Modeling and dissection of longitudinal blood pressure and hypertension phenotypes in genetic epidemiological studies

Bickeböller, Heike; Barrett, Jennifer; Jacobs, Kevin B.; Rosenberger, Albert

doi:10.1002/gepi.10287

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…This is not the case at 108.27 cM, where linkage is not indicated although p -values are small. Several other GAW13 contributions focussing on SBP as well (GAW13 group 9) reported linkage on chromosome 17, but not to the ACE gene region (for a summary, see [9]).…”

Section: Discussionmentioning

confidence: 99%

Nonparametric longitudinal allele-sharing model

et al. 2003

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Basically no methods are available for the analysis of quantitative traits in longitudinal genetic epidemiological studies. We introduce a nonparametric factorial design for longitudinal data on independent sib pairs, modelling the phenotypic quadratic differences as the dependent variable. Factors are the number of alleles shared identically by descent (IBD) and the age categories at which the dependent variable is measured, allowing for dependence due to age. To identify a linked marker a rank statistic tests the influence of IBD group on phenotypic quadratic differences. No assumptions are made on normality or variances of the dependent variable. We apply our method to 71 sib pairs from the Framingham Heart Study data provided at the Genetic Analysis Workshop 13. For all 15 available markers on chromosome 17 we analyzed the influence on systolic blood pressure. In addition, different selection strategies to sample from the whole data are discussed.

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Section: Discussionmentioning

confidence: 99%

Nonparametric longitudinal allele-sharing model

et al. 2003

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Fundus hypertonicus

Schnurrbusch

Wolf

2005

Ophthalmologe

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Cardiovascular disease is one of the major causes of mortality and morbidity in the western world. However, 30% of men and 15% of women were not aware of their elevated blood pressure. Hypertension has been defined as a multiply exceeded threshold value set by the WHO. With increasing blood pressure, mortality increases significantly. Studies have shown an increasing risk of stroke or cardiovascular disease in this patient group. The ophthalmologist plays an important role in the assessment and classification of vascular abnormalities. Typical parenchymal and vascular changes are helpful in arriving at a proper diagnosis, as well as providing a means of risk assessment and potentially helping to identify patients at risk.

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Covariance components models for longitudinal family data

Burton

Scurrah²,

Tobin³

et al. 2005

International Journal of Epidemiology

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A longitudinal family study is an epidemiological design that involves repeated measurements over time in a sample that includes families. Such studies, that may also include relative pairs and unrelated individuals, allow closer investigation of not only the factors that cause a disease to arise, but also the genetic and environmental determinants that modulate the subsequent progression of that disease. Knowledge of such determinants may pay high dividends in terms of prognostic assessment and in the development of new treatments that may be tailored to the prognostic profile of individual patients. Unfortunately longitudinal family studies are difficult to analyse. They conflate the complex within-family correlation structure of a cross-sectional family study with the correlation over time that is intrinsic to longitudinal repeated measures. Here we describe an approach to analysis that is relatively straightforward to implement, yet is flexible in its application. It represents a natural extension of a Gibbs-sampling-based approach to the analysis of cross-sectional family studies that we have described previously. The approach can be applied to pedigrees of arbitrary complexity. It is applicable to continuous traits, repeated binary disease states, and repeated counts or rates with a Poisson distribution. It not only supports the analysis of observed determinants, including measured genotypes, but also allows decomposition of the correlation structure, thereby permitting conclusions to be drawn about the effect of unobserved genes and environment on key features of disease progression, and hence to estimate the heritability of these features. We demonstrate the efficacy of our methods using a range of simulated data analyses, and illustrate its practical application to longitudinal blood pressure data measured in families from the Framingham Heart Study.

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Modeling and dissection of longitudinal blood pressure and hypertension phenotypes in genetic epidemiological studies

Cited by 5 publications

References 14 publications

Nonparametric longitudinal allele-sharing model

Nonparametric longitudinal allele-sharing model

Fundus hypertonicus

Covariance components models for longitudinal family data

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