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November 1994

Diagnostic Interview for Genetic Studies: Rationale, Unique Features, and Training

Author Affiliations

collaborators from the NIMH Genetics Initiative
From the Departments of Psychiatry at Indiana University Medical Center, Indianapolis (Dr Nurnberger and Ms York-Cooler), Columbia University, New York, NY (Drs Kaufmann, Harkavy-Friedman, and Malaspina), The Johns Hopkins University, Baltimore, Md (Dr Simpson), and Washington University, St Louis, Mo (Dr Reich); and the National Institute of Mental Health (NIMH) Division of Clinical and Treatment Research, Rockville, Md (Dr Blehar and Ms Severe). A list of the NIMH Genetics Initiative Collaborators appears at the end.

Arch Gen Psychiatry. 1994;51(11):849-859. doi:10.1001/archpsyc.1994.03950110009002

This the Diagnostic Interview for Genetic Studies (DIGS), a clinical interview especially constructed for the assessment of major mood and psychotic disorders and their spectrum conditions. The DIGS, which was developed and piloted as a collaborative effort of investigators from sites in the National Institute of Mental Health (NIMH) Genetics Initiative, has the following additional features: (1) polydiagnostic capacity; (2) a detailed assessment of the course of the illness, chronology of psychotic and mood syndromes, and comorbidity; (3) additional phenomenologic assessments of symptoms; and (4) algorithmic scoring capability. The DIGS is designed to be employed by interviewers who exercise significant clinical judgment and who summarize information in narrative form as well as in ratings. A two-phase test-retest (within-site, between-site) reliability study was carried out for DSM-III-R criteria—based major depression, bipolar disorder, schizophrenia, and schizoaffective disorder. Reliabilities using algorithms were excellent (0.73 to 0.95), except for schizoaffective disorder, for which disagreement on estimates of duration of mood syndromes relative to psychosis reduced reliability. A final best-estimate process using medical records and information from relatives as well as algorithmic diagnoses is expected to be more reliable in making these distinctions. The DIGS should be useful as part of archival data gathering for genetic studies of major affective disorders, schizophrenia, and related conditions.