ID
46155
Beskrivning
Principal Investigator: Patrick F. Sullivan, MD, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA MeSH: Depressive Disorder, Major https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000486 Our goals are to develop a comprehensive understanding of the genomics of transcription in a population based unselected sample and to discover DNA and RNA biomarkers for major depressive disorder (MDD). This work is essential to developing a more complete understanding of the biological basis of MDD, a common complex trait associated with considerable morbidity, mortality, and personal/societal cost. All biological samples have been collected from well-defined populations, and are now available. First, we conduct a "genetical genomics" or eQTL study of ~800 MZ and ~800DZ twin pairs. Each subject has been assayed for genome-wide SNPs and CNVs and gene expression from peripheral blood sampled under standardized conditions. We determine the genetic architecture (genetic and non-genetic proportions of variance via twin analyses) for every transcript, and the genome-wide associations (i.e., SNP-transcript eQTL pairs). These analyses will be expanded to consider transcriptional modules. The key deliverable is a detailed catalogue of the general and specific architecture of transcription plus raw intensity files. Second, we seek to discover DNA and RNA biomarkers relevant to MDD, capitalizing on the results of a large MDD study with repeated clinical and biological assessments; we have previously shown that PB is a reasonable proxy for CNS expression and employ an advanced modelling framework: (a) Using baseline data, we identify biomarkers for MDD by comparing ~1000 controls with ~1400 MDD cases via comparisons of SNP, CNV, expression transcripts, and transcriptional modules. (b) Using longitudinal data, we contrast gene expression signatures assessed at baseline and two years later in ~200 controls and ~500 MDD cases.
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Nyckelord
Versioner (3)
- 2022-11-16 2022-11-16 - Kristina Keller
- 2022-12-13 2022-12-13 - Kristina Keller
- 2025-01-29 2025-01-29 - Akane Nishihara
Rättsinnehavare
Patrick F. Sullivan, MD, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Uppladdad den
29 januari 2025
DOI
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Licens
Creative Commons BY 4.0
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dbGaP phs000486 Integration of Genomics and Transcriptomics in unselected Twins and in Major Depression
Subject - Consent Information
- StudyEvent: dbGaP phs000486 Integration of Genomics and Transcriptomics in unselected Twins and in Major Depression
- Eligibility Criteria
- Subject - Consent Information
- Pedigree Information
- Subject - Sample Mapping
- The dataset provides information about affection status (i.e. Major Depressive Disorder, diagnosed through application of the 'Composite International Diagnostic Interview' [CIDI]), menopausal status, BMI, and lifestyle variables, including alcohol consumption and smoking habits, plus sociodemographic data of participants. Several of the variables were measured twice, at time of the CIDI interview and at a later time during the study.
- Sample Attributes - including counts of white/red blood cells, basophils, lymphocytes, monocytes, neutophils, and time of sample draw.
Similar models
Subject - Consent Information
- StudyEvent: dbGaP phs000486 Integration of Genomics and Transcriptomics in unselected Twins and in Major Depression
- Eligibility Criteria
- Subject - Consent Information
- Pedigree Information
- Subject - Sample Mapping
- The dataset provides information about affection status (i.e. Major Depressive Disorder, diagnosed through application of the 'Composite International Diagnostic Interview' [CIDI]), menopausal status, BMI, and lifestyle variables, including alcohol consumption and smoking habits, plus sociodemographic data of participants. Several of the variables were measured twice, at time of the CIDI interview and at a later time during the study.
- Sample Attributes - including counts of white/red blood cells, basophils, lymphocytes, monocytes, neutophils, and time of sample draw.
C0441833 (UMLS CUI [1,2])
C0348080 (UMLS CUI [1,2])
C2986476 (UMLS CUI [1,3])
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