OBJECTIVES: Family and genetic studies indicate overlapping liability for major depressive disorder and bipolar disorder. The purpose of the present study was to determine whether this shared genetic liability influences clinical presentation. METHODS: A polygenic risk score for bipolar disorder, derived from a large genome-wide association meta-analysis, was generated for each subject of European-American ancestry (n = 1,274) in the Sequential Treatment Alternatives to Relieve Depression study (STAR*D) outpatient major depressive disorder cohort. A hypothesis-driven approach was used to test for association between bipolar disorder risk score and features of depression associated with bipolar disorder in the literature. Follow-up analyses were performed in two additional cohorts. RESULTS: A generalized linear mixed model including seven features hypothesized to be associated with bipolar spectrum illness was significantly associated with bipolar polygenic risk score [F = 2.07, degrees of freedom (df) = 7, p = 0.04]. Features included early onset, suicide attempt, recurrent depression, atypical depression, subclinical mania, subclinical psychosis, and severity. Post-hoc univariate analyses demonstrated that the major contributors to this omnibus association were onset of illness at age ≤ 18 years [odds ratio (OR) = 1.2, p = 0.003], history of suicide attempt (OR = 1.21, p = 0.03), and presence of at least one manic symptom (OR = 1.16, p = 0.02). The maximal variance in these traits explained by polygenic score ranged from 0.8% to 1.1%. However, analyses in two replication cohorts testing a five-feature model did not support this association. CONCLUSIONS: Bipolar genetic loading appeared to be associated with bipolar-like presentation in major depressive disorder in the primary analysis. However, the results were at most inconclusive because of lack of replication. Replication efforts were challenged by different ascertainment and assessment strategies in the different cohorts. The methodological approach described here may prove useful in applying genetic data to clarify psychiatric nosology in future studies.
OBJECTIVES: Family and genetic studies indicate overlapping liability for major depressive disorder and bipolar disorder. The purpose of the present study was to determine whether this shared genetic liability influences clinical presentation. METHODS: A polygenic risk score for bipolar disorder, derived from a large genome-wide association meta-analysis, was generated for each subject of European-American ancestry (n = 1,274) in the Sequential Treatment Alternatives to Relieve Depression study (STAR*D) outpatientmajor depressive disorder cohort. A hypothesis-driven approach was used to test for association between bipolar disorder risk score and features of depression associated with bipolar disorder in the literature. Follow-up analyses were performed in two additional cohorts. RESULTS: A generalized linear mixed model including seven features hypothesized to be associated with bipolar spectrum illness was significantly associated with bipolar polygenic risk score [F = 2.07, degrees of freedom (df) = 7, p = 0.04]. Features included early onset, suicide attempt, recurrent depression, atypical depression, subclinical mania, subclinical psychosis, and severity. Post-hoc univariate analyses demonstrated that the major contributors to this omnibus association were onset of illness at age ≤ 18 years [odds ratio (OR) = 1.2, p = 0.003], history of suicide attempt (OR = 1.21, p = 0.03), and presence of at least one manic symptom (OR = 1.16, p = 0.02). The maximal variance in these traits explained by polygenic score ranged from 0.8% to 1.1%. However, analyses in two replication cohorts testing a five-feature model did not support this association. CONCLUSIONS: Bipolar genetic loading appeared to be associated with bipolar-like presentation in major depressive disorder in the primary analysis. However, the results were at most inconclusive because of lack of replication. Replication efforts were challenged by different ascertainment and assessment strategies in the different cohorts. The methodological approach described here may prove useful in applying genetic data to clarify psychiatric nosology in future studies.
Authors: Robert M A Hirschfeld; Charles Holzer; Joseph R Calabrese; Myrna Weissman; Michael Reed; Marilyn Davies; Mark A Frye; Paul Keck; Susan McElroy; Lydia Lewis; Jonathan Tierce; Karen D Wagner; Elizabeth Hazard Journal: Am J Psychiatry Date: 2003-01 Impact factor: 18.112
Authors: Jon S Novick; Jonathan W Stewart; Stephen R Wisniewski; Ian A Cook; Radmila Manev; Andrew A Nierenberg; Jerrold F Rosenbaum; Kathy Shores-Wilson; G K Balasubramani; Melanie M Biggs; Sid Zisook; A John Rush Journal: J Clin Psychiatry Date: 2005-08 Impact factor: 4.384
Authors: A John Rush; Madhukar H Trivedi; Hicham M Ibrahim; Thomas J Carmody; Bruce Arnow; Daniel N Klein; John C Markowitz; Philip T Ninan; Susan Kornstein; Rachel Manber; Michael E Thase; James H Kocsis; Martin B Keller Journal: Biol Psychiatry Date: 2003-09-01 Impact factor: 13.382
Authors: Thilo Deckersbach; Roy H Perlis; W Gordon Frankle; Stephen M Gray; Louisa Grandin; Darin D Dougherty; Andrew A Nierenberg; Gary S Sachs Journal: CNS Spectr Date: 2004-03 Impact factor: 3.790
Authors: Roy H Perlis; Jordan W Smoller; Maurizio Fava; Jerrold F Rosenbaum; Andrew A Nierenberg; Gary S Sachs Journal: J Affect Disord Date: 2004-04 Impact factor: 4.839
Authors: Kai Xiang Lim; Frühling Rijsdijk; Saskia P Hagenaars; Adam Socrates; Shing Wan Choi; Jonathan R I Coleman; Kylie P Glanville; Cathryn M Lewis; Jean-Baptiste Pingault Journal: PLoS Med Date: 2020-06-01 Impact factor: 11.069