Segregation/Linkage Analysis for Hypertension
|First Received Date ICMJE||May 25, 2000|
|Last Updated Date||June 23, 2005|
|Start Date ICMJE||July 1982|
|Primary Completion Date||Not Provided|
|Current Primary Outcome Measures ICMJE||Not Provided|
|Original Primary Outcome Measures ICMJE||Not Provided|
|Change History||Complete list of historical versions of study NCT00005158 on ClinicalTrials.gov Archive Site|
|Current Secondary Outcome Measures ICMJE||Not Provided|
|Original Secondary Outcome Measures ICMJE||Not Provided|
|Current Other Outcome Measures ICMJE||Not Provided|
|Original Other Outcome Measures ICMJE||Not Provided|
|Brief Title ICMJE||Segregation/Linkage Analysis for Hypertension|
|Official Title ICMJE||Not Provided|
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.
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.
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.
|Study Type ICMJE||Observational|
|Study Design ICMJE||Observational Model: Natural History|
|Target Follow-Up Duration||Not Provided|
|Sampling Method||Not Provided|
|Study Population||Not Provided|
|Intervention ICMJE||Not Provided|
|Study Group/Cohort (s)||Not Provided|
* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
|Recruitment Status ICMJE||Completed|
|Enrollment ICMJE||Not Provided|
|Completion Date||June 1991|
|Primary Completion Date||Not Provided|
|Eligibility Criteria ICMJE||
No eligibility criteria
|Accepts Healthy Volunteers||No|
|Contacts ICMJE||Contact information is only displayed when the study is recruiting subjects|
|Location Countries ICMJE||Not Provided|
|NCT Number ICMJE||NCT00005158|
|Other Study ID Numbers ICMJE||1030|
|Has Data Monitoring Committee||Not Provided|
|Responsible Party||Not Provided|
|Study Sponsor ICMJE||National Heart, Lung, and Blood Institute (NHLBI)|
|Collaborators ICMJE||Not Provided|
|Investigators ICMJE||Not Provided|
|Information Provided By||National Heart, Lung, and Blood Institute (NHLBI)|
|Verification Date||June 2000|
ICMJE Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP