Computing a Synthetic Chronic Psychosocial Stress Measurement in Multiple Datasets and its Application in the Replication of G × E Interactions of the EBF1 Gene. Issue 6 (22nd July 2015)
- Record Type:
- Journal Article
- Title:
- Computing a Synthetic Chronic Psychosocial Stress Measurement in Multiple Datasets and its Application in the Replication of G × E Interactions of the EBF1 Gene. Issue 6 (22nd July 2015)
- Main Title:
- Computing a Synthetic Chronic Psychosocial Stress Measurement in Multiple Datasets and its Application in the Replication of G × E Interactions of the EBF1 Gene
- Authors:
- Singh, Abanish
Babyak, Michael A.
Brummett, Beverly H.
Jiang, Rong
Watkins, Lana L.
Barefoot, John C.
Kraus, William E.
Shah, Svati H.
Siegler, Ilene C.
Hauser, Elizabeth R.
Williams, Redford B. - Abstract:
- <abstract abstract-type="main"> <title>ABSTRACT</title> <p>Chronic psychosocial stress adversely affects health and is associated with the development of disease [Williams, <xref ref-type="link" rid="gepi21910-bib-0027">2008</xref>]. Systematic epidemiological and genetic studies are needed to uncover genetic variants that interact with stress to modify metabolic responses across the life cycle that are the proximal contributors to the development of cardiovascular disease and precipitation of acute clinical events. Among the central challenges in the field are to perform and replicate gene‐by‐environment (G × E) studies. The challenge of measurement of individual experience of psychosocial stress is magnified in this context. Although many research datasets exist that contain genotyping and disease‐related data, measures of psychosocial stress are often either absent or vary substantially across studies. In this paper, we provide an algorithm to create a synthetic measure of chronic psychosocial stress across multiple datasets, applying a consistent criterion that uses proxy indicators of stress components. We validated the computed scores of chronic psychosocial stress by observing moderately strong and significant correlations with the self‐rated chronic psychosocial stress in the Multi‐Ethnic Study of Atherosclerosis Cohort (Rho = 0.23, <italic>P</italic> &lt; 0.0001) and with the measures of depressive symptoms in five datasets (Rho = 0.15–0.42, <italic>P</italic>s =<abstract abstract-type="main"> <title>ABSTRACT</title> <p>Chronic psychosocial stress adversely affects health and is associated with the development of disease [Williams, <xref ref-type="link" rid="gepi21910-bib-0027">2008</xref>]. Systematic epidemiological and genetic studies are needed to uncover genetic variants that interact with stress to modify metabolic responses across the life cycle that are the proximal contributors to the development of cardiovascular disease and precipitation of acute clinical events. Among the central challenges in the field are to perform and replicate gene‐by‐environment (G × E) studies. The challenge of measurement of individual experience of psychosocial stress is magnified in this context. Although many research datasets exist that contain genotyping and disease‐related data, measures of psychosocial stress are often either absent or vary substantially across studies. In this paper, we provide an algorithm to create a synthetic measure of chronic psychosocial stress across multiple datasets, applying a consistent criterion that uses proxy indicators of stress components. We validated the computed scores of chronic psychosocial stress by observing moderately strong and significant correlations with the self‐rated chronic psychosocial stress in the Multi‐Ethnic Study of Atherosclerosis Cohort (Rho = 0.23, <italic>P</italic> &lt; 0.0001) and with the measures of depressive symptoms in five datasets (Rho = 0.15–0.42, <italic>P</italic>s = 0.005 to &lt;0.0001) and by comparing the distributions of the self‐rated and computed measures. Finally, we demonstrate the utility of this computed chronic psychosocial stress variable by providing three additional replications of our previous finding of gene‐by‐stress interaction with central obesity traits [Singh et al., <xref ref-type="link" rid="gepi21910-bib-0020">2015</xref>].</p> </abstract> … (more)
- Is Part Of:
- Genetic epidemiology. Volume 39:Issue 6(2015)
- Journal:
- Genetic epidemiology
- Issue:
- Volume 39:Issue 6(2015)
- Issue Display:
- Volume 39, Issue 6 (2015)
- Year:
- 2015
- Volume:
- 39
- Issue:
- 6
- Issue Sort Value:
- 2015-0039-0006-0000
- Page Start:
- 489
- Page End:
- 497
- Publication Date:
- 2015-07-22
- Subjects:
- Genetic epidemiology -- Periodicals
Heredity -- Periodicals
Medical geography -- Periodicals
614 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1098-2272 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/gepi.21910 ↗
- Languages:
- English
- ISSNs:
- 0741-0395
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4111.848000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 4010.xml