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. Author manuscript; available in PMC: 2015 Dec 1.
Published in final edited form as: Genomics. 2014 Oct 13;104(6 0 0):490–495. doi: 10.1016/j.ygeno.2014.10.003

Whole Blood Gene Expression and Interleukin-6 Levels

Honghuang Lin 1,2,*, Roby Joehanes 3,4,5,*, Luke C Pilling 6,*, Josée Dupuis 7,8, Kathryn L Lunetta 9,10, Sai-Xia Ying 11, Emelia J Benjamin 12,13,14, Dena Hernandez 15, Andrew Singleton 16, David Melzer 17, Peter J Munson 18,19,20, Daniel Levy 21,22, Luigi Ferrucci 23,*, Joanne M Murabito 24,25,*
PMCID: PMC4262595  NIHMSID: NIHMS635030  PMID: 25311648

Abstract

Background

Circulating interleukin-6 levels increase with advancing age and are a risk factor for various diseases and mortality. The characterization of gene expression profiles associated with interleukin-6 levels might suggest important molecular events underlying its regulation.

Methods and Results

We studied the association of transcriptional profiles with interleukin-6 levels in 2422 participants from Framingham Heart Study Offspring Cohort using Affymetrix Human Exon 1.0 ST Array. We identified 4139 genes that were significantly associated with interleukin-6 levels (FDR<0.05) after adjusting for age, sex and blood cell components. We then replicated 807 genes in the InCHIANTI study with 694 participants. Many of the top genes are involved in inflammation-related pathways or erythrocyte function, including JAK/Stat signaling pathway and interleukin-10 signaling pathway.

Conclusion

We identified and replicated 807 genes that were associated with circulating interleukin-6 levels. Future characterization of interleukin-6 regulation networks may facilitate the identification of additional potential targets for treating inflammation-related diseases.

Keywords: Inflammation, gene expression, interleukin-6, epidemiology

Introduction

Human interleukin-6 is a multifunctional pro-inflammatory cytokine produced by many cell types including immune cells [1], vascular smooth muscle cells [2], adipocytes and skeletal muscle [3]. Interleukin-6 has both an acute and chronic inflammatory role [4]. It mediates downstream inflammatory cascades, including the production of C-reactive protein and fibrinogen [5, 6]. The protein also provides various signals to regulate cell growth [1], immune responses [7], and acute phase protein secretion [6], Interleukin-6 is an important regulator of immune response, inflammation and hematopoiesis.

The circulating level of interleukin-6 is usually low in healthy individuals but increases in older adults, often for unclear reasons [8]. Interleukin-6 is associated with increased risk for mortality even after accounting for cardiovascular disease and its risk factors [9]. Various age-related diseases have been associated with elevated level of interleukin-6 including cardiovascular disease [10], cancer [11], and diabetes [12, 13]. The level of interleukin-6 has been recognized as an important inflammatory biomarker for the severity and risk of progression of many diseases, such as atherosclerosis [6], systemic lupus erythematosus [14], impaired hematopoiesis [15], and coronary heart disease [16]. In addition, elevated levels of interleukin-6 have been associated with cognitive decline [17], physical disability, and frailty in older adults [18–23].

The expression of many genes has been associated with the concentration of circulating interleukin-6 levels [24–26]. Meanwhile, the expression of interleukin-6 gene is also regulated by a number of genes [27, 28]. However, prior studies typically focused on a few candidate genes using a small group of selected samples, which might limit their application to the general population. The objective of the present study was to assess the association of genome-wide gene expression with interleukin-6 levels in participants from the Framingham Heart Study, a large community-based cohort. Our results were further validated in an independent cohort, the InCHIANTI Study. The overall study design is shown in Figure 1.

Figure 1. Study design.

Figure 1

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Primary analyses were adjusted for age and sex. Secondary analyses were adjusted for age, sex, and additional clinical covariates. A total of 4139 genes were found significantly associated with interleukin-6 levels (FDR<0.05), which were then tested in the replication cohort. Eight hundred and ninety-seven of them were replicated (P<0.05).

Methods

Study Samples

The Framingham Heart Study (FHS) is a longitudinal study aiming to investigate cardiovascular disease and its risk factors in the community. Three generations of participants have been enrolled since 1948 [29–31]. Every 2–8 years participants undergo a physical examination and assessment of cardiovascular disease risk factors, 12-lead electrocardiogram, along with lifestyle and medical history interview. Study samples for the current project were collected from the Offspring cohort enrolled in 1971, who are the children of the Original cohort as well as spouses of the offspring [30, 31]. Our study was limited to the participants who attended the eighth Offspring examination (2005–2008) and provided a blood sample for RNA collection. All participants gave written informed consent, and the study was approved by the Review Boards at the National Human Genome Research Institute and Boston University Medical Center.

Interleukin-6 Measurement

Fasting blood samples were obtained during the routine clinic visit, and the samples were frozen at −80°C. The interleukin-6 concentration was assayed by the quantitative enzyme-linked immunosorbent assay according to the manufacturers’ protocols (R&D Systems, Minneapolis, MN, USA). Ten percent of measures were run in duplicate. The minimum detectable concentration was 0.039 pg/ml. The mean intra-assay coefficient of variation was 4.0%[32]. More details are available at http://www.framinghamheartstudy.org/researchers/description-data/vascular-manuals/offspring_exam8_omni1_exam3_marker_manual.pdf.

Gene Expression Profiling

The gene expression profiling has been described in detail by Joehanes et al [33]. In brief, total RNA was isolated from frozen PAXgene blood tubes (PreAnalytiX, Hombrechtikon, Switzerland) and amplified using the WT-Ovation Pico RNA Amplification System (NuGEN, San Carlos, CA) according to the manufacturers’ standard operating procedures. The obtained cDNA was hybridized to the Affymetrix Human Exon 1.0 ST Array (Affymetrix, Inc., Santa Clara, CA). The raw data were quantile-normalized and log2 transformed, followed by summarization using Robust Multi-array Average [34]. The gene annotations were obtained from Affymetrix NetAffx Analysis Center (version 31). We excluded transcript clusters that were not mapped to RefSeq transcripts, resulting 17,873 distinct transcripts (17,324 distinct genes) for downstream analysis. Using partial least square method, we imputed the white blood cell and platelet counts and the percentage of lymphocytes, monocytes, eosinophils and basophils from gene expression data on measured cell counts in 2284 participants from FHS Third Generation Cohort. Given that the gene expression and interleukin-6 was not measured in the same examination for the FHS Third Generation Cohort, we did not include them in the analysis. The percentages of each imputed cell type were then normalized, where the negative predicted values were set to 0 and the sum of the percentages for all cell types were set 100%. Cross-validated estimates of prediction accuracy (R2) were 0.61, 0.41, 0.25, 0.83, 0.83, 0.81, 0.89, 0.25, for white blood cell counts, red blood cell counts, platelet counts, neutrophil percent, lymphocyte percent, monocyte percent, eosinophil percent, and basophil percent, respectively [33].

Statistical Analyses

Our primary analyses tested the association between interleukin-6 levels and gene expression. Gene expression was treated as the dependent measure and the loge of interleukin-6 concentration was treated as the exposure variable. The association was evaluated by the linear mixed effect models, in which clinical covariates, including sex, age, and differential cell counts, were cast as fixed factors, the technical covariates were cast as a random factor, and familial relatedness as random variance-covariance matrix.

The secondary analyses were adjusted for additional clinical covariates that might affect interleukin-6 levels [35]. Clinical covariates included current smoking status, systolic and diastolic blood pressures, hypertension treatment, body mass index, waist circumference, total/high-density lipoprotein cholesterol, triglycerides, lipid-lowering medication, glucose, diabetes, aspirin treatment (≥3 days per week), hormone replacement therapy, and prevalent cardiovascular disease.

We used false discovery rate (FDR)[36] to correct for multiple testing, which estimates the number of incorrectly rejected hypotheses divided by the total number of rejected hypotheses. Genes with FDR<0.05 were considered statistically significant.

All the analyses were performed using the R software package (www.r-project.org/). The linear mixed effect models were implemented in the “lme4” package, and "pedigreemm" package was used to account for pedigree information.

Replication Phase

We performed our replication on samples from the InCHIANTI study. InCHIANTI is a prospective population-based study of older adults that aims to identify risk factors for late-life disability [37, 38]. More than 1000 participants 65 years or older who lived in the Chianti area of Italy were enrolled between 1998 and 2000. RNA was collected using PAXgene technology during the participants 4th visit (2007/2008), and the Illumina Human HT12-v3 gene expression array quantified transcript expression levels. Details about the InCHIANTI interleukin-6 assay and gene expression profiling are available in the Supplemental Materials, and have been published previously [39]. Genes with nominal P<0.05 were considered as replicated. All participants gave written informed consent and the study was approved by the Italian National Institute on Research and Care of Aging (INRCA) Ethical Committee.

Results

Association of interleukin-6 with Gene Expression in FHS

A total of 2422 eligible participants from the Framingham Offspring Cohort were enrolled in our study. The descriptive characteristics of the participants (mean age 66±9 years, 54.9% women) are provided in Table 1.

Table 1.

Clinical characteristics of the study samples

Characteristics FHS (n=2422) + InCHIANTI (n=694) +
Women, n (%) 1,329 (54.9%) 381 (54.9%)
Age, year ± SD 66.4 ± 9.0 72.2 ± 15.3
Interleukin-6 levels, pg/ml 2.65 ± 2.98 3.80 ± 2.99
Smoker, n (%) 203 (8.4%) 71 (10.4%)
Body mass index, kg/m2 28.5 ± 5.4 27.1 ± 4.3
Waist circumference, cm 100 ± 14 95 ± 12
Systolic blood pressure, mm Hg 129 ± 17 132 ± 20
Diastolic blood pressure, mm Hg 73 ± 10 78 ± 11
Hypertension treatment 1,298 (53.6%) 217 (31.3%)
Total cholesterol, mg/dL 186 ± 37 205 ± 40
HDL cholesterol,mg/dL 57 ± 18 56 ± 15
Triglycerides, mg/dL 119 ± 71 124 ± 74
Glucose, mg/dL 107 ± 24 94 ± 24
Prevalent diabetes mellitus, n (%) 423 (17.5%) 97 (14.3%)
Prevalent cardiovascular disease 240 (9.9%) 188 (27.7%)
Aspirin treatment (>=3 days per week) 1,087 (44.9%) 173 (25.4%)
Lipid-lowering medication 1,061 (43.8%) 90 (13.2%)
Hormone replacement therapy 141 (5.8%) 32 (4.7%)
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Characteristics are represented by mean ± SD or n (%)

We identified 4139 genes that were significantly associated with interleukin-6 levels (FDR<5%). Among them, 1766 genes were negatively associated with interleukin-6 levels, whereas the remaining 2372 genes were positively associated. Figure 2 shows the volcano plot of all studied genes, and Table 2 shows the top associations (FDR<5.0×10−18). The most significant gene was DPEP2 (FDR=3.5×10−23), which encodes a dipeptidase that catalyzes various dipeptides including leukotriene D4 [40]. Many of the genes are involved in erythrocyte function (ALAS2, FLT3, SLC4A1, GLRX5, and STOM) or immune response (FCGR1A, PNP, and TSTA3).

Figure 2. Volcano plot of association results from primary analyses.

Figure 2

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Each dot represents one gene. The x-axis represents the beta estimation (β) of each gene, whereas the y-axis represents the log10(P). Positive effects represent that the gene were positively associated with interleukin-6 levels, whereas negative effects represent that the genes were negatively associated with interleukin-6 levels. The red dash line indicates FDR<0.05.

Table 2.

Most significant genes associated with interleukin-6 levels from the primary analysis+

Entrez Gene
ID		 Gene FHS InCHIANTI‡
Effect
size		 SE* FDR$ Effect
size		 SE* P value
64174 DPEP2 −0.07 0.01 3.5×10−23 −0.25 0.04 3.5×10−9
3084 NRG1 0.07 0.01 5.6×10−23 0.14 0.05 4.0×10−3
54762 GRAMD1C −0.13 0.01 1.0×10−22
212 ALAS2 0.22 0.02 1.0×10−22 0.18 0.05 1.2×10−4
2322 FLT3 −0.11 0.01 6.2×10−22
2209 FCGR1A 0.17 0.02 8.3×10−22 0.29 0.04 7.6×10−11
4860 PNP 0.13 0.01 1.5×10−21 0.15 0.05 1.5×10−3
6478 SIAH2 0.07 0.01 1.6×10−21 0.15 0.05 1.8×10−3
7264 TSTA3 0.14 0.01 1.6×10−21 0.12 0.05 1.0×10−2
3068 HDGF 0.10 0.01 3.5×10−21 0.10 0.05 2.8×10−2
51218 GLRX5 0.09 0.01 1.5×10−19 0.16 0.05 6.2×10−4
482 ATP1B2 0.09 0.01 1.5×10−19 −0.09 0.05 7.5×10−2
6521 SLC4A1 0.18 0.02 1.6×10−19 0.10 0.05 2.3×10−2
23500 DAAM2 −0.06 0.01 3.6×10−19
2040 STOM 0.10 0.01 5.3×10−19 0.11 0.04 8.5×10−3
5305 PIP4K2A 0.10 0.01 9.7×10−19 0.14 0.05 3.8×10−3
8991 SELENBP1 0.18 0.02 1.7×10−18 0.15 0.05 2.3×10−3
9829 DNAJC6 0.08 0.01 3.8×10−18
2766 GMPR 0.17 0.02 4.4×10−18 0.13 0.05 8.3×10−3
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Primary analyses were adjusted for age and sex

‡

Some genes were not present in InCHIANTI assay

*

SE: standard error;

$

FDR: false discovery rate

To further characterize the association of gene expression with interleukin-6 levels, we performed secondary analyses by adjusting for additional clinical covariates (see Methods). Figure 3 shows the comparison between primary and secondary analyses in terms of T-statistics. The results were highly correlated (R2=0.86). Among 4139 gene that were significant in the primary analysis, 3484 (84.2%) genes remained significant (P<0.05) in the secondary analysis.

Figure 3. Comparison of T-statistics between primary analyses and secondary analyses.

Figure 3

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The x-axis represents the T-statistics of loge(interleukin-6) from the primary analysis and y-axis represents the T-statistics of loge(interleukin-6) from the secondary analysis. Each point represents one gene. The results were highly correlated (R2=0.86).

Given the heterogeneous nature of whole blood, we also examined the association of interleukin-6 levels with each cell type in the blood. As shown in Supplemental Table 1, the white blood cell counts, particularly lymphocytes and monocytes, were most significantly associated with interleukin-6 levels.

Replication in InCHIANTI

We tested our findings in InCHIANTI for replication. Table 1 shows the descriptive characteristics of the 694 eligible participants enrolled in the study (mean age 72.2±15.3 years, 54.9% women). Among the 4139 significant genes from FHS, 2831 genes also were measured in InCHIANTI, of which 807 were significant (P<0.05) and had the same direction of effects, including 43 highly significant genes (Bonferroni P<1.8×10−5). Among the top genes from FHS (FDR<5.0×10−18) that also were available in InCHIANTI (n=15 genes), all except one were replicated, suggesting the robustness of our results despite distinct transcriptional profiling platforms. The full list of replicated genes is provided in Supplemental Table 2. We also performed meta-analysis of both cohorts, and found that all the 807 replicated genes remained significant in the meta-analysis.

Pathway Analysis

We then examined the enrichment of interleukin-6 associated genes in canonical pathways by the Ingenuity Pathway Analysis (IPA) toolbox. Sixty-four canonical pathways were significantly enriched (FDR<0.05) with replicated interleukin-6 associated genes. Table 3 shows the top 10 enriched pathways, including well-known inflammation-related pathways like JAK/Stat signaling pathway (FDR=7.2×10−4, Supplemental Figure 1) and interleukin-10 signaling pathway (FDR=2.3×10−3, Supplemental Figure 2). We also performed pathway analysis separately for genes positively associated with interleukin-6 levels (Supplemental Table 3) and genes negatively associated with interleukin-6 levels (Supplemental Table 4). An enrichment analysis on gene ontology by Gene Set Enrichment Analysis [41] found 10 gene sets were significantly enriched with interleukin-6 associated genes (FDR<5%). These gene sets were listed in Supplemental Table 5.

Table 3.

Most significant canonical pathways enriched with genes associated with interleukin-6 levels

Canonoical
pathway		 P value FDR Ratio+ Genes that were associated with
interleukin-6 levels
JAK/Stat Signaling 1.9×10−6 4.7×10−4 13/70 (0.186) FOS, BCL2L1, SOCS3, RAF1, SOCS1, STAT6, AKT1, JAK1, RRAS, CISH, CDKN1A, TYK2, STAT1
IL-10 Signaling 2.2×10−6 4.7×10−4 13/72 (0.181) IL1R2, CCR1, FOS, IKBKB, SOCS3, JAK1, IL1RN, BLVRA, TYK2, BLVRB, IL1B, MAP2K3, IL1RAP
Pancreatic Adenocarcinoma Signaling 4.7×10−6 5.6×10−4 16/116 (0.138) RAF1, E2F4, JAK1, PLD3, PA2G4, TFDP1, TYK2, TGFBR2, BCL2L1, AKT1, CDKN1A, PTGS2, CDKN1B, STAT1, NOTCH1, E2F2
Glucocorticoid Receptor Signaling 5.4×10−6 5.6×10−4 27/280 (0.0964) TAF12, IL8, RAF1, JAK1, RRAS, SGK1, CDK7, TBP, PBX1, POLR2B, FCGR1A, EP300, TGFBR2, IL1R2, FOS, BCL2L1, IKBKB, AKT1, IL1RN, GTF2E1, CDKN1A, PRKAG2, IL1B, PTGS2, FKBP5, STAT1, PPP3CA
Heme Biosynthesis II 2.0×10−5 1.7×10−3 5/10 (0.5) UROD, UROS, FECH, ALAS2, HMBS
IGF-1 Signaling 2.7×10−5 1.9×10−3 14/102 (0.137) FOS, SOCS3, RAF1, SOCS1, NOV, AKT1, JAK1, RRAS, IGF1R, PRKAG2, PDPK1, IRS2, PRKCZ, PRKAR1A
NRF2-mediated Oxidative Stress Response 3.2×10−5 1.9×10−3 20/190 (0.105) RAF1, UBB, SOD1, RRAS, DNAJA4, HERPUD1, DNAJB2, GSTO1, PRKCZ, EP300, FOS, AKT1, MGST2, MAP2K3, CDC34, FKBP5, DNAJB5, MGST3, EPHX1, GSTK1
Interferon Signaling 4.2×10−5 2.2×10−3 8/34 (0.235) IFIT3, SOCS1, OAS1, JAK1, TYK2, IFITM1, IFNGR1, STAT1
IL-6 Signaling 5.4×10−5 2.4×10−3 15/122 (0.123) IL1R2, IKBKB, FOS, SOCS3, RAF1, SOCS1, IL8, AKT1, TNFAIP6, IL1RN, RRAS, IL6R, IL1B, MAP2K3, IL1RAP
NF-κB Signaling 5.8×10−5 2.4×10−3 19/180 (0.106) RAF1, RRAS, RELB, IGF2R, PRKCZ, EP300, TGFBR2, IL1R2, IKBKB, TNIP1, AKT1, ARAF, TLR5, IL1RN, PELI1, IGF1R, IL1B, CASP8, TNFSF13B
Glioma Signaling 9.1×10−5 3.5×10−3 13/107 (0.121) RAF1, E2F4, AKT1, PA2G4, CAMK1D, TFDP1, RRAS, CDKN1A, IGF1R, IGF2R, E2F2, PRKCZ, CAMK2G
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Ratio is the number of genes that were associated with interleukin-6 levels comparing to the total number of genes in the pathway

Discussion

Interleukin-6 is a pleiotropic cytokine that regulates a variety of inflammatory responses. In our study, we investigated the association of gene expression with interleukin-6 levels in 2422 participants from FHS. A total of 4139 genes were found to be significantly associated with interleukin-6 levels; 807 genes were successfully replicated in an additional 694 participants from InCHIANTI.

Many replicated genes are involved in inflammation-related pathways and red blood cell function. FLT3 encodes a receptor tyrosine kinase that regulates hematopoiesis and immune system [42]. STOM encodes a highly conserved stomatin located in the membrane of red blood cells. The deficiency or mutation of stomatin causes hereditary stomatocytosis [43]. SLC4A1 encodes an erythrocyte plasma membrane protein that is involved in carbon dioxide transport [44]. ALAS2 encodes an erythroid-specific mitochondrially located enzyme whose mutations play an important role in the development of sideroblastic anemia [45].

Interestingly, the expression of interleukin-6 receptor (IL6R) was found to be associated with interleukin-6 levels (Discovery P=8.8×10−4; Replication P=0.015). The binding of interleukin-6 to its receptor results in the activation of multiple signaling pathways [46–49], such as JAK/Stat signaling pathway and MAPK pathway, which regulate a variety of downstream biological activities. However, we did not observe an association between IL6 expression and interleukin-6 levels (P=0.12), suggesting that increased interleukin-6 concentration in serum is not being necessarily produced by the leukocytes, and the changes in expression that are seen in the serum might be the responses of the leukocytes to the interleukin-6 in serum.

The interleukin-10 signaling pathway is one of the most significant pathways enriched with interleukin-6 related genes. Participants of the Leiden 85-plus study with an impaired cytokine production to a stimulus were found to have increased mortality [50]. A significant association was detected with the IL10 gene promoter, suggesting the maladaptive immune response was under genetic control and in turn resulted in frailty in old age. Frail older adults are known to have higher levels of interleukin-6 than non-frail older adults [51]. Genetically altered il10 mice compared to age- and sex-matched control mice develop the characteristics of human frailty including elevated interleukin-6 levels and decline in muscle strength [52]. Subsequent work demonstrated that in addition to low-grade elevation of interleukin-6, the il10 frail mice develop cardiac and vascular dysfunction with advancing age [53] and have higher mortality [54]. A better understanding of the biologic mechanisms leading to elevated interleukin-6 levels and chronic inflammation with older age may result in therapies to ameliorate age-related multi-system decline.

It is estimated that some of the inter-individual variations of interleukin-6 levels are attributable to heritability [35, 55]. Several genetic loci, such as IL6R [56] and ABO [57], have already been identified to be associated with interleukin-6 levels. Yet much of the variability in interleukin-6 levels still remains unexplained. Our study identified hundreds of interleukin-6 associated genes, which, in combination with genetic variations, may provide new insights into the regulation of interleukin-6 levels.

The gene expression in this study was measured from the whole blood, which contains a variety of cell types. Since each cell type could have specific cell responses and may result in false discovery [58–60], we thereby accounted for the relative abundance of each cell type in our analyses. To further reduce the possibility of false discovery, we applied two different platforms for gene expression profiling: Affymetrix Human Exon 1.0 ST Array for discovery and Illumina Human HT-12 v3 Array for replication. We expect that many non-replicated genes were simply due to the difference in microarray platforms and sample size (2422 vs. 694). Future increases in sample size and improvement of gene expression profiling platforms may further increase the power to identify significant genes [61, 62].

Our study has certain limitations. All participants included in this study were exclusively middle age to older adults of European descent, thus the generalizability of our findings to younger individuals or other races/ethnicities is unclear. We only measured interleukin-6 together with expression levels from the blood collected during one physical examination, but the interleukin-6 concentration may fluctuate over time [63]. Therefore our study cannot comment on longitudinal variation in the relations between gene expression and circulating interleukin-6 levels. Lastly, this study was largely limited to the association analyses, and we cannot infer causality between interleukin-6 levels and gene expression.

In conclusion, we studied the association of gene expression with interleukin-6 levels in a large community-based cohort and replicated it in another cohort. We successfully identified and replicated 807 genes that were significantly associated with interleukin-6 levels. Future characterization of interleukin-6 regulation network would enable the identification of additional potential therapeutic targets for inflammation treatment.

Supplementary Material

Highlights.

  • We studied the association of gene expression with interleukin-6 levels in 2422 participants from Framingham Heart Study Offspring Cohort, and validated the result in 694 participants from InCHIANTI study

  • We identified and replicated 807 genes that were associated with circulating interleukin-6 levels

  • Many of the interleukin-6 associated genes are involved in inflammation-related pathways or erythrocyte function

Acknowledgements

FHS gene expression profiling was funded through the Division of Intramural Research (Principal Investigator, Daniel Levy), National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD. Measurement of interleukin-6 was funded through R01 HL 064753 and R01 HL076784. This work is supported by NIH grants 1R01 HL64753 (Benjamin), R01AG028321 (Benjamin), R01AG029451 (Murabito). This study was supported in part by the Intramural Research Program, National Institute on Aging (Ferrucci). UK based work was supported by a Wellcome Trust grant to the University of Exeter, plus internal medical school funding (Melzer). The Framingham Heart Study is supported by National Heart, Lung, and Blood Institute contract N01-HC-25195.

Footnotes

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Disclosures

The authors declare no commercial conflicts of interest.

Contributor Information

Honghuang Lin, Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA; National Heart Lung and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, MA, USA.

Roby Joehanes, National Heart Lung and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, MA, USA; Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institute of Health, Bethesda, MD, USA; Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.

Luke C. Pilling, Epidemiology and Public Health, Medical School, University of Exeter, Exeter EX1 2LU, UK.

Josée Dupuis, National Heart Lung and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.

Kathryn L. Lunetta, National Heart Lung and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.

Sai-Xia Ying, Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institute of Health, Bethesda, MD, USA.

Emelia J. Benjamin, National Heart Lung and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, MA, USA; Section of Cardiovascular Medicine and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.

Dena Hernandez, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.

Andrew Singleton, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.

David Melzer, Epidemiology and Public Health, Medical School, University of Exeter, Exeter EX1 2LU, UK.

Peter J. Munson, National Heart Lung and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, MA, USA; Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institute of Health, Bethesda, MD, USA; Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.

Daniel Levy, National Heart Lung and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, MA, USA; Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.

Luigi Ferrucci, Clinical Research Branch, National Institute on Aging, Baltimore, MD, USA.

Joanne M. Murabito, National Heart Lung and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, MA, USA; Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA.

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