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Framingham Center, MA, United States

Minelli C.,European Academy Bozen Bolzano EURAC | De Grandi A.,European Academy Bozen Bolzano EURAC | Weichenberger C.X.,European Academy Bozen Bolzano EURAC | Gogele M.,European Academy Bozen Bolzano EURAC | And 23 more authors.
Genetic Epidemiology | Year: 2013

Biological plausibility and other prior information could help select genome-wide association (GWA) findings for further follow-up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts' opinions and empirical evidence to estimate the relative importance of 15 types of information at the single-nucleotide polymorphism (SNP) and gene levels. Opinions were elicited from 10 experts using a two-round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNPs established as being associated with seven disease traits through GWA meta-analysis and independent replication, with the corresponding frequency in a randomly selected set of SNPs. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta-analysis or more than one study as conferring the highest relative probability of true association, whereas previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, although location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research. © 2013 WILEY PERIODICALS, INC. Source

Fox C.S.,Lung and Blood Institutes Framingham Heart Study | Fox C.S.,Center for Population Studies | Fox C.S.,Harvard University | Woodward M.,University of Sydney | And 17 more authors.
The Lancet | Year: 2012

Background Chronic kidney disease is characterised by low estimated glomerular fi ltration rate (eGFR) and high albuminuria, and is associated with adverse outcomes. Whether these risks are modifi ed by diabetes is unknown. Methods We did a meta-analysis of studies selected according to Chronic Kidney Disease Prognosis Consortium criteria. Data transfer and analyses were done between March, 2011, and June, 2012. We used Cox proportional hazards models to estimate the hazard ratios (HR) of mortality and end-stage renal disease (ESRD) associated with eGFR and albuminuria in individuals with and without diabetes. Findings We analysed data for 1 024 977 participants (128 505 with diabetes) from 30 general population and highrisk cardiovascular cohorts and 13 chronic kidney disease cohorts. In the combined general population and highrisk cohorts with data for all-cause mortality, 75 306 deaths occurred during a mean follow-up of 8•5 years (SD 5•0). In the 23 studies with data for cardiovascular mortality, 21 237 deaths occurred from cardiovascular disease during a mean follow-up of 9•2 years (SD 4•9). In the general and high-risk cohorts, mortality risks were 1•2-1•9 times higher for participants with diabetes than for those without diabetes across the ranges of eGFR and albumin-tocreatinine ratio (ACR). With fi xed eGFR and ACR reference points in the diabetes and no diabetes groups, HR of mortality outcomes according to lower eGFR and higher ACR were much the same in participants with and without diabetes (eg, for all-cause mortality at eGFR 45 mL/min per 1•73 m2 [vs 95 mL/min per 1•73 m2], HR 1•35; 95% CI 1•18-1•55; vs 1•33; 1•19-1•48 and at ACR 30 mg/g [vs 5 mg/g], 1•50; 1•35-1•65 vs 1•52; 1•38-1•67). The overall interactions were not signifi cant. We identifi ed much the same fi ndings for ESRD in the chronic kidney disease cohorts. Interpretation Despite higher risks for mortality and ESRD in diabetes, the relative risks of these outcomes by eGFR and ACR are much the same irrespective of the presence or absence of diabetes, emphasising the importance of kidney disease as a predictor of clinical outcomes. Funding US National Kidney Foundation. Source

Thompson J.R.,University of Leicester | Gogele M.,European Academy Bozen Bolzano EURAC | Weichenberger C.X.,European Academy Bozen Bolzano EURAC | Modenese M.,European Academy Bozen Bolzano EURAC | And 23 more authors.
Genetic Epidemiology | Year: 2013

Prioritization is the process whereby a set of possible candidate genes or SNPs is ranked so that the most promising can be taken forward into further studies. In a genome-wide association study, prioritization is usually based on the P-values alone, but researchers sometimes take account of external annotation information about the SNPs such as whether the SNP lies close to a good candidate gene. Using external information in this way is inherently subjective and is often not formalized, making the analysis difficult to reproduce. Building on previous work that has identified 14 important types of external information, we present an approximate Bayesian analysis that produces an estimate of the probability of association. The calculation combines four sources of information: the genome-wide data, SNP information derived from bioinformatics databases, empirical SNP weights, and the researchers' subjective prior opinions. The calculation is fast enough that it can be applied to millions of SNPS and although it does rely on subjective judgments, those judgments are made explicit so that the final SNP selection can be reproduced. We show that the resulting probability of association is intuitively more appealing than the P-value because it is easier to interpret and it makes allowance for the power of the study. We illustrate the use of the probability of association for SNP prioritization by applying it to a meta-analysis of kidney function genome-wide association studies and demonstrate that SNP selection performs better using the probability of association compared with P-values alone. © 2012 WILEY PERIODICALS, INC. Source

O'Seaghdha C.M.,Lung and Blood Institutes Framingham Heart Study | O'Seaghdha C.M.,Center for Population Studies | O'Seaghdha C.M.,Harvard University | Hwang S.-J.,Lung and Blood Institutes Framingham Heart Study | And 8 more authors.
Journal of the American Society of Nephrology | Year: 2013

Whether novel biomarkers improve the assessment of incident kidney disease and related adverse outcomes remains to be tested in longitudinal observational studies. We tested 14 urinary biomarkers for association with incident kidney, cardiovascular, and mortality outcomes in 2948 Framingham Heart Study participants. Baseline examinations were performed between 1995 and 1998; mean follow-up was 10.1 years for renal outcomes and 11.2 years for survival analyses. Primary outcomes were incident CKD, incident albuminuria, incident cardiovascular disease, and all-cause mortality. Secondary analyses assessed incident congestive heart failure (CHF) and mortality with coexistent kidney disease. Biomarkers were tested for association with renal end points using logistic regression and incident cardiovascular and mortality outcomes in proportional hazards models; α1-microglobulin, Kim-1, and TFF-3 predicted all-cause mortality (hazard ratio per SD increase in log-transformed biomarker [HR] range, 1.15 to 1.21; 95% confidence interval [CI] range, 1.04 to 1.34; P values=0.007 to <0.001), whereas α1-microglobulin, β2- microglobulin, KIM-1, and TFF-3 associated with death with coexistent kidney disease (HR range, 1.72-2.25; 95% CI, 1.17 to 3.24; P values<0.01). KIM-1 also associated with the risk of incident CHF (HR, 1.32; 95% CI, 1.07 to 1.63; P=0.008). CTGF associated nominally with CKD (HR, 0.83; 95% CI, 0.71 to 0.98; P=0.03), but no other biomarkers associated with incident CKD or albuminuria. Addition of α1-microglobulin and TFF-3 resulted in a nonsignificant net reclassification index (NRI) of 3% for all-cause mortality beyond clinical risk factors. In conclusion, components of a panel of 14 subclinical biomarkers of kidney injury were associated with important clinical outcomes and merit additional investigation. Copyright © 2013 by the American Society of Nephrology. Source

Thanassoulis G.,McGill University | Thanassoulis G.,U.S. National Institutes of Health | Thanassoulis G.,Boston University | Peloso G.M.,U.S. National Institutes of Health | And 19 more authors.
Circulation: Cardiovascular Genetics | Year: 2012

Background-Limited data exist regarding the use of a genetic risk score (GRS) for predicting risk of incident cardiovascular disease (CVD) in US-based samples. Methods and Results-By using findings from recent genome-wide association studies, we constructed GRSs composed of 13 genetic variants associated with myocardial infarction or other manifestations of coronary heart disease (CHD) and 102 genetic variants associated with CHD or its major risk factors. We also updated the 13 single-nucleotide polymorphism (SNP) GRSs with 16 SNPs recently discovered by genome-wide association studies. We estimated the association, discrimination, and risk reclassification of each GRS for incident cardiovascular events and prevalent coronary artery calcium (CAC). In analyses adjusted for age, sex, CVD risk factors, and parental history of CVD, the 13 SNP GRSs were significantly associated with incident hard CHD (hazard ratio, 1.07; 95% CI, 1.00 -1.15; P=0.04), CVD (hazard ratio per allele, 1.05; 95% CI, 1.01-1.09; P=0.03), and high CAC (defined as >75th age- and sex-specific percentile; odds ratio per allele, 1.18; 95% CI, 1.11-1.26; P=3.4-10-7). The GRS did not improve discrimination for incident CHD or CVD but led to modest improvements in risk reclassification. However, significant improvements in discrimination and risk reclassification were observed for the prediction of high CAC. The addition of 16 newly discovered SNPs to the 13 SNP GRSs did not significantly modify these results. Conclusions-A GRS composed of 13 SNPs associated with coronary disease is an independent predictor of cardiovascular events and of high CAC, modestly improves risk reclassification for incident CHD, and significantly improves discrimination for high CAC. The addition of recently discovered SNPs did not significantly improve the performance of this GRS. © 2011 American Heart Association, Inc. Source

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