Segregation/Linkage Analysis for Hypertension

February 17, 2016 updated by: National Heart, Lung, and Blood Institute (NHLBI)
To determine the genetic components of hypertension using a series of simulation experiments designed to determine the power and validity of the then recently developed methods of segregation and linkage analysis.

Study Overview

Status

Completed

Conditions

Detailed Description

BACKGROUND:

There are two general hypotheses about the nature of the genetic component of hypertension. A single gene hypothesis visualizes hypertension as a specific disease entity determined by an autosomal dominant or incompletely dominant allele with little environmental effect. A polygenic hypothesis views hypertension as determined by a large number of genetic and environmental factors operating independently with roughly equal contributions. The evidence supporting the single gene hypothesis is based primarily on bimodal and trimodal distributions of blood pressure in the population. It has been suggested that the bimodal or trimodal distributions are the result of ascertainment bias. The evidence supporting the polygenic model is based on several studies where the distribution of blood pressure is unimodal and often skewed toward higher values in both the population and in first degree relatives of hypertensive individuals. These skewed distributions can be approximately normalized using log transformations.

In this study, a particular effort was made to detect major genes. A major gene is said to exist in a particular sample if an appreciable amount of the variability of a trait in that sample is due to segregation of alleles at a single locus. The presence of a major gene does not preclude the existence of other genetic or environmental effects. In the last decade three general models have been proposed to detect the presence of a major gene. The transmission probability model is a general model for the genetic analysis of pedigree data which tests for Mendelian segregation ratios and is a generalization of the traditional methods of segregation analysis. This model has little power to differentiate between single gene and polygenic inheritance although it may be able to detect some kinds of non-single gene transmission. This method has been extended to allow analysis of multivariate traits, testing of a wide variety of hypotheses concerning modes of transmission and various ascertainment corrections. Major genes identified with this model include hypercholesterolemia, dopamine-beta-hydroxylase, and catechol-o-methytransferase.

The mixed model includes both a single locus and a multi-locus component and is designed to distinguish between the two. The model assumes that all transmission from one generation to the next that cannot be accounted for by classical polygenic inheritance is due to segregation of alleles at a single locus. It is ideal for detecting a major gene in the presence of polygenic inheritance provided that no other type of transmission is occurring. This model has been extended to include an environmental correlation among sibs. Major loci identified with this model include PTC, IgE and congenital glaucoma. The unified model is a mixed model with the single locus component parameterized in terms of transmission probabilities, and is a combination of the two previous models. Several research groups have developed methodologies to overcome the computational difficulties presented by this combined model.

DESIGN NARRATIVE:

The study was divided into two parts, the analysis of the methodologies and the application of the methodologies in the genetic analysis of hypertension. In the first part of the study, the power, robustness, and validity of three genetic models of segregation and linkage analysis were considered: the transmission probability model; the mixed model; and the unified model which was also a mixed model with the single locus component parameterized in terms of transmission probabilities. The methods of segregation and linkage analysis found to be most satisfactory were then applied to the analysis of data on five large pedigrees in collaboration with Wright State University and to the analysis of ten large pedigrees ascertained as part of the Bogalusa Heart Study. A determination was made of the effects of partitioning large families into nuclear families and performing segregation and linkage on these nuclear families.

The study completion date listed in this record was obtained from the "End Date" entered in the Protocol Registration and Results System (PRS) record.

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

No older than 100 years (ADULT, OLDER_ADULT, CHILD)

Accepts Healthy Volunteers

No

Genders Eligible for Study

Male

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

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Sponsor

National Heart, Lung, and Blood Institute (NHLBI)

Publications and helpful links

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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

July 1, 1982

Study Completion (ACTUAL)

June 1, 1991

Study Registration Dates

First Submitted

May 25, 2000

First Submitted That Met QC Criteria

May 25, 2000

First Posted (ESTIMATE)

May 26, 2000

Study Record Updates

Last Update Posted (ESTIMATE)

February 18, 2016

Last Update Submitted That Met QC Criteria

February 17, 2016

Last Verified

June 1, 2000

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 1030
  • R01HL028522 (NIH)

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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