Variance components linkage analysis for adjusted systolic blood pressure in the Framingham Heart Study

Byng, M C; Fisher, Sheila; Lewis, Cathryn M.; Whittaker, John C.

doi:10.1186/1471-2156-4-s1-s4

Cited by 2 publications

(7 citation statements)

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“…The first involved selecting a single time point or constructing a simple summary measure. Byng et al [2003] used three representations: the maximum SBP, lifetime average SBP, and SBP at age 40. Wilcox et al [2003] used the untreated maximum lifetime measurement as part of the construction of a multivariate phenotype.…”

Section: Traits Related To Systolic Blood Pressurementioning

confidence: 99%

“…In addition to repeated measures over time, some of the measures were influenced by treatment effects. Byng et al [2003] considered treatment a binary ''clinical'' covariate and included it in one of three types of adjustments for SBP. Lin et al [2003] and Slager and Iturria [2003] chose not to adjust for hypertension treatment.…”

Section: Traits Related To Systolic Blood Pressurementioning

confidence: 99%

“…Slager and Iturria [2003] took the mean of generalized-estimating-equation residuals as a new phenotype. Byng et al [2003] also used two sets of adjustment factors in their consideration of point or summary measures. A special challenge to the accuracy of modelling of SBP is the fact of treatment for hypertension during the course of the research for some subjects.…”

Section: Traits Related To Systolic Blood Pressurementioning

confidence: 99%

“…Wilcox et al [2003] used the maximum untreated SBP. The linkage analyses of derived phenotypes were performed using the programs GENEHUNTER (variance components [Byng et al, 2003], and HasemanElston regression and nonparametric quantitative trait loci (QTL) analysis [Lin et al, 2003]), SOLAR [Slager and Iturria, 2003;Palmer et al, 2003;Wilcox et al, 2003], and GENEHUNTER-PLUS [Wilcox et al, 2003]. …”

Section: Traits Related To Systolic Blood Pressurementioning

confidence: 99%

“…This special kind of data was used by the participants of Presentation Group 1 to create new phenotypes representing the pattern of longitudinal change of the provided phenotypes. The traits analyzed for the Framingham data were systolic blood pressure (SBP) [Byng et al, 2003;Golla et al, 2003;Lin et al, 2003;Palmer et al, 2003;Slager and Iturria, 2003], body weight or body-mass index (BMI) [Golla et al, 2003;Strug et al, 2003], type 2 diabetes [Jun et al, 2003], and a composite trait that is related to metabolic syndrome [Wilcox et al, 2003]. For the simulated data, the traits of interest were glucose levels [Corbett et al, 2003;Cordell et al, 2003], cholesterol levels [Kim et al, 2003], and BMI change and hypertension [Yoo et al, 2003].…”

Section: Introductionmentioning

confidence: 99%

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Genetic analysis of phenotypes derived from longitudinal data: Presentation Group 1 of Genetic Analysis Workshop 13

et al. 2003

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The participants of Presentation Group 1 used the GAW13 data to derive new phenotypes, which were then analyzed for linkage and, in one case, for association to the genetic markers. Since the trait measurements ranged over longer time periods, the participants looked at the time dependence of particular traits in addition to the trait itself. The phenotypes analyzed with the Framingham data can be roughly divided into 1) body weight-related traits, which also include a type 2 diabetes progression trait, and 2) traits related to systolic blood pressure. Both trait classes are associated with metabolic syndrome. For traits related to body weight, linkage was consistently identified by at least two participating groups to genetic regions on chromosomes 4, 8, 11, and 18. For systolic blood pressure, or its derivatives, at least two groups obtained linkage for regions on chromosomes 4, 6, 8, 11, 14, 16, and 19. Five of the 13 participating groups focused on the simulated data. Due to the rather sparse grid of microsatellite markers, an association analysis for several traits was not successful. Linkage analysis of hypertension and body mass index using LODs and heterogeneity LODs (HLODs) had low power. For the glucose phenotype, a combination of random coefficient regression models and variance component linkage analysis turned out to be strikingly powerful in the identification of a trait locus simulated on chromosome 5. Haseman-Elston regression methods, applied to the same phenotype, had low power, but the above-mentioned chromosome 5 locus was not included in this analysis.

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Section: Traits Related To Systolic Blood Pressurementioning

confidence: 99%

Section: Traits Related To Systolic Blood Pressurementioning

confidence: 99%

Section: Traits Related To Systolic Blood Pressurementioning

confidence: 99%

Section: Traits Related To Systolic Blood Pressurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genetic analysis of phenotypes derived from longitudinal data: Presentation Group 1 of Genetic Analysis Workshop 13

et al. 2003

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On Testing an Unspecified Function Through a Linear Mixed Effects Model with Multiple Variance Components

Wang

Chen

2012

Biometrics

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Summary We examine a generalized F-test of a nonparametric function through penalized splines and a linear mixed effects model representation. With a mixed effects model representation of penalized splines, we imbed the test of an unspecified function into a test of some fixed effects and a variance component in a linear mixed effects model with nuisance variance components under the null. The procedure can be used to test a nonparametric function or varying-coefficient with clustered data, compare two spline functions, test the significance of an unspecified function in an additive model with multiple components, and test a row or a column effect in a two-way analysis of variance model. Through a spectral decomposition of the residual sum of squares, we provide a fast algorithm for computing the null distribution of the test, which significantly improves the computational efficiency over bootstrap. The spectral representation reveals a connection between the likelihood ratio test (LRT) in a multiple variance components model and a single component model. We examine our methods through simulations, where we show that the power of the generalized F-test may be higher than the LRT, depending on the hypothesis of interest and the true model under the alternative. We apply these methods to compute the genome-wide critical value and p-value of a genetic association test in a genome-wide association study (GWAS), where the usual bootstrap is computationally intensive (up to 108 simulations) and asymptotic approximation may be unreliable and conservative.

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Variance components linkage analysis for adjusted systolic blood pressure in the Framingham Heart Study

Cited by 2 publications

References 4 publications

Genetic analysis of phenotypes derived from longitudinal data: Presentation Group 1 of Genetic Analysis Workshop 13

Genetic analysis of phenotypes derived from longitudinal data: Presentation Group 1 of Genetic Analysis Workshop 13

On Testing an Unspecified Function Through a Linear Mixed Effects Model with Multiple Variance Components

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