Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation

Michael H Cho; George R Washko; Thomas J Hoffmann; Gerard J Criner; Eric A Hoffman; Fernando J Martinez; Nan Laird; John J Reilly; Edwin K Silverman

doi:10.1186/1465-9921-11-30

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Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation

Journal article

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Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation

Michael H Cho, George R Washko, Thomas J Hoffmann, Gerard J Criner, Eric A Hoffman, Fernando J Martinez, Nan Laird, John J Reilly and Edwin K Silverman

Respiratory research, Vol.11(1), pp.30-30

2010

DOI: 10.1186/1465-9921-11-30

PMCID: PMC2850331

PMID: 20233420

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Abstract

Background: Numerous studies have demonstrated associations between genetic markers and COPD, but results have been inconsistent. One reason may be heterogeneity in disease definition. Unsupervised learning approaches may assist in understanding disease heterogeneity. Methods: We selected 31 phenotypic variables and 12 SNPs from five candidate genes in 308 subjects in the National Emphysema Treatment Trial (NETT) Genetics Ancillary Study cohort. We used factor analysis to select a subset of phenotypic variables, and then used cluster analysis to identify subtypes of severe emphysema. We examined the phenotypic and genotypic characteristics of each cluster. Results: We identified six factors accounting for 75% of the shared variability among our initial phenotypic variables. We selected four phenotypic variables from these factors for cluster analysis: 1) post-bronchodilator FEV1 percent predicted, 2) percent bronchodilator responsiveness, and quantitative CT measurements of 3) apical emphysema and 4) airway wall thickness. K-means cluster analysis revealed four clusters, though separation between clusters was modest: 1) emphysema predominant, 2) bronchodilator responsive, with higher FEV1; 3) discordant, with a lower FEV1 despite less severe emphysema and lower airway wall thickness, and 4) airway predominant. Of the genotypes examined, membership in cluster 1 (emphysema-predominant) was associated with TGFB1 SNP rs1800470. Conclusions: Cluster analysis may identify meaningful disease subtypes and/or groups of related phenotypic variables even in a highly selected group of severe emphysema subjects, and may be useful for genetic association studies.

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Title: Subtitle: Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation
Creators: Michael H Cho - Channing Laboratory, Brigham & Women's Hospital, Boston, MA, USA
George R Washko - Division of Pulmonary and Critical Care Medicine, Brigham & Women's Hospital, Boston, MA, USA
Thomas J Hoffmann - Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
Gerard J Criner - Division of Pulmonary and Critical Care, Temple University School of Medicine, Philadelphia, PA, USA
Eric A Hoffman - Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
Fernando J Martinez - Division of Pulmonary and Critical Care Medicine, University of Michigan Health System, Ann Arbor, MI, USA
Nan Laird - Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
John J Reilly - University of Pittsburgh Medical Center, Pittsburgh, PA, USA
Edwin K Silverman - Channing Laboratory, Brigham & Women's Hospital, Boston, MA, USA
Resource Type: Journal article
Publication Details: Respiratory research, Vol.11(1), pp.30-30
DOI: 10.1186/1465-9921-11-30
PMID: 20233420
PMCID: PMC2850331
NLM abbreviation: Respir Res
ISSN: 1465-9921
eISSN: 1465-993X
Publisher: BioMed Central
Language: English
Date published: 2010
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
Record Identifier: 9984051779202771

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