Manuel Mattheisen

Beyond GWAS in COPD: Probing the Landscape between Gene-Set Associations, Genome-Wide Associations and Protein-Protein Interaction Networks

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Beyond GWAS in COPD : Probing the Landscape between Gene-Set Associations, Genome-Wide Associations and Protein-Protein Interaction Networks. / McDonald, Merry-Lynn Noelle; Mattheisen, Manuel; Cho, Michael H; Liu, Yang-Yu; Harshfield, Benjamin; Hersh, Craig P; Bakke, Per; Gulsvik, Amund; Lange, Christoph; Beaty, Terri H; Silverman, Edwin K.

In: Human Heredity, Vol. 78, No. 3, 27.08.2014, p. 131-139.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

McDonald, M-LN, Mattheisen, M, Cho, MH, Liu, Y-Y, Harshfield, B, Hersh, CP, Bakke, P, Gulsvik, A, Lange, C, Beaty, TH & Silverman, EK 2014, 'Beyond GWAS in COPD: Probing the Landscape between Gene-Set Associations, Genome-Wide Associations and Protein-Protein Interaction Networks', Human Heredity, vol. 78, no. 3, pp. 131-139. https://doi.org/10.1159/000365589

APA

McDonald, M-L. N., Mattheisen, M., Cho, M. H., Liu, Y-Y., Harshfield, B., Hersh, C. P., Bakke, P., Gulsvik, A., Lange, C., Beaty, T. H., & Silverman, E. K. (2014). Beyond GWAS in COPD: Probing the Landscape between Gene-Set Associations, Genome-Wide Associations and Protein-Protein Interaction Networks. Human Heredity, 78(3), 131-139. https://doi.org/10.1159/000365589

CBE

McDonald M-LN, Mattheisen M, Cho MH, Liu Y-Y, Harshfield B, Hersh CP, Bakke P, Gulsvik A, Lange C, Beaty TH, Silverman EK. 2014. Beyond GWAS in COPD: Probing the Landscape between Gene-Set Associations, Genome-Wide Associations and Protein-Protein Interaction Networks. Human Heredity. 78(3):131-139. https://doi.org/10.1159/000365589

MLA

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Author

McDonald, Merry-Lynn Noelle ; Mattheisen, Manuel ; Cho, Michael H ; Liu, Yang-Yu ; Harshfield, Benjamin ; Hersh, Craig P ; Bakke, Per ; Gulsvik, Amund ; Lange, Christoph ; Beaty, Terri H ; Silverman, Edwin K. / Beyond GWAS in COPD : Probing the Landscape between Gene-Set Associations, Genome-Wide Associations and Protein-Protein Interaction Networks. In: Human Heredity. 2014 ; Vol. 78, No. 3. pp. 131-139.

Bibtex

@article{f16cd6b8f8bd4277bc0c114f1bc26537,
title = "Beyond GWAS in COPD: Probing the Landscape between Gene-Set Associations, Genome-Wide Associations and Protein-Protein Interaction Networks",
abstract = "Objectives: To use a systems biology approach to integrate genotype and protein-protein interaction (PPI) data to identify disease network modules associated with chronic obstructive pulmonary disease (COPD) and to perform traditional pathway analysis. Methods: We utilized a standard gene-set association approach (FORGE) using gene-based association analysis and gene-set definitions from the molecular signatures database (MSigDB). As a discovery step, we analyzed GWAS results from 2 well-characterized COPD cohorts: COPDGene and GenKOLS. We used a third well-characterized COPD case-control cohort for replication: ECLIPSE. Next, we used dmGWAS, a method that integrates GWAS results with PPI, to identify COPD disease modules. Results: No gene-sets reached experiment-wide significance in either discovery population. We identified a consensus network of 10 genes identified in modules by integrating GWAS results with PPI that replicated in COPDGene, GenKOLS, and ECLIPSE. Members of 4 gene-sets were enriched among these 10 genes: (i) lung adenocarcinoma tumor-sequencing genes, (ii) IL-7 pathway genes, (iii) kidney cell response to arsenic, and (iv) CD4 T-cell responses. Further, several genes have also been associated with pathophysiology relevant to COPD including KCNK3, NEDD4L, and RIN3. In particular, KCNK3 has been associated with pulmonary arterial hypertension, a common complication in advanced COPD. Conclusion: We report a set of new genes that may influence the etiology of COPD that would not have been identified using traditional GWAS and pathway analyses alone. {\textcopyright} 2014 S. Karger AG, Basel.",
author = "McDonald, {Merry-Lynn Noelle} and Manuel Mattheisen and Cho, {Michael H} and Yang-Yu Liu and Benjamin Harshfield and Hersh, {Craig P} and Per Bakke and Amund Gulsvik and Christoph Lange and Beaty, {Terri H} and Silverman, {Edwin K}",
year = "2014",
month = aug,
day = "27",
doi = "10.1159/000365589",
language = "English",
volume = "78",
pages = "131--139",
journal = "Human Heredity",
issn = "0001-5652",
publisher = "S. Karger AG",
number = "3",

}

RIS

TY - JOUR

T1 - Beyond GWAS in COPD

T2 - Probing the Landscape between Gene-Set Associations, Genome-Wide Associations and Protein-Protein Interaction Networks

AU - McDonald, Merry-Lynn Noelle

AU - Mattheisen, Manuel

AU - Cho, Michael H

AU - Liu, Yang-Yu

AU - Harshfield, Benjamin

AU - Hersh, Craig P

AU - Bakke, Per

AU - Gulsvik, Amund

AU - Lange, Christoph

AU - Beaty, Terri H

AU - Silverman, Edwin K

PY - 2014/8/27

Y1 - 2014/8/27

N2 - Objectives: To use a systems biology approach to integrate genotype and protein-protein interaction (PPI) data to identify disease network modules associated with chronic obstructive pulmonary disease (COPD) and to perform traditional pathway analysis. Methods: We utilized a standard gene-set association approach (FORGE) using gene-based association analysis and gene-set definitions from the molecular signatures database (MSigDB). As a discovery step, we analyzed GWAS results from 2 well-characterized COPD cohorts: COPDGene and GenKOLS. We used a third well-characterized COPD case-control cohort for replication: ECLIPSE. Next, we used dmGWAS, a method that integrates GWAS results with PPI, to identify COPD disease modules. Results: No gene-sets reached experiment-wide significance in either discovery population. We identified a consensus network of 10 genes identified in modules by integrating GWAS results with PPI that replicated in COPDGene, GenKOLS, and ECLIPSE. Members of 4 gene-sets were enriched among these 10 genes: (i) lung adenocarcinoma tumor-sequencing genes, (ii) IL-7 pathway genes, (iii) kidney cell response to arsenic, and (iv) CD4 T-cell responses. Further, several genes have also been associated with pathophysiology relevant to COPD including KCNK3, NEDD4L, and RIN3. In particular, KCNK3 has been associated with pulmonary arterial hypertension, a common complication in advanced COPD. Conclusion: We report a set of new genes that may influence the etiology of COPD that would not have been identified using traditional GWAS and pathway analyses alone. © 2014 S. Karger AG, Basel.

AB - Objectives: To use a systems biology approach to integrate genotype and protein-protein interaction (PPI) data to identify disease network modules associated with chronic obstructive pulmonary disease (COPD) and to perform traditional pathway analysis. Methods: We utilized a standard gene-set association approach (FORGE) using gene-based association analysis and gene-set definitions from the molecular signatures database (MSigDB). As a discovery step, we analyzed GWAS results from 2 well-characterized COPD cohorts: COPDGene and GenKOLS. We used a third well-characterized COPD case-control cohort for replication: ECLIPSE. Next, we used dmGWAS, a method that integrates GWAS results with PPI, to identify COPD disease modules. Results: No gene-sets reached experiment-wide significance in either discovery population. We identified a consensus network of 10 genes identified in modules by integrating GWAS results with PPI that replicated in COPDGene, GenKOLS, and ECLIPSE. Members of 4 gene-sets were enriched among these 10 genes: (i) lung adenocarcinoma tumor-sequencing genes, (ii) IL-7 pathway genes, (iii) kidney cell response to arsenic, and (iv) CD4 T-cell responses. Further, several genes have also been associated with pathophysiology relevant to COPD including KCNK3, NEDD4L, and RIN3. In particular, KCNK3 has been associated with pulmonary arterial hypertension, a common complication in advanced COPD. Conclusion: We report a set of new genes that may influence the etiology of COPD that would not have been identified using traditional GWAS and pathway analyses alone. © 2014 S. Karger AG, Basel.

U2 - 10.1159/000365589

DO - 10.1159/000365589

M3 - Journal article

C2 - 25171373

VL - 78

SP - 131

EP - 139

JO - Human Heredity

JF - Human Heredity

SN - 0001-5652

IS - 3

ER -