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Framingham Center, MA, United States

Velagaleti R.S.,The NHLBIs Framingham Heart Study | O'Donnell C.J.,U.S. National Institutes of Health | O'Donnell C.J.,Massachusetts General Hospital
Heart Failure Clinics | Year: 2010

Cardiovascular disease is the leading cause of death in men and women, and heart failure (HF) is associated with high rates of morbidity and mortality. Most common forms of HF are non-mendelian and the evidence for heritability is modest. Study of the genetic susceptibility to HF has been limited to patients with rare familial forms of HF and candidate gene association studies in patients with distinct subtypes of HF. However, with the completion of the human genome project and the development of the HapMap template, new large-scale genome-wide association studies are possible. This article reviews the status of these and other important developments in genomics, in particular genome-wide sequencing, and other "omics". Source


Chen M.-H.,Boston University | Liu X.,Boston University | Wei F.,University of West Georgia | Larson M.G.,Boston University | And 5 more authors.
Genetic Epidemiology | Year: 2011

Genome-wide association studies (GWAS) have been frequently conducted on general or isolated populations with related individuals. However, there is a lack of consensus on which strategy is most appropriate for analyzing dichotomous phenotypes in general pedigrees. Using simulation studies, we compared several strategies including generalized estimating equations (GEE) strategies with various working correlation structures, generalized linear mixed model (GLMM), and a variance component strategy (denoted LMEBIN) that treats dichotomous outcomes as continuous with special attentions to their performance with rare variants, rare diseases, and small sample sizes. In our simulations, when the sample size is not small, for type I error, only GEE and LMEBIN maintain nominal type I error in most cases with exceptions for GEE with very rare disease and genetic variants. GEE and LMEBIN have similar statistical power and slightly outperform GLMM when the prevalence is low. In terms of computational efficiency, GEE with sandwich variance estimator outperforms GLMM and LMEBIN. We apply the strategies to GWAS of gout in the Framingham Heart Study. Based on our results, we would recommend using GEE ind-san in the GWAS for common variants and GEE ind-fij or LMEBIN for rare variants for GWAS of dichotomous outcomes with general pedigrees. © 2011 Wiley Periodicals, Inc. Source


Chen M.-H.,Boston University | Larson M.G.,The NHLBIs Framingham Heart Study | Larson M.G.,Boston University | Hsu Y.-H.,Harvard University | And 6 more authors.
BMC Genetics | Year: 2010

Background: Genome-wide association (GWA) studies that use population-based association approaches may identify spurious associations in the presence of population admixture. In this paper, we propose a novel three-stage approach that is computationally efficient and robust to population admixture and more powerful than the family-based association test (FBAT) for GWA studies with family data.We propose a three-stage approach for GWA studies with family data. The first stage is to perform linear regression ignoring phenotypic correlations among family members. SNPs with a first stage p-value below a liberal cut-off (e.g. 0.1) are then analyzed in the second stage that employs a linear mixed effects (LME) model that accounts for within family correlations. Next, SNPs that reach genome-wide significance (e.g. 10-6for 34,625 genotyped SNPs in this paper) are analyzed in the third stage using FBAT, with correction of multiple testing only for SNPs that enter the third stage. Simulations are performed to evaluate type I error and power of the proposed method compared to LME adjusting for 10 principal components (PC) of the genotype data. We also apply the three-stage approach to the GWA analyses of uric acid in Framingham Heart Study's SNP Health Association Resource (SHARe) project.Results: Our simulations show that whether or not population admixture is present, the three-stage approach has no inflated type I error. In terms of power, using LME adjusting PC is only slightly more powerful than the three-stage approach. When applied to the GWA analyses of uric acid in the SHARe project of FHS, the three-stage approach successfully identified and confirmed three SNPs previously reported as genome-wide significant signals.Conclusions: For GWA analyses of quantitative traits with family data, our three-stage approach provides another appealing solution to population admixture, in addition to LME adjusting for genetic PC. © 2010 Chen et al; licensee BioMed Central Ltd. Source


Marioni R.E.,University of Edinburgh | Marioni R.E.,University of Queensland | Shah S.,University of Queensland | McRae A.F.,University of Queensland | And 35 more authors.
Genome Biology | Year: 2015

Background: DNA methylation levels change with age. Recent studies have identified biomarkers of chronological age based on DNA methylation levels. It is not yet known whether DNA methylation age captures aspects of biological age. Results: Here we test whether differences between people's chronological ages and estimated ages, DNA methylation age, predict all-cause mortality in later life. The difference between DNA methylation age and chronological age ({increment}age) was calculated in four longitudinal cohorts of older people. Meta-analysis of proportional hazards models from the four cohorts was used to determine the association between {increment}age and mortality. A 5-year higher {increment}age is associated with a 21% higher mortality risk, adjusting for age and sex. After further adjustments for childhood IQ, education, social class, hypertension, diabetes, cardiovascular disease, and APOE e4 status, there is a 16% increased mortality risk for those with a 5-year higher {increment}age. A pedigree-based heritability analysis of {increment}age was conducted in a separate cohort. The heritability of {increment}age was 0.43. Conclusions: DNA methylation-derived measures of accelerated aging are heritable traits that predict mortality independently of health status, lifestyle factors, and known genetic factors. © 2015 Marioni et al.; licensee BioMed Central. Source

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