Publications by authors named "Larry D Atwood"

Genetic loci for body mass index (BMI) in adolescence and young adulthood, a period of high risk for weight gain, are understudied, yet may yield important insight into the etiology of obesity and early intervention. To identify novel genetic loci and examine the influence of known loci on BMI during this critical time period in late adolescence and early adulthood, we performed a two-stage meta-analysis using 14 genome-wide association studies in populations of European ancestry with data on BMI between ages 16 and 25 in up to 29 880 individuals. We identified seven independent loci (P < 5.

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There is evidence across several species for genetic control of phenotypic variation of complex traits, such that the variance among phenotypes is genotype dependent. Understanding genetic control of variability is important in evolutionary biology, agricultural selection programmes and human medicine, yet for complex traits, no individual genetic variants associated with variance, as opposed to the mean, have been identified. Here we perform a meta-analysis of genome-wide association studies of phenotypic variation using ∼170,000 samples on height and body mass index (BMI) in human populations.

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Myopia is a complex genetic disorder and a common cause of visual impairment among working age adults. Genome-wide association studies have identified susceptibility loci on chromosomes 15q14 and 15q25 in Caucasian populations of European ancestry. Here, we present a confirmation and meta-analysis study in which we assessed whether these two loci are also associated with myopia in other populations.

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Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10(-8) to P = 10(-190)). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions.

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Objective: White matter hyperintensities (WMHs) detectable by magnetic resonance imaging are part of the spectrum of vascular injury associated with aging of the brain and are thought to reflect ischemic damage to the small deep cerebral vessels. WMHs are associated with an increased risk of cognitive and motor dysfunction, dementia, depression, and stroke. Despite a significant heritability, few genetic loci influencing WMH burden have been identified.

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Height is a classic complex trait with common variants in a growing list of genes known to contribute to the phenotype. Using a genecentric genotyping array targeted toward cardiovascular-related loci, comprising 49,320 SNPs across approximately 2000 loci, we evaluated the association of common and uncommon SNPs with adult height in 114,223 individuals from 47 studies and six ethnicities. A total of 64 loci contained a SNP associated with height at array-wide significance (p < 2.

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Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals.

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Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects).

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Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait.

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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.

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Extensive efforts have been aimed at understanding the genetic underpinnings of complex diseases that affect humans. Numerous genome-wide association studies have assessed the association of genes with human disease, including the Framingham Heart Study (FHS), which genotyped 550,000 SNPs in 9,000 participants. The success of such efforts requires high rates of consent by participants, which is dependent on ethical oversight, communications, and trust between research participants and investigators.

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Background And Purpose: Previous studies examining genetic associations with MRI-defined brain infarct have yielded inconsistent findings. We investigated genetic variation underlying covert MRI infarct in persons without histories of transient ischemic attack or stroke. We performed meta-analysis of genome-wide association studies of white participants in 6 studies comprising the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.

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Genetic Analysis Workshop 16 (GAW16) Problem 2 presented data from the Framingham Heart Study (FHS), an observational, prospective study of risk factors for cardiovascular disease begun in 1948. Data have been collected in three generations of family participants in the study and the data presented for GAW16 included phenotype data from all three generations, with four examinations of data collected repeatedly for the first two generations. The trait data consisted of information on blood pressure, hypertension treatment, lipid levels, diabetes and blood glucose, smoking, alcohol consumed, weight, and coronary heart disease incidence.

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Heritability and genetic and environmental correlations of total and regional brain volumes were estimated from a large, generally healthy, community-based sample, to determine if there are common elements to the genetic influence of brain volumes and white matter hyperintensity (WMH) volume. There were 1538 Framingham Heart Study participants with brain volume measures from quantitative magnetic resonance imaging who were free of stroke and other neurologic disorders that might influence brain volumes and who were members of families with at least 2 Framingham Heart Study participants. Heritability was estimated using variance component methodology and adjusting for the components of the Framingham stroke risk profile.

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Central abdominal fat is a strong risk factor for diabetes and cardiovascular disease. To identify common variants influencing central abdominal fat, we conducted a two-stage genome-wide association analysis for waist circumference (WC). In total, three loci reached genome-wide significance.

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This chapter provides an introduction to the Framingham Heart Study and the genetic research related to cardiovascular diseases conducted in this unique population. It briefly describes the origins of the study, the risk factors that contribute to heart disease, and the approaches taken to discover the genetic basis of some of these risk factors. The genetic architecture of several biological risk factors has been explained using family studies, segregation analysis, heritability, and phenotypic and genetic correlations.

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Background: Cardiovascular disease (CVD) and its most common manifestations--including coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF)--are major causes of morbidity and mortality. In many industrialized countries, cardiovascular disease (CVD) claims more lives each year than any other disease. Heart disease and stroke are the first and third leading causes of death in the United States.

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Background: Obesity is related to multiple cardiovascular disease (CVD) risk factors as well as CVD and has a strong familial component. We tested for association between SNPs on the Affymetrix 100K SNP GeneChip and measures of adiposity in the Framingham Heart Study.

Methods: A total of 1341 Framingham Heart Study participants in 310 families genotyped with the Affymetrix 100K SNP GeneChip had adiposity traits measured over 30 years of follow up.

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Background: Brain magnetic resonance imaging (MRI) and cognitive tests can identify heritable endophenotypes associated with an increased risk of developing stroke, dementia and Alzheimer's disease (AD). We conducted a genome-wide association (GWA) and linkage analysis exploring the genetic basis of these endophenotypes in a community-based sample.

Methods: A total of 705 stroke- and dementia-free Framingham participants (age 62 +9 yrs, 50% male) who underwent volumetric brain MRI and cognitive testing (1999-2002) were genotyped.

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Background: The Framingham Heart Study (FHS), founded in 1948 to examine the epidemiology of cardiovascular disease, is among the most comprehensively characterized multi-generational studies in the world. Many collected phenotypes have substantial genetic contributors; yet most genetic determinants remain to be identified. Using single nucleotide polymorphisms (SNPs) from a 100K genome-wide scan, we examine the associations of common polymorphisms with phenotypic variation in this community-based cohort and provide a full-disclosure, web-based resource of results for future replication studies.

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Although previous genome scans have searched for quantitative-trait loci (QTLs) influencing variation in blood pressure (BP), few have investigated the rate of change in BP over time as a phenotype. Here, we compare results from genomewide scans to localize QTLs for systolic, diastolic, and mean arterial BPs (SBP, DBP, and MBP, respectively) and for rates of change in systolic, diastolic, and mean arterial BPs (rSBP, rDBP, and rMBP, respectively), with use of the longitudinal data collected about Mexican Americans of the San Antonio Family Heart Study (SAFHS). Significant evidence of linkage was found for rSBP (LOD 4.

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Background: The blood pressure (BP) and heart rate responses to exercise treadmill testing predict incidence of cardiovascular disease, but the genetic determinants of hemodynamic and chronotropic responses to exercise are largely unknown.

Methods And Results: We assessed systolic BP, diastolic BP, and heart rate during the second stage of the Bruce protocol and at the third minute of recovery in 2982 Framingham Offspring participants (mean age 43 years; 53% women). With use of residuals from multivariable models adjusted for clinical correlates of exercise treadmill testing responses, we estimated the heritability (variance-components methods), genetic linkage (multipoint quantitative trait analyses), and association with 235 single-nucleotide polymorphisms in 14 candidate genes selected a priori from neurohormonal pathways for their potential role in exercise treadmill testing responses.

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Background: Arterial stiffness is reported in numerous family studies to be heritable. Linkage analysis has identified genomic regions that likely harbor genes contributing to its phenotypic expression. We sought to identify loci contributing to arterial stiffness in a large group of African-American hypertensive families.

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For nearly 60 years, the Framingham Heart Study has examined the natural history, risk factors, and prognosis of cardiovascular, lung, and other diseases. Recruitment of the Original Cohort began in 1948. Twenty-three years later, 3,548 children of the Original Cohort, along with 1,576 of their spouses, enrolled in the Offspring Cohort.

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Background: Studies of model-based linkage analysis show that trait or marker model misspecification leads to decreasing power or increasing Type I error rate. An increase in Type I error rate is seen when marker related parameters (e.g.

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