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. Prior investigations have reported several single gene variants associated with CHD, stroke, HF, and AF. We report a community-based genome-wide association study of major CVD outcomes.In 1345 Framingham Heart Study participants from the largest 310 pedigrees , we analyzed associations of 70,987 qualifying SNPs (Affymetrix 100K GeneChip) to four major CVD outcomes: major atherosclerotic CVD , major CHD , AF (n = 151), and HF (n = 73). Participants free of the condition at entry were included in proportional hazards models. We analyzed model-based deviance residuals using generalized estimating equations to test associations between SNP genotypes and traits in additive genetic models restricted to autosomal SNPs with minor allele frequency ≥0.10, genotype call rate ≥0.80, and Hardy-Weinberg equilibrium p-value ≥ 0.001.-5. The lowest p-values for each CVD trait were as follows: major CVD, rs499818, p = 6.6 × 10-6; major CHD, rs2549513, p = 9.7 × 10-6; AF, rs958546, p = 4.8 × 10-6; HF: rs740363, p = 8.8 × 10-6. Of note, we found associations of a 13 Kb region on chromosome 9p21 with major CVD (p 1.7 – 1.9 × 10-5) and major CHD (p 2.5 – 3.5 × 10-4) that confirm associations with CHD in two recently reported genome-wide association studies. Also, rs10501920 in CNTN5 was associated with AF (p = 9.4 × 10-6) and HF (p = 1.2 × 10-4). Complete results for these phenotypes can be found at the dbgap website http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.Six associations yielded p < 10No association attained genome-wide significance, but several intriguing findings emerged. Notably, we replicated associations of chromosome 9p21 with major CVD. Additional studies are needed to validate these results. Finding genetic variants associated with CVD may point to novel disease pathways and identify potential targeted preventive therapies. Cardiovascular disease (CVD) and its most common manifestations, coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF) are major causes of morbidity and mortality. In many industrialized countries CVD claims more lives each year than any other disease. In the United States, for example, heart disease and stroke are the first and third leading causes of death . At age Prior Framingham Heart Study research points to strong familial patterns of CVD, HF, and AF -8 and suWe report results of a genome-wide association study of four CVD outcomes in community-based Framingham Heart Study participants who were enrolled without regard to disease status. Analysis for each specific outcome was restricted to those free of the condition at baseline. We also provide association results for previously reported candidate genes and candidate regions for these CVD outcomes.In 1948, 5209 men and women from Framingham, Massachusetts, who were between 28 and 62 years of age, were recruited to participate in the Framingham Heart Study . PeriodiAll participants in both cohorts who were free of a specific condition at enrollment were analyzed for onset of that endpoint during follow up through the end of 2004. All suspected CVD events were reviewed and adjudicated by a panel of three Framingham physician investigators after review of all available Framingham Heart Study examination records, hospitalization records, and physician notes, using previously published criteria .For these analyses, we considered four groups of events: major CHD events included recognized myocardial infarction, coronary insufficiency, and death due to CHD; major atherosclerotic CVD events included major CHD plus atherothrombotic stroke; the remaining groups were HF and AF. Myocardial infarction was diagnosed by the presence of 2 out of 3 clinical criteria: new diagnostic Q-waves on ECG, prolonged ischemic chest discomfort, and elevation of serum biomarkers of myocardial necrosis. CHD death was established upon review of all available records, if the cause of death was probably CHD and no other cause could be ascribed.Atherothrombotic brain infarction was defined as a non-embolic acute-onset focal neurological deficit of vascular etiology that persisted for more than 24 hours or an ischemic infarct was documented at autopsy.History of interim hospitalizations and symptoms of HF were obtained at each clinic examination; outside medical records were evaluated for participants who did not attend an examination. Three physicians reviewed all suspected interim events using Framingham Heart Study clinic notes, external physician reports and hospitalization records. HF was diagnosed when at least two major criteria were present, or one major and two minor criteria. Major criteria were paroxysmal nocturnal dyspnea, pulmonary rales, distended jugular veins, enlarging heart size on chest radiography, acute pulmonary edema, hepatojugular reflux, third heart sound, jugular venous pressure of 16 cm or greater, weight loss of 4.5 kg or greater in response to diuresis, pulmonary edema, visceral congestion, or cardiomegaly on autopsy. Minor criteria counted only if not attributed to another disease. Minor criteria were bilateral ankle edema, nocturnal cough, shortness of breath on ordinary exertion, hepatomegaly, pleural effusion, vital capacity decreased by one third from previous maximum, and heart rate ≥120 beats/min.AF was diagnosed when, upon review by a study cardiologist, AF or atrial flutter was present on an ECG obtained from a routine Framingham clinic examination or from a hospital or physician record. HF was defined on the basis of review of medical records and the finding of concurrent presence of two major or one major plus two minor criteria .The accompanying Overview  providesa priori not to perform linkage analysis on outcomes residuals. The distribution of observed p values for the four CVD outcomes was compared to that which would be expected under the null hypothesis of no genetic associations with outcomes.Proportional-hazards models were used to analyze time to each endpoint, stratified by cohort, using covariate values obtained at enrollment. Models were adjusted for (i) sex and age, or (ii) sex, age and multiple covariates. For CVD and CHD, covariates included smoking, diabetes, systolic BP, anti-hypertensive treatment and total cholesterol; for HF, covariates were smoking, diabetes, systolic BP, anti-hypertensive treatment and body mass index; for AF, covariates were diabetes, systolic BP, anti-hypertensive therapy and valve disease. Deviance residuals estimated from each model were standardized  to form the phenotypes analyzed with genetic models. For genotype-phenotype association analyses, we assumed an additive-allele model of inheritance and we conducted association tests using regression models with generalized estimating equations (GEE), as well as family-based association testing using FBAT. Due to relatively small numbers of outcome events and non-normality of the deviance residuals, we decided GEE and FBAT additive genetic effect models also were run for SNPs in or near candidate genes for each of the CVD outcomes. Candidate genes were selected after separate literature searches for each outcome. All SNPs across the interval extending from 200 Kb proximal to the start to 200 kb beyond the end of each gene were eligible if the minor allele frequency was ≥0.1, the genotype call rate was ≥0.8, and the Hardy-Weinberg equilibrium p value was ≥0.001.Four primary phenotypes were analyzed: major atherosclerotic CVD n = 142), major CHD (n = 118), HF (n = 73), and AF (n = 151). Covariates for each outcome are listed in Table 42, major-5 and another 31 with p values < 10-4  and HF (p = 1.2 × 10-4). Three SNPs near PHACTR1 were associated with major CVD  and one of these was associated with major CHD . Among GEE results for HF was rs939698 (p = 3.6 × 10-4) in RYR2, which has been implicated in arrhythmogenic right ventricular dysplasia/cardiomyopathy  in our analyses using GEE or FBAT additive genetic models. With dramatic declines in the cost of high throughput genotyping, selective genotyping of SNPs with suggestive evidence of association can be considered. Two-stage approaches – genome-wide association followed by selective genotyping – have been adopted as a practical and efficient strategy for pursuing initial genome-wide results [We provide results of genome-wide association for 4 CVD outcomes of great public health impact: major CVD, major CHD, AF, and HF. No associations attained genome-wide significance [4.4 × 10 results ,26.RYR2  with HF. The ryanodine receptor has been implicated in arrhythmogenic right ventricular dysplasia/cardiomyopathy [Results of GEE and FBAT associations pointed to few candidate genes of obvious interest for any CVD outcomes. One intriguing result was the association of myopathy ,27, a ra2 above 0.80) and it is uncertain whether the concordant results represent statistically correlated chance findings or indicate regions of heightened interest.The lowest p values we identified may be purely by chance. The number of events (maximum of 142 for major CVD) was small to detect association, but would be sufficient to detect a SNP with high minor allele frequency in linkage disequilibrium with a causal variant that contributed high risk. This was the case for a genome-wide association study of age-related macular degeneration – only 96 cases and 50 controls were sufficient to identify genome-wide association with complement factor H . SometimALOX5AP , GJA4 , MEF2A , and PCSK9  in relation to CHD risk. In contrast, candidate gene results for HF and AF provided little evidence of replication of previously reported associations. Null results of these associations may be due in part to poor coverage of the candidates by the SNPs on the 100K chip and the modest number of events available for analysis. Our results can be compared with other genome-wide associations of similar phenotypes. We observed strong association of major CVD with 3 SNPs in the region of chromosome 9 that was recently reported to be associated with MI and CHD in multiple samples [Candidate gene results for the 4 CVD outcomes provided suggestive confirmation of prior associations reported for  samples ,23. This-3, and at significance level 10-6 we had 84% power for QTL heritability 2% [This investigation has several limitations. This study used CVD cases that were identified through careful surveillance of a community-based sample with multigenerational participation. Recruitment of original and offspring cohort participants began long before DNA collection, which occurred in recent years. Thus, most CVD cases were prevalent at the time of DNA collection. For CVD outcomes (such as these) with substantial mortality risk, a survival bias may have been introduced by this study design; individuals with early CVD events had to survive and attend a later clinic examination at which DNA was collected. Another limitation is the modest number of events included in analyses, in particular for HF, where only 73 events were available for analysis. For continuous traits, we had 78% power to detect a SNP with QTL heritability of 1% at significance level 10ility 2% . In the These association results for major CVD outcomes extend experience with genome-wide association studies. Replication studies are needed and will be used to guide future genotyping and resequencing efforts. Finding genetic variants associated with CVD may facilitate the identification of high risk patients and aid in identifying targeted future approaches to prevention and treatment of CVD.AF = atrial fibrillation; CHD = coronary heart disease; HF = heart failure; CVD = cardiovascular disease; FBAT = family based association test; GEE = generalized estimating equation.The authors declare that they have no competing interests.MGL participated in study design, data collection, statistical analysis, interpretation of results, and manuscript preparation. LDA contributed to the design and analysis. EJB contributed to the design of analyses, acquisition and interpretation of data, provided critical manuscript revisions. LAC contributed to the design and analysis. RBD contributed to the design and analysis. CSF contributed to data acquisition and approved the final version of the manuscript. DRG contributed to project design and data acquisition. CYG participated in statistical analysis. NLHC contributed to the design and analysis. SJH participated in statistical analysis and manuscript preparation. JMM participated in acquisition of data, interpretation, revising & approval of final manuscript. CNC participated in the analysis and interpretation of data and critical review of the manuscript. CJOD participated in the analysis and interpretation of data and critical review of the manuscript. SS participated in data collection, definition of phenotypes and review of the manuscript. RSV participated in data collection, interpretation of analyses and review of the manuscript. TJW contributed to data acquisition data, interpretation of data analysis, and revision of the manuscript for important intellectual content. PAW participated in data collection and project conception and design. DL contributed to project conception and design, interpretation of results, and drafting the manuscript.All authors approved the final manuscript.


A response to Parienti JJ, Verdon R and Massari V: Methodological standards in non-inferiority AIDS trials: moving from adherence to compliance. BMC Med Res Meth 2006, 6:46 Given tThe main purpose of the 2NN study was to compare four different treatment strategies[et al. present in Table 2 the results of two additional equivalence analyses using the 2NN data; between the off-label use of NVP once daily and EFV, and between the experimental regimen NVP+EFV and EFV. Not only is this against the intention of the 2NN investigators, these analyses only have around 60% power to demonstrate a difference in efficacy that is smaller than 10% (the a-priori set limit).Parienti et al. present an equivalence analysis for an on-treatment population (OT) to make up for the absence of this analysis in the original report. We agree with the authors that the use of an OT analysis in an equivalence test has the smallest risk of dilution of the results. However, because the 2NN was an open-label study with change of treatment as one of the components of treatment failure (the primary outcome), an OT population could severely bias the outcome. Unfortunately, the data that the authors use to perform this analysis are not from an OT population but from the population that completed the 48 weeks of follow-up. This population is a mix of those who are still on their allocated treatment and those who are not. Deducing the number of patients changing allocated treatment (and arriving at the OT-population) from the reasons for treatment failure is misplaced due to the 'competing risks' inherent to the composite primary outcome[For the main comparison of the 2NN study Parienti y outcome. The difWe do agree with the authors that our wording in the conclusion of the abstract and the body of the manuscript, is ambiguous and might be misinterpreted. For the main comparison , we report throughout the discussion that the difference between NVP twice daily and EFV was not significantly different ('similar') in a head-to-head comparison, but that equivalence of the two strategies could not be demonstrated. It would have been clearer to refrain from the term 'similar' in this respect and stick to the terminology that fits the equivalence design of the study. We used this wording to incorporate the conventional superiority analysis and the clinical interpretation of the results. The relatively small differences in primary and secondary efficacy endpoint of the study  and the absence of large differences in the incidence of adverse events make that we see nevirapine and efavirenz as indeed interchangeable drugs, despite the lack of formal equivalence in the primary efficacy outcome.et al. report in their Table 2 that we used the same confusing terminology for the other equivalent analyses. This is not the case and does not do justice to the 2NN study. We never performed other equivalence analyses than for the primary comparison.Having said this, Parienti et al. In a non-inferiority study the main question is to assess whether a new drug is not worse off than the current standard of care. In this situation there is no room for the new drug to show superior activity. As a result, only the lower limit of the confidence interval around the difference between the drugs compared is of interest. In an equivalence study there is a-priory a situation of equipoise with respect to the efficacy of two drugs. Although the main aim is to test whether the two drugs have equivalent efficacy and can therefore be used interchangeably, there is room for each of the drugs to outperform the other. Therefore, both sides of the confidence interval are of interest. For this reason we chose for an equivalence study. NVP and EFV were widely used at the time the study was conducted. Without a previous randomized comparison between the two drugs, there was no formal golden standard against which non-inferiority should be tested, although in general EFV was believed to be more effective than NVP based on data from observational cohort studies.We would also like to make a more theoretical note. A non-inferiority design and an equivalence design are closely linked but the underlying idea is fundamentally different and goes beyond the type of confidence interval used as described by Parienti et al could have contributed more to this debate.A critical appraisal of published results from studies on the efficacy of antiretroviral drugs is most welcome since it feeds the scientific debate and points out over-interpretation of the results by investigators or drug companies. However, misrepresentation of results and conclusions of the original studies in this debate will undermine the credibility of these studies with HIV-researchers and clinical practitioners. An easy way to avoid this from happening is to contact the authors of the original studies to provide the appropriate details. If such a strategy would have been followed here, the review of Parienti 2NN: double non-nucleoside studyEFV: efavirenzNVP: nevirapineOT: on-treatmentAll authors have received honorarium for lectures and travel grants from different pharmaceutical companies, including Boehringer Ingelheim (the manufacturer of nevirapine) and Bristol-Myers Squibb (the manufacturer of efavirenz)FvL wrote the first draft and subsequent versions of the manuscript.FWW and JMA contributed to all versions of the manuscript.All authors approved the final version of the manuscript.The pre-publication history for this paper can be accessed here:


Saccharomyces cerevisiae cellular morphology using a very sensitive high-throughput imaging platform. We quantified 501 morphological parameters in over 50,000 yeast cells from a cross between two wild-type divergent backgrounds. Extensive morphological differences were found between these backgrounds. The genetic architecture of the traits was complex, with evidence of both epistasis and transgressive segregation. We mapped quantitative trait loci (QTL) for 67 traits and discovered 364 correlations between traits segregation and inheritance of gene expression levels. We validated one QTL by the replacement of a single base in the genome. This study illustrates the natural diversity and complexity of cellular traits among natural yeast strains and provides an ideal framework for a genetical genomics dissection of multiple traits. Our results did not overlap with results previously obtained from systematic deletion strains, showing that both approaches are necessary for the functional exploration of genomes.Functional genomics relies on two essential parameters: the sensitivity of phenotypic measures and the power to detect genomic perturbations that cause phenotypic variations. In model organisms, two types of perturbations are widely used. Artificial mutations can be introduced in virtually any gene and allow the systematic analysis of gene function via mutants fitness. Alternatively, natural genetic variations can be associated to particular phenotypes via genetic mapping. However, the access to genome manipulation and breeding provided by model organisms is sometimes counterbalanced by phenotyping limitations. Here we investigated the natural genetic diversity of  S. cerevisiae cells (baker's yeast) differs from one strain to another, but these differences were usually described qualitatively. We used a high-throughput imaging platform to study the morphology of yeast cells when they divide. Cells were stained with three fluorescent dyes so that their periphery, their DNA, and their actin could be recognized, and their images were analysed by a specialized software program. Numerous morphological differences were found between two distant strains of S. cerevisiae. By crossing these two strains, we performed quantitative genetics: several loci controlling morphological variations were found on the genome, and correlations were made between gene expression and morphology changes. Using bioinformatics, we showed that the results obtained do not overlap with previous results obtained from yeast cells in which specific genes are deleted. The study, therefore, illustrates how mutagenesis and the use of natural genetic variations provide complementary knowledge.A familiar face or a dog breed is easily recognized because morphology of individuals differs according to their genetic backgrounds. For single-cell organisms, morphology reduces to the shape and size of cellular features. Microbiologists noticed that the shape of  Yeast genetics has long been powered by the ease of conducting genome manipulation and mutagenic screens. These experiments are usually performed on a restricted panel of laboratory strain backgrounds that serve as standards. Variability between backgrounds is often viewed as a problem that must be minimized by using nearly isogenic strains whenever possible. As an alternative approach, several recent studies have used wild-type strains from divergent backgrounds to identify regulators of specific phenotypes such as high-temperature growth , sporulacis- and trans-regulatory variations and providing evidence for “master” regulators [In addition, exploring strain-to-strain variation in yeast or other model systems is essential to our understanding of the regulation of complex traits . For exagulators . Similargulators ,11. Sincgulators . FurtherFunctional annotations of genomes are highly dependent on the sensitivity and scale of phenotypic tests . ConsequWe have applied this phenotypic characterization to a cross between two wild-type divergent yeast strains, which had previously been used in the study of the segregation of gene expression traits . As descAMN1 gene. For simplicity, strains BY4716 and YEF1946 will, nonetheless, be referred to as BY and RM hereafter. Samples from nine independent cultures of each strain were characterized by triple-staining fluorescent microscopy and automated cell imaging as described previously [p < 0.001, 143 traits showed significant difference. A permutation test determined that only one trait was expected to differ by chance at this p-value. The morphological differences between the two strains were found at different stages of the cell cycle and reflected various cellular aspects  and strain YEF1946, isogenic to RM11-1a, which is a haploid derivative of a wine strain (kindly provided by E. Foss). The original RM11-1a could not be used because of its clumpy nature , which weviously . At leas aspects . After n aspects A and 1B; aspects C. No dif aspects D. Mother aspects E and 1F, aspects G, and ce aspects . Surpris aspects . However aspects . Thus, oIn a previous study, the segregation of gene expression levels was studied across 112 F1 segregants from BY × RM . We usedTo estimate the complexity of the genetic control, we looked for cases of transgressive segregation or epistasis. We applied tests previously adapted for multiple traits  and founp < 9.04 × 10−5 and p < 3.43 × 10−5, respectively. A permutation test determined that these p-values corresponded to FDR = 0.10 and FDR = 0.05, respectively. A total of seven distinct loci controlling specific morphological features were identified at FDR = 0.05 . Among 247 traits showing heritability greater than 50%, 104 and 29 could be correlated to the expression level of at least one gene at |R| > 0.565 and |R| > 0.62, respectively. A permutation test determined that these correlation values corresponded to FDR = 0.1 and FDR = 0.05, respectively. At FDR = 0.1, a total of 364 correlations involved the expression levels of 103 genes (FLO11 (YIR019C) and ECM34  were correlated to brightness differences of the cell wall. To look more systematically for such consistencies, we clustered hierarchically the 104 traits and 103 genes involved, and examined territories of the correlation map containing several gene/traits correlations (PET117 (YER058W), SAL1 (YNL083W), SCO1 (YBR037C), YNR036C, DBF20 (YPR111w), and YHR080C were correlated to nine traits measuring the position of DNA in the mother cell and in the bud after nuclear division. This suggested a link between DNA positioning and the GO terms “protein metabolism”  and “mitochondrion” . Since DNA positioning estimation can be affected by mitochondrial DNA staining, it is very likely that these variations in mitochondrial activities between BY and RM cells are associated with different regional distributions of mitochondrial DNA. Expression of 22 genes, including TOP2 (YNL088W) and MSH2 (YOL090W), were correlated to 18 traits also describing DNA positioning in mother cells and buds after nuclear division. These correlations linked these traits to many GO terms reflecting DNA metabolism, including “DNA-dependent DNA replication”  and “DNA repair” . This association suggests that differences in nuclear DNA metabolism drive differences in DNA positioning. Expression levels of SEH1 (YGL100W), YHR200W, HTA1 (YDR225W), GRD19 (YOR357C), and HHT2 (YNL031C) were correlated to ten traits describing the position of DNA during cellular division, the size of the bud neck, and the position of DNA in the mother cell after nuclear division. These traits were therefore associated to GO terms “chromatin assembly or disassembly” . This suggests that differences in DNA intracellular distribution probably result from differences in chromatin dynamics throughout nuclear division. Finally, expression levels of 23 genes, many of which being involved in pheromone response, were correlated to 39 traits describing the mother cell shape  and “response to pheromone” . This association strongly suggested that cell shape differences resulted from differences in the activation of the pheromone response pathway between the two genetic backgrounds. A direct explanation of this is given in the next section.In the study from Brem et al. , all 62 03 genes . We founelations . We founGPA1 (YHR005C) gene , and seven of these differed accordingly between BY and BY-gpa1I469S (p < 0.05)  can be due to a reduced statistical power when comparing strains , as compared to linkage test , or to additional undetected QTL with opposing effects.We then sought to identify polymorphisms responsible for morphological differences. We focused on 16 cell-elongation traits linked to a locus on Chromosome VIII that contained the 5C) gene . A previes in BY . Respons of GPA1 . Of the  < 0.05) . Values In a separate study, the cellular morphological alterations due to single gene disruptions were investigated in the context of the BY genetic background . For allS. cerevisiae backgrounds. The appearance of yeast is therefore polymorphic, as is the appearance of two unrelated human beings or animals. Out of curiosity, we asked colleagues to distinguish BY4716 from YEF1946 liquid cultures under the microscope . From their responses, it was obvious that no difference would have been characterized without the fluorescence staining and automated quantification that we used. We found significant QTL for only 27 of the 143 traits differing between the two backgrounds. Differences in actin distribution in buds, bud shape and size, early bud growth, thickness of the cell wall, or neck width, as well as most traits related to DNA distribution prior to nuclear division could not be correlated to genomic loci. This can result from the small heritability of some of the phenotypes, as mentioned above for parameters that can be affected by the abundance and distribution of mitochondria. It can also result from the high complexity of the genetic control, whereby many loci contribute to the phenotype, but their small individual effect prevents their detection. Conversely, 40 of the 67 traits for which QTL were found were not in the list of the significant differences between parental strains. This was the case for phenotype CCV115_C describing the variation of mother-cell axis ratio across the sample (mapped to Chromosome X), and phenotype DCV194_C describing signal heretogeneities in mother cell DNA (mapped to Chromosome XV 930000). There are several possible explanations for this: some of these linkage results might be false positives (which we would expect to be possible for one or two but certainly not 40 phenotypes), alleles acting in the opposite direction might shade their effects in the parental strains (which would be consistent with the extent of transgressive segregation), or simply because statistical power was sometimes higher during mapping .We found extensive cellular morphological variations between two wild-type Notably, only 12 out of the 67 traits for which significant linkage was found were mapped to more than one place of the genome. Considering the large extent of transgressive segregation and epistasis, it is very likely that other loci contribute to morphological variations, and detecting only one or two per trait is probably a statistical power limitation (<100 segregants).We did not test additional wild backgrounds, but one could very well apply a similar protocol to many divergent strains and compare their morphological distance to their genetic or transcriptomic divergence. Such a study may indicate whether morphological differences co-evolved with genetic or regulatory divergence, or if they were driven by other differences such as environmental conditions.FLO11, which is involved in cell-surface variation within yeast cultures [p-values between these loci and the corresponding morphological trait: 92 cases showed p < 0.002, which is an acceptable threshold to account for multiplicity when considering only 224 candidate loci instead of the entire genome. Although most of these cases reflected genetic linkages already identified in our genome scan, they suggested eight additional loci. This indirect mapping of QTL via the use of eQTL seemed therefore promising. However, we tried but failed to validate candidate genes at one of these loci by engineering and characterizing strains where alleles were replaced. We consider that further investigation of these eight loci needs to be done before declaring them as valid QTL. The approach is nonetheless likely to be helpful in studies combining eQTL and phenotypic mapping.One of the challenging aims of modern and future quantitative genetics is the simultaneous dissection of multiple traits. For example, clinical phenotypes are now collected systematically from cohorts of individuals, and molecular phenotypes such as biochemical dosage or gene expression profiling can be included in mapping strategies ,22. We scultures . In sevecultures , whereasGPA1 genotype (p = 0.15), whereas its coefficient of variation was mapped to GPA1, and this mapping was further validated by the gpa1-I469S strain (p = 0.01). This argues that the gpa1I469S polymorphism does not affect the shape of all cells but rather the fraction of cells that are elliptic. These observations imply that genetic variation can affect cell-to-cell variability of cellular traits, without necessarily influencing the mean trait itself. This is particularly important when considering genetic susceptibility to common human or animal phenotypes. If genotypes affect the distribution of a phenotype among “identical” cells of a tissue, such genotypes are likely to modulate the phenotype penetrance. It is therefore tempting to propose a nondeterministic view of genetic predisposition, whereby incomplete penetrance does not only result from environmental exposures but also from levels of cell-to-cell heterogeneity.Interestingly, several traits that we mapped were not direct measures of cellular features but coefficients of variation of such measures across the sample. In several cases, the measure itself  was not linked to the QTL controlling its coefficient of variation. For example, parameter C13_C, which measured the fitness of the mother cell for ellipse, was not mapped and was not correlated to the RAD50 (YNL250W) results in accumulation of large budded cells arrested or delayed in G2/M because of the failure to repair damaged DNA, but one can imagine that natural sequence variations in the gene might provoke more subtle alterations in the DNA repair system. Third, RM polymorphisms might provide gain-of-function alleles as compared to BY. In this case, the effects are likely to differ from the consequences of a null mutation. Finally, the QTL approach is far from exhaustive because it only interrogates genes containing functional polymorphisms between the backgrounds considered. We also note that in our case, although genome annotation was essential to characterize the effect of GPA1 alleles, data from systematic mutagenesis could not provide candidate QTL. The power of the candidate-gene approach for QTL mapping has been debated [We showed that the natural polymorphisms affecting yeast morphological traits do not preferentially reside in genes in which deletions affect these traits. This comparison of two alternative genomic approaches (systematic mutagenesis versus QTL mapping) was possible because both studies were performed on the same platform. We conclude that these two approaches are complementary for the functional study of genomes of model organisms. This is important since both approaches are widely used and heavily funded ,26. Ther debated , and a pgpa1I469S [Strains used were BY4716  and YEF1pa1I469S  and BYxR7 cells/ml in synthetic C medium at 30 °C. Cells were fixed with 3.7% formaldehyde and stained with rhodamine-phalloidine, fluorescein isothiocyanate-concanavalin A, and DAPI. Cells were observed using an Axioplan 2 microscope with 100× Plan-neofluar objective lens . Digital images were acquired with CoolSNAP cooled-CCD camera  and MetaMorph Software . Images were processed with CalMorph [S. cerevisiae, nine cultures for either BY4716 or YEF1946 strain were used. For QTL mapping, three cultures for each segregant were used.Microscopic observation and data processing were essentially similar as previously described . BrieflyCalMorph  to generp = 0.015, Wilcoxon Mann-Whitney).For every trait, its coefficient of variation across the 126 replicated cultures of the wild-type strain described in Ohya et al.  was comphttp://www.r-project.org). Heritability was measured as (VarS − VarE)/VarS, where VarS is the variance among segregants, and VarE is an estimate of the environmental variance calculated on the parental replicates. Since three independent phenotypic values were available for each segregant, the variance among segregants was computed three times on independent series. VarS was estimated by the average of these three variances.All statistical analyses were made using purpose-developed C codes or using R software . FDR was the Nfalse(j0)/Nactual(j0) ratio, where Nactual(j0) was the number of real traits showing j > j0. The estimated total number of transgressive traits, Nactual(j0) − Nfalse(j0), was highest for j0 = 6 and was 157, 128, and 124 in the three replicated series of segregants data, respectively. This corresponded to one-fourth of the total number of traits. At j0 = 18, FDR was <0.05 and 34 traits were significant in all three series  and the number of traits with detected linkage.For each trait with heritability greater than 50%, the genome was scanned for QTL as follows. Triplicates were averaged to give a single phenotypic value for each segregant. Marker–trait association was tested using the Wilcoxon Mann-Whitney test, and significance was assessed by permutation test as described previously [Since all tests were nonparametric, we worked on all 501 traits without distinguishing the set of 254 traits that were previously determined to fit normality  .R were computed for each pair of messenger level and morphological trait. For a given R0 cutoff, the number of traits showing |R| > R0, with at least one expression level, was computed after permuting the segregant indexes. This number was averaged across 100 permutations. FDR was then determined as the ratio between this number of expected false positives and the number of traits correlated to expression levels prior to permutation. To mine for traits/GO-terms associations, we clustered the 103 genes and 104 traits correlated at FDR = 0.1 and visually examined the correlation map for regions enriched in genes/traits correlations (http://db.yeastgenome.org/cgi-bin/GO/goTermFinder) to infer significance of GO term enrichments.To test for association between expression levels  and traits (247 traits with heritability >50%), Pearson correlation coefficients elations . Gene lip < 0.0001 in the Ohya et al. 2005 dataset). This was the case for only one of the 13 bins. This search involved a high number  of gene/trait combinations, and finding one positive bin could therefore result from chance only. To test this, we reassigned the 13 bins to random places on the genome and re-examined it to determine if deletions of genes in their vicinity could explain one of their linked traits. We ran this test five times and obtained hits for zero, two, zero, three, and two bins at the respective runs. Thus, obtaining one positive bin in the actual data was not statistically significant.We segmented the genome into 20-kb bins and assigned each of the 67 traits mapped at FDR = 0.05 to the bin showing highest genetic linkage as in . Traits Table S1(102 KB XLS)Click here for additional data file.Table S2(21 KB XLS)Click here for additional data file.Table S3(24 KB XLS)Click here for additional data file.Table S4(37 KB XLS)Click here for additional data file.Table S5(47 KB XLS)Click here for additional data file.


The aim of the present study was to model apparent serum half-lives of polybrominated diphenyl ethers (PBDEs) with 7–10 bromine substituents. Workers with occupational exposure to PBDEs have elevated serum levels of PBDEs, but these substances are also found in the general population and are ubiquitous environmental contaminants. The calculations were based on exposure assessments of rubber workers (manufactured flame-retarded rubber compound) and electronics dismantlers who donated blood during a period with no work-related exposures to PBDEs, and referents without any known occupational exposure . The workers had previously been found to have elevated levels of high- and medium-brominated diphenyl ethers compared with the referent populations. We performed nonlinear mixed-effects modeling of kinetics, using data from previous and present chemical analyses. The calculated apparent half-life for decabromodiphenyl ether (BDE-209) was 15 days . The three nona-BDEs and four octa-BDE congeners were found to have half-lives of 18–39 and 37–91 days, respectively. BDE-209 has a short half-life in human blood. Because BDE-209 is commonly present in humans in general, the results of this study imply that humans must be more or less continuously exposed to BDE-209 to sustain the serum concentrations observed. BDE-209 is more readily transformed and/or eliminated than are lower brominated diphenyl ether congeners, and human health risk must be assessed accordingly. Polybrominated diphenyl ethers (PBDEs) have for decades been applied as additive flame retardants in polymers in electronics and electric goods, and in rubber, plastics, and textiles . The tecIt is not yet fully understood how humans are exposed to the PBDEs, but ingestion (food and dust) and inhalation seem to be important routes of exposure. Many PBDEs have been reported to be present in food , in ambit1/2 = 2.4 days) for BDE-209 in rats dosed with this congener. Also, gray seals experimentally dosed with BDE-209 indicated a short half-life (8–13 days) of this compound . The serum was centrifuged, transferred to dark-colored acetone-washed glass bottles, frozen, and kept at –20°C until the start of the chemical analysis. Informed consent was obtained from all subjects, and the study was approved by the Ethics Committee at Lund University, Sweden (protocol LU 227-97).Solvents, reference standards, and other chemicals used in analysis of serum samples, as well as the instrumental support of the work, have been reported previously .m/z = 79, 81] as previously described in detail for the individual studies  varied between data sets, we had to recalculate uniform LOQs for the present study. For all serum samples analyzed, we used a signal-to-noise (S:N) ratio > 5 to define the LOQ, when no interference was present in the blank samples. If interferences were present in the blank samples, the amount of the analyte in the sample had to be at least five times the average blank level to be accepted. The amounts of PBDEs in blank samples, if any, were subtracted from sample concentrations before being reported. When LOQs differed between sets of data recorded and analyzed at different time points, the LOQ was set as the highest measured LOQ (based on S:N ratio or blank sample levels) or the lowest quantified value in all data sets.For modeling of the half-life of each PBDE congener, we assumed that each subject had a certain, constant non-work-related exposure independent of profession. Each of the occupationally exposed workers also had a work-related exposure considered to be at a steady state before vacation but different between groups and subjects. Considering the start of the vacation as day 0, Equation 1 for each subject can be expressed as follows:Based on the results from the cross-sectional studies, we selected groups for inclusion in the models. Clerks, cleaners, and abattoir workers were considered occupationally unexposed in all half-life calculations. We considered the electronics dismantlers to be occupationally exposed to all PBDE congeners studied. Four rubber mixers were considered to be occupationally exposed to deca-, nona-, and octa-BDEs but not to lower brominated congeners. The remaining three rubber millers had PBDE concentrations that did not differ from the referents and were considered occupationally unexposed.The rubber millers and abattoir workers  were sampled in 2000, whereas the female referents were sampled in 1997. Thus, we added a separate addition factor for millers and abattoir workers to the model . For BDE-209 the inclusion of this addition factor yielded a better fit of the model but did not change the half-life estimate. For all other congeners, there were no differences in the model fits, and this additional factor was set to zero.In total, 107 observations from 68 subjects for BDE-209 and BDE-183 were available for the calculations. Because octa- and nona-BDEs had not been determined for cleaners and clerks, 67 observations from 28 individuals were available. For serum concentrations below the uniform LOQ, the level was set to LOQ/2, and the values could thus be used in the kinetic calculations.We used NONMEM  and 34 in two rubber workers with a rich data set. We assumed a monoexponential decline on top of the baseline (background) level. We used NLIN Procedure in SAS .t1/2 was 15 days . For the three nona-BDEs , the calculated t1/2 values were 18, 39, and 28 days, respectively, with the shortest half-life for the PBDE con-gener with three ortho-bromines (BDE-206). The four octa-BDE isomers  had calculated half-lives of 72, 85, 37, and 91 days, respectively, with the shortest half-life for BDE-203. BDE-183 had a calculated half-life of 94 days . The apparent half-lives for BDE-153 and several other medium- and low-brominated congeners could not be determined but were indicated to be much longer than our 1-month observation period (BDE-153 data in PBDE serum concentrations from four electronics dismantlers and four rubber mixers sampled two to seven times from the beginning to the end of their vacation are given in In two rubber mixers with a rich data set, a traditional elimination model yielded similar results for BDE-209, with half-lives of 14 and 16 days, respectively, calculated between days 10 and 34, to ensure that the uptake phase was over .We observed a rapid uptake of BDE-209 after an isolated 1-day period of deca-BDE exposure during production of a batch of flame-retarded rubber . The levInitial findings in a small number of electronics dismantlers had indicated that the half-life of BDE-209 might be shorter than the half-lives of PBDEs with a lower degree of bromination . In the Experimental data are well in accordance with our observations. A rapid clearance of BDE-209 has been indicated in carp  and ratsThe general pattern of decreasing half-lives of PBDE congeners with increasing number of bromine substituents was thus expected. Within each group of isomers, such as the nona-BDEs and octa-BDEs, the estimated apparent half-lives varied nominally, albeit with wide and overlapping confidence intervals. It can be noted, however, that the PBDE congeners with the shortest half-lives within each group of isomers have a hydrogen substituent in one of the phenyl rings and the other ring fully brominated. No further conclusions can yet be drawn from these observations.The metabolism of BDE-209 is not yet fully elucidated. In the rat, There are yet no other half-life calculations based on observational human data. Estimates based on animal experimental data have been published previously for BDE-47, BDE-99, BDE-100, BDE-153, and BDE-154 , indicatBDE-209 has been shown to have developmental neurotoxicity in mice , and its


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.Adult participants (n = 1345) of the largest 310 pedigrees in the FHS, many biologically related, were genotyped with the 100K Affymetrix GeneChip. These genotypes were used to assess their contribution to 987 phenotypes collected in FHS over 56 years of follow up, including: cardiovascular risk factors and biomarkers; subclinical and clinical cardiovascular disease; cancer and longevity traits; and traits in pulmonary, sleep, neurology, renal, and bone domains. We conducted genome-wide variance components linkage and population-based and family-based association tests.http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.The participants were white of European descent and from the FHS Original and Offspring Cohorts . This overview summarizes the methods, selected findings and limitations of the results presented in the accompanying series of 17 manuscripts. The presented association results are based on 70,897 autosomal SNPs meeting the following criteria: minor allele frequency ≥ 10%, genotype call rate ≥ 80%, Hardy-Weinberg equilibrium p-value ≥ 0.001, and satisfying Mendelian consistency. Linkage analyses are based on 11,200 SNPs and short-tandem repeats. Results of phenotype-genotype linkages and associations for all autosomal SNPs are posted on the NCBI dbGaP website at We have created a full-disclosure resource of results, posted on the dbGaP website, from a genome-wide association study in the FHS. Because we used three analytical approaches to examine the association and linkage of 987 phenotypes with thousands of SNPs, our results must be considered hypothesis-generating and need to be replicated. Results from the FHS 100K project with NCBI web posting provides a resource for investigators to identify high priority findings for replication. Cardiovascular diseases are major illnesses among Americans, affecting about a third of the population  and resulting in more than 870,000 cardiovascular disease deaths annually . Cardiovhttp://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 at the National Center for Biotechnology Information (NCBI), where these comprehensive results are posted and may be browsed in the context of multiple genomic tracks including Entrez Gene, RefSeq, dbSNP, genetic markers, and OMIM. The deposition of these data in a public repository is consistent with the long tradition of publishing preliminary results from the FHS to benefit the wider scientific community.In this manuscript, we summarize the strategies that we pursued to conduct the 100K genome-wide study, providing an overview for a series of 17 companion manuscripts (Table 1 of the Overview) describing associations with specific collections of traits -42. The To organize the evaluation of the rich resource of data collected over nearly 60 years of follow up, we established a set of "Phenotype Working Groups" that included clinicians, epidemiologists, geneticists, and biostatisticians. These groups specified the traits to be studied, along with covariate adjustment and subgroups for analyses. In all, 987 phenotypes were examined for association, 835 for linkage. Some phenotypes are the same trait with different covariate adjustments, at different examinations or evaluated in different subgroups. For example, many traits were evaluated with both age and sex adjustment as well as with additional multivariable adjustments, yielding more than one phenotype for analysis. Each manuscript in this series provides a platform for the web posted results. Not every trait is described in the manuscripts; rather, the purpose of each manuscript is to introduce the trait areas and to present a brief summary of the results. In the present manuscript, we describe the general approach to analysis of the traits, provide an overview of some results, and discuss the limitations of the studies.The Framingham Heart Study (FHS) began in 1948 with recruitment of 5209 men and women (2336 men and 2873 women) between the ages of 28 and 62 years in the town of Framingham, Massachusetts, about 20 miles west of Boston -46. Theset al. [In 1971, an Offspring Cohort of 5124 men and women, who were adult children of Original Cohort members or were spouses of these offspring, was recruited and has been examined every four to eight years since ,48. The et al.  and Dawbet al. .In the late 1980s and through the 1990s, DNA was collected from living study participants. As many of the Original Cohort members were deceased by that time, these DNAs were mostly collected in Offspring Study participants. During the mid- to late-1990s, 1702 DNA samples were genotyped by the Mammalian Genotyping Service in the largest 330 two-generation pedigrees consisting of 2885 Framingham Study participants. These pedigrees were used for linkage analyses of blood pressure , lipids th examination. Participants in the Offspring Cohort return every 4 to 8 years for similar examinations and the 8th examination is currently underway.Original Cohort study subjects return to the Study every two years for a detailed medical history, physical examination and laboratory tests. The Original Cohort subjects are currently in their 29http://www.nhlbi.nih.gov/about/framingham/policies/index.htm was established. Only subjects with lymphoblast cell lines were included on the plate set, although a substantial number of the DNA samples on the plate set were derived from whole blood or buffy coat. The family plate set was used for genotyping of the Affymetrix 100K GeneChip. After cleaning the genotyping data, the study sample comprised 1345 FHS participants, 278 from the Original and 1087 Offspring Cohorts.In the early 2000s, a family DNA plate set with 1,399 participants from these 330 pedigrees Given the breadth of phenotypes, we established 8 larger Phenotype Working Groups, overseeing 17 discrete phenotypic domains, as follows: 1. Blood pressure, arterial stiffness, echocardiography, endothelial function and exercise testing; 2. Metabolic traits including anthropometry, glycemic traits and lipids; 3. Pulmonary function and sleep; 4. Systemic biomarkers, including inflammatory and thrombotic factors; 5. Subclinical atherosclerosis; 6. Renal and endocrine function; 7. Longevity and aging, including brain and bone aging phenotypes; 8. Cardiovascular disease outcomes, cancer, electrocardiography and heart rate variability. In addition, we established a statistical and analytical methodology group. These groups were convened by the FHS Genetic Steering Committee to define phenotypes to be evaluated, including the covariates used in analyses, to review results of linkage and association analyses, to foster communication among various Framingham investigators who were working on different traits, and to suggest possible follow-up strategies.For the 100K genome-wide project, each Working Group defined the phenotypes to be studied. Since most traits have well established factors that contribute to their variation, each group created a set of residuals from multivariable regression models accounting for the primary known covariates, in order to control for confounding from these variables and to increase the ability to detect genetic signals. For quantitative traits, the adjusted standardized residuals were generated using linear regression models. For qualitative traits, we used a variety of approaches including Cox proportional hazards with Martingale residuals for time-to-event  traits and logistic regression with deviance residuals for dichotomous traits. These methods are described below. In some cases, several different covariate adjustments were used for a single trait. Each manuscript describes the specific adjustments that were applied. We used residuals from regression models that included all subjects with traits in each Cohort, rather than limiting analyses to those who were genotyped, to produce residuals based on all subjects with phenotypic values, regardless of availability of genotypic data. This approach avoids potential biases in covariate adjustment based only upon the subset of individuals with both genotype and phenotype data and produces robust estimates of covariate effects.http://research.marshfieldclinic.org/genetics. A set of 401 microsatellite markers)[Genomic DNA derived from whole blood or buffy coat was phenol-chloroform extracted and DNA from immortalized lymphoblast cell lines was salt-precipitate extracted. Genotyping of the 100K SNPs in FHS families was performed through an ancillary study to Drs. Michael Christman and Alan Herbert at Boston University School of Medicine in the Department of Genetics and Genomics using the GeneChip Human Mapping 100K set from Affymetrix, following the manufacturer's protocol as previously described . Genotyp markers), coverin markers). An addiA total of 1380 individuals were successfully genotyped. First, familial relationships were checked using the sib_kin utility in the Aspex software package . BecauseFor Hardy-Weinberg equilibrium (HWE) testing, we randomly selected one individual per family to form a sample of unrelated individuals. Then, for each of the 100K SNPs, the observed genotype frequencies were compared to those expected under HWE using an exact chi-square test statistic  implemenWe found 38,062 SNPs with MAF <10% , an additional 2346 SNPs with genotyping call rates <80% and still another 1595 with HWE p-value < 0.001. We used a genotyping call rate cutpoint of 80% in part because of the use of the less accurate DM algorithm. SNPs with low MAF, low genotyping call rates and inadequate HWE produced unstable results, so they were excluded from our association results reported in this set of manuscripts, leaving 70,987 SNPs. Results for all autosomal SNPs are reported on the dbGaP website, regardless of MAF, genotyping call rates or HWE p-values; however, filters for these factors are provided on the dbGaP website.Both microsatellites previously genotyped by the Mammalian Genotyping Service and SNPs from the 100K were used to calculate identity by descent probabilities. We constructed genetic maps using all microsatellite NCBI genetic markers with Marshfield genetic location available and whose physical order and genetic order were consistent. Using this NCBI Marshfield map as our skeleton, we applied linear interpolation from physical to genetic distance to obtain approximate genetic locations (in centiMorgans) for all SNPs in the 100K set with known physical location.Because current linkage analysis software cannot handle the marker density available from a 100K scan, we selected a subset of 10,592 SNPs to supplement 613 genome scan microsatellite markers available on 1886 members of the largest 330 Framingham families. We selected SNPs to minimize linkage disequilibrium (LD) because current linkage software assumes that markers are in linkage equilibrium, and violation of this assumption has been shown to create spurious linkage evidence in certain contexts ,67. ThusVariance component linkage analyses were performed on residuals of up to 1341 individuals in 310 full pedigrees. Four of the 1345 subjects were the only person in a pedigree and were excluded from linkage analyses since they contributed no information. Multipoint probabilities of IBD between relative pairs were computed at each genetic marker location with the program MERLIN, Version 0.10.2. Due to size limitations for exact identity by descent (IBD) multipoint computation in MERLIN software , the 310We applied population-based and family-based methods to test for association between the 100K SNPs and residual phenotypes using an additive model unless otherwise specified. We used family-based association test methods, implemented in the program FBAT, Version 1.5.5 ,71, to tFor the population-based approach, we used generalized estimating equation (GEE)  regressiOf the 1345 subjects who satisfied appropriate familial relationships and who were considered in the presentation of results in these manuscripts, 258 were Original Cohort participants (90 men and 168 women) and 1087 were Offspring Cohort participants (527 men and 560 women). Table http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Each manuscript describes the traits that were studied and presents some results. These manuscripts are not intended to be comprehensive and generally do not include results for all phenotypes and covariate adjustment schemes that were studied and presented on the website. Full listings of all traits evaluated are provided in Additional file We present 17 manuscripts, each displaying selected results for an epidemiologically related group of traits. Table Allele frequencies for the 100K Affymetrix GeneChip in the Framingham sample are displayed in Figure http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 include all autosomal SNPs, regardless of genotyping call rate, HWE p-value or MAF; adjustable filters for these factors are provided. In our manuscripts, we present results for SNPs that satisfy genotyping call rates ≥ 80%, HWE p-value ≥ 0.001, and MAF ≥ 10%. The proportion of SNPs satisfying a genotyping call rate of ≥80% is 97.4%; 91% of SNPs satisfied a call rate of ≥90%.The results displayed on the NCBI dbGaP We expect only a small percentage of all tested SNPs to be truly associated with any phenotype. Therefore, to obtain an approximation of the null distribution of p-values, we examined the distribution of p-values for 415 phenotypes from the Metabolic Working Group and 14 CVD event phenotypes. If one assumes that only a few true associations exist for each phenotype, these p-value distributions approximate the null distribution, because only a few SNPs out of the large total number tested would be expected to exceed any critical value due to true associations. Table -7 and 10 for 10-8). The excess is higher for the full set of SNPs reported on the website. Here we found that the average proportion ranged from 1.2 times larger for nominal level of 0.05 to 19 times greater for nominal level of 10-5 and 2500 times greater for nominal level 10-8.Many of the phenotypes in the Metabolic Working Group were approximately normally distributed  and thus may reflect the situation for which the assumptions of the analytical methods were generally satisfied. We display two sets of results in Table -5 and 67 times greater for nominal 10-8 than expected. Thus, GEE p-values need to be interpreted with care, as SNPs in the lowest p-value range have the potential to be especially enriched with false positives.The CVD phenotypes represent an extreme case, as the phenotypes were residuals from survival models, were generally bimodal, and do not satisfy general assumptions for normality. We see the same general pattern that we observed for the Metabolic Working Group phenotypes with somewhat conservative FBAT tests and excess numbers of small p-values for GEE tests. As might be expected, the CVD phenotypes revealed larger excesses of small GEE p-values, with the average proportion of SNPs falling below specified alpha levels ranging from 1.08  to 2.4 times greater for nominal level of 10-6 we found this ratio varied from 9.5 to 614 for call rate > 95% and 6.8 to 667 for call rate < 80%. Thus, we used a liberal genotyping call rate of > 80% for presentation of results in our manuscripts to err on the side of including a result rather than not, even though we expect nearly all results to be false positives.We also examined the dependence of the p-value distribution for GEE results on the genotyping call rate for the Metabolic Working Group phenotypes. Our sample was not ascertained on trait status; so genotyping failures were likely to be randomly distributed. Therefore, one might expect that the effect of genotyping error on type I error would be more modest than for case-control studies). As expeFigure As in any GWAS, we expect that most results with small p-values are false positives. The p-value distributions support this notion and further suggest that the GEE results may have more false positives than one would expect. Table 2 in each manuscript ranks results by p-value, but each paper also pursues its own strategy to identify which results may be more worthy of follow up in Table 3 of each manuscript, usually by considering evidence from several sources such as correlated traits.-8, we have more than 80% power to detect a SNP explaining 4% or more total phenotypic variation when 60% or more individuals are phenotyped. With higher MAF, the power remains similar for the same QTL heritability (data not shown). Thus, we have sufficient power to detect SNPs explaining ≥4% or more of the phenotypic variance using the population-based GEE association test approach, controlling for multiple testing for a single trait. The effect size for a specific QTL heritability, defined as the increase/decrease of the phenotype value with one copy increment of the allele tested, depends on the MAF of the SNP. For example, for a SNP explaining 4% of the phenotypic variation, the effect size is 0.47 times the phenotypic standard deviation (SD) for a MAF of 0.1, and 0.28 SD for a MAF of 0.5.To assess the power of the population-based association approach with GEE, we simulated a trait following a normal distribution with 30% polygenic heritability in this sample of 1345 subjects. We generated a SNP with MAF 0.10 and assumed that the SNP was the QTL with an additive effect and QTL heritability varying from 1% to 5%. We also varied the proportion of phenotyped individuals from 60% to 100%, as some traits were not available in all subjects genotyped. The phenotype and genotype data were simulated using SOLAR simqtl, Version 3.0.4. We tested the association between the SNP and the trait using GEE. One thousand replicates were performed for each scenario. The results are displayed in Table Beyond simple examination of individual p-values for single tests, the authors of each manuscript developed their own synthetic strategies to prioritize SNPs that may be worthy of follow up (results shown in Table 3 of each manuscript). Many strategies considered results among similar traits. For example, the Subclinical Working Group created four subgroups of traits: (1) ankle brachial index phenotypes, (2) common carotid IMT phenotypes, (3) internal carotid IMT phenotypes, and (4) multidetector computed tomography (MDCT) coronary calcification phenotypes . Within The main results for each Working Group are presented in the individual manuscripts of this series. Here we highlight some results that address some of our expectations.OR10J1 and FCER1A or OR10J3  [-8) and rs2808629 (p = 3.19*10-8)) within the 1.5 support interval of this peak [-06 by GEE and 0.0045 by FBAT) [Whereas strategies for genetic studies have been undergoing substantial changes in recent years, partly due to changes in the laboratory, we hypothesized that genomic regions that harbor significant linkage results would also contain significant association results. One example of the concordance of linkage and association results was noted for monocyte chemoattractant protein-1 (MCP1), an inflammatory marker, where a region of linkage  for MCP1 concentrations also contains two SNPs on chromosome 1 within the 1.5 support interval for the linkage peak and within 60 kb of the genes  by GEE) . We founhis peak . Whereasby FBAT) . HoweverWe did not expect the same SNPs to appear in many manuscripts, as cardiovascular disease is complex and involves a large and varied number of pathways for its development. In contrast, some manuscripts report on correlated traits. Thus, we examined overlap among the top 500 SNPs associated with the phenotypes across 3 Metabolic Working Groups: glycemic/diabetes phenotypes, lipid phenotypes and obesity phenotypes. Of 11 SNPs found in more than one group, none were found among the top 500 SNPs in all three groups. However, 7 SNPs were found in the glycemia and obesity groups, 2 in glycemia and lipid groups and 2 in lipid and obesity groups -40.APOE. On the other hand, we found that SNP rs7007797 in the LPL gene was associated with both HDL and triglycerides [-16) [2 = 1) with the Arg/Gln FVII SNP previously shown to account for 9% of the total phenotypic variance [SORL 1, a gene recently related to the risk of Alzheimer's disease [-6; rs726601; FBAT p = 8.2 × 10-4) [WRN gene that causes premature aging are associated with age at death and morbidity-free survival at age 65 years [AGT gene was associated with both systolic and diastolic blood pressure [NOS1AP locus with electrocardiographic QT interval duration was replicated with p-values ranging from 0.0001–0.0009 for 4 partially correlated SNPs [ACE associated with blood pressure [PPARG P12A (rs1801282) was not associated with diabetes or related traits including body mass index [In Table 4 of each manuscript, we investigated whether our results replicated previous reports in the literature. The 100K chip does not contain many SNPs in well-known lipid genes, such as ycerides ; we replycerides . Strong es [-16) , which rvariance . Similarvariance . Two SNP × 10-4) . We foun65 years . The LD pressure . The assted SNPs . In contpressure . PPARG Pss index . Some ofCST3 gene was highly associated with serum cystatin-C levels  [We have also identified a few results that are biologically compelling, although replication of the SNP association is warranted. For example, we found that a SNP (rs1158167) near the 5*10-09) . This SN5*10-09) .et al. [et al. [While we have been preparing this series of manuscripts, several genome-wide studies have been published -86. Someet al.  and McPh [et al. . While ohttp://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. We note that the genotypes in this sample have also been evaluated by Drs. Michael Christman and Alan Herbert. Some of their results are reported on line as described by Herbert et al. [We have presented a brief description of the methods and a few selected results derived from analyses of the 100K Affymetrix GeneChip with a large number of FHS traits, ranging from CVD events and subclinical measures to traditional cardiovascular risk factors of diabetes, lipid levels, blood pressure and also including more novel biomarker measures that reflect modern hypotheses, such as the role of inflammatory pathways in the development of CVD. We have also reported on a number of neurological, renal, cancer and aging traits, including longevity (age at death) and bone mass and structure. None of these manuscripts provide a comprehensive report. Rather, the purpose of this set of manuscripts is to provide a brief summary of the results and to introduce readers to the data posted on the dbGaP website t et al. .First, the present investigation represents a comprehensive GWAS analysis of numerous phenotypes in a large community-based cohort. To our knowledge, it is the largest GWAS performed in an observational cohort in terms of the number of phenotypes analyzed and web posted. Second, we exploited the phenotypic diversity and richness of the Framingham Offspring Study database to analyze a set of phenotypes that were for the most part collected by detailed, direct measurements of study participants. Further, many of the phenotypes are quantitative traits. Phenotypes have been broadly categorized into seventeen different domains for manuscripts in this supplement. It is noteworthy that key risk factor phenotypes, such as blood pressure and lipid levels, were collected at multiple examinations, and thus we were able to conduct analyses using time-averaged traits, maximizing the scientific yield from the longitudinal prospective design of our cohort study. Further, several recently collected phenotypes, in particular biomarkers and imaging measures, were collected using highly reproducible, state-of-the-art modalities. Correlated phenotypes facilitated the assessment of pleiotropy by seeking associations of SNPs with such phenotypes. These investigations occurred primarily among the variables in each individual manuscript. Finally, for most phenotypes, there was evidence for a significant heritable component from FHS or other studies. We acknowledge that some phenotypic domains may represent analytical constructs, rather than truly distinct groups from a biological standpoint.Several aspects of our investigation merit comment. Third, we have web-posted the results of all analyses on autosomes on dbGaP, including results without statistical evidence of association, so that investigators world-wide can access the data freely and mine them in silico for hypothesis generation, inclusion in meta-analysis, and direct comparisons with their own results. In addition to the freely posted aggregate results, participant-specific genotypic and phenotypic data are available for distribution for further analyses to approved scientific investigators world-wide via the NCBI/NHLBI and consistent with Framingham Study data distribution policies (see http://www.nhlbi.nih.gov/about/framingham/policies/index.htm). For the purpose of publication, reference to these analyses may be made by referring to either the appropriate manuscript or the specific URL for web-posted data. Fourth, the simultaneous and full-disclosure release (on the web) of all association and linkage results of phenotypes encompassing at least 17 different domains in a cohesive and comprehensive manner signifies the tremendous teamwork of numerous FHS investigators, statisticians, programmers, and others. Most importantly, this effort would not be possible without the full cooperation and commitment of the FHS participants, who continue to attend Study examinations in an effort to further the scientific knowledge of factors that lead to heart disease and other traits.Fifth, as in any genome-wide association study with a large number of SNPs, most results that are considered statistically significant by a conventional p < 0.05 may be falsely positive; so it is difficult to decide what results are important. Not only do we have a large number of statistical tests for each phenotype, but we also have numerous phenotypes. Thus, considering multiple testing in the interpretation of results is of paramount importance. There are several approaches to address the issue of multiple testing, such as Bonferroni correction, permutation testing and false discovery rates. To conduct permutation testing for all of the traits that we considered is prohibitively time-consuming, particularly in preserving heritability of the traits with family data. Further, with correlated traits it is difficult to decide what traits should be included in a permutation testing strategy. One approach to controlling the false-positive rate in genome-wide association studies is to set a stringent threshold for declaring statistical significance. According to the report of the International HapMap Consortium, complete testing of common variants (MAF > 0.05) in each 500 kb is equivalent to performing 150 independent tests in white populations of European descent [-8. Thus, for a single trait, one could use this threshold. Several results do fall below this threshold  may represent true positive findings. The limited power to detect SNPs of small effect sizes offered by the analysis of our relatively modest sample size of ~1300 participants contributes to this phenomenon as well; we only have high power to detect a SNP explaining 4% or more of the phenotypic variance in the population-based GEE association test; the power of FBAT and variance component linkage analysis is even lower.e HapMap . FurtherAdditionally, for several of the analyzed phenotypes we did not observe any overlap between the top SNP-phenotype associations noted in GEE and FBAT analyses. The inherent differences in the two analytical methods especially in the context of the modest sample sizes, particularly for FBAT with small numbers of informative trios, may contribute to this phenomenon. FBAT is limited by the number of informative transmissions and although we suspect that there is little population stratification in our sample , GEE is Ninth, we were limited in our ability to replicate genetic variants previously reported to be associated with phenotypes in our database because specific coverage of such genetic variation in these candidates was limited in the Affymetrix 100K GeneChip. We view such analyses as more illustrative of the potential utility of our GWAS, rather than as definitive evidence for or against an association described with a putative candidate gene in the published literature.CRP gene  and facs series ). Thus, Finally, the Framingham Study participants were white of European descent and predominantly middle-aged to elderly. Hence, the genetic associations may not be generalizable to other ethnicities/races or to younger individuals.http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Interested investigators can also access the data through a standing protocol, described at http://www.nhlbi.nih.gov/about/framingham/policies/index.htm. Key to interpretation of these results is replication and evaluation of these results in other cohorts and ultimately, functional studies. We encourage investigators to examine the results and to pursue the genetic signals therein in their own cohorts. In the near future we will provide results and data from approximately 550,000 SNPs on more than 9000 participants from three generations in the FHS SNP Health Association Resource (SHARe) project. Data will be available to qualified investigators through an application process to dbGaP. It is our hope that the results from these two genome-wide association studies will lead to a much deeper understanding of the role of common genetic variation in the development of cardiovascular disease and its risk factors.In summary, the results from the FHS 100K association and linkage studies described herein and posted on the NCBI website provide a GWAS resource for investigators. We have presented a description of the methods and general strategies used for analysis of the 100K Affymetrix GeneChip in relation to a broad range of traits measured in the FHS. Brief descriptions of results of these analyses are provided a series of 17 manuscripts, with results for all autosomal SNPs genotyped successfully displayed at FBAT = family-based association test; GEE = generalized estimating equations; GWAS = Genome wide association study; IBD = identity by descent; LD = linkage disequilibrium; LOD = logarithm of the odds; SNP = single nucleotide polymorphism.Dr. Meigs currently has research grants from GlaxoSmithKline and Wyeth, and serves on safety or advisory boards for GlaxoSmithKline, Merck, and Lilly.All authors have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; LAC, EJB, SD, ALD, JD, KF, CSF, DJG, SK, DPK, MGL, DL, KLL, MDM, JBM, CNC, GTO, CJO, SS, RSV, JBW, QY and LDA have been involved in drafting the manuscript or revising it critically for important intellectual content; and All authors have given final approval of the version to be published.In addition, MDM, ZYW: imported, performed QC, processed, organized, and displayed all results within the NCBI dbGaP.Phenotypes for population-based GEE analyses.Click here for filePhenotypes for family-based FBAT analyses.Click here for filePhenotypes for linkage analyses.Click here for file


Blood lipid levels including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) are highly heritable. Genome-wide association is a promising approach to map genetic loci related to these heritable phenotypes.In 1087 Framingham Heart Study Offspring cohort participants , we conducted genome-wide analyses (Affymetrix 100K GeneChip) for fasting blood lipid traits. Total cholesterol, HDL-C, and TG were measured by standard enzymatic methods and LDL-C was calculated using the Friedewald formula. The long-term averages of up to seven measurements of LDL-C, HDL-C, and TG over a ~30 year span were the primary phenotypes. We used generalized estimating equations (GEE), family-based association tests (FBAT) and variance components linkage to investigate the relationships between SNPs  and multivariable-adjusted residuals. We pursued a three-stage replication strategy of the GEE association results with 287 SNPs (P < 0.001 in Stage I) tested in Stage II  and 40 SNPs  tested in Stage III .2 = 0.66, 0.69, 0.58, respectively; each P < 0.0001). Of 70,987 tests for each of the phenotypes, two SNPs had p < 10-5 in GEE results for LDL-C, four for HDL-C, and one for TG. For each multivariable-adjusted phenotype, the number of SNPs with association p < 10-4 ranged from 13 to 18 and with p < 10-3, from 94 to 149. Some results confirmed previously reported associations with candidate genes including variation in the lipoprotein lipase gene (LPL) and HDL-C and TG . The full set of GEE, FBAT and linkage results are posted at the database of Genotype and Phenotype (dbGaP). After three stages of replication, there was no convincing statistical evidence for association  between any of the tested SNPs and lipid phenotypes.Long-term averages of LDL-C, HDL-C, and TG were highly heritable  or reduced genomic coverage of the 100K array. GWAS in FHS using a denser genome-wide genotyping platform and a better-powered replication strategy may identify novel loci underlying blood lipids. APOE/PCSK9 with LDL-C [CETP/LIPC/LPL with HDL-C [APOA5/LPL with TG [Blood lipid levels are a major contributor to atherosclerotic cardiovascular disease . Currentth LDL-C -5, CETP/th HDL-C -9, and A with TG ,11, amonRecent advances in genomics enable a genome-wide association study (GWAS), an approach in which a substantial fraction of common genetic variation is tested for a role in determining phenotypic variation . These aWe hypothesized that common genetic variants explain a proportion of the inter-individual variability in LDL-C, HDL-C, and TG. Accordingly, we conducted genome-wide linkage and association studies for these three phenotypes in Framingham Heart Study (FHS) participants.Of the 1345 FHS participants who are part of the family plate set (see Executive Summary), we focused our analyses on the 1087 participants from the Offspring cohort who had Affymetrix 100K genotypes. Lipid phenotypes were measured at various examinations as described in Table 2, body mass index (weight in kg divided by the height in m2), alcohol intake (drinks per week), current cigarette smoking , menopausal status , and hormone replacement therapy .Blood lipids were measured from fasting venous blood collected at each of seven clinical examination time points extending from 1971 to 2001. Total cholesterol, HDL-C, and TG were measured by standard enzymatic methods. LDL-C was calculated using the Friedewald formula, with a missing value assigned for participants with a measured TG > 400 mg/dL. Clinical covariates utilized in phenotypic regression modeling included age at the time of blood lipid measurement, age2, age3, and examination year (by decade). The ''expected'' residual was generated within each sex and 10 year-age-group as the average of ''calculated'' residuals equal or greater than the treated individual's ''calculated'' residual.Commonly-used lipid lowering therapies affect total cholesterol and TG. To account for treatment effect, we imputed total cholesterol and TG values for those treated with lipid-lowering therapy. The imputation procedure was modeled after prior work on imputing blood pressure values for those on antihypertensive medication . For eacLipoprotein subclass profiles were measured by a commercially available proton NMR spectroscopic assay  on plasma samples stored at -70°C as described previously . The parAll analyses were based on the Affymetrix 100K GeneChip genotyping data generated in Framingham Heart Study participants as described previously . In orde2) and Model 2 . For quantitative covariates , the mean value across examinations was used as a covariate. For categorical covariates, the proportion of exams scored as 'yes' was used. The residual MeanLDL-C, MeanHDL-C, and MeanlogTG values from Model 1 and Model 2 served as the primary phenotypes.TG levels were log-transformed to approximate a normal distribution. For each blood lipid phenotype, the long-term average of 4 to 7 serial measurements was used as the primary phenotype. Participants contributing fewer than 4 of 7 measures of a given phenotype were excluded from that analysis. MeanLDL-C, MeanHDL-C, and MeanTG were adjusted for covariates in sex-specific linear regression models. Two sets of phenotypic models were created: Model 1  package . Using tReplication genotyping was attempted in three independent sample sets: a) the FHS unrelated plate set; b) Genetics of Lipid Lowering Drugs and Diet Network (GOLDN); and c) Malmö Diet and Cancer Study – Cardiovascular Cohort (MDC-CC).The second stage consisted of ~1450 biologically unrelated individuals from the FHS unrelated plate set. The third stage consisted of ~1450 participants from GOLDN and ~5200 participants from MDC-CC. GOLDN is a family-based sample recruited from two National Heart, Lung, and Blood Institute's Family Heart Study field centers . The Family Heart Study is a multi-center, population-based cohort designed to study the genetic and environmental determinations of cardiovascular disease.The MDC study is a community-based prospective epidemiologic cohort of 28,098 persons recruited for a baseline examination between 1991 and 1996. From this cohort, 6103 persons were randomly selected to participate in the MDC-CC which sought to investigate risk factors for cardiovascular disease. Of the MDC-CC participants, 5466 had DNA and lipid phenotypes available. Individuals on lipid lowering therapy and with outlier values of LDL-C, HDL-C, or TG (top 0.5% of the distribution) were excluded, leaving 5212 individuals available for the SNP-lipid association analyses2 only). We next conducted a joint analysis of Stage I (GWAS 100K data) and Stage II (FHS unrelated plate set). The joint analysis consisted of a weighted average of the beta estimates and standard errors from Stages I and II and used the inverse of the variance in each stage as weights.For follow-up into Stage II (the FHS unrelated plate set), we selected all SNPs in the GWAS with an association P < 0.001 for the MeanLDL-C, MeanHDL-C, or MeanTG phenotypes from the minimally-adjusted phenotypic model  did not differ by increasing minor allele copy number. In GOLDN, to account for correlated observations due to family relationships we used linear mixed-effects methods in SOLAR.For follow-up into Stage III (GOLDN and MDC-CC), we selected for genotyping all SNPs with a P < 0.001 in the joint analysis of Stages I and II. For genotype-phenotype association analyses in MDC-CC and GOLDN, we assumed an additive model of inheritance. In MDC-CC, we conducted multivariable linear regression analyses to test the null hypothesis that LDL-C, HDL-C, or TG residuals  of LDL-C, HDL-C, and TG .Clinical characteristics of the FHS sample of 1345 subjects are presented in the Executive Summary. Table Heritability estimates for long-term average lipid phenotypes  were greater than those from single time-point measurements Table . For exa-9), rs524802 (p = 7.6*10-7), and rs7007075 (p = 7.7*10-6), respectively (Table -8), rs10495594 (p = 5.1*10-5), and rs1449866 (p = 1.8*10-5), respectively  approached a null distribution but with a slight excess of low P values. For example, for the MeanLDL-C, whereas one would expect 1% of SNPs to demonstrate a P < 0.01 by chance, we found that 1.34% of SNPs displayed a P < 0.01. Similar results were seen for meanHDL-C and meanTG (data not shown).We evaluated the association results for a SNP and each of a set of four correlated phenotypes – ApoA-I, LDLNMRsm, MeanHDL-C, and MeanTG Table . Several2 = 1) to the previously studied rs328  [2 > 0.5 threshold) in the APOE, PCSK9, CETP, LIPC, and APOA5 genes, we were unable to confirm these other previously reported associations  in the lipoprotein lipase (LPL) was associated with MeanHDL-C (p = 0.0005) and MeanTG (p = 0.002) Table . This SNs S447X) . The min-5) between any of the tested SNPs and lipid phenotypes.Replication is critical to distinguish true positives from false ones in a GWAS. We pursued a three-stage replication strategy with 287 SNPs (P < 0.001 in Stage I) tested in Stage II  and 40 SNPs  tested in Stage III . Results are displayed in Table We examined associations of Affymetrix 100K SNPs and lipid traits in FHS and identified putative associations with lipid phenotypes. We studied the long-term average of up to 7 measurements each of LDL-C, HDL-C, and TG as the primary phenotypes and for one phenotype, the MeanLDL-C, we observed a nominal P that exceeded genome-wide significance . HoweverGWAS offers the potential to identify novel genetic variants/loci that are associated with blood lipid variation, unlimited by our current knowledge of lipoprotein biology. However, a central limitation of GWAS is that the true signals are mixed amidst a large number of false positive results. Validation in additional samples is required to distinguish the true positives from the false ones.-8, we estimated that we had 89% power to detect a quantitative trait locus explaining 2% of phenotypic variance, 48% power to detect a locus explaining 1% of the variance, and 13% power to detect a locus explaining 0.5% of the variance.Replication of initial GWAS findings using a staged design has been suggested to minimize genotyping cost and maximize statistical power ,24. An iGCKR) as a novel locus associated with TG [GCKR SNP (rs780094) explained the greatest proportion of blood TG variance in the Diabetes Genetics Initiative study. However, on the Affymetrix 100K array, there are no SNPs within the 60 kb spanning GCKR.With our replication effort, we failed to identify any novel loci related to blood lipids. At least two potential explanations are possible. First, our study design had limited statistical power to detect common SNPs that explain ≤1% of trait variance. In the Diabetes Genetics Initiative genome-wide association study for blood lipid traits, we recently showed that for lipid traits, there are few common variants that explain >2% of the variance and most SNPs explain <1% of trait variance . To have with TG . Of any This study is distinguished by the availability of serial lipid phenotypes over a 30-year time span, the community-based nature of the collection, and the routine ascertainment of covariates in a standardized clinical examination. We acknowledge several limitations. These include the lack of validation for the imputation methodology used to address lipid lowering therapy, limited statistical power due to sample size, and confinement to a single ancestral group – whites of European ancestry.Using a 100K genome-wide scan, we present association and linkage results for a rich set of lipid phenotypes in FHS. This resource may be useful for comparisons with other GWAS currently in progress. GWAS in FHS using a denser genome-wide genotyping platform and a better-powered replication strategy may identify novel loci underlying blood lipids.FBAT = family-based association test; GEE = generalized estimating equations.The authors declare that they have no competing interests.SK, AM, SD, GP, JMO, and LAC participated in the design of the study and the interpretation of the data. AM, GP, and SD conducted the statistical analyses. SK drafted the manuscript. AS, CG, LG, and NPB generated replication genotype data and analyses. OM and MOM provided replication samples and conducted association analyses in the Malmo Diet and Cancer Study. DKA and JMO provided replication samples and led the generation of lipid phenotypes in the GOLDN study. SD, SK, RD, JMO, and LAC revised the manuscript critically for important intellectual content. All authors read and approved the above manuscript.