GENETICS OF BIPOLAR DISORDER IN LATINO POPULATIONS

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 2007 OBJECTIVE: The goal of this project is to collect diagnostic information and DNA samples from 385 families of Latino descent, each with a minimum of two siblings affected with BPI (DSM-IV diagnosis) in order to detect BPI susceptibility loci in this population. Endophenotypes defined by neurocognitive and structural MRI tests related to BPI will be validated in these pedigrees and genes which contribute to these biologic variables will also be mapped through covariate quantitative trait analyses. RESEARCH PLAN AND METHODS: Imaging data will be acquired for a subset of subjects (from approximately 30 families) at the San Antonio site in order to validate structural imaging measures as endophenotypes. We will obtain MRIs for approximately 60 subjects a year (15 siblings with BPI, 30 unaffected siblings, and 15 controls) over a four year period. We have extensive experience in obtaining these MRIs for BP subjects and controls and have done so for 140 subjects over the last two years. We will use a GE/Elscint Prestige 2-T high-field magnetic resonance (MR) scanner, located at the UTHSCSA Research Imaging Center (RIC). The scans will be performed by a highly trained MR technician. A sagittal scout series is first obtained to verify patient position, image quality, and clarity of the full extent of the aqueduct of sylvius to locate a midline sagittal image. 3D T1-FFE (T-1 weighted fast field echo) will be performed in the coronal plane with TR of 25 ms, TE of 5 ms, flip angle of 40o, field of view (FOV) 240 mm x 220 mm, slice thickness of 1.0 mm, NEX=2 and matrix size of 256x192, to obtain images covering the entire brain. We will additionally use a spin echo sequence to obtain T2 and proton density images in the axial plane and screen for neuroradiological abnormalities. The total time to collect the 3D T1-FFE and spin echo sequence is approximately 25 minutes. The proposed regions-of-interest (ROIs) will be identified and delineated according to previously published methods for anatomical MRI measurements of these particular brain regions. The anatomical regions will be identified in 3 anatomical planes, in reference to standard anatomical brain atlases, and examination of the three-dimensional volume-rendered view of these structures will be used to facilitate identification of boundaries. All morphometric measures will be performed by raters well trained to do these specific procedures, and who have achieved inter-rater reliability of over 0.90 for these specific measurements. The volume of gray and white matter and cerebrospinal fluid (CSF) in the brain ROIs will be estimated using the 3D T1-FFE MRI data and a semi-automated segmentation method based on the generation of histogram of gray, white matter and cerebrospinal fluid pixel intensities. This method uses a histogram approach to determine the thresholds for separation of gray matter, white matter, and CSF, as per method previously described 180,181,186,187. With the proposed sample size of 60 affected individuals and 60 healthy comparison subjects, we will have 80% power (1-b%) to reject the null hypothesis (a=0.05) of no between group difference for a contrast with small effect size (minimum effect size = 0.25). Given the effect sizes of case-control contrast for the anterior cingulate (effect size=1.08) and left amygdala (effect size=0.64) reported above, we anticipate power to detect between group differences to vary from 96 to 99.9%. Assuming that volumetric measurements for unaffected relatives are midway between affected and unrelated individuals, with 120 unaffected family members and 60 healthy comparison subjects, we anticipate power to detect between group differences to range from 59 to 99.6%, depending on the ROI.