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Revisiting schizophrenia linkage data in the NIMH Repository: reanalysis of regularized data across multiple studies.

Authors :
Vieland VJ
Walters KA
Lehner T
Azaro M
Tobin K
Huang Y
Brzustowicz LM
Source :
The American journal of psychiatry [Am J Psychiatry] 2014 Mar; Vol. 171 (3), pp. 350-9.
Publication Year :
2014

Abstract

Objective: The Combined Analysis of Psychiatric Studies (CAPS) project conducted extensive review and regularization across studies of all schizophrenia linkage data available as of 2011 from the National Institute of Mental Health-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI). The authors reanalyzed the data using statistical methods tailored to accumulation of evidence across multiple, potentially highly heterogeneous, sets of data.<br />Method: Data were subdivided based on contributing study, major population group, and presence or absence within families of schizophrenia with a substantial affective component. The posterior probability of linkage (PPL) statistical framework was used to sequentially update linkage evidence across these data subsets (omnibus results).<br />Results: While some loci previously implicated using the HGI data were also identified in the present omnibus analysis (2q36.1, 15q23), others were not. Several loci were found that had not previously been reported in the HGI samples but are supported by independent linkage or association studies (3q28, 12q23.1, 11p11.2, Xq26.1). Not surprisingly, differences were seen across population groups. Of particular interest are signals on 11p15.3, 11p11.2, and Xq26.1, for which data from families with a substantial affective component support linkage while data from the remaining families provide evidence against linkage. All three of these loci overlap with loci reported in independent studies of bipolar disorder or mixed bipolar-schizophrenia samples.<br />Conclusions: Public data repositories provide the opportunity to leverage large multisite data sets for studying complex disorders. Analysis with a statistical method specifically designed for such data enables us to extract new information from an existing data resource.

Details

Language :
English
ISSN :
1535-7228
Volume :
171
Issue :
3
Database :
MEDLINE
Journal :
The American journal of psychiatry
Publication Type :
Academic Journal
Accession number :
24170318
Full Text :
https://doi.org/10.1176/appi.ajp.2013.11121766