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Genome-wide linkage scan of 98 bipolar pedigrees and analysis of clinical covariates
Genome-wide linkage scan of 98 bipolar pedigrees and analysis of clinical covariates
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Genome-wide linkage scan of 98 bipolar pedigrees and analysis of clinical covariates
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Genome-wide linkage scan of 98 bipolar pedigrees and analysis of clinical covariates
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Genome-wide linkage scan of 98 bipolar pedigrees and analysis of clinical covariates
Genome-wide linkage scan of 98 bipolar pedigrees and analysis of clinical covariates
Journal Article

Genome-wide linkage scan of 98 bipolar pedigrees and analysis of clinical covariates

2007
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Overview
Despite compelling evidence that genetic factors contribute to bipolar disorder (BP), attempts to identify susceptibility genes have met with limited success. This may be due to the genetic heterogeneity of the disorder. We sought to identify susceptibility loci for BP in a genome-wide linkage scan with and without clinical covariates that might reflect the underlying heterogeneity of the disorder. We genotyped 428 subjects in 98 BP families at the Center for Inherited Disease Research with 402 microsatellite markers. We first carried out a non-parametric linkage analysis with MERLIN, and then reanalyzed the data with LODPAL to incorporate clinical covariates for age at onset (AAO), psychosis and comorbid anxiety. We sought to further examine the top findings in the covariate analysis in an independent sample of 64 previously collected BP families. In the non-parametric linkage analysis, three loci were nominally significant under a narrow diagnostic model and seven other loci were nominally significant under a broader model. The top findings were on chromosomes 2q24 and 3q28. The covariate analyses yielded additional evidence for linkage on 3q28 with AAO in the primary and independent samples. Although none of the linked loci were genome-wide significant, their congruence with prior results and, for the covariate analyses, their identification in two separate samples increases the likelihood that they are true positives and deserve further investigation. These findings further demonstrate the value of considering clinical features that may reflect the underlying heterogeneity of disease in order to facilitate gene mapping.