Christopher J O'Donnell

Publications (398) View all

• Article: Common variants in and near IRS1 and subclinical cardiovascular disease in the Framingham Heart Study.

  Soo Lim, Jaeyoung Hong, Ching-Ti Liu, Marie-France Hivert, Charles C White, Joanne M Murabito, Christopher J O'Donnell, Josée Dupuis, Jose C Florez, James B Meigs
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  ABSTRACT: OBJECTIVE: Common variants at the 2q36.3-IRS1 locus are associated with insulin resistance (IR), type 2 diabetes (T2D) and coronary artery disease (CAD) in large-scale association studies. We tested the hypothesis that variants at this locus are associated with subclinical atherosclerosis traits. METHODS: We studied 2740 Framingham Heart Study participants (54.9% women; mean age 57.8 years) with measures of coronary artery or abdominal aortic calcium, internal and common carotid intima-media thickness, and ankle-brachial index (ABI). We tested 1) four SNPs previously shown to be associated with IR (rs2972146, rs2943650), T2D (rs2943641) or CAD (rs2943634) and 2) any SNP at 2q36.3-IRS1, for association with subclinical atherosclerosis traits, adjusting for atherosclerosis risk factors. We set type 1 error rate for test 1) as 0.05/5 traits = P < 0.01, and for test 2) as 0.05 divided by the effective number of independent tests, divided by 5 for the number of traits analyzed. RESULTS: We found no association between the four known SNPs and subclinical atherosclerosis, but identified one SNP (rs10167219, r(2) with rs2943634 = 0.07) at 2q36.3 that was significantly associated with ABI (corrected P = 0.009). However, rs10167219 was not associated with ABI (P = 0.70) in 35,404 participants in a published ABI association study. CONCLUSION: Common variants at the 2q36.3-IRS1 locus were not associated with subclinical atherosclerosis traits in this study which was adequately powered to find associations with moderate effect size. Although IR and T2D may be mechanistically linked to CAD via subclinical atherosclerosis, an alternate mechanism for the IR-T2D-CAD associations at 2q36.3-IRS1 must be postulated.
  Atherosclerosis 04/2013; · 3.79 Impact Factor
• Article: A Systems Biology Framework Identifies Molecular Underpinnings of Coronary Heart Disease.

  Tianxiao Huan, Bin Zhang, Zhi Wang, Roby Joehanes, Jun Zhu, Andrew D Johnson, Saixia Ying, Peter J Munson, Nalini Raghavachari, Richard Wang, [......], Themistocles L Assimes, Ruth McPherson, Nilesh J Samani, Heribert Schunkert, Qingying Meng, Christine Suver, Christopher J O'Donnell, Jonathan Derry, Xia Yang, Daniel Levy
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  ABSTRACT: OBJECTIVE: Genetic approaches have identified numerous loci associated with coronary heart disease (CHD). The molecular mechanisms underlying CHD gene-disease associations, however, remain unclear. We hypothesized that genetic variants with both strong and subtle effects drive gene subnetworks that in turn affect CHD.Approach and Results-We surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 age- and sex-matched controls. Twenty-four coexpression modules were identified, including 1 case-specific and 1 control-specific differential module (DM). The DMs were enriched for genes involved in B-cell activation, immune response, and ion transport. By integrating the DMs with altered gene expression-associated single-nucleotide polymorphisms and with results of genome-wide association studies of CHD and its risk factors, the control-specific DM was implicated as CHD causal based on its significant enrichment for both CHD and lipid expression-associated single-nucleotide polymorphisms. This causal DM was further integrated with tissue-specific Bayesian networks and protein-protein interaction networks to identify regulatory key driver genes. Multitissue key drivers (SPIB and TNFRSF13C) and tissue-specific key drivers (eg, EBF1) were identified. CONCLUSIONS: Our network-driven integrative analysis not only identified CHD-related genes, but also defined network structure that sheds light on the molecular interactions of genes associated with CHD risk.
  Arteriosclerosis Thrombosis and Vascular Biology 03/2013; · 6.37 Impact Factor
• Article: Gene Expression Signatures of Coronary Heart Disease.

  Roby Joehanes, Saixia Ying, Tianxiao Huan, Andrew D Johnson, Nalini Raghavachari, Richard Wang, Poching Liu, Kimberly A Woodhouse, Shurjo K Sen, Kahraman Tanriverdi, Paul Courchesne, Jane E Freedman, Christopher J O'Donnell, Daniel Levy, Peter J Munson
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  ABSTRACT: OBJECTIVE: To identify transcriptomic biomarkers of coronary heart disease (CHD) in 188 cases with CHD and 188 age- and sex-matched controls who were participants in the Framingham Heart Study.Approach and Results-A total of 35 genes were differentially expressed in cases with CHD versus controls at FDR<0.5, including GZMB, TMEM56, and GUK1. Cluster analysis revealed 3 gene clusters associated with CHD, 2 linked to increased erythrocyte production and a third to reduced natural killer and T cell activity in cases with CHD. Exon-level results corroborated and extended the gene-level results. Alternative splicing analysis suggested that GUK1 and 38 other genes were differentially spliced in cases with CHD versus controls. Gene Ontology analysis linked ubiquitination and T-cell-related pathways with CHD. CONCLUSIONS: Two bioinformatically defined groups of genes show consistent associations with CHD. Our findings are consistent with the hypotheses that hematopoesis is upregulated in CHD, possibly reflecting a compensatory mechanism, and that innate immune activity is disrupted in CHD or altered by its treatment. Transcriptomic signatures may be useful in identifying pathways associated with CHD and point toward novel therapeutic targets for its treatment and prevention.
  Arteriosclerosis Thrombosis and Vascular Biology 03/2013; · 6.37 Impact Factor
• Article: Distribution, Determinants, and Normal Reference Values of Thoracic and Abdominal Aortic Diameters by Computed Tomography (from the Framingham Heart Study).

  Ian S Rogers, Joseph M Massaro, Quynh A Truong, Amir A Mahabadi, Matthias F Kriegel, Caroline S Fox, George Thanassoulis, Eric M Isselbacher, Udo Hoffmann, Christopher J O'Donnell
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  ABSTRACT: Current screening and detection of asymptomatic aortic aneurysms is based largely on uniform cut-point diameters. The aims of this study were to define normal aortic diameters in asymptomatic men and women in a community-based cohort and to determine the association between aortic diameters and traditional risk factors for cardiovascular disease. Measurements of the diameters of the ascending thoracic aorta (AA), descending thoracic aorta (DTA), infrarenal abdominal aorta (IRA), and lower abdominal aorta (LAA) were acquired from 3,431 Framingham Heart Study (FHS) participants. Mean diameters were stratified by gender, age, and body surface area. Univariate associations with risk factor levels were examined, and multivariate linear regression analysis was used to assess the significance of covariate-adjusted relations with aortic diameters. For men, the average diameters were 34.1 mm for the AA, 25.8 mm for the DTA, 19.3 mm for the IRA, and 18.7 mm for the LAA. For women, the average diameters were 31.9 mm for the AA, 23.1 mm for the DTA, 16.7 mm for the IRA, and 16.0 mm for the LAA. The mean aortic diameters were strongly correlated (p <0.0001) with age and body surface area in age-adjusted analyses, and these relations remained significant in multivariate regression analyses. Positive associations of diastolic blood pressure with AA and DTA diameters in both genders and pack-years of cigarette smoking with DTA diameter in women and IRA diameter in men and women were observed. In conclusion, average diameters of the thoracic and abdominal aorta by computed tomography are larger in men compared with women, vary significantly with age and body surface area, and are associated with modifiable cardiovascular disease risk factors, including diastolic blood pressure and cigarette smoking.
  The American journal of cardiology 03/2013; · 3.58 Impact Factor
• Article: Overlap Between Common Genetic Polymorphisms Underpinning Kidney Traits and Cardiovascular Disease Phenotypes: The CKDGen Consortium.

  Matthias Olden, Alexander Teumer, Murielle Bochud, Cristian Pattaro, Anna Köttgen, Stephen T Turner, Rainer Rettig, Ming-Huei Chen, Abbas Dehghan, Francois Bastardot, [......], Gary F Mitchell, Joshua C Bis, Christopher J O'Donnell, Ching-Yu Cheng, Xueling Sim, David S Siscovick, Josef Coresh, W H Linda Kao, Caroline S Fox, Conall M O'Seaghdha
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  ABSTRACT: BACKGROUND: Chronic kidney disease is associated with cardiovascular disease. We tested for evidence of a shared genetic basis to these traits. STUDY DESIGN: We conducted 2 targeted analyses. First, we examined whether known single-nucleotide polymorphisms (SNPs) underpinning kidney traits were associated with a series of vascular phenotypes. Additionally, we tested whether vascular SNPs were associated with markers of kidney damage. Significance was set to 1.5×10-4 (0.05/325 tests). SETTING & PARTICIPANTS: Vascular outcomes were analyzed in participants from the AortaGen (20,634), CARDIoGRAM (86,995), CHARGE Eye (15,358), CHARGE IMT (31,181), ICBP (69,395), and NeuroCHARGE (12,385) consortia. Tests for kidney outcomes were conducted in up to 67,093 participants from the CKDGen consortium. PREDICTOR: We used 19 kidney SNPs and 64 vascular SNPs. OUTCOMES & MEASUREMENTS: Vascular outcomes tested were blood pressure, coronary artery disease, carotid intima-media thickness, pulse wave velocity, retinal venular caliber, and brain white matter lesions. Kidney outcomes were estimated glomerular filtration rate and albuminuria. RESULTS: In general, we found that kidney disease variants were not associated with vascular phenotypes (127 of 133 tests were nonsignificant). The one exception was rs653178 near SH2B3 (SH2B adaptor protein 3), which showed direction-consistent association with systolic (P = 9.3 ×10-10) and diastolic (P = 1.6 ×10-14) blood pressure and coronary artery disease (P = 2.2 ×10-6), all previously reported. Similarly, the 64 SNPs associated with vascular phenotypes were not associated with kidney phenotypes (187 of 192 tests were nonsignificant), with the exception of 2 high-correlated SNPs at the SH2B3 locus (P = 1.06 ×10-07 and P = 7.05 ×10-08). LIMITATIONS: The combined effect size of the SNPs for kidney and vascular outcomes may be too low to detect shared genetic associations. CONCLUSIONS: Overall, although we confirmed one locus (SH2B3) as associated with both kidney and cardiovascular disease, our primary findings suggest that there is little overlap between kidney and cardiovascular disease risk variants in the overall population. The reciprocal risks of kidney and cardiovascular disease may not be genetically mediated, but rather a function of the disease milieu itself.
  American Journal of Kidney Diseases 03/2013; · 5.43 Impact Factor