Novel Approaches in Linkage Analysis for Complex Traits

Status Completed

Clinical Trial Summary

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

Clinical Trial 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 Design

N/A

Related Conditions & MeSH terms

Clinical Trial Details

NCT number	NCT00049855
Study type	Observational
Source	Mayo Clinic
Contact
Status	Completed
Phase	N/A
Start date	September 2002
Completion date	February 2005

View Details

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Clinical trials are conducted in a series of steps, called phases - each phase is designed to answer a separate research question.

Phase 1: Researchers test a new drug or treatment in a small group of people for the first time to evaluate its safety, determine a safe dosage range, and identify side effects.
Phase 2: The drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.
Phase 3: The drug or treatment is given to large groups of people to confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug or treatment to be used safely.
Phase 4: Studies are done after the drug or treatment has been marketed to gather information on the drug's effect in various populations and any side effects associated with long-term use.