
Am J Psychiatry 157:1552-1562, October 2000
© 2000 American Psychiatric Association
Genetic Epidemiology of Major Depression: Review and Meta-Analysis
Patrick F. Sullivan, M.D., F.R.A.N.Z.C.P.,
Michael C. Neale, Ph.D., and
Kenneth S. Kendler, M.D.
Abstract
OBJECTIVE: The authors conducted a meta-analysis of relevant data from primary studies of the genetic epidemiology of major depression.METHOD: The authors searched MEDLINE and the reference lists of previous review articles to identify relevant primary studies. On the basis of a review of family, adoption, and twin studies that met specific inclusion criteria, the authors derived quantitative summary statistics. RESULTS: Five family studies met the inclusion criteria. The odds ratios for proband (subjects with major depression or comparison subjects) versus first-degree relative status (affected or unaffected with major depression) were homogeneous across the five studies (Mantel-Haenszel odds ratio=2.84, 95% CI=2.313.49). No adoption study met the inclusion criteria, but the results of two of the three reports were consistent with genetic influences on liability to major depression. Five twin studies met the inclusion criteria, and their statistical summation suggested that familial aggregation was due to additive genetic effects (point estimate of heritability of liability=37%, 95% CI=31%42%), with a minimal contribution of environmental effects common to siblings (point estimate=0%, 95% CI=0%5%), and substantial individual-specific environmental effects/measurement error (point estimate=63%, 95% CI=58%67%). The literature suggests that recurrence best predicts the familial aggregation of major depression. CONCLUSIONS: Major depression is a familial disorder, and its familiality mostly or entirely results from genetic influences. Environmental influences specific to an individual are also etiologically significant. Major depression is a complex disorder that does not result from either genetic or environmental influences alone but rather from both. These findings are notably consistent across samples and methods and are likely to be generally applicable.
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