Figures
Abstract
Despite evidence of the clustering of metabolic syndrome components, current approaches for identifying unifying genetic mechanisms typically evaluate clinical categories that do not provide adequate etiological information. Here, we used data from 19,486 European American and 6,287 African American Candidate Gene Association Resource Consortium participants to identify loci associated with the clustering of metabolic phenotypes. Six phenotype domains (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic state, central obesity, and elevated plasma glucose) encompassing 19 quantitative traits were examined. Principal components analysis was used to reduce the dimension of each domain such that >55% of the trait variance was represented within each domain. We then applied a statistically efficient and computational feasible multivariate approach that related eight principal components from the six domains to 250,000 imputed SNPs using an additive genetic model and including demographic covariates. In European Americans, we identified 606 genome-wide significant SNPs representing 19 loci. Many of these loci were associated with only one trait domain, were consistent with results in African Americans, and overlapped with published findings, for instance central obesity and FTO. However, our approach, which is applicable to any set of interval scale traits that is heritable and exhibits evidence of phenotypic clustering, identified three new loci in or near APOC1, BRAP, and PLCG1, which were associated with multiple phenotype domains. These pleiotropic loci may help characterize metabolic dysregulation and identify targets for intervention.
Author Summary
The metabolic syndrome represents a clustering of metabolic phenotypes (e.g. elevated blood pressure, cholesterol levels, and plasma glucose, as well as abdominal obesity) and is associated with an increased risk of atherosclerosis and type 2 diabetes. Although multiple genes influencing the specific metabolic syndrome components have been reported, few studies have evaluated the genetic underpinnings of the syndrome as a whole. Here, we describe an approach to evaluate multiple clustered traits, which allows us to test whether common genetic variants influence the co-occurrence of one or more metabolic phenotypes. By examining approximately 20,000 European American and 6,200 African American participants from five studies, we show that three regions on chromosomes 12, 19, and 20 are associated with multiple metabolic phenotypes. These genetic variants are highly intriguing candidates that may increase our understanding of the biologic basis of the clustering of metabolic phenotypes and help identify targets for early intervention.
Citation: Avery CL, He Q, North KE, Ambite JL, Boerwinkle E, Fornage M, et al. (2011) A Phenomics-Based Strategy Identifies Loci on APOC1, BRAP, and PLCG1 Associated with Metabolic Syndrome Phenotype Domains. PLoS Genet 7(10): e1002322. https://doi.org/10.1371/journal.pgen.1002322
Editor: Greg Gibson, Georgia Institute of Technology, United States of America
Received: April 19, 2011; Accepted: August 11, 2011; Published: October 13, 2011
This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Funding: PAGE: Support for the Genetic Epidemiology of Causal Variants Across the Life Course (CALiCo) was provided through the National Human Genome Research Institute's Population Architecture Using Genomics and Epidemiology (PAGE) network (U01 HG004803). EAGLE work was funded by NIH/NHGRI grant U01 HG004798. The WHI program is funded by NIH/NHLBI, U.S. Department of Health and Human Services, through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, 44221, and U01 HG004790. Funding support for the Multiethnic Cohort study was provided through the National Cancer Institute (R37CA54281, R01 CA63, P01CA33619, U01CA136792, and U01CA98758) and NHGRI (U01 HG004802). CARe: The authors wish to acknowledge the support of the National Heart, Lung, and Blood Institute and the contributions of the research institutions, study investigators, field staff, and study participants in creating this resource for biomedical research. The following five parent studies contributed parent study data, ancillary study data, and DNA samples through the Broad Institute (N01-HC-65226) to create this genotype/phenotype data base for wide dissemination to the biomedical research community: Atherosclerosis Risk in Communities (ARIC): University of North Carolina at Chapel Hill (N01-HC-55015), Baylor Medical College (N01-HC-55016), University of Mississippi Medical Center (N01-HC-55021), University of Minnesota (N01-HC-55019), Johns Hopkins University (N01-HC-55020), University of Texas, Houston (N01-HC-55017); Cardiovascular Health Study (CHS): University of Washington (N01-HC-85079), Wake Forest University (N01-HC-85080), Johns Hopkins University (N01-HC-85081), University of Pittsburgh (N01-HC-85082), University of California Davis (N01-HC-85083), University of California Irvine (N01-HC-85084), New England Medical Center (N01-HC-85085), University of Vermont (N01-HC-85086), Georgetown University (N01-HC-35129), Johns Hopkins University (N01 HC-15103), University of Wisconsin (N01-HC-75150), Geisinger Clinic (N01-HC-45133), University of Washington (N01 HC-55222, U01 HL080295); Coronary Artery Risk in Young Adults (CARDIA): University of Alabama at Birmingham (N01-HC-48047), University of Minnesota (N01-HC-48048), Northwestern University (N01-HC-48049), Kaiser Foundation Research Institute (N01-HC-48050), University of Alabama at Birmingham (N01-HC-95095), Tufts-New England Medical Center (N01-HC-45204), Wake Forest University (N01-HC-45205), Harbor-UCLA Research and Education Institute (N01-HC-05187), University of California Irvine (N01-HC-45134, N01-HC-95100); Framingham Heart Study (FHS): Boston University (N01-HC-25195); Multi-Ethnic Study of Atherosclerosis (MESA): University of Washington (N01-HC-95159), Regents of the University of California (N01-HC-95160), Columbia University (N01-HC-95161), Johns Hopkins University (N01-HC-95162), University of Minnesota (N01-HC-95163), Northwestern University (N01-HC-95164), Wake Forest University (N01-HC-95165), University of Vermont (N01-HC-95166), New England Medical Center (N01-HC-95167), Johns Hopkins University (N01-HC-95168), Harbor-UCLA Research and Education Institute (N01-HC-95169). CLA was supported by grant K99-HL-098458 from the NHLBI. JBM was supported by grants K24 DK080140 and R01 DK078616 from the NIDDK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The metabolic syndrome represents metabolic dysregulation expressed as the clustering of several physiologic risk factors and is associated with an increased risk of atherosclerosis and type 2 diabetes [1]. The core metabolic syndrome domains are abdominal obesity, atherogenic dyslipidemia, elevated blood pressure, elevated plasma glucose, a pro-thrombotic state, and a pro-inflammatory state [2], which are represented to varying degrees in commonly used metabolic syndrome scoring systems [3]–[7].
Several lines of evidence support a genetic basis underlying the core metabolic syndrome domains. Measures of metabolic domains cluster in families [8] and heritability estimates range from 16% for systolic blood pressure to 60% for high-density lipoprotein (HDL) cholesterol [9]. Genome-wide association (GWA) studies have also identified common variants in CETP, LPL, APOA5, and GCKR that influence the co-occurrence of metabolic domain phenotypes [10], [11].
Despite evidence of the clustering of metabolic domain phenotypes, current approaches for identifying unifying genetic mechanisms (i.e. pleiotropy) remain largely focused on clinical categories that do not provide adequate etiological information [12]. As an alternative, a phenomics approach that assembles coherent sets of phenotypic features that extend across individual measurements and diagnostic boundaries creates the opportunity for novel genetic investigations of established biological pathways and complements the traditional GWA study or candidate gene-based strategy focused on individual phenotypes [13]–[15]. In addition to making use of existing knowledge on process-related information or pathways, a multi-phenotype phenomics approach also may provide greater statistical power than analyses of individual phenotypes [16] and improve the ability to detect effects of small magnitude [17]. Although several authors have advocated the use of such strategies [15], [18], [19], the approach is implemented infrequently.
This study evaluated evidence of pleiotropy in clustered metabolic domains using data from five well characterized population-based studies composed of approximately 20,000 European American and 6,200 African American participants: the Atherosclerosis Risk in Communities (ARIC) study, the Coronary Artery Risk Development in Young Adults (CARDIA) study, the Cardiovascular Health Study (CHS), the Framingham Heart Study (FHS), and the Multi-Ethnic Study of Atherosclerosis (MESA). Six phenotype domains (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic state, central obesity and elevated plasma glucose) encompassing 19 quantitative traits were examined. After dimension reduction, we applied a statistically efficient and computationally feasible multivariate approach that related the phenotype domains to 250,000 imputed SNPs. Our approach, which is applicable to studies of heritable, clustered interval scale outcomes, identified several genome-wide significant loci associated with multiple phenotype domains, which may help characterize metabolic dysregulation and identify targets for intervention.
Results
After excluding duplicate samples (N = 56), first- and second-degree relatives (N = 1,152) in all studies except the family-based Framingham Heart Study, and individuals identified as genetic outliers (N = 20), there were 19,468 European American and 6,287 African American Candidate Gene Association Resource Consortium (CARe) participants available for analysis. As expected, CARDIA participants (mean age: 25 years) had better cardiovascular health profiles, including lower low density lipoprotein concentrations, markers of vascular inflammation, and blood pressure levels when compared to the older cohorts (Tables S1, S2, S3, S4, S5).
Eight principle components were used to characterize the six metabolic syndrome trait domains (Figure 1): one principal component each for vascular dysfunction, elevated plasma glucose, pro-thrombotic state and central obesity and two principal components for atherogenic dyslipidemia and vascular inflammation. Correlation between the principal components, which served as the eight phenotypes of interest, was modest and consistent across studies and racial groups. As an example, race- specific results from the ARIC Study are presented in Tables S6, S7.
ARIC and CARDIA were the only studies with full phenotype data for all 19 of the variables used to define the metabolic trait domains. Although apolipoprotein A1 and B measurements were unavailable in three cohorts, the high correlations with high-density and low-density lipoprotein concentrations (r>0.70 in ARIC data, Tables S8, S9) suggested that all five cohorts provided similar atherogenic dyslipidemia phenotypes. A similarly high correlation was observed between von Willebrand factor and factor VIII in the ARIC data, implying a common pro-thrombotic phenotype in studies missing either measurement. The modest correlation between systemic markers of inflammation in the MESA study, which did not measure white blood cell count and uric acid concentration, suggests that this study may contribute a slightly different vascular inflammation phenotype. The MESA study also did not assay factor VII, suggesting that this study also contributed a somewhat different pro-thrombotic phenotype. However, a sensitivity analysis excluding pro-thrombotic and inflammation principal components estimated in the MESA study yielded comparable results.
In European Americans, we identified 606 SNPs representing 19 loci that were associated with at least one metabolic trait domain (Table 1, Figure 2) at the genome-wide significance level (P<2.13×10−7; the SNP with the lowest P – value chosen if multiple significant SNPs were identified for a given locus) and these results were consistent across the multiple large cohorts (Table S10 and Figure S1). Several of these loci overlapped results in African Americans (Table 2, Figure 3), including associations with LPL, ABO, VWF, CTEP, and LDLR. In addition to these 19 loci, we also identified 15 additional secondary signals in European Americans, defined as genome-wide significant SNPs (the SNP with the lowest P – value chosen if multiple significant SNPs were identified for a given locus) in very low linkage disequilibrium (LD) (r2<0.05) with the most significant SNP and within the same 1,000-kb region (Table S11). To verify the independent contributions of these additional loci, we performed a conditional analysis using the most significant SNP at each significant locus as a covariate. Thirteen of these signals remained significant, including one APOC1 variant, after adjusting for the primary signals.
Previously identified loci associated with single metabolic trait domains
The strongest signal for both European American and African American participants was located on chromosome 9 in the ABO gene (P<1.0×10−300 and P = 6.1×10−75, respectively). These signals overlap earlier findings between factor VIII and von Willebrand factor with ABO [20]. Nine additional loci in European Americans and eight loci in African Americans demonstrated effects limited to one metabolic syndrome trait domain that have already been reported in the GWA literature and are therefore not considered further: ABCA1, APOB, CD36, CELSR2, CETP, CRP, F7, LDLR, LIPC, PVRL2, TRIB1, VWF, and ZNF259.
Previously identified loci associated multiple trait domains
Six loci were associated with at least two trait domains in European Americans: GCKR, ABCB11, LPL, HNF1A, FTO, and SUGP1, results which overlap published associations identified through GWA studies for individual trait components. For example, several GWA studies have identified associations between GCKR and elevated plasma glucose [21], atherogenic dyslipidemia [22], and vascular inflammation [23]–[25]. GCKR is a plausible unifying mechanism for the clustering of metabolic domains, as the protein inhibits glucokinase, the predominant glucose phosphorylating enzyme [26]. HNF1A, which encodes the transcription factor hepatocyte nuclear factor (HNF)-1a, also suggests a common pathogenic background, as previous GWA studies have identified associations with atherogenic dyslipidemia [27], vascular inflammation [28], and type 2 diabetes [29]. Of note, FTO was the only previously identified and consistently replicated obesity locus we identified.
Candidate genes at new loci
The strongest new pleiotropic signal in European Americans was for rs4420638 (P 1.7×10−57), located approximately 0.32 kilobases (kb) downstream of APOC1 and associated with elevated plasma glucose (P = 8.7×10−4), atherogenic dyslipidemia (1×10−31), vascular inflammation (P = 5×10−12), and central obesity (P = 1.2×10−6). Although associations between APOC1 with atherogenic dyslipidemia [22], [30], [31] and vascular inflammation [32], [33] have been reported and replicated in the GWA study literature, we consider it a novel locus due to the strong and previously unreported associations with elevated plasma glucose and central obesity. Localizing this signal is challenging, as the region contains a 48-kb gene cluster that also includes the APOE and pseudo-APOC’ genes [34]. However, the modest levels of linkage disequilibrium (Figure 4), the presence of a second signal (Table S11), studies which demonstrate that mice overexpressing human APOC1 show a marked reduction in the update of fatty acids into adipocytes [35], and the fact the physiological role of APOC1 is less well established than APOE, APOB, and APOA1 [36] all support further evaluation and fine mapping of APOC1.
Positions are from NCBI build 36 and recombination rates are estimated from HapMap phase II CEU data. SNPs are represented by circles, and the large blue diamond is the SNP with the lowest P-value. Circle color represents correlation with the top SNP: blue indicates weak correlation and red indicates strong correlation. Recombination rate is plotted in the background and known genes in the region are shown at the bottom of the plot.
The second new locus was rs11065987 (P = 2.9×10−10), located approximately 9.9 kb upstream of BRAP and associated with atherogenic dyslipidemia (3.1×10−3), vascular dysfunction (2.2×10−4), and central obesity (9.7×10−3). Initial reports suggested that the BRAP protein binds the breast cancer suppressor protein BRCA1 [37]. BRAP is also known to modulate mitogen activated protein kinase signaling [38], an established cell survival, growth, differentiation, transformation, and proinflammatory pathway [39].
The GWA study literature provides few clues that link BRAP with metabolic trait domains, as associations have only been identified for alanine aminotransferase [24] and esophageal cancer [40], both in populations of Japanese descent. However, the recombination rate (cM/Mb) is low from approximately 110.3 Mb to 111.5 Mb (Figure 4) and this extended region includes loci associated with type 1 diabetes [41], [42], vascular dysfunction [43], and waist-hip ratio [44]. The ATXN2 gene, located 27 kb from the index SNP, is an intriguing candidate gene. Expansion of a CAG repeat in the ataxin-2 protein causes the neurodegenerative disease spinocerebellar ataxia type 2. However, instead of a neurodegenerative phenotype, ATXN2-deficient rodents exhibited phenotypes characterized by abdominal obesity, insulin resistance, and marked hepatosteatosis (i.e. lipid accumulation in the liver) [45]. Linkage studies of obesity in humans have also associated this region with BMI and total fat percentage [46].
A third genome-wide significant signal was identified for rs753381 (P = 4.3×10−8), a missense mutation in PLCG1 that results in a change from an isoleucine to a threonine. PLCG1 encodes a protein that catalyzes the formation of inositol 1,4,5-trisphosphate and diacylglycerol from phosphatidylinositol 4,5-bisphosphate and plays an important role in the intracellular transduction of receptor-mediated tyrosine kinase activators [47]. Few epidemiologic studies of PLCG1 or neighboring genes have been published. However, mice nullizygous for PLCG1 stop growing mid-gestation and show no evidence of vasculogenesis [48]. Vasculogenesis has been associated with insulin resistance [49], plasminogen activator inhibitor-1(PAI-1) concentration [50], hyperglycemia, and adiponectin levels [51]. This suggests that PLCG1 may contribute to the clustering of metabolic domains in a more subtle manner, such as through small alterations in the structure of the PLCG1 protein. Thus, the missense mutation we identified would serve as a highly intriguing candidate SNP for further study.
Discussion
In this study composed of approximately 20,000 European American and 6,200 African American participants, we identified three new loci associated with multiple metabolic trait domains: APOC1, BRAP, and PLCG1. These loci were in or near genes previously associated with atherogenic dyslipidemia, vascular inflammation, type I diabetes, vascular dysfunction, and central adiposity. No previous genome-wide or gene-centric studies examining evidence for pleiotropy in metabolic domains has detected these loci at genome-wide significant levels.
The pathogenesis of the clustering of metabolic phenotypes remains poorly understood, although it is likely that a sedentary lifestyle, combined with dietary patterns and genetic susceptibility factors, contribute. Candidate genes associated with metabolic syndrome phenotypes largely reflect current knowledge of established pathways regulating obesity, free fatty acid metabolism, insulin sensitivity, lipid metabolism, and inflammation. Although candidate gene and GWA studies have successfully identified loci influencing variation in these pathways, studies examining genetic factors influencing the co-occurrence of metabolic phenotypes are limited. Additionally, those that examine the clustering of syndromic components using the pre-defined clinical cutpoints are largely inconsistent or inconclusive. This general lack of success may reflect ongoing controversy over metabolic syndrome definitions, leading to phenotypic heterogeneity and inconsistent genetic findings across studies [52]. The utility of studying the syndrome as a binary entity as opposed to a series of component traits is also debated [12], especially since the dichotomization of interval scale traits will discard information.
Methods for examining evidence of pleiotropy remain uncommon in the GWA literature and most likely reflect the lack of methodologies and software that are scalable to GWA studies. In this paper, we present a statistically efficient and computational feasible approach to testing for pleiotropy on a genome-wide scale. Our method is applicable to population-based and family studies and identified several associations that would not have been identified through typical univariate analyses. The approach presented herein is also not limited to metabolic phenotypes. Instead, our method could be applied to any set of interval scale traits that are heritable and exhibit evidence of phenotypic clustering.
Although alternative analytic approaches were available, for example estimating principal components for all traits simultaneously, we focused on the phenotype clusters presented in Figure 1. First, evaluating the nineteen phenotypes of interest as six domains of interest is biologically plausible given evidence of phenotypic clustering. It was also easier to interpret principal components that were derived in separate phenotype domains rather than components estimated simultaneously. Additionally, estimating principal components within each phenotype domains ensured that each domain was sufficiently represented in the analysis.
Challenges to the approach presented herein include careful phenotype curation, made more difficult by the inclusion of 19 traits across multiple cohorts that were not measured with a common protocol. Only the ARIC and CARDIA studies had full phenotype information on all 19 traits and CHS was the only study with all traits measured during a single visit. The use of a multivariate phenotype comprised of 19 variables also limited the number of contributing cohorts and the identification of replication cohorts, as few studies have such comprehensive phenotypic data. Nonetheless, we were able to identify approximately 25,000 participants from studies that used standardized, comparable protocols and many of the associations were consistent across cohorts.
Further challenges that are not unique to large scale genetic studies incorporating a phenomics approach include the consistency of results across populations defined by age, race, sex, or other demographic characteristics. For example, the three new loci identified in the European American population were not detected in the African American population. Given a modest sample size of 6,287 participants it is difficult to determine whether an inability to generalize results to the African American population reflects different patterns of LD, varying environmental contexts, or limited statistical power. Variation in mean age between contributing cohorts, which ranged from 25 years in the CARDIA study to 72 years in the CHS, could introduce additional heterogeneity, as associations between metabolic phenotypes have been shown to diminish with age [53]. Finally, marked variation in the prevalence of the metabolic syndrome by gender, regardless of clinical definition, suggest the possibility of sex-specific metabolic syndrome effects [54]. Analyses that examine modification by sex, age, and other important clinical covariates are therefore warranted.
Our use of the IBC array, which is composed of variants implicated in cardiovascular, inflammatory, hemostasis/coagulation, and metabolic pathways, was beneficial in that it allowed us to leverage the wealth of information on pathways implicated in metabolic disturbances while reducing multiple testing penalties. Admittedly this approach was limited in that it potentially excludes novel pathways not captured by the IBC chip. Although imputation allowed us to increase the number of variants, genome-wide approaches might identify additional pleiotropic loci.
In summary, our results support phenomics as a complementary approach that leverages phenotypic variation for the evaluation of pleiotropy, a clear limitation of existing studies examining the metabolic syndrome using clinical definitions. Our approach, which is applicable to studies of heritable, clustered interval scale outcomes, also takes advantage of the wealth of phenotype data available in longitudinal cohort studies as well as emerging analytical and bioinformatics approaches. Ultimately, these results support the presence of genetic variants with pleiotropic effects on adiposity, inflammation, glucose regulation, dyslipidemia, vascular dysfunction and thrombosis. Such loci may help characterize metabolic dysregulation and identify targets for intervention.
Materials and Methods
Study population
This study arose from a collaboration between investigators from two National Institute of Health funded consortia examining the genetic basis of common complex diseases: the Population Architecture using Genomics and Epidemiology (PAGE) study, a National Human Genome Research Institute funded effort examining the epidemiologic architecture of common genetic variation that have been reproducibly associated with human diseases and traits [55] and the CARe Consortium [56], a National Heart, Lung, and Blood Institute-supported resource for genetic analyses examining cardiovascular phenotypes. Briefly, PAGE investigators participating in the phenomics working group wanted to extend existing efforts examining evidence for pleiotropy in approximately 300 replicated genetic variants [57] to include a more comprehensive evaluation of common SNPs. A collaboration between PAGE and CARe investigators was therefore initiated, and used data from five CARe studies of European American and African American with adequate phenotype data: ARIC, CARDIA, CHS, FHS, and MESA. All participating institutions and CARe sites obtained Institutional Review Board approval for this study. Additional information on the participating CARe studies is provided in Text S1.
Genotyping
The Institute for the Translational Medicine and Therapeutics (ITMAT)-Broad-CARe (IBC) genotyping array [58] was used to evaluate approximately 2,100 genes related to cardiovascular, inflammatory, hemostasis/coagulation, and metabolic phenotypes and pathways. The IBC array tagging approach was designed to capture maximal genetic information for both common and lower frequency SNPs (<5% minor allele frequency (MAF)) in HapMap as well as European American and African American populations. The array included 49,320 SNPs, 15,000 of which were gene variants not present in HapMap. Additional details of the SNP selection and tagging approach are given in Text S1.
Imputation of untyped and missing SNP genotypes was performed using MACH 1.0.16. [59] For the European samples, phased haplotypes from the CEU founders of HapMap 2 were used as reference. For African American populations, a combined CEU+YRI reference panel was created that includes SNPs segregating in both CEU and YRI, as well as SNPs segregating in one panel and monomorphic and non-missing in the other. Imputation for the IBC array was performed in two steps. First, individuals with pedigree relatedness or cryptic relatedness were filtered. A subset of individuals was randomly extracted from each panel and used to generate recombination and error rate estimates for the corresponding sample. Second, these rates were used to impute all sample individuals across the entire reference panel. Before cleaning, there were an average of 246,740 (range: 245,816, 247,505) and 227,224 (range: 225,111, 229,061) imputed SNPs in the European American and African American study populations, respectively. Imputation results were then filtered at an imputation quality limit of 0.30 and a MAF threshold of 0.01, yielding 235,077 (95.3% of total) and 227,222 (96.2% of total) SNPs for analysis in European American and African American participants, respectively.
Phenotypes
The clustered risk factors of interest were characterized as a six-domain phenotype: atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic state, elevated plasma glucose, and central obesity (Figure 1). These domains were constructed a priori based on a review of literature examining clustering in metabolic phenotypes, placing specific emphasis on the National Cholesterol Education Program’s Adult Treatment Panel III report [4], [60]. Nineteen variables were then selected to represent one of the six domains with preference for variables measured in at least four of the contributing cohort studies or variables that were highly correlated with available measures. Measurement protocols for each variable by study are provided in Table S21. We assessed normality, and transformations were used when variables exhibited excessive skewness or kurtosis as determined by numerical summary information and visual inspection of histograms and normal probability plots. Dimension reduction using principal components analysis was then performed for each phenotype domain separately in each race/ethnic and study population. For example, principal components for the vascular inflammation domain were calculated using the following traits: albumin, C reactive protein, fibrinogen, uric acid, and white blood cell count. Principal components were chosen so that>55% of the variance for each domain was explained (Tables S12, S13, S14, S15, S16, S17, S18, S19, S20). This threshold was chosen because all of the first (waist circumference, pro-thrombotic state, elevated plasma glucose, and vascular dysfunction) and the sum of first and second (vascular inflammation and atherogenic dyslipidemia) principal components exceeded 55% across all studies and racial/ethnic groups.
Statistical methods
For each phenotype, we fit a linear regression model relating the phenotype to the SNP genotype under the additive mode of inheritance; the model includes environmental variables (i.e., age, sex and study center) as well as the first ten principal components from EIGENSTRAT to adjust for population substructure [61]. Ten population substructure components were included because each component was associated with at least one of the eight phenotypes of interest in at least one study. If the SNP genotype is not associated with any phenotype domain, then the regression coefficients for the SNP genotype are zero in all eight linear models. We tested this global null hypothesis by constructing a multivariate test statistic based on the joint distribution of the score statistics from the eight linear models, which accounted for the correlation between the eight phenotypes. We chose the score statistic because it is computationally efficient and numerically stable. The test statistic is referred to the chi-squared distribution with eight degrees of freedom. The genome-wide significance level was set as P<2.13×10−7 (i.e. 0.05/235,077). Q-Q plots by race are not presented, as our use of a gene-centric array highly enriched for metabolic loci complicated the identification of markers with low prior probabilities of association (i.e. “null markers”) for all phenotypes of interest. The data from each cohort were analyzed separately and the results were combined via meta-analysis as described in Text S2. All analyses were stratified by race and were performed in SAS 9.1 and C++. Further details are given in the Text S2.
Supporting Information
Figure S1.
Forest plots of univariate effect estimates and 95% confidence intervals for eight metabolic dimensions in n = 19,468 European American participants from five cohorts.
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Table S1.
Baseline characteristics of ARIC Study participants (N = 11,757) by race.
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Table S2.
Baseline characteristics of CARDIA Study participants (N = 2,712) by race.
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Table S3.
Baseline characteristics of CHS Study participants (N = 4,627) by race.
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Table S4.
Baseline characteristics of Framingham Heart Study participants (N = 2,789).
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Table S5.
Baseline characteristics of MESA Study participants (N = 3,870) by race.
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Table S6.
Pearson correlation coefficient estimates for 8 principal components used to characterize the six metabolic domains in n = 9,068 European American ARIC participants.
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Table S7.
Pearson correlation coefficient estimates for 8 principal components used to characterize the six metabolic domains in n = 2,689 African American ARIC participants.
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Table S8.
Pearson correlation coefficient estimates for 19 phenotypes used to characterize the six metabolic domains in n = 9,068 European American ARIC participants.
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Table S9.
Pearson correlation coefficient estimates for 19 phenotypes used to characterize the six metabolic domains in n = 2,689 African American ARIC participants.
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Table S10.
Overall and study-specific multivariate P-values for 19 known, confirmed, or possible new loci for metabolic trait dimensions in n = 19,468 European American from five studies.
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Table S11.
Fifteen potential secondary signals for metabolic trait dimensions in n = 19,468 European Americans from five studies.
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Table S12.
Percent variance explained by principal components used to characterize the metabolic trait dimensions, estimated in n = 9,068 European American ARIC participants.
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Table S13.
Percent variance explained by principal components used to characterize the metabolic trait dimensions, estimated in n = 2,712 African American ARIC participants.
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Table S14.
Percent variance explained by principal components used to characterize the metabolic trait dimensions, estimated in n = 1,433 CARDIA European American participants.
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Table S15.
Percent variance explained by principal components used to characterize the metabolic trait dimensions, estimated in n = 1,279 CARDIA African American participants.
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Table S16.
Percent variance explained by principal components used to characterize the metabolic trait dimensions, estimated in n = 3,892 European American CHS participants.
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Table S17.
Percent variance explained by principal components used to characterize the metabolic trait dimensions, estimated in n = 735 African American CHS participants.
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Table S18.
Percent variance explained by principal components used to characterize the metabolic trait dimensions, estimated in n = 2,789 European American FHS participants.
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Table S19.
Percent variance explained by principal components used to characterize the metabolic trait dimensions, estimated in n = 2,286 European American MESA participants.
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Table S20.
Percent variance explained by principal components used to characterize the metabolic trait dimensions, estimated in n = 1,584 African American MESA participants.
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Table S21.
Measurement protocols for 19 phenotypes used to measure metabolic trait dimensions by study.
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Author Contributions
Conceived and designed the experiments: CLA QH GH DYL. Analyzed the data: CLA QH DYL. Wrote the paper: CLA QH GH DY. Provided genetic data: EB MF JBM JSP BMP JIR KDT GH. Critical review of manuscript: KEN JLA EB MF LAH CK JBM JSP SAP BMP MDR JIR KDT LRW.
References
- 1. Isomaa B, Almgren P, Tuomi T, Forsen B, Lahti K, et al. (2001) Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care 24: 683–689.
- 2. Grundy SM (2006) Drug therapy of the metabolic syndrome: minimizing the emerging crisis in polypharmacy. Nat Rev Drug Discov 5: 295–309.
- 3. Alberti KG, Zimmet P, Shaw J (2006) Metabolic syndrome–a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med 23: 469–480.
- 4. Treatment PanelATP III (2002) Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 106: 3143–3421.
- 5. Balkau B, Charles MA (1999) Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet Med 16: 442–443.
- 6. Einhorn D, Reaven GM, Cobin RH, Ford E, Ganda OP, et al. (2003) American College of Endocrinology position statement on the insulin resistance syndrome. Endocr Pract 9: 237–252.
- 7. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, et al. (2005) Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 112: 2735–2752.
- 8. Bosy-Westphal A, Onur S, Geisler C, Wolf A, Korth O, et al. (2007) Common familial influences on clustering of metabolic syndrome traits with central obesity and insulin resistance: the Kiel obesity prevention study. Int J Obes (Lond) 31: 784–790.
- 9. Lin HF, Boden-Albala B, Juo SH, Park N, Rundek T, et al. (2005) Heritabilities of the metabolic syndrome and its components in the Northern Manhattan Family Study. Diabetologia 48: 2006–2012.
- 10. Zabaneh D, Balding DJ (2010) A genome-wide association study of the metabolic syndrome in Indian Asian men. PLoS ONE 5: e11961.
- 11. Kraja AT, Vaidya D, Pankow JS, Goodarzi MO, Assimes TL, et al. (2011) A Bivariate Genome-Wide Approach to Metabolic Syndrome: STAMPEED Consortium. Diabetes 60: 1329–1339.
- 12. Muller MJ, Bosy-Westphal A, Krawczak M (2010) Genetic studies of common types of obesity: a critique of the current use of phenotypes. Obes Rev 11: 612–618.
- 13. Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nat Rev Genet 11: 855–866.
- 14. Hegele RA (2004) Phenomics, lipodystrophy, and the metabolic syndrome. Trends Cardiovasc Med 14: 133–137.
- 15. Bilder RM, Sabb FW, Cannon TD, London ED, Jentsch JD, et al. (2009) Phenomics: The systematic study of phenotypes on a genome-wide scale. Neuroscience 164: 30–42.
- 16. Amos CI, Laing AE (1993) A comparison of univariate and multivariate tests for genetic linkage. Genet Epidemiol 10: 671–676.
- 17. Jiang C, Zeng ZB (1995) Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140: 1111–1127.
- 18. Bilder RM (2008) Phenomics: building scaffolds for biological hypotheses in the post-genomic era. Biol Psychiatry 63: 439–440.
- 19. Freimer N, Sabatti C (2003) The human phenome project. Nat Genet 34: 15–21.
- 20. Smith NL, Chen MH, Dehghan A, Strachan DP, Basu S, et al. (2010) Novel associations of multiple genetic loci with plasma levels of factor VII, factor VIII, and von Willebrand factor: The CHARGE (Cohorts for Heart and Aging Research in Genome Epidemiology) Consortium. Circulation 121: 1382–1392.
- 21. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, et al. (2010) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42: 105–116.
- 22. Waterworth DM, Ricketts SL, Song K, Chen L, Zhao JH, et al. (2010) Genetic variants influencing circulating lipid levels and risk of coronary artery disease. Arterioscler Thromb Vasc Biol 30: 2264–2276.
- 23. Ridker PM, Pare G, Parker A, Zee RY, Danik JS, et al. (2008) Loci related to metabolic-syndrome pathways including LEPR,HNF1A, IL6R, and GCKR associate with plasma C-reactive protein: the Women's Genome Health Study. Am J Hum Genet 82: 1185–1192.
- 24. Kamatani Y, Matsuda K, Okada Y, Kubo M, Hosono N, et al. (2010) Genome-wide association study of hematological and biochemical traits in a Japanese population. Nat Genet 42: 210–215.
- 25. Kolz M, Johnson T, Sanna S, Teumer A, Vitart V, et al. (2009) Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations. PLoS Genet 5: e1000504.
- 26. Matschinsky FM, Glaser B, Magnuson MA (1998) Pancreatic beta-cell glucokinase: closing the gap between theoretical concepts and experimental realities. Diabetes 47: 307–315.
- 27. Kathiresan S, Willer CJ, Peloso GM, Demissie S, Musunuru K, et al. (2009) Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet 41: 56–65.
- 28. Okada Y, Takahashi A, Ohmiya H, Kumasaka N, Kamatani Y, et al. (2011) Genome-wide association study for C-reactive protein levels identified pleiotropic associations in the IL6 locus. Hum Mol Genet 20: 1224–1231.
- 29. Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, et al. (2010) Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet 42: 579–589.
- 30. Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, et al. (2008) Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet 40: 161–169.
- 31. Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, et al. (2008) Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet 40: 189–197.
- 32. Elliott P, Chambers JC, Zhang W, Clarke R, Hopewell JC, et al. (2009) Genetic Loci associated with C-reactive protein levels and risk of coronary heart disease. JAMA 302: 37–48.
- 33. Dehghan A, Dupuis J, Barbalic M, Bis JC, Eiriksdottir G, et al. (2011) Meta-analysis of genome-wide association studies in >80 000 subjects identifies multiple loci for C-reactive protein levels. Circulation 123: 731–738.
- 34. Li WH, Tanimura M, Luo CC, Datta S, Chan L (1988) The apolipoprotein multigene family: biosynthesis, structure, structure-function relationships, and evolution. J Lipid Res 29: 245–271.
- 35. Jong MC, Voshol PJ, Muurling M, Dahlmans VE, Romijn JA, et al. (2001) Protection from obesity and insulin resistance in mice overexpressing human apolipoprotein C1. Diabetes 50: 2779–2785.
- 36. Soutar AK, Garner CW, Baker HN, Sparrow JT, Jackson RL, et al. (1975) Effect of the human plasma apolipoproteins and phosphatidylcholine acyl donor on the activity of lecithin: cholesterol acyltransferase. Biochemistry 14: 3057–3064.
- 37. Li S, Ku CY, Farmer AA, Cong YS, Chen CF, et al. (1998) Identification of a novel cytoplasmic protein that specifically binds to nuclear localization signal motifs. J Biol Chem 273: 6183–6189.
- 38. Matheny SA, Chen C, Kortum RL, Razidlo GL, Lewis RE, et al. (2004) Ras regulates assembly of mitogenic signalling complexes through the effector protein IMP. Nature 427: 256–260.
- 39. Ory S, Morrison DK (2004) Signal transduction: implications for Ras-dependent ERK signaling. Curr Biol 14: R277–278.
- 40. Cui R, Kamatani Y, Takahashi A, Usami M, Hosono N, et al. (2009) Functional variants in ADH1B and ALDH2 coupled with alcohol and smoking synergistically enhance esophageal cancer risk. Gastroenterology 137: 1768–1775.
- 41. Barrett JC, Clayton DG, Concannon P, Akolkar B, Cooper JD, et al. (2009) Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet 41: 703–707.
- 42. Cooper JD, Smyth DJ, Smiles AM, Plagnol V, Walker NM, et al. (2008) Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci. Nat Genet 40: 1399–1401.
- 43. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, et al. (2009) Genome-wide association study of blood pressure and hypertension. Nat Genet 41: 677–687.
- 44. Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, et al. (2009) A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 41: 527–534.
- 45. Lastres-Becker I, Brodesser S, Lutjohann D, Azizov M, Buchmann J, et al. (2008) Insulin receptor and lipid metabolism pathology in ataxin-2 knock-out mice. Hum Mol Genet 17: 1465–1481.
- 46. Li WD, Dong C, Li D, Zhao H, Price RA (2004) An obesity-related locus in chromosome region 12q23-24. Diabetes 53: 812–820.
- 47. Berridge MJ, Irvine RF (1984) Inositol trisphosphate, a novel second messenger in cellular signal transduction. Nature 312: 315–321.
- 48. Liao HJ, Kume T, McKay C, Xu MJ, Ihle JN, et al. (2002) Absence of erythrogenesis and vasculogenesis in Plcg1-deficient mice. J Biol Chem 277: 9335–9341.
- 49. He Z, Opland DM, Way KJ, Ueki K, Bodyak N, et al. (2006) Regulation of vascular endothelial growth factor expression and vascularization in the myocardium by insulin receptor and PI3K/Akt pathways in insulin resistance and ischemia. Arterioscler Thromb Vasc Biol 26: 787–793.
- 50. Xiang G, Schuster MD, Seki T, Kocher AA, Eshghi S, et al. (2004) Down-regulation of plasminogen activator inhibitor 1 expression promotes myocardial neovascularization by bone marrow progenitors. J Exp Med 200: 1657–1666.
- 51. Mouquet F, Cuilleret F, Susen S, Sautiere K, Marboeuf P, et al. (2009) Metabolic syndrome and collateral vessel formation in patients with documented occluded coronary arteries: association with hyperglycaemia, insulin-resistance, adiponectin and plasminogen activator inhibitor-1. Eur Heart J 30: 840–849.
- 52. Pollex RL, Hegele RA (2006) Genetic determinants of the metabolic syndrome. Nat Clin Pract Cardiovasc Med 3: 482–489.
- 53. Meigs JB (2000) Invited commentary: insulin resistance syndrome? Syndrome X? Multiple metabolic syndrome? A syndrome at all? Factor analysis reveals patterns in the fabric of correlated metabolic risk factors. Am J Epidemiol 152: 908-911; discussion 912:
- 54. Meigs JB, Wilson PW, Nathan DM, D'Agostino RB Sr, Williams K, et al. (2003) Prevalence and characteristics of the metabolic syndrome in the San Antonio Heart and Framingham Offspring Studies. Diabetes 52: 2160–2167.
- 55.
Matise TC, Ambite JL, Buyske S, Cole SA, Crawford DC, et al. (2010) The next PAGE in understanding complex traits: study design for analysis of Population Architecture using Genetics and Epidemiology. Am J Epidemiol. In press.
- 56. Musunuru K, Lettre G, Young T, Farlow DN, Pirruccello JP, et al. (2010) Candidate gene association resource (CARe): design, methods, and proof of concept. Circ Cardiovasc Genet 3: 267–275.
- 57. Pendergrass SA, Brown-Gentry KD, Dudek S, Ambite JL, Avery CL, et al. (2010) Phenotype-wide association study (PheWAS) for exploration of novel SNP and phenotype relationships within PAGE. Genet Epidemiol 35: 410–422.
- 58. Keating BJ, Tischfield S, Murray SS, Bhangale T, Price TS, et al. (2008) Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies. PLoS ONE 3: e3583.
- 59. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34: 816–834.
- 60. Grundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C (2004) Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 109: 433–438.
- 61. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38: 904–909.