Jonathan T Heinzman1, Karin F Hoth1, Michael H Cho2, Phuwanat Sakornsakolpat3, Elizabeth A Regan4, Barry J Make5, Gregory L Kinney6, Frederick S Wamboldt7, Kristen E Holm5, Nicholas Bormann1, Julian Robles1, Victor Kim8, Anand S Iyer9, Edwin K Silverman2, James D Crapo5, Shizhong Han10, James B Potash10, Gen Shinozaki11. 1. Department of Psychiatry, University of Iowa Hospitals and Clinics, Iowa City, IA, USA. 2. Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA. 3. Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA. 4. Department of Medicine, National Jewish Health, Denver, CO, USA; Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, CO, USA. 5. Department of Medicine, National Jewish Health, Denver, CO, USA. 6. Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, CO, USA. 7. Department of Medicine, National Jewish Health, Denver, CO, USA; Department of Psychiatry, University of Colorado School of Medicine at the Anschutz Medical Campus, Aurora, CO, USA. 8. Department of Thoracic Medicine and Surgery, Temple University School of Medicine, Philadelphia, PA, USA. 9. Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA; Lung Health Center, University of Alabama at Birmingham, Birmingham, AL, USA; Health Services, Outcomes, and Effectiveness Research Training Program, University of Alabama at Birmingham, Birmingham, AL, USA. 10. Department of Psychiatry, University of Iowa Hospitals and Clinics, Iowa City, IA, USA; Department of Psychiatry, Johns Hopkins University, Baltimore, MD, USA. 11. Department of Psychiatry, University of Iowa Hospitals and Clinics, Iowa City, IA, USA. Electronic address: gen-shinozaki@uiowa.edu.
Abstract
BACKGROUND: Large sample GWAS is needed to identify genetic factors associated with depression. This study used genome-wide genotypic and phenotypic data from the COPDGene study to identify genetic risk factors for depression. METHODS: Data were from 9716 COPDGene subjects with ≥10 pack-year history. Depression was defined as antidepressant use and/or a HADS depression subscale score ≥8. Non-Hispanic White (6576) and African-American (3140) subsets were analyzed. A GWAS pipeline identified SNPs associated with depression in each group. Network analysis software analyzed gene interactions through common biological pathways, genetic interactions, and tissue-specific gene expression. RESULTS: The mean age was 59.4 years (SD 9.0) with 46.5% female subjects. Depression was in 24.7% of the NHW group (1622) and 12.5% of the AA group (391). No SNPs had genome-wide significance. One of the top SNPs, rs12036147 (p = 1.28 × 10-6), is near CHRM3. Another SNP was near MDGA2 (rs17118176, p = 3.52 × 10-6). Top genes formed networks for synaptic transmission with a statistically significant level of more co-expression in brain than other tissues, particularly in the basal ganglia (p = 1.00 × 10-4). LIMITATIONS: Limitations included a depression definition based on antidepressant use and a limited HADS score subgroup, which could increase false negatives in depressed patients not on antidepressants. Antidepressants used for smoking cessation in non-depressed patients could lead to false positives. CONCLUSIONS: Systems biology analysis identified statistically significant pathways whereby multiple genes influence depression. The gene set pathway analysis and COPDGene data can help investigate depression in future studies.
BACKGROUND: Large sample GWAS is needed to identify genetic factors associated with depression. This study used genome-wide genotypic and phenotypic data from the COPDGene study to identify genetic risk factors for depression. METHODS: Data were from 9716 COPDGene subjects with ≥10 pack-year history. Depression was defined as antidepressant use and/or a HADS depression subscale score ≥8. Non-Hispanic White (6576) and African-American (3140) subsets were analyzed. A GWAS pipeline identified SNPs associated with depression in each group. Network analysis software analyzed gene interactions through common biological pathways, genetic interactions, and tissue-specific gene expression. RESULTS: The mean age was 59.4 years (SD 9.0) with 46.5% female subjects. Depression was in 24.7% of the NHW group (1622) and 12.5% of the AA group (391). No SNPs had genome-wide significance. One of the top SNPs, rs12036147 (p = 1.28 × 10-6), is near CHRM3. Another SNP was near MDGA2 (rs17118176, p = 3.52 × 10-6). Top genes formed networks for synaptic transmission with a statistically significant level of more co-expression in brain than other tissues, particularly in the basal ganglia (p = 1.00 × 10-4). LIMITATIONS: Limitations included a depression definition based on antidepressant use and a limited HADS score subgroup, which could increase false negatives in depressedpatients not on antidepressants. Antidepressants used for smoking cessation in non-depressedpatients could lead to false positives. CONCLUSIONS: Systems biology analysis identified statistically significant pathways whereby multiple genes influence depression. The gene set pathway analysis and COPDGene data can help investigate depression in future studies.
Authors: Ronald C Kessler; Patricia Berglund; Olga Demler; Robert Jin; Kathleen R Merikangas; Ellen E Walters Journal: Arch Gen Psychiatry Date: 2005-06
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