Novel Approaches in Linkage Analysis for Complex Traits

This study has been completed.
Sponsor:
Collaborator:
Information provided by:
Mayo Clinic
ClinicalTrials.gov Identifier:
NCT00049855
First received: November 14, 2002
Last updated: April 15, 2014
Last verified: April 2014
  Purpose

To develop new statistical methods to explore genetic mechanisms that contribute to the development of hypertension.


Condition
Cardiovascular Diseases
Heart Diseases
Hypertension

Study Type: Observational

Resource links provided by NLM:


Further study details as provided by Mayo Clinic:

Study Start Date: September 2002
Study Completion Date: February 2005
Primary Completion Date: February 2005 (Final data collection date for primary outcome measure)
Detailed Description:

BACKGROUND:

Hypertension affects 50 million Americans and is the single greatest risk factor contributing to diseases of the brain, heart, and kidneys. There is a strong evidence that hypertension has a genetic basis. The study will develop novel approaches to better understand the genetic mechanisms contributing to measures of blood pressure (BP) level, diagnostic category (hypertension versus normotension) and correlated traits.

DESIGN NARRATIVE:

This genetic epidemiology study will develop novel approaches to better understand the genetic mechanisms contributing to measures of blood pressure (BP) level, diagnostic category (hypertension versus normotension) and correlated traits. The first aim is to localize genes influencing measures of blood pressure levels, diagnostic category and their correlates. This will be done by applying genome-wide multivariate linkage analyses based on the variance components approach and utilizing clusters of traits correlated with measures of blood pressure and/or diagnostics category. The second aim is to develop exploratory diagnostic tools for linkage analysis of complex traits to further enhance our ability to localize genes influencing measures of blood pressure, diagnostic category and their correlates. This will be done by extending the diagnostic tools used in regression analysis to the variance components approach used for linkage analysis of quantitative traits. In this study for example, it can be used to identify outlier families since previous studies have shown that families with outlier values yield false-positive results. Tree-structure models will also be extended to pedigree data. Tree-based modeling is an exploratory technique for uncovering structure in the data. The use of tree-structure models is advantageous because no assumptions are necessary to explore the data structure or to derive parsimonious model. These models are accurate classifiers (binary outcome) and predictors (quantitative outcomes). All these tools will be incorporated in the S-Plus software as a function. S-Plus was selected due to its capability and flexibility for analyzing large data sets.

  Eligibility

Genders Eligible for Study:   Both
Accepts Healthy Volunteers:   No
Criteria

No eligibility criteria

  Contacts and Locations
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Please refer to this study by its ClinicalTrials.gov identifier: NCT00049855

Sponsors and Collaborators
Mayo Clinic
Investigators
Investigator: Mariza De Andrade Mayo Clinic
  More Information

Publications:
ClinicalTrials.gov Identifier: NCT00049855     History of Changes
Other Study ID Numbers: 536-00, R01HL071917
Study First Received: November 14, 2002
Last Updated: April 15, 2014
Health Authority: United States: Federal Government

Additional relevant MeSH terms:
Cardiovascular Diseases
Heart Diseases
Hypertension
Vascular Diseases

ClinicalTrials.gov processed this record on August 26, 2014