0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Original Investigation |

Multisystem Component Phenotypes of Bipolar Disorder for Genetic Investigations of Extended Pedigrees FREE

Scott C. Fears, MD, PhD1; Susan K. Service, MS1; Barbara Kremeyer, PhD2; Carmen Araya, Lic3; Xinia Araya, Lic3; Julio Bejarano, MS3; Margarita Ramirez, Lic3; Gabriel Castrillón, BSc4; Juliana Gomez-Franco, MD5; Maria C. Lopez, MSW5; Gabriel Montoya, MD, MSc5; Patricia Montoya, MA5; Ileana Aldana, MPH1; Terri M. Teshiba, BA1; Zvart Abaryan, BSc1; Noor B. Al-Sharif, BSc1; Marissa Ericson, PhD1; Maria Jalbrzikowski, PhD1; Jurjen J. Luykx, MD, PhD1,6; Linda Navarro, MS1; Todd A. Tishler, PhD1; Lori Altshuler, MD1; George Bartzokis, MD1; Javier Escobar, MD7; David C. Glahn, PhD8,9; Jorge Ospina-Duque, MD5; Neil Risch, PhD10; Andrés Ruiz-Linares, MD, PhD11; Paul M. Thompson, PhD1; Rita M. Cantor, PhD1; Carlos Lopez-Jaramillo, MD, PhD5,12; Gabriel Macaya, PhD3; Julio Molina, MD1,13; Victor I. Reus, MD14; Chiara Sabatti, PhD15; Nelson B. Freimer, MD1; Carrie E. Bearden, PhD1
[+] Author Affiliations
1Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
2Wellcome Trust Sanger Institute, Hinxton, England
3Cell and Molecular Biology Research, Universidad de Costa Rica, San Pedro de Montes de Oca, Costa Rica
4Instituto de Alta Tecnología Médica de Antioquia, Medellín, Colombia
5Grupo de Investigación en Psiquiatría, Departamento de Psiquiatría, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
6Department of Psychiatry, ZNA Stuivenberg, Antwerp, Belgium
7Department of Psychiatry and Family Medicine, University of Medicine and Dentistry of New Jersey–Robert Wood Johnson Medical School, New Brunswick
8Department of Psychiatry, Yale University, New Haven, Connecticut
9Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut
10Institute for Human Genetics, University of California, San Francisco
11Department of Genetics, Evolution, and Environment, University College London, London, England
12Mood Disorders Program, Hospital San Vicente Fundacion, Medellín, Colombia
13BioCiencias Lab, Guatemala, Guatemala
14Department of Psychiatry, University of California, San Francisco
15Department of Health Research and Policy, Stanford University, Stanford, California
JAMA Psychiatry. 2014;71(4):375-387. doi:10.1001/jamapsychiatry.2013.4100.
Text Size: A A A
Published online

Importance  Genetic factors contribute to risk for bipolar disorder (BP), but its pathogenesis remains poorly understood. A focus on measuring multisystem quantitative traits that may be components of BP psychopathology may enable genetic dissection of this complex disorder, and investigation of extended pedigrees from genetically isolated populations may facilitate the detection of specific genetic variants that affect BP as well as its component phenotypes.

Objective  To identify quantitative neurocognitive, temperament-related, and neuroanatomical phenotypes that appear heritable and associated with severe BP (bipolar I disorder [BP-I]) and therefore suitable for genetic linkage and association studies aimed at identifying variants contributing to BP-I risk.

Design, Setting, and Participants  Multigenerational pedigree study in 2 closely related, genetically isolated populations: the Central Valley of Costa Rica and Antioquia, Colombia. A total of 738 individuals, all from Central Valley of Costa Rica and Antioquia pedigrees, participated; among them, 181 have BP-I.

Main Outcomes and Measures  Familial aggregation (heritability) and association with BP-I of 169 quantitative neurocognitive, temperament, magnetic resonance imaging, and diffusion tensor imaging phenotypes.

Results  Of 169 phenotypes investigated, 126 (75%) were significantly heritable and 53 (31%) were associated with BP-I. About one-quarter of the phenotypes, including measures from each phenotype domain, were both heritable and associated with BP-I. Neuroimaging phenotypes, particularly cortical thickness in prefrontal and temporal regions as well as volume and microstructural integrity of the corpus callosum, represented the most promising candidate traits for genetic mapping related to BP based on strong heritability and association with disease. Analyses of phenotypic and genetic covariation identified substantial correlations among the traits, at least some of which share a common underlying genetic architecture.

Conclusions and Relevance  To our knowledge, this is the most extensive investigation of BP-relevant component phenotypes to date. Our results identify brain and behavioral quantitative traits that appear to be genetically influenced and show a pattern of BP-I association within families that is consistent with expectations from case-control studies. Together, these phenotypes provide a basis for identifying loci contributing to BP-I risk and for genetic dissection of the disorder.

Figures in this Article

Bipolar disorder (BP) encompasses a broad range of phenotypic features. However, most research into its etiology has focused on the overall syndrome16 rather than on its components. Although genome-wide association studies have identified the first replicated loci contributing to BP susceptibility,36 the small relative risk attributed to these loci may reflect the complex genetic nature of the disorder. This possibility motivates efforts to identify heritable BP-associated quantitative traits for which the genetic basis is simpler and for which higher-impact variants may be detected.712

We describe our investigation, in 26 pedigrees selected for multiple cases of severe BP (bipolar I disorder [BP-I]), of quantitative traits hypothesized to represent components of the biology underlying BP. Previous studies of these measures demonstrated association with BP, deficits in euthymic individuals with BP, and values in family members without BP that are intermediate between those of their relatives with BP and control participants. These phenotypes assay temperament,1315 perceptual creativity,1618 neurocognitive function,1921 and neuroanatomy (via structural magnetic resonance imaging [MRI] and diffusion tensor imaging [DTI]).2224 We also measured sleep, activity, and circadian rhythms, analyses of which are ongoing and will be reported separately.

Previously described pedigrees, including many of those evaluated here,2528 show BP segregation patterns suggesting the transmission of high-impact risk alleles. However, linkage studies of such pedigrees have yielded equivocal results, presumably because BP is genetically complex even within these families.3 The feasibility of identifying rare, high-impact variants through next-generation sequencing has stimulated renewed interest in pedigree studies; however, even with this technology, the etiological complexity of BP hinders the identification of risk variants. We hypothesize that BP results from the confluence of multiple etiological processes, each of which alone may be simpler to unravel. Investigation of quantitative component phenotypes in pedigrees from population isolates such as the genetically related isolates of the Central Valley of Costa Rica (CVCR) and Antioquia, Colombia (ANT),2931 from which we recruited the pedigrees investigated herein, may lead to a better understanding of the heritable components of the disorder and at the same time simplify the search for specific genetic risk factors.

We report results from evaluations of the most extensive set of putative BP component phenotypes yet assessed within any study sample. For each measure, we describe its degree of familial aggregation (an indicator of heritability [h2]) and of association with BP-I. These results suggest multiple phenotypes for genetic investigations of BP-I across the domains of temperament, neurocognition, and neuroanatomy.

Sample

We investigated pedigrees from ANT (11) and CVCR (15), ascertained in previous genetic studies2528,3236 through hospitals and clinics in each country, using genealogic information to extend each pedigree. To prioritize pedigree branches for quantitative phenotyping, we recruited nuclear families including at least 1 member with known BP-I (based on the Diagnostic Interview for Genetics Studies37,38 and/or extensive medical records), available parents, and at least 2 siblings without BP-I (eAppendix 1 in Supplement). Families varied considerably in size (12-355 members; mean, 55 members) and in the number of individuals phenotyped in this study (3-177 individuals; mean, 29 individuals) (Table 1). Written informed consent was obtained from each participant. Institutional review boards at participating institutions approved all study procedures.

Table Graphic Jump LocationTable 1.  Sample Characteristics by Country and Family
Clinical Assessments

To establish DSM-IV diagnoses, we used a best-estimate process modified from previous procedures33 (eAppendix 1 in Supplement) and including diagnostic interviews using Spanish versions of the Mini International Neuropsychiatric Interview39 and the Diagnostic Interview for Genetics Studies. Individuals designated as having BP-I had a best-estimate diagnosis of BP-I, unipolar mania, or schizoaffective disorder, bipolar type, as in previous studies.27,33,40 The Young Mania Rating Scale41 and the 17-item Hamilton Depression Rating Scale42 were administered at the time of assessment and identified individuals with significant mood symptoms (Young Mania Rating Scale Score >14 or Hamilton Depression Rating Scale score >14), whom we excluded from analyses of temperament and neurocognitive measures.

Temperament and Neurocognitive Assessment

Temperament and neurocognitive measures, assessed in 738 subjects, had previously demonstrated heritability and association to BP1316,2224 (Table 2). The temperament battery, 15 measures generated from 7 instruments (eAppendix 1 in Supplement), included multiple dimensions categorized into 4 subdomains: affective temperament, impulsivity/risk taking, perceptual creativity, and delusion proneness (Table 2). The neurocognitive battery (eAppendix 1 in Supplement) included a computerized neuropsychological evaluation51 and paper-and-pencil measures of verbal abilities, inhibitory control,55 and declarative memory.52

Table Graphic Jump LocationTable 2.  Behavioral Measures to Generate Phenotypes
Neuroimaging

We acquired T1-weighted structural neuroimages on 1.5-T scanners from 527 subjects (285 from CVCR and 242 from ANT) (eAppendix 1 in Supplement), implementing protocols for acquisition of DTIs in ANT only. We used Freesurfer software,57,58 with manual inspection of intermediate steps in the processing stream to correct common errors, to generate 96 structural MRI phenotypes, including measures of volume, surface area, and cortical thickness (Table 3, eTable 1 in Supplement).61,62

Table Graphic Jump LocationTable 3.  Neuroimaging Measures to Generate Phenotypes

We determined DTI phenotypes (eAppendix 1 in Supplement) with Functional MRI of the Brain (FMRIB) Software Library software59,60 using the Johns Hopkins University probabilistic tractography atlas63 to determine and customize regions of interest, which we limited to tracts previously associated with BP.6466 In total, we generated 18 DTI phenotypes across 3 categories: fractional anisotropy, indicating the degree of anisotropy; axial diffusivity, or diffusivity along the major axis of diffusion; and radial diffusivity, an average of the diffusivities along the 2 minor axes6770 (Table 3, eTable 1 in Supplement).

Statistical Analysis

We assessed familial aggregation of traits using SOLAR version 6.3.6 software,71 which implements a variance component method to estimate the proportion of phenotypic variance due to additive genetic factors (narrow-sense heritability). This model partitions total variability into polygenic and environmental components. The environmental component is unique to individuals, while the polygenic component is shared between individuals as a function of their pedigree kinship. If the variance in phenotype Y due to the polygenic component is designated as σg2 and the environmental component as σe2, then in this model Var(Y) = σg2 + σe2, and the covariance between phenotype values of individuals i and j is Cov(Yi, Yj) = 2(φij)(σg2), where φij is the kinship between individuals i and j.

Variance components analysis is sensitive to outliers and nonnormal trait distributions. To guard against potential statistical artifacts induced by skewed distributions, we used, prior to analysis, a rank-based procedure72 to inverse normal transform all phenotypes. This transformation, implemented within SOLAR, is standard in variance component analyses as it does not induce correlations between relatives or lead to inflated estimates of heritability.73

We regressed all phenotypes on 3 covariates (sex, age, and country). Additional covariates included years of education (temperament and neurocognitive measures), body weight (T1-weighted and DTI variables), intracranial volume (volume measurements from T1-weighted images), and total cortical surface area (regional surface area measures). We implemented regressions in SOLAR with pedigree structures using residuals from these models in all further analyses.

We tested for difference in trait means between individuals with and without a diagnosis of BP-I (BP-I association analyses), using SOLAR to account for dependencies among relatives. We controlled the family-wise error rate at the 0.05 level, using a Bonferroni-corrected threshold for each test (heritability and BP-I association; P < 2.96 × 10−4). We used published evidence to assign each trait an expected a priori direction of change, designating them as BP-I associated only if the difference was in the a priori assigned direction, therefore using a 1-tailed test (eTable 1 in Supplement).

We estimated phenotypic correlations for all trait pairs. Genetic correlations were estimated for all pairs in which both traits were significantly heritable using SOLAR.74 Graphs of the estimated correlation structures used methods described in eAppendix 1 in the Supplement.

Sample Characteristics

Table 1 shows summary statistics for the sample by family; eTable 2 in the Supplement provides additional clinical characterization of the 181 participants who met best-estimate criteria for BP-I. We excluded 5 individuals with elevated Young Mania Rating Scale or Hamilton Depression Rating Scale scores from analyses of neurocognitive and temperament data, and we excluded 5 additional individuals from BP-I association analyses (but not from heritability analyses) because a BP-I diagnosis could be neither confirmed nor excluded.

Heritability and Association With BP-I

Of the 169 traits examined, 126 (75%) were significantly heritable, 53 (31%) were significantly associated with BP-I, and 41 (24%) were both heritable and associated with BP-I (Figure 1, eTable 1 in Supplement). These results were robust with respect to phenotype variations across pedigrees and countries (data not shown) and to outliers (eAppendix 2 and eFigure in Supplement); for secondary analyses of the effects of medications and duration of illness on trait values, see eAppendix 3 in the Supplement. Results within each domain are described here.

Place holder to copy figure label and caption
Figure 1.
Summary of Analyses of Heritability and Association With Bipolar I Disorder

The results of analyses of heritability and of association with bipolar I disorder (BP-I) are shown as 2 histograms stacked on top of each other. Inner histogram purple bars show the magnitude of the heritability estimate for each component phenotype, and the blue box next to the trait name at the outer edge of the plot indicates estimates that passed the significance threshold. Outer histogram shows the magnitude of the estimated regression coefficient for the BP-I association test. Orange bars show positive coefficients representing traits that are higher in participants with BP-I compared with family members without BP-I. Green bars show negative coefficients representing traits that are lower in participants with BP-I. A red box at the outer edge of the circle indicates traits that exceeded the significance threshold for association with BP-I. AIM indicates Abstraction, Inhibition, and Working Memory Task; BART, Balloon Analogue Risk Task; CVLT, California Verbal Learning Test; IP-CPT, Identical Pairs Continuous Performance Test; MRI, magnetic resonance imaging; PCET, Penn Conditional Exclusion Test; SCAP, Spatial Capacity Delayed Response Test; SST, Stop Signal Task; TEMPS, Temperament Evaluation of Memphis, Pisa, Paris, and San Diego; TONI, Test of Nonverbal Intelligence; VWM, verbal working memory; WASI, Wechsler Abbreviated Scale of Intelligence; and WMS, Wechsler Memory Scale.

Graphic Jump Location
Temperament

Six of the 15 temperament measures demonstrated significant heritability, although overall this domain showed the lowest estimates of additive genetic influence (h2 of approximately 0.18-0.30). In contrast, 3 temperament traits displayed the strongest BP-I associations of all 169 measures: Temperament Evaluation of Memphis, Pisa, Paris, and San Diego cyclothymia scale, Barratt Impulsiveness Scale, and Peters et al Delusions Inventory. Delusion proneness (Peters et al Delusions Inventory) and perceptual creativity (Barron-Welsh Art Scale dislike subscale) were both heritable and associated with BP-I, while risk-taking propensity (Balloon Analogue Risk Task) was neither heritable nor associated with BP-I.

Neurocognition

Some measures from all domains assessed showed significant heritability and BP-I associations. Most measures of processing speed, long-term memory, and verbal fluency were significantly heritable (13 of 19); within this heritable subset, most were associated with BP-I (9 of 13). Within working memory assessments, verbal but not spatial tasks showed evidence of heritability, and participants with BP-I showed significant impairment on measures of sustained attention (Identical Pairs Continuous Performance Test), spatial working memory (Spatial Capacity Delayed Response Test), and verbal working memory tasks (letter-number sequencing). Measures of inhibitory control (Stroop Color-Word Interference Test and Stop Signal Task) showed evidence for impairment in participants with BP-I; among these measures, the Stroop measures (Stroop Color-Word Interference Test trials, time, and number of errors) were also heritable. Nonverbal abstract reasoning measures (Abstraction, Inhibition, and Working Memory Task, Test of Nonverbal Intelligence, matrix reasoning) were neither significantly heritable nor associated with BP-I.

Neuroimaging

Most neuroimaging phenotypes (approximately 88%) were significantly heritable, and a substantial number of these measures were significantly associated with BP-I. Several global measures differed between participants with BP-I and their relatives without BP-I (decreased total cerebral gray and white matter and cerebellar volumes, with corresponding increases in third-ventricle volume). Localized reductions were also observed in several structures (Figure 2), including hippocampus and ventral diencephalon (while amygdala and thalamus showed a similar trend). The T1-weighted and DTI sequences provided convergent evidence for BP-I–related changes in the corpus callosum; participants with BP-I showed decreases in volume (total corpus callosum and 4 of the 5 corpus callosal subdivisions) and overall fractional anisotropy, while increased radial diffusivity in the splenium of the corpus callosum indicated reduced white matter integrity.

Place holder to copy figure label and caption
Figure 2.
Structural Neuroimaging Phenotypes

A, Results of the heritability and bipolar I disorder (BP-I) association analyses of volumetric magnetic resonance imaging phenotypes. The 3 representative T1-weighted coronal magnetic resonance images depict the results of the Freesurfer segmentation overlaid as colored masks selected to better distinguish the anatomy. Mask colors are not related to the results. The colors of the text labels indicate structures that showed significant evidence of familial aggregation (blue) and structures that were both heritable and associated with BP-I (magenta). B, Cortical thickness phenotypes and results of the heritability and BP-I association analysis for cortical gray matter thickness. The medial surface is rotated upward by 60° to provide a view of the ventral surface.

Graphic Jump Location

Compared with relatives without BP-I, participants with BP-I displayed widespread reduction of cortical thickness in heteromodal association regions in most of the prefrontal and temporal cortex, including the superior temporal gyrus, inferior temporal gyrus, fusiform, and lingual regions (Figure 2B). Most lateral prefrontal cortex regions, including all subregions of the inferior frontal gyrus and lateral orbitofrontal cortex, were significantly thinner in participants with BP-I. In contrast, the medial orbitofrontal region was neither heritable nor associated with BP-I. Another exception to the overall pattern of findings was the superior frontal gyrus, which showed BP-I–associated gray matter reduction but was not significantly heritable. Most measures of regional surface area were heritable but were not significantly associated with BP-I.

Evaluation of Between-Trait Phenotypic and Genetic Correlations

Using false discovery rate methods, we determined thresholds (t) for rejecting the null hypothesis of correlation = 0; t = 2.58 SEs from 0 for phenotypic correlations (ρp) and 2.81 SEs from 0 for genetic correlations (ρg). About 20% of trait pairs (2117 of 10 585) exceeded t for ρp and 10% of heritable pairs (539 of 5460) exceeded t for ρg. Schematic representations (eAppendix 1 in Supplement) of the networks of phenotypic and genetic correlations (Figure 3) demonstrate the clustering of phenotypes by domain, showing no clear separation between heritable and nonheritable traits (circles and squares, respectively). Similarly, BP-I–associated traits showed no distinct clustering (nodes with a red border). The network structure of the genetic correlations was sparser than, but qualitatively similar to, that of phenotypic correlations. Traits mainly clustered within phenotypic domains, but some genetic correlations across domains were observed, such as Stroop errors with rostral middle frontal and inferior parietal surface area (Figure 3B; nodes 34, 87, and 107).

Place holder to copy figure label and caption
Figure 3.
Network Graph of Correlations Among Phenotypes

Network representations of pairwise phenotypic correlations (A) and genetic correlations (B). All trait pairs were included in the phenotypic correlation analysis, and only pairs in which both traits were heritable were included in the genetic correlation analysis. Nodes are colored according to their assigned subdomain (see Subdomain column in eTable 1 in Supplement). Circular nodes indicate significantly heritable phenotypes; square nodes, nonheritable phenotypes. Traits that were significantly associated with bipolar I disorder have a red border. Nodes are connected with an edge when the hypothesis of correlation = 0 was rejected using false discovery rate–controlled thresholds. Numbers correspond to plot identification numbers for phenotypes detailed in eTable 1 in the Supplement. MRI indicates magnetic resonance imaging. B, Examples of genetically correlated traits mentioned in the text include the hippocampus (67), amygdala (56), and surface area of the pars opercularis (97) as well as Stroop Color-Word Interference Test errors (34) with surface area measures from the inferior parietal (87) and rostral middle frontal (107) regions of interest.

Graphic Jump Location

Through the most comprehensive evaluation to date of BP component phenotypes, we delineated measures that may help elucidate the genetic contribution to BP-I risk. Gauging the potential informativeness of traits based on their heritability and association with BP-I, we can divide them into 4 groups.

Measures that demonstrate both heritability and association with BP-I (group 1) are the most promising phenotypes for identifying loci contributing to disease risk, as shown for other neuropsychiatric disorders.75 Analyses at loci linked to and/or associated with both BP-I and a group 1 phenotype will suggest the degree of BP-I genetic risk directly attributable to that measure; some loci may, of course, contribute to trait variability but not to disease risk.

All domains that we assessed include group 1 phenotypes. Some phenotypes in this group, such as delusion proneness,76 appear broadly characteristic of the major psychoses. Others, such as perceptual creativity, appear specific to BP predisposition7779; individuals diagnosed as having BP are overrepresented in creative occupations compared with individuals diagnosed as having other psychiatric disorders or with the general population.78,79 Many individuals with BP consider heightened creativity a positive aspect of their condition,80 which should fuel efforts to elucidate the mechanisms underlying this association.

Among the neurocognitive processes in group 1, the BP-I associations reflect impairments in processing speed, verbal learning and memory, category fluency, and inhibitory control, mirroring findings from previous BP and schizophrenia case-control, family, and pedigree studies.20,21,51,8185 Such phenotypes could contribute to the shared risk between these disorders suggested by recent genome-wide association studies.86

Group 1 neuroimaging measures provide the first confirmation in families of BP-related anatomical variations previously identified through case-control studies.8792 Although generally in accord with structural MRI findings from prior studies, our results identified larger zones of BP-I–associated gray matter reduction, which may reflect the greater size and reduced ethnic heterogeneity of the sample. We identified significant volume reduction and cortical thinning in 2 prefrontal systems implicated in BP pathogenesis: (1) a corticocognitive network anchored in the dorsolateral and ventrolateral prefrontal cortex, including all subdivisions of the inferior frontal gyrus, which plays a role in attention, working memory, and inhibitory control and shows attenuated activation in functional MRI studies of individuals with BP9398; and (2) a ventral-limbic system implicated in emotional reactivity, involving the hippocampus, amygdala, and orbitofrontal cortex.87,8991 Further, the reduced corpus callosum volume and white matter integrity align with twin studies suggesting genetically influenced alterations of this structure in BP.99,100 Gray matter reduction in temporal structures, including the superior temporal, lingual, and fusiform gyri, is noteworthy given the involvement of these structures in facial emotion identification, a process impaired in individuals with BP and adolescents at high risk.101105

Numerous phenotypes, including most of the neuroimaging measures, were heritable but not associated with BP-I (group 2). The lack of difference in cortical surface area between participants with BP-I and their relatives without BP-I supports previous evidence dissociating this measure from cortical thickness abnormalities characteristic of the disorder.92 Similarly, neurocognitive traits in this category have consistently demonstrated heritability in twin and family samples84,106113 but have shown inconsistent association with BP-I.20,21,81,114

A third set of phenotypes showed BP-I association but were not heritable (group 3), suggesting they may be predominantly influenced by environmental or disease-specific factors. Previous studies have proposed that temperament is a key contributor to BP genetic risk,115 but we found little evidence for heritability of several measures associated with emotional reactivity (cyclothymic, irritable, and depressive temperament, aggression, and impulsivity) that were elevated in our participants with BP-I.

Our results for neurocognitive traits are remarkably similar to those reported in the only previously published study of such traits in BP pedigrees,51 with 3 exceptions. First, we did not find significant heritability for face memory (which was impaired in participants with BP-I in both studies). Second, we observed significant impairment in participants with BP-I on measures of sustained attention and spatial working memory. As deficits in these domains may index psychotic symptoms, regardless of diagnosis,116 this discordance may reflect the larger percentage of patients in our sample with a lifetime history of psychosis. Finally, we found lower heritability for nonverbal abstract reasoning. As we report heritability estimates corrected for demographic variables, comparisons with the prior study are with its similarly corrected estimates.

We identified extensive correlation among measures within each phenotypic domain, including phenotype clusters consistently implicated in BP pathology. Some such clusters also showed evidence of shared genetic influence (eg, limbic regions with the pars opercularis of the inferior frontal gyrus98). This analysis also suggests shared genetic influence among select measures across domains, eg, that between Stroop test performance and surface area MRI measures.

Our ascertainment strategy emphasized close family relationships, enhancing the power for quantitative genetic analyses; however, the shared genetic and environmental backgrounds of our participants would tend to make them more similar to each other compared with cases and independently ascertained controls and reduce power to identify phenotypic associations with BP-I. Two scenarios may explain group differences observed for some phenotypes: participants with BP-I may carry risk alleles with strong and/or nonadditive phenotypic effects, and/or they may have experienced different environmental exposures, either prior to illness onset or as a consequence of the disorder. As the ascertainment of the pedigrees themselves and of the specific individuals evaluated within them were nonrandom with respect to clinical diagnosis, our data are not suitable for assessing the genetic relationship between these phenotypes and BP-I.

Although prior evidence supported the selection of each measure that we evaluated, the use of alternative measures could have yielded discrepant outcomes. While such discrepancies may reflect incompatibilities in the theoretical underpinnings of different instruments (eg, for temperament scales), identification of genetic coassociations between BP-I and specific component measures will accelerate the standardization of phenotyping.

Our findings establish a core set of measures across multiple domains as component phenotypes for identifying the genetic basis of BP-I risk. Overall, the profile of brain and behavioral impairments in these pedigrees is similar to those identified previously in case-control samples. We therefore anticipate that while specific genetic variants contributing to these phenotypes and to BP-I risk may be distinct to the CVCR and ANT population isolates, they could suggest genes that also influence disease risk in other populations.

Corresponding Author: Carrie E. Bearden, PhD, Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Dr S, Room 3506, Los Angeles, CA 90095 (cbearden@mednet.ucla.edu).

Submitted for Publication: June 5, 2013; final revision received September 19, 2013; accepted October 16, 2013.

Published Online: February 12, 2014. doi:10.1001/jamapsychiatry.2013.4100.

Author Contributions: Drs Freimer and Bearden had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Service, Kremeyer, Abaryan, Ericson, Altshuler, Escobar, Ospina-Duque, Risch, Ruiz-Linares, Lopez-Jaramillo, Macaya, Reus, Sabatti, Freimer, Bearden.

Acquisition of data: C. Araya, X. Araya, Bejarano, Ramirez, Castrillón, Gomez-Franco, Lopez, G. Montoya, P. Montoya, Aldana, Teshiba, Luykx, Tishler, Bartzokis, Escobar, Ospina-Duque, Lopez-Jaramillo, Macaya, Molina, Reus, Freimer, Bearden.

Analysis and interpretation of data: Fears, Service, Castrillón, Abaryan, Al-Sharif, Ericson, Jalbrzikowski, Navarro, Glahn, Risch, Thompson, Cantor, Reus, Sabatti, Freimer, Bearden.

Drafting of the manuscript: Fears, Ramirez, Castrillón, Lopez, G. Montoya, P. Montoya, Teshiba, Al-Sharif, Ericson, Glahn, Risch, Lopez-Jaramillo, Molina, Sabatti, Freimer, Bearden.

Critical revision of the manuscript for important intellectual content: Fears, Service, Kremeyer, C. Araya, X. Araya, Bejarano, Gomez-Franco, Aldana, Abaryan, Jalbrzikowski, Luykx, Navarro, Tishler, Altshuler, Bartzokis, Escobar, Glahn, Ospina-Duque, Ruiz-Linares, Thompson, Cantor, Lopez-Jaramillo, Macaya, Reus, Sabatti, Freimer, Bearden.

Statistical analysis: Fears, Service, Castrillón, Abaryan, Ericson, Jalbrzikowski, Navarro, Glahn, Risch, Cantor, Lopez-Jaramillo, Sabatti, Freimer.

Obtained funding: Altshuler, Lopez-Jaramillo, Reus, Freimer, Bearden.

Administrative, technical, and material support: Fears, Kremeyer, C. Araya, X. Araya, Bejarano, Ramirez, Castrillón, Gomez-Franco, Lopez, G. Montoya, P. Montoya, Aldana, Teshiba, Abaryan, Al-Sharif, Jalbrzikowski, Luykx, Tishler, Altshuler, Bartzokis, Escobar, Ospina-Duque, Thompson, Lopez-Jaramillo, Macaya, Molina, Freimer.

Study supervision: Abaryan, Bartzokis, Escobar, Ruiz-Linares, Lopez-Jaramillo, Macaya, Reus, Freimer, Bearden.

Conflict of Interest Disclosures: Dr Altshuler has received advisory board honoraria from Sepracor, Takeda Pharmaceuticals North America, H. Lundbeck A/S, and Sunovion Pharmaceuticals and has been a consultant for Eli Lilly. No other disclosures were reported.

Funding/Support: This work was supported by grants R01MH075007, R01MH095454, P30NS062691 (Dr Freimer), K23MH074644-01 (Dr Bearden), K08MH086786 (Dr Fears), and R01HG006695 (Dr Sabatti) from the National Institutes of Health and by Colciencias and Codi–University of Antioquia (Dr Lopez-Jaramillo).

Role of the Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Correction: This article was corrected on April 3, 2014, for an omission in Funding/Support.

Goodwin  FK, Jamison  KR. Manic-Depressive Illness: Bipolar Disorders and Recurrent Depression. New York, NY: Oxford University Press; 2007.
McGuffin  P, Rijsdijk  F, Andrew  M, Sham  P, Katz  R, Cardno  A.  The heritability of bipolar affective disorder and the genetic relationship to unipolar depression. Arch Gen Psychiatry. 2003;60(5):497-502.
PubMed   |  Link to Article
Fears  SC, Mathews  CM, Freimer  NF. Genetic linkage analysis of psychiatric disorders. In: Kaplan and Sadock's Comprehensive Textbook of Psychiatry. Philadelphia, PA: Lippincott Williams & Wilkins; 2009:320-332.
Ferreira  MA, O’Donovan  MC, Meng  YA,  et al; Wellcome Trust Case Control Consortium.  Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet. 2008;40(9):1056-1058.
PubMed   |  Link to Article
Sklar  P, Smoller  JW, Fan  J,  et al.  Whole-genome association study of bipolar disorder. Mol Psychiatry. 2008;13(6):558-569.
PubMed   |  Link to Article
Sullivan  PF, Daly  MJ, O’Donovan  M.  Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet. 2012;13(8):537-551.
PubMed   |  Link to Article
Bearden  CE, Freimer  NB.  Endophenotypes for psychiatric disorders: ready for primetime? Trends Genet. 2006;22(6):306-313.
PubMed   |  Link to Article
Cannon  TD, Keller  MC.  Endophenotypes in the genetic analyses of mental disorders. Annu Rev Clin Psychol. 2006;2:267-290.
PubMed   |  Link to Article
Gottesman  II, Gould  TD.  The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry. 2003;160(4):636-645.
PubMed   |  Link to Article
Kendler  KS, Neale  MC.  Endophenotype: a conceptual analysis. Mol Psychiatry. 2010;15(8):789-797.
PubMed   |  Link to Article
Preston  GA, Weinberger  DR.  Intermediate phenotypes in schizophrenia: a selective review. Dialogues Clin Neurosci. 2005;7(2):165-179.
PubMed
Walters  JT, Owen  MJ.  Endophenotypes in psychiatric genetics. Mol Psychiatry. 2007;12(10):886-890.
PubMed   |  Link to Article
Akiskal  HS, Kilzieh  N, Maser  JD,  et al.  The distinct temperament profiles of bipolar I, bipolar II and unipolar patients. J Affect Disord. 2006;92(1):19-33.
PubMed   |  Link to Article
Karam  EG, Salamoun  MM, Yeretzian  JS,  et al.  The role of anxious and hyperthymic temperaments in mental disorders: a national epidemiologic study. World Psychiatry. 2010;9(2):103-110.
PubMed
Vázquez  GH, Kahn  C, Schiavo  CE,  et al.  Bipolar disorders and affective temperaments: a national family study testing the “endophenotype” and “subaffective” theses using the TEMPS-A Buenos Aires. J Affect Disord. 2008;108(1-2):25-32.
PubMed   |  Link to Article
Srivastava  S, Childers  ME, Baek  JH,  et al.  Toward interaction of affective and cognitive contributors to creativity in bipolar disorders: a controlled study. J Affect Disord. 2010;125(1-3):27-34.
PubMed   |  Link to Article
Santosa  CM, Strong  CM, Nowakowska  C, Wang  PW, Rennicke  CM, Ketter  TA.  Enhanced creativity in bipolar disorder patients: a controlled study. J Affect Disord. 2007;100(1-3):31-39.
PubMed   |  Link to Article
Simeonova  DI, Chang  KD, Strong  C, Ketter  TA.  Creativity in familial bipolar disorder. J Psychiatr Res. 2005;39(6):623-631.
PubMed   |  Link to Article
Glahn  DC, Bearden  CE, Niendam  TA, Escamilla  MA.  The feasibility of neuropsychological endophenotypes in the search for genes associated with bipolar affective disorder. Bipolar Disord. 2004;6(3):171-182.
PubMed   |  Link to Article
Arts  B, Jabben  N, Krabbendam  L, van Os  J.  Meta-analyses of cognitive functioning in euthymic bipolar patients and their first-degree relatives. Psychol Med. 2008;38(6):771-785.
PubMed   |  Link to Article
Bora  E, Yucel  M, Pantelis  C.  Cognitive endophenotypes of bipolar disorder: a meta-analysis of neuropsychological deficits in euthymic patients and their first-degree relatives. J Affect Disord. 2009;113(1-2):1-20.
PubMed   |  Link to Article
Fusar-Poli  P, Howes  O, Bechdolf  A, Borgwardt  S.  Mapping vulnerability to bipolar disorder: a systematic review and meta-analysis of neuroimaging studies. J Psychiatry Neurosci. 2012;37(3):170-184.
PubMed   |  Link to Article
Langan  C, McDonald  C.  Neurobiological trait abnormalities in bipolar disorder. Mol Psychiatry. 2009;14(9):833-846.
PubMed   |  Link to Article
Foland-Ross  LC, Thompson  PM, Sugar  CA,  et al.  Investigation of cortical thickness abnormalities in lithium-free adults with bipolar I disorder using cortical pattern matching. Am J Psychiatry. 2011;168(5):530-539.
PubMed   |  Link to Article
McInnes  LA, Escamilla  MA, Service  SK,  et al.  A complete genome screen for genes predisposing to severe bipolar disorder in two Costa Rican pedigrees. Proc Natl Acad Sci U S A. 1996;93(23):13060-13065.
PubMed   |  Link to Article
Service  S, Molina  J, Deyoung  J,  et al.  Results of a SNP genome screen in a large Costa Rican pedigree segregating for severe bipolar disorder. Am J Med Genet B Neuropsychiatr Genet. 2006;141B(4):367-373.
PubMed   |  Link to Article
Herzberg  I, Jasinska  A, García  J,  et al.  Convergent linkage evidence from two Latin-American population isolates supports the presence of a susceptibility locus for bipolar disorder in 5q31-34. Hum Mol Genet. 2006;15(21):3146-3153.
PubMed   |  Link to Article
Kremeyer  B, García  J, Müller  H,  et al.  Genome-wide linkage scan of bipolar disorder in a Colombian population isolate replicates loci on chromosomes 7p21-22, 1p31, 16p12 and 21q21-22 and identifies a novel locus on chromosome 12q. Hum Hered. 2010;70(4):255-268.
PubMed   |  Link to Article
Carvajal-Carmona  LG, Ophoff  R, Service  S,  et al.  Genetic demography of Antioquia (Colombia) and the Central Valley of Costa Rica. Hum Genet. 2003;112(5-6):534-541.
PubMed
Reich  D, Patterson  N, Campbell  D,  et al.  Reconstructing Native American population history. Nature. 2012;488(7411):370-374.
PubMed   |  Link to Article
Service  S, DeYoung  J, Karayiorgou  M,  et al.  Magnitude and distribution of linkage disequilibrium in population isolates and implications for genome-wide association studies. Nat Genet. 2006;38(5):556-560.
PubMed   |  Link to Article
Jasinska  AJ, Service  S, Jawaheer  D,  et al.  A narrow and highly significant linkage signal for severe bipolar disorder in the chromosome 5q33 region in Latin American pedigrees. Am J Med Genet B Neuropsychiatr Genet. 2009;150B(7):998-1006.
PubMed   |  Link to Article
Freimer  NB, Reus  VI, Escamilla  M,  et al.  An approach to investigating linkage for bipolar disorder using large Costa Rican pedigrees. Am J Med Genet. 1996;67(3):254-263.
PubMed   |  Link to Article
Escamilla  MA, Spesny  M, Reus  VI,  et al.  Use of linkage disequilibrium approaches to map genes for bipolar disorder in the Costa Rican population. Am J Med Genet. 1996;67(3):244-253.
PubMed   |  Link to Article
Freimer  NB, Reus  VI, Escamilla  MA,  et al.  Genetic mapping using haplotype, association and linkage methods suggests a locus for severe bipolar disorder (BPI) at 18q22-q23. Nat Genet. 1996;12(4):436-441.
PubMed   |  Link to Article
Ophoff  RA, Escamilla  MA, Service  SK,  et al.  Genomewide linkage disequilibrium mapping of severe bipolar disorder in a population isolate. Am J Hum Genet. 2002;71(3):565-574.
PubMed   |  Link to Article
Nurnberger  JI  Jr, Blehar  MC, Kaufmann  CA,  et al; NIMH Genetics Initiative.  Diagnostic interview for genetic studies: rationale, unique features, and training. Arch Gen Psychiatry. 1994;51(11):849-859, discussion 863-864.
PubMed   |  Link to Article
Palacio  CA, García  J, Arbeláez  MP,  et al.  Validation of the Diagnostic Interview for Genetic Studies (DIGS) in Colombia [in Spanish]. Biomedica. 2004;24(1):56-62.
PubMed
Sheehan  DV, Lecrubier  Y, Sheehan  KH,  et al.  The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10J Clin Psychiatry. 1998;59(suppl 20):22-33.
PubMed
Hong  KS, McInnes  LA, Service  SK,  et al.  Genetic mapping using haplotype and model-free linkage analysis supports previous evidence for a locus predisposing to severe bipolar disorder at 5q31-33. Am J Med Genet B Neuropsychiatr Genet. 2004;125B(1):83-86.
PubMed   |  Link to Article
Young  RC, Biggs  JT, Ziegler  VE, Meyer  DA.  A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry. 1978;133:429-435.
PubMed   |  Link to Article
Hamilton  M.  A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56-62.
PubMed   |  Link to Article
Peters  E, Joseph  S, Day  S, Garety  P.  Measuring delusional ideation: the 21-item Peters et al Delusions Inventory (PDI). Schizophr Bull. 2004;30(4):1005-1022.
PubMed   |  Link to Article
Barron  F, Welsh  GS.  Artistic perception as a possible factor in personality style: its measurement by a figure preference test. J Psychol. 1952;33:199-203. doi:10.1080/00223980.1952.9712830.
Link to Article
Akiskal  HS, Akiskal  KK.  TEMPS: Temperament Evaluation of Memphis, Pisa, Paris and San Diego. J Affect Disord. 2005;85(1-2):1-2.
PubMed   |  Link to Article
Buss  AH, Perry  M.  The aggression questionnaire. J Pers Soc Psychol. 1992;63(3):452-459.
PubMed   |  Link to Article
Patton  JH, Stanford  MS, Barratt  ES.  Factor structure of the Barratt Impulsiveness Scale. J Clin Psychol. 1995;51(6):768-774.
PubMed   |  Link to Article
Kolin  EA, Price  L, Zoob  I.  Development of a sensation-seeking scale. J Consult Psychol. 1964;28:477-482.
PubMed   |  Link to Article
Zuckerman  M, Link  K.  Construct validity for the sensation-seeking scale. J Consult Clin Psychol. 1968;32(4):420-426.
PubMed   |  Link to Article
Lejuez  CW, Read  JP, Kahler  CW,  et al.  Evaluation of a behavioral measure of risk taking: the Balloon Analogue Risk Task (BART). J Exp Psychol Appl. 2002;8(2):75-84.
PubMed   |  Link to Article
Glahn  DC, Almasy  L, Barguil  M,  et al.  Neurocognitive endophenotypes for bipolar disorder identified in multiplex multigenerational families. Arch Gen Psychiatry. 2010;67(2):168-177.
PubMed   |  Link to Article
Wechsler  D. Wechsler Adult Intelligence Scale—Fourth Edition (WAIS-IV). San Antonio, TX: Harcourt Assessment; 2008.
Glahn  DC, Cannon  TD, Gur  RE, Ragland  JD, Gur  RC.  Working memory constrains abstraction in schizophrenia. Biol Psychiatry. 2000;47(1):34-42.
PubMed   |  Link to Article
Kurtz  MM, Ragland  JD, Moberg  PJ, Gur  RC.  The Penn Conditional Exclusion Test: a new measure of executive-function with alternate forms of repeat administration. Arch Clin Neuropsychol. 2004;19(2):191-201.
PubMed   |  Link to Article
Stroop  JR.  Studies of interference in serial verbal reactions. J Exp Psychol Gen. 1992;121(1):15-23.
Link to Article
Brown  L, Sherbenou  RJ, Johnsen  SK. Test of Nonverbal Intelligence: A Language-Free Measure of Cognitive Ability. Austin, TX: Pro-Ed; 1997.
Dale  AM, Fischl  B, Sereno  MI.  Cortical surface-based analysis, I: segmentation and surface reconstruction. Neuroimage. 1999;9(2):179-194.
PubMed   |  Link to Article
Fischl  B, Sereno  MI, Dale  AM.  Cortical surface-based analysis, II: inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9(2):195-207.
PubMed   |  Link to Article
Smith  SM, Jenkinson  M, Woolrich  MW,  et al.  Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23(suppl 1):S208-S219.
PubMed   |  Link to Article
Jenkinson  M, Beckmann  CF, Behrens  TE, Woolrich  MW, Smith  SM.  FSL. Neuroimage. 2012;62(2):782-790.
PubMed   |  Link to Article
Panizzon  MS, Fennema-Notestine  C, Eyler  LT,  et al.  Distinct genetic influences on cortical surface area and cortical thickness. Cereb Cortex. 2009;19(11):2728-2735.
PubMed   |  Link to Article
Raznahan  A, Shaw  P, Lalonde  F,  et al.  How does your cortex grow? J Neurosci. 2011;31(19):7174-7177.
PubMed   |  Link to Article
Oishi  K, Zilles  K, Amunts  K,  et al.  Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. Neuroimage. 2008;43(3):447-457.
PubMed   |  Link to Article
Mahon  K, Burdick  KE, Ikuta  T,  et al.  Abnormal temporal lobe white matter as a biomarker for genetic risk of bipolar disorder. Biol Psychiatry. 2013;73(2):177-182.
PubMed   |  Link to Article
Sexton  CE, Mackay  CE, Ebmeier  KP.  A systematic review of diffusion tensor imaging studies in affective disorders. Biol Psychiatry. 2009;66(9):814-823.
PubMed   |  Link to Article
Sprooten  E, Sussmann  JE, Clugston  A,  et al.  White matter integrity in individuals at high genetic risk of bipolar disorder. Biol Psychiatry. 2011;70(4):350-356.
PubMed   |  Link to Article
Bartzokis  G, Lu  PH, Heydari  P,  et al.  Multimodal magnetic resonance imaging assessment of white matter aging trajectories over the lifespan of healthy individuals. Biol Psychiatry. 2012;72(12):1026-1034.
PubMed   |  Link to Article
Budde  MD, Xie  M, Cross  AH, Song  SK.  Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: a quantitative pixelwise analysis. J Neurosci. 2009;29(9):2805-2813.
PubMed   |  Link to Article
Song  SK, Sun  SW, Ramsbottom  MJ, Chang  C, Russell  J, Cross  AH.  Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage. 2002;17(3):1429-1436.
PubMed   |  Link to Article
Song  SK, Sun  SW, Ju  WK, Lin  SJ, Cross  AH, Neufeld  AH.  Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage. 2003;20(3):1714-1722.
PubMed   |  Link to Article
Almasy  L, Blangero  J.  Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998;62(5):1198-1211.
PubMed   |  Link to Article
Van der Waerden  BL.  Order tests for the two-sample problem and their power. Indag Math. 1952;14:453-458.
Pilia  G, Chen  WM, Scuteri  A,  et al.  Heritability of cardiovascular and personality traits in 6148 Sardinians. PLoS Genet. 2006;2(8):e132.
PubMed   |  Link to Article
Williams  JT, Van Eerdewegh  P, Almasy  L, Blangero  J.  Joint multipoint linkage analysis of multivariate qualitative and quantitative traits, I: likelihood formulation and simulation results. Am J Hum Genet. 1999;65(4):1134-1147.
PubMed   |  Link to Article
Cruchaga  C, Kauwe  JS, Harari  O,  et al; GERAD Consortium; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Alzheimer Disease Genetic Consortium (ADGC).  GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer’s disease. Neuron. 2013;78(2):256-268.
PubMed   |  Link to Article
Schürhoff  F, Szöke  A, Méary  A,  et al.  Familial aggregation of delusional proneness in schizophrenia and bipolar pedigrees. Am J Psychiatry. 2003;160(7):1313-1319.
PubMed   |  Link to Article
Jamison  KR.  Great wits and madness: more near allied? Br J Psychiatry. 2011;199(5):351-352.
PubMed   |  Link to Article
Kyaga  S, Lichtenstein  P, Boman  M, Hultman  C, Långström  N, Landén  M.  Creativity and mental disorder: family study of 300 000 people with severe mental disorder. Br J Psychiatry. 2011;199(5):373-379.
PubMed   |  Link to Article
Kyaga  S, Landén  M, Boman  M, Hultman  CM, Långström  N, Lichtenstein  P.  Mental illness, suicide and creativity: 40-year prospective total population study. J Psychiatr Res. 2013;47(1):83-90.
PubMed   |  Link to Article
Parker  G, Paterson  A, Fletcher  K, Blanch  B, Graham  R.  The “magic button question” for those with a mood disorder: would they wish to re-live their condition? J Affect Disord. 2012;136(3):419-424.
PubMed   |  Link to Article
Balanzá-Martínez  V, Rubio  C, Selva-Vera  G,  et al.  Neurocognitive endophenotypes (endophenocognitypes) from studies of relatives of bipolar disorder subjects: a systematic review. Neurosci Biobehav Rev. 2008;32(8):1426-1438.
PubMed   |  Link to Article
Robinson  LJ, Thompson  JM, Gallagher  P,  et al.  A meta-analysis of cognitive deficits in euthymic patients with bipolar disorder. J Affect Disord. 2006;93(1-3):105-115.
PubMed   |  Link to Article
Torres  IJ, Boudreau  VG, Yatham  LN.  Neuropsychological functioning in euthymic bipolar disorder: a meta-analysis. Acta Psychiatr Scand Suppl. 2007;(434):17-26.
PubMed
Greenwood  TA, Braff  DL, Light  GA,  et al.  Initial heritability analyses of endophenotypic measures for schizophrenia: the Consortium on the Genetics of Schizophrenia. Arch Gen Psychiatry. 2007;64(11):1242-1250.
PubMed   |  Link to Article
Gur  RE, Nimgaonkar  VL, Almasy  L,  et al.  Neurocognitive endophenotypes in a multiplex multigenerational family study of schizophrenia. Am J Psychiatry. 2007;164(5):813-819.
PubMed   |  Link to Article
Steinberg  S, de Jong  S, Mattheisen  M,  et al; GROUP; Wellcome Trust Case Control Consortium 2.  Common variant at 16p11.2 conferring risk of psychosis. Mol Psychiatry. 2014;19(1):108-114.
PubMed   |  Link to Article
Arnone  D, Cavanagh  J, Gerber  D, Lawrie  SM, Ebmeier  KP, McIntosh  AM.  Magnetic resonance imaging studies in bipolar disorder and schizophrenia: meta-analysis. Br J Psychiatry. 2009;195(3):194-201.
PubMed   |  Link to Article
Bora  E, Fornito  A, Yücel  M, Pantelis  C.  Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder. Biol Psychiatry. 2010;67(11):1097-1105.
PubMed   |  Link to Article
Hallahan  B, Newell  J, Soares  JC,  et al.  Structural magnetic resonance imaging in bipolar disorder: an international collaborative mega-analysis of individual adult patient data. Biol Psychiatry. 2011;69(4):326-335.
PubMed   |  Link to Article
Kempton  MJ, Geddes  JR, Ettinger  U, Williams  SC, Grasby  PM.  Meta-analysis, database, and meta-regression of 98 structural imaging studies in bipolar disorder. Arch Gen Psychiatry. 2008;65(9):1017-1032.
PubMed   |  Link to Article
McDonald  C, Zanelli  J, Rabe-Hesketh  S,  et al.  Meta-analysis of magnetic resonance imaging brain morphometry studies in bipolar disorder. Biol Psychiatry. 2004;56(6):411-417.
PubMed   |  Link to Article
Rimol  LM, Hartberg  CB, Nesvåg  R,  et al.  Cortical thickness and subcortical volumes in schizophrenia and bipolar disorder. Biol Psychiatry. 2010;68(1):41-50.
PubMed   |  Link to Article
Houenou  J, Frommberger  J, Carde  S,  et al.  Neuroimaging-based markers of bipolar disorder: evidence from two meta-analyses. J Affect Disord. 2011;132(3):344-355.
PubMed   |  Link to Article
Chen  CH, Suckling  J, Lennox  BR, Ooi  C, Bullmore  ET.  A quantitative meta-analysis of fMRI studies in bipolar disorder. Bipolar Disord. 2011;13(1):1-15.
PubMed   |  Link to Article
Cusi  AM, Nazarov  A, Holshausen  K, Macqueen  GM, McKinnon  MC.  Systematic review of the neural basis of social cognition in patients with mood disorders. J Psychiatry Neurosci. 2012;37(3):154-169.
PubMed   |  Link to Article
Kupferschmidt  DA, Zakzanis  KK.  Toward a functional neuroanatomical signature of bipolar disorder: quantitative evidence from the neuroimaging literature. Psychiatry Res. 2011;193(2):71-79.
PubMed   |  Link to Article
Townsend  J, Altshuler  LL.  Emotion processing and regulation in bipolar disorder: a review. Bipolar Disord. 2012;14(4):326-339.
PubMed   |  Link to Article
Roberts  G, Green  MJ, Breakspear  M,  et al.  Reduced inferior frontal gyrus activation during response inhibition to emotional stimuli in youth at high risk of bipolar disorder. Biol Psychiatry. 2013;74(1):55-61.
PubMed   |  Link to Article
Bearden  CE, van Erp  TG, Dutton  RA,  et al.  Mapping corpus callosum morphology in twin pairs discordant for bipolar disorder. Cereb Cortex. 2011;21(10):2415-2424.
PubMed   |  Link to Article
van der Schot  AC, Vonk  R, Brans  RG,  et al.  Influence of genes and environment on brain volumes in twin pairs concordant and discordant for bipolar disorder. Arch Gen Psychiatry. 2009;66(2):142-151.
PubMed   |  Link to Article
Brotman  MA, Guyer  AE, Lawson  ES,  et al.  Facial emotion labeling deficits in children and adolescents at risk for bipolar disorder. Am J Psychiatry. 2008;165(3):385-389.
PubMed   |  Link to Article
Brotman  MA, Skup  M, Rich  BA,  et al.  Risk for bipolar disorder is associated with face-processing deficits across emotions. J Am Acad Child Adolesc Psychiatry. 2008;47(12):1455-1461.
PubMed   |  Link to Article
Mercer  L, Becerra  R.  A unique emotional processing profile of euthymic bipolar disorder? a critical review. J Affect Disord. 2013;146(3):295-309.
PubMed   |  Link to Article
Samamé  C, Martino  DJ, Strejilevich  SA.  Social cognition in euthymic bipolar disorder: systematic review and meta-analytic approach. Acta Psychiatr Scand. 2012;125(4):266-280.
PubMed   |  Link to Article
Adleman  NE, Kayser  RR, Olsavsky  AK,  et al.  Abnormal fusiform activation during emotional-face encoding assessed with functional magnetic resonance imaging. Psychiatry Res. 2013;212(2):161-163.
PubMed   |  Link to Article
Ando  J, Ono  Y, Wright  MJ.  Genetic structure of spatial and verbal working memory. Behav Genet. 2001;31(6):615-624.
PubMed   |  Link to Article
Corvin  A, Donohoe  G, Hargreaves  A, Gallagher  L, Gill  M.  The cognitive genetics of neuropsychiatric disorders. Curr Top Behav Neurosci. 2012;12:579-613.
PubMed
Donohoe  G, Deary  IJ, Glahn  DC, Malhotra  AK, Burdick  KE.  Neurocognitive phenomics: examining the genetic basis of cognitive abilities. Psychol Med. 2013;43(10):2027-2036.
PubMed   |  Link to Article
Greenwood  TA, Beeri  MS, Schmeidler  J,  et al.  Heritability of cognitive functions in families of successful cognitive aging probands from the Central Valley of Costa Rica. J Alzheimers Dis. 2011;27(4):897-907.
PubMed
Hervey  AS, Greenfield  K, Gualtieri  CT.  Heritability in cognitive performance: evidence using computer-based testing. J Genet Psychol. 2012;173(1):112-118.
PubMed   |  Link to Article
Lee  T, Mosing  MA, Henry  JD,  et al; OATS Research Team.  Genetic influences on four measures of executive functions and their covariation with general cognitive ability: the Older Australian Twins Study. Behav Genet. 2012;42(4):528-538.
PubMed   |  Link to Article
Oliver  BR, Plomin  R.  Twins’ Early Development Study (TEDS): a multivariate, longitudinal genetic investigation of language, cognition and behavior problems from childhood through adolescence. Twin Res Hum Genet. 2007;10(1):96-105.
PubMed   |  Link to Article
van Soelen  IL, Brouwer  RM, van Leeuwen  M, Kahn  RS, Hulshoff Pol  HE, Boomsma  DI.  Heritability of verbal and performance intelligence in a pediatric longitudinal sample. Twin Res Hum Genet. 2011;14(2):119-128.
PubMed   |  Link to Article
Bearden  CE, Hoffman  KM, Cannon  TD.  The neuropsychology and neuroanatomy of bipolar affective disorder: a critical review. Bipolar Disord. 2001;3(3):106-150, discussion 151-153.
PubMed   |  Link to Article
Evans  L, Akiskal  HS, Keck  PE  Jr,  et al.  Familiality of temperament in bipolar disorder: support for a genetic spectrum. J Affect Disord. 2005;85(1-2):153-168.
PubMed   |  Link to Article
Glahn  DC, Bearden  CE, Barguil  M,  et al.  The neurocognitive signature of psychotic bipolar disorder. Biol Psychiatry. 2007;62(8):910-916.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.
Summary of Analyses of Heritability and Association With Bipolar I Disorder

The results of analyses of heritability and of association with bipolar I disorder (BP-I) are shown as 2 histograms stacked on top of each other. Inner histogram purple bars show the magnitude of the heritability estimate for each component phenotype, and the blue box next to the trait name at the outer edge of the plot indicates estimates that passed the significance threshold. Outer histogram shows the magnitude of the estimated regression coefficient for the BP-I association test. Orange bars show positive coefficients representing traits that are higher in participants with BP-I compared with family members without BP-I. Green bars show negative coefficients representing traits that are lower in participants with BP-I. A red box at the outer edge of the circle indicates traits that exceeded the significance threshold for association with BP-I. AIM indicates Abstraction, Inhibition, and Working Memory Task; BART, Balloon Analogue Risk Task; CVLT, California Verbal Learning Test; IP-CPT, Identical Pairs Continuous Performance Test; MRI, magnetic resonance imaging; PCET, Penn Conditional Exclusion Test; SCAP, Spatial Capacity Delayed Response Test; SST, Stop Signal Task; TEMPS, Temperament Evaluation of Memphis, Pisa, Paris, and San Diego; TONI, Test of Nonverbal Intelligence; VWM, verbal working memory; WASI, Wechsler Abbreviated Scale of Intelligence; and WMS, Wechsler Memory Scale.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.
Structural Neuroimaging Phenotypes

A, Results of the heritability and bipolar I disorder (BP-I) association analyses of volumetric magnetic resonance imaging phenotypes. The 3 representative T1-weighted coronal magnetic resonance images depict the results of the Freesurfer segmentation overlaid as colored masks selected to better distinguish the anatomy. Mask colors are not related to the results. The colors of the text labels indicate structures that showed significant evidence of familial aggregation (blue) and structures that were both heritable and associated with BP-I (magenta). B, Cortical thickness phenotypes and results of the heritability and BP-I association analysis for cortical gray matter thickness. The medial surface is rotated upward by 60° to provide a view of the ventral surface.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.
Network Graph of Correlations Among Phenotypes

Network representations of pairwise phenotypic correlations (A) and genetic correlations (B). All trait pairs were included in the phenotypic correlation analysis, and only pairs in which both traits were heritable were included in the genetic correlation analysis. Nodes are colored according to their assigned subdomain (see Subdomain column in eTable 1 in Supplement). Circular nodes indicate significantly heritable phenotypes; square nodes, nonheritable phenotypes. Traits that were significantly associated with bipolar I disorder have a red border. Nodes are connected with an edge when the hypothesis of correlation = 0 was rejected using false discovery rate–controlled thresholds. Numbers correspond to plot identification numbers for phenotypes detailed in eTable 1 in the Supplement. MRI indicates magnetic resonance imaging. B, Examples of genetically correlated traits mentioned in the text include the hippocampus (67), amygdala (56), and surface area of the pars opercularis (97) as well as Stroop Color-Word Interference Test errors (34) with surface area measures from the inferior parietal (87) and rostral middle frontal (107) regions of interest.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Sample Characteristics by Country and Family
Table Graphic Jump LocationTable 2.  Behavioral Measures to Generate Phenotypes
Table Graphic Jump LocationTable 3.  Neuroimaging Measures to Generate Phenotypes

References

Goodwin  FK, Jamison  KR. Manic-Depressive Illness: Bipolar Disorders and Recurrent Depression. New York, NY: Oxford University Press; 2007.
McGuffin  P, Rijsdijk  F, Andrew  M, Sham  P, Katz  R, Cardno  A.  The heritability of bipolar affective disorder and the genetic relationship to unipolar depression. Arch Gen Psychiatry. 2003;60(5):497-502.
PubMed   |  Link to Article
Fears  SC, Mathews  CM, Freimer  NF. Genetic linkage analysis of psychiatric disorders. In: Kaplan and Sadock's Comprehensive Textbook of Psychiatry. Philadelphia, PA: Lippincott Williams & Wilkins; 2009:320-332.
Ferreira  MA, O’Donovan  MC, Meng  YA,  et al; Wellcome Trust Case Control Consortium.  Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet. 2008;40(9):1056-1058.
PubMed   |  Link to Article
Sklar  P, Smoller  JW, Fan  J,  et al.  Whole-genome association study of bipolar disorder. Mol Psychiatry. 2008;13(6):558-569.
PubMed   |  Link to Article
Sullivan  PF, Daly  MJ, O’Donovan  M.  Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet. 2012;13(8):537-551.
PubMed   |  Link to Article
Bearden  CE, Freimer  NB.  Endophenotypes for psychiatric disorders: ready for primetime? Trends Genet. 2006;22(6):306-313.
PubMed   |  Link to Article
Cannon  TD, Keller  MC.  Endophenotypes in the genetic analyses of mental disorders. Annu Rev Clin Psychol. 2006;2:267-290.
PubMed   |  Link to Article
Gottesman  II, Gould  TD.  The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry. 2003;160(4):636-645.
PubMed   |  Link to Article
Kendler  KS, Neale  MC.  Endophenotype: a conceptual analysis. Mol Psychiatry. 2010;15(8):789-797.
PubMed   |  Link to Article
Preston  GA, Weinberger  DR.  Intermediate phenotypes in schizophrenia: a selective review. Dialogues Clin Neurosci. 2005;7(2):165-179.
PubMed
Walters  JT, Owen  MJ.  Endophenotypes in psychiatric genetics. Mol Psychiatry. 2007;12(10):886-890.
PubMed   |  Link to Article
Akiskal  HS, Kilzieh  N, Maser  JD,  et al.  The distinct temperament profiles of bipolar I, bipolar II and unipolar patients. J Affect Disord. 2006;92(1):19-33.
PubMed   |  Link to Article
Karam  EG, Salamoun  MM, Yeretzian  JS,  et al.  The role of anxious and hyperthymic temperaments in mental disorders: a national epidemiologic study. World Psychiatry. 2010;9(2):103-110.
PubMed
Vázquez  GH, Kahn  C, Schiavo  CE,  et al.  Bipolar disorders and affective temperaments: a national family study testing the “endophenotype” and “subaffective” theses using the TEMPS-A Buenos Aires. J Affect Disord. 2008;108(1-2):25-32.
PubMed   |  Link to Article
Srivastava  S, Childers  ME, Baek  JH,  et al.  Toward interaction of affective and cognitive contributors to creativity in bipolar disorders: a controlled study. J Affect Disord. 2010;125(1-3):27-34.
PubMed   |  Link to Article
Santosa  CM, Strong  CM, Nowakowska  C, Wang  PW, Rennicke  CM, Ketter  TA.  Enhanced creativity in bipolar disorder patients: a controlled study. J Affect Disord. 2007;100(1-3):31-39.
PubMed   |  Link to Article
Simeonova  DI, Chang  KD, Strong  C, Ketter  TA.  Creativity in familial bipolar disorder. J Psychiatr Res. 2005;39(6):623-631.
PubMed   |  Link to Article
Glahn  DC, Bearden  CE, Niendam  TA, Escamilla  MA.  The feasibility of neuropsychological endophenotypes in the search for genes associated with bipolar affective disorder. Bipolar Disord. 2004;6(3):171-182.
PubMed   |  Link to Article
Arts  B, Jabben  N, Krabbendam  L, van Os  J.  Meta-analyses of cognitive functioning in euthymic bipolar patients and their first-degree relatives. Psychol Med. 2008;38(6):771-785.
PubMed   |  Link to Article
Bora  E, Yucel  M, Pantelis  C.  Cognitive endophenotypes of bipolar disorder: a meta-analysis of neuropsychological deficits in euthymic patients and their first-degree relatives. J Affect Disord. 2009;113(1-2):1-20.
PubMed   |  Link to Article
Fusar-Poli  P, Howes  O, Bechdolf  A, Borgwardt  S.  Mapping vulnerability to bipolar disorder: a systematic review and meta-analysis of neuroimaging studies. J Psychiatry Neurosci. 2012;37(3):170-184.
PubMed   |  Link to Article
Langan  C, McDonald  C.  Neurobiological trait abnormalities in bipolar disorder. Mol Psychiatry. 2009;14(9):833-846.
PubMed   |  Link to Article
Foland-Ross  LC, Thompson  PM, Sugar  CA,  et al.  Investigation of cortical thickness abnormalities in lithium-free adults with bipolar I disorder using cortical pattern matching. Am J Psychiatry. 2011;168(5):530-539.
PubMed   |  Link to Article
McInnes  LA, Escamilla  MA, Service  SK,  et al.  A complete genome screen for genes predisposing to severe bipolar disorder in two Costa Rican pedigrees. Proc Natl Acad Sci U S A. 1996;93(23):13060-13065.
PubMed   |  Link to Article
Service  S, Molina  J, Deyoung  J,  et al.  Results of a SNP genome screen in a large Costa Rican pedigree segregating for severe bipolar disorder. Am J Med Genet B Neuropsychiatr Genet. 2006;141B(4):367-373.
PubMed   |  Link to Article
Herzberg  I, Jasinska  A, García  J,  et al.  Convergent linkage evidence from two Latin-American population isolates supports the presence of a susceptibility locus for bipolar disorder in 5q31-34. Hum Mol Genet. 2006;15(21):3146-3153.
PubMed   |  Link to Article
Kremeyer  B, García  J, Müller  H,  et al.  Genome-wide linkage scan of bipolar disorder in a Colombian population isolate replicates loci on chromosomes 7p21-22, 1p31, 16p12 and 21q21-22 and identifies a novel locus on chromosome 12q. Hum Hered. 2010;70(4):255-268.
PubMed   |  Link to Article
Carvajal-Carmona  LG, Ophoff  R, Service  S,  et al.  Genetic demography of Antioquia (Colombia) and the Central Valley of Costa Rica. Hum Genet. 2003;112(5-6):534-541.
PubMed
Reich  D, Patterson  N, Campbell  D,  et al.  Reconstructing Native American population history. Nature. 2012;488(7411):370-374.
PubMed   |  Link to Article
Service  S, DeYoung  J, Karayiorgou  M,  et al.  Magnitude and distribution of linkage disequilibrium in population isolates and implications for genome-wide association studies. Nat Genet. 2006;38(5):556-560.
PubMed   |  Link to Article
Jasinska  AJ, Service  S, Jawaheer  D,  et al.  A narrow and highly significant linkage signal for severe bipolar disorder in the chromosome 5q33 region in Latin American pedigrees. Am J Med Genet B Neuropsychiatr Genet. 2009;150B(7):998-1006.
PubMed   |  Link to Article
Freimer  NB, Reus  VI, Escamilla  M,  et al.  An approach to investigating linkage for bipolar disorder using large Costa Rican pedigrees. Am J Med Genet. 1996;67(3):254-263.
PubMed   |  Link to Article
Escamilla  MA, Spesny  M, Reus  VI,  et al.  Use of linkage disequilibrium approaches to map genes for bipolar disorder in the Costa Rican population. Am J Med Genet. 1996;67(3):244-253.
PubMed   |  Link to Article
Freimer  NB, Reus  VI, Escamilla  MA,  et al.  Genetic mapping using haplotype, association and linkage methods suggests a locus for severe bipolar disorder (BPI) at 18q22-q23. Nat Genet. 1996;12(4):436-441.
PubMed   |  Link to Article
Ophoff  RA, Escamilla  MA, Service  SK,  et al.  Genomewide linkage disequilibrium mapping of severe bipolar disorder in a population isolate. Am J Hum Genet. 2002;71(3):565-574.
PubMed   |  Link to Article
Nurnberger  JI  Jr, Blehar  MC, Kaufmann  CA,  et al; NIMH Genetics Initiative.  Diagnostic interview for genetic studies: rationale, unique features, and training. Arch Gen Psychiatry. 1994;51(11):849-859, discussion 863-864.
PubMed   |  Link to Article
Palacio  CA, García  J, Arbeláez  MP,  et al.  Validation of the Diagnostic Interview for Genetic Studies (DIGS) in Colombia [in Spanish]. Biomedica. 2004;24(1):56-62.
PubMed
Sheehan  DV, Lecrubier  Y, Sheehan  KH,  et al.  The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10J Clin Psychiatry. 1998;59(suppl 20):22-33.
PubMed
Hong  KS, McInnes  LA, Service  SK,  et al.  Genetic mapping using haplotype and model-free linkage analysis supports previous evidence for a locus predisposing to severe bipolar disorder at 5q31-33. Am J Med Genet B Neuropsychiatr Genet. 2004;125B(1):83-86.
PubMed   |  Link to Article
Young  RC, Biggs  JT, Ziegler  VE, Meyer  DA.  A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry. 1978;133:429-435.
PubMed   |  Link to Article
Hamilton  M.  A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56-62.
PubMed   |  Link to Article
Peters  E, Joseph  S, Day  S, Garety  P.  Measuring delusional ideation: the 21-item Peters et al Delusions Inventory (PDI). Schizophr Bull. 2004;30(4):1005-1022.
PubMed   |  Link to Article
Barron  F, Welsh  GS.  Artistic perception as a possible factor in personality style: its measurement by a figure preference test. J Psychol. 1952;33:199-203. doi:10.1080/00223980.1952.9712830.
Link to Article
Akiskal  HS, Akiskal  KK.  TEMPS: Temperament Evaluation of Memphis, Pisa, Paris and San Diego. J Affect Disord. 2005;85(1-2):1-2.
PubMed   |  Link to Article
Buss  AH, Perry  M.  The aggression questionnaire. J Pers Soc Psychol. 1992;63(3):452-459.
PubMed   |  Link to Article
Patton  JH, Stanford  MS, Barratt  ES.  Factor structure of the Barratt Impulsiveness Scale. J Clin Psychol. 1995;51(6):768-774.
PubMed   |  Link to Article
Kolin  EA, Price  L, Zoob  I.  Development of a sensation-seeking scale. J Consult Psychol. 1964;28:477-482.
PubMed   |  Link to Article
Zuckerman  M, Link  K.  Construct validity for the sensation-seeking scale. J Consult Clin Psychol. 1968;32(4):420-426.
PubMed   |  Link to Article
Lejuez  CW, Read  JP, Kahler  CW,  et al.  Evaluation of a behavioral measure of risk taking: the Balloon Analogue Risk Task (BART). J Exp Psychol Appl. 2002;8(2):75-84.
PubMed   |  Link to Article
Glahn  DC, Almasy  L, Barguil  M,  et al.  Neurocognitive endophenotypes for bipolar disorder identified in multiplex multigenerational families. Arch Gen Psychiatry. 2010;67(2):168-177.
PubMed   |  Link to Article
Wechsler  D. Wechsler Adult Intelligence Scale—Fourth Edition (WAIS-IV). San Antonio, TX: Harcourt Assessment; 2008.
Glahn  DC, Cannon  TD, Gur  RE, Ragland  JD, Gur  RC.  Working memory constrains abstraction in schizophrenia. Biol Psychiatry. 2000;47(1):34-42.
PubMed   |  Link to Article
Kurtz  MM, Ragland  JD, Moberg  PJ, Gur  RC.  The Penn Conditional Exclusion Test: a new measure of executive-function with alternate forms of repeat administration. Arch Clin Neuropsychol. 2004;19(2):191-201.
PubMed   |  Link to Article
Stroop  JR.  Studies of interference in serial verbal reactions. J Exp Psychol Gen. 1992;121(1):15-23.
Link to Article
Brown  L, Sherbenou  RJ, Johnsen  SK. Test of Nonverbal Intelligence: A Language-Free Measure of Cognitive Ability. Austin, TX: Pro-Ed; 1997.
Dale  AM, Fischl  B, Sereno  MI.  Cortical surface-based analysis, I: segmentation and surface reconstruction. Neuroimage. 1999;9(2):179-194.
PubMed   |  Link to Article
Fischl  B, Sereno  MI, Dale  AM.  Cortical surface-based analysis, II: inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9(2):195-207.
PubMed   |  Link to Article
Smith  SM, Jenkinson  M, Woolrich  MW,  et al.  Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23(suppl 1):S208-S219.
PubMed   |  Link to Article
Jenkinson  M, Beckmann  CF, Behrens  TE, Woolrich  MW, Smith  SM.  FSL. Neuroimage. 2012;62(2):782-790.
PubMed   |  Link to Article
Panizzon  MS, Fennema-Notestine  C, Eyler  LT,  et al.  Distinct genetic influences on cortical surface area and cortical thickness. Cereb Cortex. 2009;19(11):2728-2735.
PubMed   |  Link to Article
Raznahan  A, Shaw  P, Lalonde  F,  et al.  How does your cortex grow? J Neurosci. 2011;31(19):7174-7177.
PubMed   |  Link to Article
Oishi  K, Zilles  K, Amunts  K,  et al.  Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. Neuroimage. 2008;43(3):447-457.
PubMed   |  Link to Article
Mahon  K, Burdick  KE, Ikuta  T,  et al.  Abnormal temporal lobe white matter as a biomarker for genetic risk of bipolar disorder. Biol Psychiatry. 2013;73(2):177-182.
PubMed   |  Link to Article
Sexton  CE, Mackay  CE, Ebmeier  KP.  A systematic review of diffusion tensor imaging studies in affective disorders. Biol Psychiatry. 2009;66(9):814-823.
PubMed   |  Link to Article
Sprooten  E, Sussmann  JE, Clugston  A,  et al.  White matter integrity in individuals at high genetic risk of bipolar disorder. Biol Psychiatry. 2011;70(4):350-356.
PubMed   |  Link to Article
Bartzokis  G, Lu  PH, Heydari  P,  et al.  Multimodal magnetic resonance imaging assessment of white matter aging trajectories over the lifespan of healthy individuals. Biol Psychiatry. 2012;72(12):1026-1034.
PubMed   |  Link to Article
Budde  MD, Xie  M, Cross  AH, Song  SK.  Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: a quantitative pixelwise analysis. J Neurosci. 2009;29(9):2805-2813.
PubMed   |  Link to Article
Song  SK, Sun  SW, Ramsbottom  MJ, Chang  C, Russell  J, Cross  AH.  Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage. 2002;17(3):1429-1436.
PubMed   |  Link to Article
Song  SK, Sun  SW, Ju  WK, Lin  SJ, Cross  AH, Neufeld  AH.  Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage. 2003;20(3):1714-1722.
PubMed   |  Link to Article
Almasy  L, Blangero  J.  Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998;62(5):1198-1211.
PubMed   |  Link to Article
Van der Waerden  BL.  Order tests for the two-sample problem and their power. Indag Math. 1952;14:453-458.
Pilia  G, Chen  WM, Scuteri  A,  et al.  Heritability of cardiovascular and personality traits in 6148 Sardinians. PLoS Genet. 2006;2(8):e132.
PubMed   |  Link to Article
Williams  JT, Van Eerdewegh  P, Almasy  L, Blangero  J.  Joint multipoint linkage analysis of multivariate qualitative and quantitative traits, I: likelihood formulation and simulation results. Am J Hum Genet. 1999;65(4):1134-1147.
PubMed   |  Link to Article
Cruchaga  C, Kauwe  JS, Harari  O,  et al; GERAD Consortium; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Alzheimer Disease Genetic Consortium (ADGC).  GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer’s disease. Neuron. 2013;78(2):256-268.
PubMed   |  Link to Article
Schürhoff  F, Szöke  A, Méary  A,  et al.  Familial aggregation of delusional proneness in schizophrenia and bipolar pedigrees. Am J Psychiatry. 2003;160(7):1313-1319.
PubMed   |  Link to Article
Jamison  KR.  Great wits and madness: more near allied? Br J Psychiatry. 2011;199(5):351-352.
PubMed   |  Link to Article
Kyaga  S, Lichtenstein  P, Boman  M, Hultman  C, Långström  N, Landén  M.  Creativity and mental disorder: family study of 300 000 people with severe mental disorder. Br J Psychiatry. 2011;199(5):373-379.
PubMed   |  Link to Article
Kyaga  S, Landén  M, Boman  M, Hultman  CM, Långström  N, Lichtenstein  P.  Mental illness, suicide and creativity: 40-year prospective total population study. J Psychiatr Res. 2013;47(1):83-90.
PubMed   |  Link to Article
Parker  G, Paterson  A, Fletcher  K, Blanch  B, Graham  R.  The “magic button question” for those with a mood disorder: would they wish to re-live their condition? J Affect Disord. 2012;136(3):419-424.
PubMed   |  Link to Article
Balanzá-Martínez  V, Rubio  C, Selva-Vera  G,  et al.  Neurocognitive endophenotypes (endophenocognitypes) from studies of relatives of bipolar disorder subjects: a systematic review. Neurosci Biobehav Rev. 2008;32(8):1426-1438.
PubMed   |  Link to Article
Robinson  LJ, Thompson  JM, Gallagher  P,  et al.  A meta-analysis of cognitive deficits in euthymic patients with bipolar disorder. J Affect Disord. 2006;93(1-3):105-115.
PubMed   |  Link to Article
Torres  IJ, Boudreau  VG, Yatham  LN.  Neuropsychological functioning in euthymic bipolar disorder: a meta-analysis. Acta Psychiatr Scand Suppl. 2007;(434):17-26.
PubMed
Greenwood  TA, Braff  DL, Light  GA,  et al.  Initial heritability analyses of endophenotypic measures for schizophrenia: the Consortium on the Genetics of Schizophrenia. Arch Gen Psychiatry. 2007;64(11):1242-1250.
PubMed   |  Link to Article
Gur  RE, Nimgaonkar  VL, Almasy  L,  et al.  Neurocognitive endophenotypes in a multiplex multigenerational family study of schizophrenia. Am J Psychiatry. 2007;164(5):813-819.
PubMed   |  Link to Article
Steinberg  S, de Jong  S, Mattheisen  M,  et al; GROUP; Wellcome Trust Case Control Consortium 2.  Common variant at 16p11.2 conferring risk of psychosis. Mol Psychiatry. 2014;19(1):108-114.
PubMed   |  Link to Article
Arnone  D, Cavanagh  J, Gerber  D, Lawrie  SM, Ebmeier  KP, McIntosh  AM.  Magnetic resonance imaging studies in bipolar disorder and schizophrenia: meta-analysis. Br J Psychiatry. 2009;195(3):194-201.
PubMed   |  Link to Article
Bora  E, Fornito  A, Yücel  M, Pantelis  C.  Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder. Biol Psychiatry. 2010;67(11):1097-1105.
PubMed   |  Link to Article
Hallahan  B, Newell  J, Soares  JC,  et al.  Structural magnetic resonance imaging in bipolar disorder: an international collaborative mega-analysis of individual adult patient data. Biol Psychiatry. 2011;69(4):326-335.
PubMed   |  Link to Article
Kempton  MJ, Geddes  JR, Ettinger  U, Williams  SC, Grasby  PM.  Meta-analysis, database, and meta-regression of 98 structural imaging studies in bipolar disorder. Arch Gen Psychiatry. 2008;65(9):1017-1032.
PubMed   |  Link to Article
McDonald  C, Zanelli  J, Rabe-Hesketh  S,  et al.  Meta-analysis of magnetic resonance imaging brain morphometry studies in bipolar disorder. Biol Psychiatry. 2004;56(6):411-417.
PubMed   |  Link to Article
Rimol  LM, Hartberg  CB, Nesvåg  R,  et al.  Cortical thickness and subcortical volumes in schizophrenia and bipolar disorder. Biol Psychiatry. 2010;68(1):41-50.
PubMed   |  Link to Article
Houenou  J, Frommberger  J, Carde  S,  et al.  Neuroimaging-based markers of bipolar disorder: evidence from two meta-analyses. J Affect Disord. 2011;132(3):344-355.
PubMed   |  Link to Article
Chen  CH, Suckling  J, Lennox  BR, Ooi  C, Bullmore  ET.  A quantitative meta-analysis of fMRI studies in bipolar disorder. Bipolar Disord. 2011;13(1):1-15.
PubMed   |  Link to Article
Cusi  AM, Nazarov  A, Holshausen  K, Macqueen  GM, McKinnon  MC.  Systematic review of the neural basis of social cognition in patients with mood disorders. J Psychiatry Neurosci. 2012;37(3):154-169.
PubMed   |  Link to Article
Kupferschmidt  DA, Zakzanis  KK.  Toward a functional neuroanatomical signature of bipolar disorder: quantitative evidence from the neuroimaging literature. Psychiatry Res. 2011;193(2):71-79.
PubMed   |  Link to Article
Townsend  J, Altshuler  LL.  Emotion processing and regulation in bipolar disorder: a review. Bipolar Disord. 2012;14(4):326-339.
PubMed   |  Link to Article
Roberts  G, Green  MJ, Breakspear  M,  et al.  Reduced inferior frontal gyrus activation during response inhibition to emotional stimuli in youth at high risk of bipolar disorder. Biol Psychiatry. 2013;74(1):55-61.
PubMed   |  Link to Article
Bearden  CE, van Erp  TG, Dutton  RA,  et al.  Mapping corpus callosum morphology in twin pairs discordant for bipolar disorder. Cereb Cortex. 2011;21(10):2415-2424.
PubMed   |  Link to Article
van der Schot  AC, Vonk  R, Brans  RG,  et al.  Influence of genes and environment on brain volumes in twin pairs concordant and discordant for bipolar disorder. Arch Gen Psychiatry. 2009;66(2):142-151.
PubMed   |  Link to Article
Brotman  MA, Guyer  AE, Lawson  ES,  et al.  Facial emotion labeling deficits in children and adolescents at risk for bipolar disorder. Am J Psychiatry. 2008;165(3):385-389.
PubMed   |  Link to Article
Brotman  MA, Skup  M, Rich  BA,  et al.  Risk for bipolar disorder is associated with face-processing deficits across emotions. J Am Acad Child Adolesc Psychiatry. 2008;47(12):1455-1461.
PubMed   |  Link to Article
Mercer  L, Becerra  R.  A unique emotional processing profile of euthymic bipolar disorder? a critical review. J Affect Disord. 2013;146(3):295-309.
PubMed   |  Link to Article
Samamé  C, Martino  DJ, Strejilevich  SA.  Social cognition in euthymic bipolar disorder: systematic review and meta-analytic approach. Acta Psychiatr Scand. 2012;125(4):266-280.
PubMed   |  Link to Article
Adleman  NE, Kayser  RR, Olsavsky  AK,  et al.  Abnormal fusiform activation during emotional-face encoding assessed with functional magnetic resonance imaging. Psychiatry Res. 2013;212(2):161-163.
PubMed   |  Link to Article
Ando  J, Ono  Y, Wright  MJ.  Genetic structure of spatial and verbal working memory. Behav Genet. 2001;31(6):615-624.
PubMed   |  Link to Article
Corvin  A, Donohoe  G, Hargreaves  A, Gallagher  L, Gill  M.  The cognitive genetics of neuropsychiatric disorders. Curr Top Behav Neurosci. 2012;12:579-613.
PubMed
Donohoe  G, Deary  IJ, Glahn  DC, Malhotra  AK, Burdick  KE.  Neurocognitive phenomics: examining the genetic basis of cognitive abilities. Psychol Med. 2013;43(10):2027-2036.
PubMed   |  Link to Article
Greenwood  TA, Beeri  MS, Schmeidler  J,  et al.  Heritability of cognitive functions in families of successful cognitive aging probands from the Central Valley of Costa Rica. J Alzheimers Dis. 2011;27(4):897-907.
PubMed
Hervey  AS, Greenfield  K, Gualtieri  CT.  Heritability in cognitive performance: evidence using computer-based testing. J Genet Psychol. 2012;173(1):112-118.
PubMed   |  Link to Article
Lee  T, Mosing  MA, Henry  JD,  et al; OATS Research Team.  Genetic influences on four measures of executive functions and their covariation with general cognitive ability: the Older Australian Twins Study. Behav Genet. 2012;42(4):528-538.
PubMed   |  Link to Article
Oliver  BR, Plomin  R.  Twins’ Early Development Study (TEDS): a multivariate, longitudinal genetic investigation of language, cognition and behavior problems from childhood through adolescence. Twin Res Hum Genet. 2007;10(1):96-105.
PubMed   |  Link to Article
van Soelen  IL, Brouwer  RM, van Leeuwen  M, Kahn  RS, Hulshoff Pol  HE, Boomsma  DI.  Heritability of verbal and performance intelligence in a pediatric longitudinal sample. Twin Res Hum Genet. 2011;14(2):119-128.
PubMed   |  Link to Article
Bearden  CE, Hoffman  KM, Cannon  TD.  The neuropsychology and neuroanatomy of bipolar affective disorder: a critical review. Bipolar Disord. 2001;3(3):106-150, discussion 151-153.
PubMed   |  Link to Article
Evans  L, Akiskal  HS, Keck  PE  Jr,  et al.  Familiality of temperament in bipolar disorder: support for a genetic spectrum. J Affect Disord. 2005;85(1-2):153-168.
PubMed   |  Link to Article
Glahn  DC, Bearden  CE, Barguil  M,  et al.  The neurocognitive signature of psychotic bipolar disorder. Biol Psychiatry. 2007;62(8):910-916.
PubMed   |  Link to Article

Correspondence

CME
Also Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
Please click the checkbox indicating that you have read the full article in order to submit your answers.
Your answers have been saved for later.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
Submit a Comment

Multimedia

Supplement.

eAppendix 1. Methods

eAppendix 2. Outlier Analysis

eAppendix 3. Secondary Analyses: Effects of Medication and Clinical Course

eReferences

eTable 1. Summary of Heritability and Association Analysis for All Phenotypes

eTable 2. Clinical Characteristics and History of Medication Use for BP-I Subjects

eFigure. Distribution of Group 1 Trait Values by Family

Supplemental Content

Some tools below are only available to our subscribers or users with an online account.

2,516 Views
11 Citations

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Multimedia

Author Interview

audio player

Articles Related By Topic
Related Collections
PubMed Articles
Jobs
×