Novel Approaches in Linkage Analysis for Complex Traits

April 15, 2014 updated by: Mayo Clinic
To develop new statistical methods to explore genetic mechanisms that contribute to the development of hypertension.

Study Overview

Status

Completed

Conditions

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.

Study Type

Observational

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

No eligibility criteria

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Mayo Clinic

Collaborators

National Heart, Lung, and Blood Institute (NHLBI)

Investigators

  • Mariza De Andrade, Mayo Clinic

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start

September 1, 2002

Primary Completion (Actual)

February 1, 2005

Study Completion (Actual)

February 1, 2005

Study Registration Dates

First Submitted

November 14, 2002

First Submitted That Met QC Criteria

November 14, 2002

First Posted (Estimate)

November 15, 2002

Study Record Updates

Last Update Posted (Estimate)

April 17, 2014

Last Update Submitted That Met QC Criteria

April 15, 2014

Last Verified

April 1, 2014

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 536-00
  • R01HL071917 (U.S. NIH Grant/Contract)

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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