Candidate: Dr. Peter Castaldi is a physician completing a period of F32-funded support. On July 1st, 2009 he will begin a full-time position at Tufts Medical Center and the Institute for Clinical Research and Health Policy Studies (ICRHPS). This position involves a 25% clinical commitment. His principal research interests are the genetic epidemiology of COPD and the translation of genomic discoveries into clinical practice and public health. His particular interests are genetic meta-analysis, gene-environment interaction, and predictive modeling with regression-based and machine-learning methods. His immediate goals are 1.) to identify novel genetic associations with COPD susceptibility and COPD- related phenotypes through the combined analysis of multiple genome-wide association (GWA) studies, 2.) to identify epistatic and gene-by-smoking interactions, and 3.) to develop accurate predictive models in chronic obstructive pulmonary disease (COPD) using clinical and genomic information. His long-term goal is to be an independent investigator with expertise in bioinformatics. His vision for achieving this goal involves developing expertise in bioinformatics so as to be able to participate in and eventually lead multidisciplinary teams in the application of computational methods to genomic datasets in order to answer important clinical questions that will improve the care of patients and population health. Environment: Dr. Castaldi will receive training in a rich, interdisciplinary environment. His principal mentor, Dr. Joseph Lau, is the head of the Center for Clinical Evidence Synthesis in the ICRHPS, and he is a worldwide leader in the field of meta-analysis and evidence synthesis. At Tufts, in addition to regular meetings with Dr. Lau, Peter will receive training in genetic evidence synthesis from leaders in the field, including Drs. John Ioannidis and Tom Trikalinos. The co-mentor of this application, Dr. Edwin Silverman, is a leading researcher in COPD genetics at the Channing Laboratory. At the Channing Laboratory, Peter will receive excellent training in respiratory genetics and genetic epidemiology, and he will have resources to state of the art high-throughput genotyping, next-generation sequencing technologies, and bioinformatics support. Dr. Castaldi will also continue his collaboration with Dr. Donna Slonim in the Tufts Computer Science Department, who will provide assistance with application of computational algorithms to genomic data and guidance as Dr. Castaldi continues to build a practical and theoretical foundation in Bioinformatics. Research: COPD is a major cause of morbidity and mortality that is of increasing public health importance. While the principal risk factor for COPD, smoking, is well-established, there is variable susceptibility in the general population to the lung damage caused by cigarette smoke. There is strong evidence supporting a genetic component to COPD susceptibility. Understanding how genes and environment interact to produce clinical COPD will allow for more accurate diagnostic tools and open new avenues of investigation for the development of COPD therapies. We propose to 1.) identify novel genetic associations with COPD susceptibility and 4 COPD-related phenotypes by performing meta-analysis on patient-level data from 4 large COPD GWA studies, 2.) identify gene-by-smoking and gene-gene interactions, and 3.) develop predictive models for COPD susceptibility and COPD-related phenotypes. In order to maximize the information obtained from genomic data, we will combine data from multiple studies (the National Emphysema Treatment Trial Genetics Ancillary Study, the Norway Case-Control Study, COPDGene, and ECLIPSE - total sample size=7,962) to increase power and employ regression-based and machine-learning methods to identify complex patterns of interaction in genotype data. Our study is designed to both explore and subsequently rigorously validate discovered main effect and interaction associations. Using predictive models, we will quantify the incremental predictive benefit of including genetic main effects and genetic interaction data to traditional clinical variables. Relevance: The proposed work will identify new genes associated with COPD and place them in a multivariate context so as to develop a better understanding of how genetic differences and environmental exposures contribute to the development of COPD. The models generated by this work will facilitate the translation of genomic discoveries to clinical practice and public health, in keeping with the NHLBI's mission to promote the prevention and treatment of heart, lung, and blood diseases and enhance the health of all individuals so that they can live longer and more fulfilling lives.

Public Health Relevance

The proposed work will identify new genes associated with COPD and place them in a multivariate context so as to develop a better understanding of how genetic differences and environmental exposures contribute to the development of COPD. The models generated by this work will facilitate the translation of genomic discoveries to clinical practice and public health, in keeping with the NHLBI's mission to promote the prevention and treatment of heart, lung, and blood diseases and enhance the health of all individuals so that they can live longer and more fulfilling lives. Relevance: The proposed work will identify new genes associated with COPD and place them in a multivariate context so as to develop a better understanding of how genetic differences and environmental exposures contribute to the development of COPD. The models generated by this work will facilitate the translation of genomic discoveries to clinical practice and public health, in keeping with the NHLBI's mission to promote the prevention and treatment of heart, lung, and blood diseases and enhance the health of all individuals so that they can live longer and more fulfilling lives.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Clinical Investigator Award (CIA) (K08)
Project #
5K08HL102265-03
Application #
8261100
Study Section
Special Emphasis Panel (ZHL1-CSR-U (F1))
Program Officer
Tigno, Xenia
Project Start
2010-05-01
Project End
2013-04-30
Budget Start
2012-05-01
Budget End
2013-04-30
Support Year
3
Fiscal Year
2012
Total Cost
$128,250
Indirect Cost
$9,500
Name
Tufts University
Department
Type
DUNS #
079532263
City
Boston
State
MA
Country
United States
Zip Code
02111
Castaldi, Peter J; Dy, Jennifer; Ross, James et al. (2014) Cluster analysis in the COPDGene study identifies subtypes of smokers with distinct patterns of airway disease and emphysema. Thorax 69:415-22
Basma, Hesham; Gunji, Yoko; Iwasawa, Shunichiro et al. (2014) Reprogramming of COPD lung fibroblasts through formation of induced pluripotent stem cells. Am J Physiol Lung Cell Mol Physiol 306:L552-65
Castaldi, Peter J; Cho, Michael H; San José Estépar, Raúl et al. (2014) Genome-wide association identifies regulatory Loci associated with distinct local histogram emphysema patterns. Am J Respir Crit Care Med 190:399-409
Castaldi, Peter J; San Jose Estepar, Raul; Mendoza, Carlos S et al. (2013) Distinct quantitative computed tomography emphysema patterns are associated with physiology and function in smokers. Am J Respir Crit Care Med 188:1083-90
Cho, Michael H; Castaldi, Peter J; Wan, Emily S et al. (2012) A genome-wide association study of COPD identifies a susceptibility locus on chromosome 19q13. Hum Mol Genet 21:947-57
Castaldi, P J; Demeo, D L; Hersh, C P et al. (2011) Impact of non-linear smoking effects on the identification of gene-by-smoking interactions in COPD genetics studies. Thorax 66:903-9
Castaldi, Peter J; Dahabreh, Issa J; Ioannidis, John P A (2011) An empirical assessment of validation practices for molecular classifiers. Brief Bioinform 12:189-202
Castaldi, Peter J; Cho, Michael H; Litonjua, Augusto A et al. (2011) The association of genome-wide significant spirometric loci with chronic obstructive pulmonary disease susceptibility. Am J Respir Cell Mol Biol 45:1147-53