Your browser doesn't support javascript.
loading
Proteomic networks and related genetic variants associated with smoking and chronic obstructive pulmonary disease.
Konigsberg, Iain R; Vu, Thao; Liu, Weixuan; Litkowski, Elizabeth M; Pratte, Katherine A; Vargas, Luciana B; Gilmore, Niles; Abdel-Hafiz, Mohamed; Manichaikul, Ani; Cho, Michael H; Hersh, Craig P; DeMeo, Dawn L; Banaei-Kashani, Farnoush; Bowler, Russell P; Lange, Leslie A; Kechris, Katerina J.
Afiliación
  • Konigsberg IR; Department of Biomedical Informatics, School of Medicine, University of Colorado - Anschutz Medical Campus, Aurora, CO, USA.
  • Vu T; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA.
  • Liu W; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA.
  • Litkowski EM; Department of Biomedical Informatics, School of Medicine, University of Colorado - Anschutz Medical Campus, Aurora, CO, USA.
  • Pratte KA; Department of Medicine, University of Michigan, Ann Arbor, MI, USA.
  • Vargas LB; Department of Medicine, National Jewish Health, Denver, CO, USA.
  • Gilmore N; Department of Biomedical Informatics, School of Medicine, University of Colorado - Anschutz Medical Campus, Aurora, CO, USA.
  • Abdel-Hafiz M; Department of Biomedical Informatics, School of Medicine, University of Colorado - Anschutz Medical Campus, Aurora, CO, USA.
  • Manichaikul A; Department of Computer Science and Engineering, University of Colorado - Denver, Denver, CO, USA.
  • Cho MH; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
  • Hersh CP; Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • DeMeo DL; Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Banaei-Kashani F; Channing Division of Network Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Bowler RP; Department of Computer Science and Engineering, University of Colorado - Denver, Denver, CO, USA.
  • Lange LA; Department of Medicine, National Jewish Health, Denver, CO, USA.
  • Kechris KJ; Department of Biomedical Informatics, School of Medicine, University of Colorado - Anschutz Medical Campus, Aurora, CO, USA.
BMC Genomics ; 25(1): 825, 2024 Sep 02.
Article en En | MEDLINE | ID: mdl-39223457
ABSTRACT

BACKGROUND:

Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features.

METHODS:

Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed a genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS.

RESULTS:

We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts.

CONCLUSIONS:

In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fumar / Enfermedad Pulmonar Obstructiva Crónica / Proteómica / Estudio de Asociación del Genoma Completo Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: BMC Genomics Asunto de la revista: GENETICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Fumar / Enfermedad Pulmonar Obstructiva Crónica / Proteómica / Estudio de Asociación del Genoma Completo Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: BMC Genomics Asunto de la revista: GENETICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido