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Polygenic Traits

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

Polygenic Traits

Polygenic Traits

Longitudinal Studies

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...

Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

Multiple Allele Traits

Multiple Allele Traits

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Cluster analysis in phenotyping a Portuguese population.

C C Loureiro¹, P Sa-Couto², A Todo-Bom³

¹Pneumology Unit, Hospitais da Universidade de Coimbra, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal; Centre of Pneumology, Faculty of Medicine, University of Coimbra, Portugal.

Revista Portuguesa De Pneumologia

|September 8, 2015

Summary

This study identified five distinct asthma phenotypes in Portuguese patients using cluster analysis. Key factors for distinguishing these asthma types include age of onset, obesity, and lung function measures.

Keywords:

Asthma Cluster analysis Phenotypes

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Area of Science:

Pulmonology
Clinical Immunology
Biostatistics

Background:

Asthma phenotyping aids understanding of disease mechanisms.
Inflammatory biomarkers enhance clinical parameter-based cluster analysis.
This approach was novel for Portuguese asthmatic patients.

Purpose of the Study:

To identify distinct asthma phenotypes in Portuguese patients.
Utilize cluster analysis on a secondary medical care population.
Characterize asthma subtypes within this specific demographic.

Main Methods:

Recruited consecutive asthma patients from an outpatient clinic.
Applied Ward's clustering method for phenotype identification.
Utilized standard asthma evaluation procedures and GINA guidelines.

Main Results:

Analyzed data from 57 out of 72 enrolled patients.
Identified five distinct asthma clusters (C1-C5).
Clusters varied by onset age, severity, obesity, inflammation (e.g., Th2, eosinophilic), and lung function.

Conclusions:

Identified asthma clusters largely align with larger studies.
Age at onset, obesity, lung function, FeNO, and severity are key distinguishing variables.
This phenotyping provides a refined understanding of asthma in the Portuguese population.