families that provide the most evidence for association. A permutation procedure is used to approximate the distribution of the APL-OSA statistic under the null hypothesis that there is no relationship between the family-specific covariate and the family-specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL-OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL-OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL-OSA to a family study of age-related macular degeneration, where cigarette smoking was used as a covariate.
Genetic Epidemiology 06/2008; 32(7):627-37. · 3.44 Impact Factor
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Beth S Sutton,
David R Crosslin,
Svati H Shah,
Sarah C Nelson,
Anthony Bassil,
A Brent Hale,
Carol Haynes,
Pascal J Goldschmidt-Clermont,
Jeffery M Vance,
David Seo,
William E Kraus,
Simon G Gregory, Elizabeth R Hauser
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ABSTRACT: Platelet-activating factor acetylhydrolase (PLA2G7) is a potent pro- and anti-inflammatory molecule that has been implicated in multiple inflammatory disease processes, including cardiovascular disease. The goal of this study was to investigate the genetic effects of PLA2G7 on coronary artery disease (CAD) risk in two large, independent datasets with CAD. Using a haplotype tagging (ht) approach, 19 ht single nucleotide polymorphisms (SNPs) were genotyped in CATHGEN case-control samples (cases = 806 and controls = 267) and in the GENECARD Family Study (n = 1101 families, 2954 individuals). Single SNP analysis using logistic regression revealed nine SNPs with significant association in all CATHGEN subjects (P = 0.0004-0.02). CATHGEN cases were further stratified into subgroups based on age of CAD onset (AOO) and severity of disease; 599 young affecteds (YA, AOO <56) and 207 old affected (OA, AOO >56). Significant genetic effects were observed in both OA and YA (P = 0.0001-0.02). The GENECARD probands demonstrated results similar to those seen in the YA CATHGEN cases (P = 0.002-0.05). Of the 19 SNPs genotyped, 3 SNPs result in nonsynonymous coding changes (I198T, A379V and R92H). Two of the coding SNPs, R92H and A379V, constitute two of the most significantly associated SNPs, even after Bonferroni correction and appear to represent independent associations (r(2) = 0.09). Multiple additional polymorphisms in low linkage disequilibrium with these coding SNPs were also strongly associated. In summary, PLA2G7 represents an important, potentially functional candidate in the pathophysiology of CAD based on replicated associations using two independent datasets and multiple statistical approaches. Further functional studies involving a combination of risk alleles are warranted.
Human Molecular Genetics 06/2008; 17(9):1318-28. · 7.64 Impact Factor
