OBJECTIVE: The purpose of this study was to assemble and validate a database of phenotypic variables that were collected from families with bipolar disorder as a resource for genetic and other biological studies. METHOD: Participants were ascertained for two bipolar disorder genetic linkage studies: the University of Chicago, Johns Hopkins, and National Institute of Mental Health (NIMH) Intramural Program (CHIP) Collaboration and the NIMH Genetics Initiative project. All participants underwent detailed, phenotypic assessment with either the Schedule for Affective Disorders and Schizophrenia-Lifetime Version or one of four versions of the Diagnostic Interview for Genetic Studies. Clinicians reviewed the interview items and derived variable definitions that were used to extract data from the original datasets. The combined data were subjected to range and logic assessments, and a subset was re-verified against the original data. Inconsistent data and variables that were deemed unreliable were excluded. Several of the resulting variables were characterized in the total cohort and tested for familial clustering, heritability, and statistical power in genetic linkage and association studies. RESULTS: The combined database of phenotypic variables contained 197 variables on 5,721 subjects in 1,177 families. Deoxyribonucleic acid (DNA) samples are available for 5,373 of these subjects. The clinical presentation of bipolar disorder varied markedly. Most subjects suffered from serious and often disabling illness. Many phenotypic variables are strongly familial, and some quantitative variables are highly heritable. The cohort assembled in this study offers substantial power to carry out genetic linkage and association studies that use specific clinical features as covariates or as primary phenotypes. CONCLUSIONS: This is the largest database of phenotypic variables yet assembled for bipolar disorder, and it is now available to the research community. Researchers and clinicians can use this database to explore the connections between phenomenology and genetics in a cohort that is adequately powered to detect even modest genetic effects in bipolar disorder.
OBJECTIVE: The purpose of this study was to assemble and validate a database of phenotypic variables that were collected from families with bipolar disorder as a resource for genetic and other biological studies. METHOD:Participants were ascertained for two bipolar disorder genetic linkage studies: the University of Chicago, Johns Hopkins, and National Institute of Mental Health (NIMH) Intramural Program (CHIP) Collaboration and the NIMH Genetics Initiative project. All participants underwent detailed, phenotypic assessment with either the Schedule for Affective Disorders and Schizophrenia-Lifetime Version or one of four versions of the Diagnostic Interview for Genetic Studies. Clinicians reviewed the interview items and derived variable definitions that were used to extract data from the original datasets. The combined data were subjected to range and logic assessments, and a subset was re-verified against the original data. Inconsistent data and variables that were deemed unreliable were excluded. Several of the resulting variables were characterized in the total cohort and tested for familial clustering, heritability, and statistical power in genetic linkage and association studies. RESULTS: The combined database of phenotypic variables contained 197 variables on 5,721 subjects in 1,177 families. Deoxyribonucleic acid (DNA) samples are available for 5,373 of these subjects. The clinical presentation of bipolar disorder varied markedly. Most subjects suffered from serious and often disabling illness. Many phenotypic variables are strongly familial, and some quantitative variables are highly heritable. The cohort assembled in this study offers substantial power to carry out genetic linkage and association studies that use specific clinical features as covariates or as primary phenotypes. CONCLUSIONS: This is the largest database of phenotypic variables yet assembled for bipolar disorder, and it is now available to the research community. Researchers and clinicians can use this database to explore the connections between phenomenology and genetics in a cohort that is adequately powered to detect even modest genetic effects in bipolar disorder.
Authors: Shanker Swaminathan; Daniel L Koller; Tatiana Foroud; Howard J Edenberg; Xiaoling Xuei; Alexander B Niculescu; John I Nurnberger Journal: J Affect Disord Date: 2015-03-14 Impact factor: 4.839
Authors: Pamela DeRosse; Todd Lencz; Katherine E Burdick; Samuel G Siris; John M Kane; Anil K Malhotra Journal: Schizophr Bull Date: 2008-07-15 Impact factor: 9.306
Authors: Jiajun Shi; Judith A Badner; Eiji Hattori; James B Potash; Virginia L Willour; Francis J McMahon; Elliot S Gershon; Chunyu Liu Journal: Am J Med Genet B Neuropsychiatr Genet Date: 2008-10-05 Impact factor: 3.568