Genome-wide association study identifies a novel locus for cannabis dependence.

Despite moderate heritability, only one study has identified genome-wide significant loci for cannabis-related phenotypes. We conducted meta-analyses of genome-wide association study data on 2080 cannabis-dependent cases and 6435 cannabis-exposed controls of European descent. A cluster of correlated single-nucleotide polymorphisms (SNPs) in a novel region on chromosome 10 was genome-wide significant (lowest P=1.3E-8). Among the SNPs, rs1409568 showed enrichment for H3K4me1 and H3K427ac marks, suggesting its role as an enhancer in addiction-relevant brain regions, such as the dorsolateral prefrontal cortex and the angular and cingulate gyri. This SNP is also predicted to modify binding scores for several transcription factors. We found modest evidence for replication for rs1409568 in an independent cohort of African American (896 cases and 1591 controls; P=0.03) but not European American (EA; 781 cases and 1905 controls) participants. The combined meta-analysis (3757 cases and 9931 controls) indicated trend-level significance for rs1409568 (P=2.85E-7). No genome-wide significant loci emerged for cannabis dependence criterion count (n=8050). There was also evidence that the minor allele of rs1409568 was associated with a 2.1% increase in right hippocampal volume in an independent sample of 430 EA college students (fwe-P=0.008). The identification and characterization of genome-wide significant loci for cannabis dependence is among the first steps toward understanding the biological contributions to the etiology of this psychiatric disorder, which appears to be rising in some developed nations.

Interhemispheric white matter integrity in young people with bipolar disorder and at high genetic risk.

BACKGROUND: White matter (WM) impairments have been reported in patients with bipolar disorder (BD) and those at high familial risk of developing BD. However, the distribution of these impairments has not been well characterized. Few studies have examined WM integrity in young people early in the course of illness and in individuals at familial risk who have not yet passed the peak age of onset. METHOD: WM integrity was examined in 63 BD subjects, 150 high-risk (HR) individuals and 111 participants with no family history of mental illness (CON). All subjects were aged 12 to 30 years. RESULTS: This young BD group had significantly lower fractional anisotropy within the genu of the corpus callosum (CC) compared with the CON and HR groups. Moreover, the abnormality in the genu of the CC was also present in HR participants with recurrent major depressive disorder (MDD) (n = 16) compared with CON participants. CONCLUSIONS: Our findings provide important validation of interhemispheric abnormalities in BD patients. The novel finding in HR subjects with recurrent MDD - a group at particular risk of future hypo/manic episodes - suggests that this may potentially represent a trait marker for BD, though this will need to be confirmed in longitudinal follow-up studies.

Genome-wide association data suggest ABCB1 and immune-related gene sets may be involved in adult antisocial behavior.

Adult antisocial behavior (AAB) is moderately heritable, relatively common and has adverse consequences for individuals and society. We examined the molecular genetic basis of AAB in 1379 participants from a case-control study in which the cases met criteria for alcohol dependence. We also examined whether genes of interest were expressed in human brain. AAB was measured using a count of the number of Antisocial Personality Disorder criteria endorsed under criterion A from the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV). Participants were genotyped on the Illumina Human 1M BeadChip. In total, all single-nucleotide polymorphisms (SNPs) accounted for 25% of the variance in AAB, although this estimate was not significant (P=0.09). Enrichment tests indicated that more significantly associated genes were over-represented in seven gene sets, and most were immune related. Our most highly associated SNP (rs4728702, P=5.77 × 10(-7)) was located in the protein-coding adenosine triphosphate-binding cassette, sub-family B (MDR/TAP), member 1 (ABCB1). In a gene-based test, ABCB1 was genome-wide significant (q=0.03). Expression analyses indicated that ABCB1 was robustly expressed in the brain. ABCB1 has been implicated in substance use, and in post hoc tests we found that variation in ABCB1 was associated with DSM-IV alcohol and cocaine dependence criterion counts. These results suggest that ABCB1 may confer risk across externalizing behaviors, and are consistent with previous suggestions that immune pathways are associated with externalizing behaviors. The results should be tempered by the fact that we did not replicate the associations for ABCB1 or the gene sets in a less-affected independent sample.

Confirmatory test of two factors and four subtypes of bipolar disorder based on lifetime psychiatric co-morbidity.

BACKGROUND: The first aim was to use confirmatory factor analysis (CFA) to test a hypothesis that two factors (internalizing and externalizing) account for lifetime co-morbid DSM-IV diagnoses among adults with bipolar I (BPI) disorder. The second aim was to use confirmatory latent class analysis (CLCA) to test the hypothesis that four clinical subtypes are detectible: pure BPI; BPI plus internalizing disorders only; BPI plus externalizing disorders only; and BPI plus internalizing and externalizing disorders. METHOD: A cohort of 699 multiplex BPI families was studied, ascertained and assessed (1998-2003) by the National Institute of Mental Health Genetics Initiative Bipolar Consortium: 1156 with BPI disorder (504 adult probands; 594 first-degree relatives; and 58 more distant relatives) and 563 first-degree relatives without BPI. Best-estimate consensus DSM-IV diagnoses were based on structured interviews, family history and medical records. MPLUS software was used for CFA and CLCA. RESULTS: The two-factor CFA model fit the data very well, and could not be improved by adding or removing paths. The four-class CLCA model fit better than exploratory LCA models or post-hoc-modified CLCA models. The two factors and four classes were associated with distinctive clinical course and severity variables, adjusted for proband gender. Co-morbidity, especially more than one internalizing and/or externalizing disorder, was associated with a more severe and complicated course of illness. The four classes demonstrated significant familial aggregation, adjusted for gender and age of relatives. CONCLUSIONS: The BPI two-factor and four-cluster hypotheses demonstrated substantial confirmatory support. These models may be useful for subtyping BPI disorders, predicting course of illness and refining the phenotype in genetic studies.

A genome-wide association study of alcohol-dependence symptom counts in extended pedigrees identifies C15orf53.

Several studies have identified genes associated with alcohol-use disorders (AUDs), but the variation in each of these genes explains only a small portion of the genetic vulnerability. The goal of the present study was to perform a genome-wide association study (GWAS) in extended families from the Collaborative Study on the Genetics of Alcoholism to identify novel genes affecting risk for alcohol dependence (AD). To maximize the power of the extended family design, we used a quantitative endophenotype, measured in all individuals: number of alcohol-dependence symptoms endorsed (symptom count (SC)). Secondary analyses were performed to determine if the single nucleotide polymorphisms (SNPs) associated with SC were also associated with the dichotomous phenotype, DSM-IV AD. This family-based GWAS identified SNPs in C15orf53 that are strongly associated with DSM-IV alcohol-dependence symptom counts (P=4.5 × 10(-8), inflation-corrected P=9.4 × 10(-7)). Results with DSM-IV AD in the regions of interest support our findings with SC, although the associations were less significant. Attempted replications of the most promising association results were conducted in two independent samples: nonoverlapping subjects from the Study of Addiction: Genes and Environment (SAGE) and the Australian Twin Family Study of AUDs (OZALC). Nominal association of C15orf53 with SC was observed in SAGE. The variant that showed strongest association with SC, rs12912251 and its highly correlated variants (D'=1, r(2) 0.95), have previously been associated with risk for bipolar disorder.

Enrichment of cis-regulatory gene expression SNPs and methylation quantitative trait loci among bipolar disorder susceptibility variants.

We conducted a systematic study of top susceptibility variants from a genome-wide association (GWA) study of bipolar disorder to gain insight into the functional consequences of genetic variation influencing disease risk. We report here the results of experiments to explore the effects of these susceptibility variants on DNA methylation and mRNA expression in human cerebellum samples. Among the top susceptibility variants, we identified an enrichment of cis regulatory loci on mRNA expression (eQTLs), and a significant excess of quantitative trait loci for DNA CpG methylation, hereafter referred to as methylation quantitative trait loci (mQTLs). Bipolar disorder susceptibility variants that cis regulate both cerebellar expression and methylation of the same gene are a very small proportion of bipolar disorder susceptibility variants. This finding suggests that mQTLs and eQTLs provide orthogonal ways of functionally annotating genetic variation within the context of studies of pathophysiology in brain. No lymphocyte mQTL enrichment was found, suggesting that mQTL enrichment was specific to the cerebellum, in contrast to eQTLs. Separately, we found that using mQTL information to restrict the number of single-nucleotide polymorphisms studied enhances our ability to detect a significant association. With this restriction a priori informed by the observed functional enrichment, we identified a significant association (rs12618769, P(bonferroni)<0.05) from two other GWA studies (TGen+GAIN; 2191 cases and 1434 controls) of bipolar disorder, which we replicated in an independent GWA study (WTCCC). Collectively, our findings highlight the importance of integrating functional annotation of genetic variants for gene expression and DNA methylation to advance the biological understanding of bipolar disorder.

Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction.

We have used a translational convergent functional genomics (CFG) approach to identify and prioritize genes involved in schizophrenia, by gene-level integration of genome-wide association study data with other genetic and gene expression studies in humans and animal models. Using this polyevidence scoring and pathway analyses, we identify top genes (DISC1, TCF4, MBP, MOBP, NCAM1, NRCAM, NDUFV2, RAB18, as well as ADCYAP1, BDNF, CNR1, COMT, DRD2, DTNBP1, GAD1, GRIA1, GRIN2B, HTR2A, NRG1, RELN, SNAP-25, TNIK), brain development, myelination, cell adhesion, glutamate receptor signaling, G-protein-coupled receptor signaling and cAMP-mediated signaling as key to pathophysiology and as targets for therapeutic intervention. Overall, the data are consistent with a model of disrupted connectivity in schizophrenia, resulting from the effects of neurodevelopmental environmental stress on a background of genetic vulnerability. In addition, we show how the top candidate genes identified by CFG can be used to generate a genetic risk prediction score (GRPS) to aid schizophrenia diagnostics, with predictive ability in independent cohorts. The GRPS also differentiates classic age of onset schizophrenia from early onset and late-onset disease. We also show, in three independent cohorts, two European American and one African American, increasing overlap, reproducibility and consistency of findings from single-nucleotide polymorphisms to genes, then genes prioritized by CFG, and ultimately at the level of biological pathways and mechanisms. Finally, we compared our top candidate genes for schizophrenia from this analysis with top candidate genes for bipolar disorder and anxiety disorders from previous CFG analyses conducted by us, as well as findings from the fields of autism and Alzheimer. Overall, our work maps the genomic and biological landscape for schizophrenia, providing leads towards a better understanding of illness, diagnostics and therapeutics. It also reveals the significant genetic overlap with other major psychiatric disorder domains, suggesting the need for improved nosology.

Genome-wide linkage analysis of 972 bipolar pedigrees using single-nucleotide polymorphisms.

Because of the high costs associated with ascertainment of families, most linkage studies of Bipolar I disorder (BPI) have used relatively small samples. Moreover, the genetic information content reported in most studies has been less than 0.6. Although microsatellite markers spaced every 10 cM typically extract most of the genetic information content for larger multiplex families, they can be less informative for smaller pedigrees especially for affected sib pair kindreds. For these reasons we collaborated to pool family resources and carried out higher density genotyping. Approximately 1100 pedigrees of European ancestry were initially selected for study and were genotyped by the Center for Inherited Disease Research using the Illumina Linkage Panel 12 set of 6090 single-nucleotide polymorphisms. Of the ~1100 families, 972 were informative for further analyses, and mean information content was 0.86 after pruning for linkage disequilibrium. The 972 kindreds include 2284 cases of BPI disorder, 498 individuals with bipolar II disorder (BPII) and 702 subjects with recurrent major depression. Three affection status models (ASMs) were considered: ASM1 (BPI and schizoaffective disorder, BP cases (SABP) only), ASM2 (ASM1 cases plus BPII) and ASM3 (ASM2 cases plus recurrent major depression). Both parametric and non-parametric linkage methods were carried out. The strongest findings occurred at 6q21 (non-parametric pairs LOD 3.4 for rs1046943 at 119 cM) and 9q21 (non-parametric pairs logarithm of odds (LOD) 3.4 for rs722642 at 78 cM) using only BPI and schizoaffective (SA), BP cases. Both results met genome-wide significant criteria, although neither was significant after correction for multiple analyses. We also inspected parametric scores for the larger multiplex families to identify possible rare susceptibility loci. In this analysis, we observed 59 parametric LODs of 2 or greater, many of which are likely to be close to maximum possible scores. Although some linkage findings may be false positives, the results could help prioritize the search for rare variants using whole exome or genome sequencing.

Ethnic disparities in the perception of ethical risks from psychiatric genetic studies.

To examine if ethnic differences in concerns about unfavorable consequences from psychiatric genetic studies, existing between non-Hispanic Black and White populations, persist among participants in an actual genetic study of bipolar disorder. Historically, minority subjects have been less willing to participate in such studies. Participants in the US Bipolar Genome Study (BIGS) were assessed on six items of concerns in the Questionnaire on Genetic Risk (QGR). Each item had five response categories, ranging from "not at all" concerned to "very concerned." Responses from Black (N = 188) and White participants (N = 1,065) formed the base for this analysis. Concerns about unfavorable consequences of conducting psychiatric genetic studies were prevalent in the whole sample. Concern for medical insurance was most prevalent (63.4%), followed by job concern (58.8%) and stigma (57.4%). Racial discrimination was less prevalent (28.1%). Blacks endorsed significantly stronger concerns for all consequences except the medical insurance item (P < 0.008). The most significant ethnic disparity in concerns was for racial discrimination (P < 0.0001). Associations between levels of concern and ethnicity remained significant after adjustments for other factors in multivariate models. Ethnic differences (Blacks vs. Whites) in perceived concerns about unfavorable consequences from participation persist among participants in an actual psychiatric genetic study. This suggests that other factors may play a more critical role in the decision not to participate. Future studies should investigate more comprehensive sources of barriers to consenting for ongoing psychiatric genetic studies in representative samples, incorporating assessments from non-participants as well as participants.

Identification of blood biomarkers for psychosis using convergent functional genomics.

There are to date no objective clinical laboratory blood tests for psychotic disease states. We provide proof of principle for a convergent functional genomics (CFG) approach to help identify and prioritize blood biomarkers for two key psychotic symptoms, one sensory (hallucinations) and one cognitive (delusions). We used gene expression profiling in whole blood samples from patients with schizophrenia and related disorders, with phenotypic information collected at the time of blood draw, then cross-matched the data with other human and animal model lines of evidence. Topping our list of candidate blood biomarkers for hallucinations, we have four genes decreased in expression in high hallucinations states (Fn1, Rhobtb3, Aldh1l1, Mpp3), and three genes increased in high hallucinations states (Arhgef9, Phlda1, S100a6). All of these genes have prior evidence of differential expression in schizophrenia patients. At the top of our list of candidate blood biomarkers for delusions, we have 15 genes decreased in expression in high delusions states (such as Drd2, Apoe, Scamp1, Fn1, Idh1, Aldh1l1), and 16 genes increased in high delusions states (such as Nrg1, Egr1, Pvalb, Dctn1, Nmt1, Tob2). Twenty-five of these genes have prior evidence of differential expression in schizophrenia patients. Predictive scores, based on panels of top candidate biomarkers, show good sensitivity and negative predictive value for detecting high psychosis states in the original cohort as well as in three additional cohorts. These results have implications for the development of objective laboratory tests to measure illness severity and response to treatment in devastating disorders such as schizophrenia.

Coming to grips with complex disorders: genetic risk prediction in bipolar disorder using panels of genes identified through convergent functional genomics.

We previously proposed and provided proof of principle for the use of a complementary approach, convergent functional genomics (CFG), combining gene expression and genetic data, from human and animal model studies, as a way of mining the existing GWAS datasets for signals that are there already, but did not reach significance using a genetics-only approach [Le-Niculescu et al., 2009b]. CFG provides a fit-to-disease prioritization of genes that leads to generalizability in independent cohorts, and counterbalances the fit-to-cohort prioritization inherent in classic genetic-only approaches, which have been plagued by poor reproducibility across cohorts. We have now extended our previous work to include more datasets of GWAS, and more recent evidence from other lines of work. In essence our analysis is the most comprehensive integration of genetics and functional genomics to date in the field of bipolar disorder. Biological pathway analyses identified top canonical pathways, and epistatic interaction testing inside these pathways has identified genes that merit future follow-up as direct interactors (intra-pathway epistasis, INPEP). Moreover, we have put together a panel of best P-value single nucleotide polymorphisms (SNPs), based on the top candidate genes we identified. We have developed a genetic risk prediction score (GRPS) based on our panel, and demonstrate how in two independent test cohorts the GRPS differentiates between subjects with bipolar disorder and normal controls, in both European-American and African-American populations. Lastly, we describe a prototype of how such testing could be used to categorize disease risk in individuals and aid personalized medicine approaches, in psychiatry and beyond.

Item response modeling of DSM-IV mania symptoms in two representative US epidemiological samples.

BACKGROUND: There is considerable debate surrounding the effective measurement of DSM-IV symptoms used to assess manic disorders in epidemiological samples. METHOD: Using two nationally representative datasets, the National Epidemiological Survey of Alcohol and Related Conditions (NESARC, n=43,093 at wave 1, n=34,653 at 3-year follow-up) and the National Comorbidity Survey - Replication (NCS-R, n=9282), we examined the psychometric properties of symptoms used to assess DSM-IV mania. The predictive utility of the mania factor score was tested using the 3-year follow-up data in NESARC. RESULTS: Criterion B symptoms were unidimensional (single factor) in both samples. The symptoms assessing flight of ideas, distractibility and increased goal-directed activities had high factor loadings (0.70-0.93) with moderate rates of endorsement, thus providing good discrimination between individuals with and without mania. The symptom assessing grandiosity performed less well in both samples. The quantitative mania factor score was a good predictor of more severe disorders at the 3-year follow-up in the NESARC sample, even after controlling for a past history of DSM-IV diagnosis of manic disorder. CONCLUSIONS: These analyses suggest that questions based on some DSM symptoms effectively discriminate between individuals at high and low liability to mania, but others do not. A quantitative mania factor score may aid in predicting recurrence for patients with a history of mania. Methods for assessing mania using structured interviews in the absence of clinical assessment require further refinement.

Convergent functional genomics of genome-wide association data for bipolar disorder: comprehensive identification of candidate genes, pathways and mechanisms.

Given the mounting convergent evidence implicating many more genes in complex disorders such as bipolar disorder than the small number identified unambiguously by the first-generation Genome-Wide Association studies (GWAS) to date, there is a strong need for improvements in methodology. One strategy is to include in the next generation GWAS larger numbers of subjects, and/or to pool independent studies into meta-analyses. We propose and provide proof of principle for the use of a complementary approach, convergent functional genomics (CFG), as a way of mining the existing GWAS datasets for signals that are there already, but did not reach significance using a genetics-only approach. With the CFG approach, the integration of genetics with genomics, of human and animal model data, and of multiple independent lines of evidence converging on the same genes offers a way of extracting signal from noise and prioritizing candidates. In essence our analysis is the most comprehensive integration of genetics and functional genomics to date in the field of bipolar disorder, yielding a series of novel (such as Klf12, Aldh1a1, A2bp1, Ak3l1, Rorb, Rora) and previously known (such as Bdnf, Arntl, Gsk3b, Disc1, Nrg1, Htr2a) candidate genes, blood biomarkers, as well as a comprehensive identification of pathways and mechanisms. These become prime targets for hypothesis driven follow-up studies, new drug development and personalized medicine approaches.

Identifying blood biomarkers for mood disorders using convergent functional genomics.

There are to date no objective clinical laboratory blood tests for mood disorders. The current reliance on patient self-report of symptom severity and on the clinicians' impression is a rate-limiting step in effective treatment and new drug development. We propose, and provide proof of principle for, an approach to help identify blood biomarkers for mood state. We measured whole-genome gene expression differences in blood samples from subjects with bipolar disorder that had low mood vs those that had high mood at the time of the blood draw, and separately, changes in gene expression in brain and blood of a mouse pharmacogenomic model. We then integrated our human blood gene expression data with animal model gene expression data, human genetic linkage/association data and human postmortem brain data, an approach called convergent functional genomics, as a Bayesian strategy for cross-validating and prioritizing findings. Topping our list of candidate blood biomarker genes we have five genes involved in myelination (Mbp, Edg2, Mag, Pmp22 and Ugt8), and six genes involved in growth factor signaling (Fgfr1, Fzd3, Erbb3, Igfbp4, Igfbp6 and Ptprm). All of these genes have prior evidence of differential expression in human postmortem brains from mood disorder subjects. A predictive score developed based on a panel of 10 top candidate biomarkers (five for high mood and five for low mood) shows sensitivity and specificity for high mood and low mood states, in two independent cohorts. Our studies suggest that blood biomarkers may offer an unexpectedly informative window into brain functioning and disease state.

Phenomic, convergent functional genomic, and biomarker studies in a stress-reactive genetic animal model of bipolar disorder and co-morbid alcoholism.

We had previously identified the clock gene D-box binding protein (Dbp) as a potential candidate gene for bipolar disorder and for alcoholism, using a Convergent Functional Genomics (CFG) approach. Here we report that mice with a homozygous deletion of DBP have lower locomotor activity, blunted responses to stimulants, and gain less weight over time. In response to a chronic stress paradigm, these mice exhibit a diametric switch in these phenotypes. DBP knockout mice are also activated by sleep deprivation, similar to bipolar patients, and that activation is prevented by treatment with the mood stabilizer drug valproate. Moreover, these mice show increased alcohol intake following exposure to stress. Microarray studies of brain and blood reveal a pattern of gene expression changes that may explain the observed phenotypes. CFG analysis of the gene expression changes identified a series of novel candidate genes and blood biomarkers for bipolar disorder, alcoholism, and stress reactivity.

Association of NFKB1, which encodes a subunit of the transcription factor NF-kappaB, with alcohol dependence.

A broad region on chromosome 4q has been linked to alcohol dependence (alcoholism). We hypothesized that such broad linkage regions represent the combined action of multiple genes. Seeking to identify genes within that region that are associated with alcoholism, we have tested the association of NFKB1, located at 4q24, with alcoholism. NFKB1 encodes a 105 kDa transcription inhibitor that is cleaved to the 50 kDa DNA-binding subunit of the ubiquitous transcription factor NF-kappaB. NF-kappaB regulates many genes relevant to brain function, and its actions can be potentiated by ethanol; thus, NFKB1 is an excellent candidate gene for alcoholism. Nineteen SNPs in and near NFKB1 were analyzed in a sample of 219 multiplex alcoholic families of European American descent. Family-based association analyses detected significant evidence of association with eight SNPs and marginal evidence for five more. The association was driven by the affected individuals with earlier onset of alcoholism (55% of the sample with onset < or =21 years). Further analysis of the age of onset as a quantitative variable provided evidence for the association of 12 SNPs in this gene. Thus, variations in NFKB1 appear to affect the risk for alcoholism, particularly contributing to an earlier onset of the disease.

Convergent Functional Genomics of bipolar disorder: from animal model pharmacogenomics to human genetics and biomarkers.

Progress in understanding the genetic and neurobiological basis of bipolar disorder(s) has come from both human studies and animal model studies. Until recently, the lack of concerted integration between the two approaches has been hindering the pace of discovery, or more exactly, constituted a missed opportunity to accelerate our understanding of this complex and heterogeneous group of disorders. Our group has helped overcome this "lost in translation" barrier by developing an approach called convergent functional genomics (CFG). The approach integrates animal model gene expression data with human genetic linkage/association data, as well as human tissue (postmortem brain, blood) data. This Bayesian strategy for cross-validating findings extracts meaning from large datasets, and prioritizes candidate genes, pathways and mechanisms for subsequent targeted, hypothesis-driven research. The CFG approach may also be particularly useful for identification of blood biomarkers of the illness.

Towards understanding the schizophrenia code: an expanded convergent functional genomics approach.

Identifying genes for schizophrenia through classical genetic approaches has proven arduous. Here, we present a comprehensive convergent analysis that translationally integrates brain gene expression data from a relevant pharmacogenomic mouse model (involving treatments with a psychomimetic agent - phencyclidine (PCP), and an anti-psychotic - clozapine), with human genetic linkage data and human postmortem brain data, as a Bayesian strategy of cross validating findings. Topping the list of candidate genes, we have three genes involved in GABA neurotransmission (GABRA1, GABBR1, and GAD2), one gene involved in glutamate neurotransmission (GRIA2), one gene involved in neuropeptide signaling (TAC1), two genes involved in synaptic function (SYN2 and KCNJ4), six genes involved in myelin/glial function (CNP, MAL, MBP, PLP1, MOBP and GFAP), and one gene involved in lipid metabolism (LPL). These data suggest that schizophrenia is primarily a disorder of brain functional and structural connectivity, with GABA neurotransmission playing a prominent role. These findings may explain the EEG gamma band abnormalities detected in schizophrenia. The analysis also revealed other high probability candidates genes (neurotransmitter signaling, other structural proteins, ion channels, signal transduction, regulatory enzymes, neuronal migration/neurite outgrowth, clock genes, transcription factors, RNA regulatory genes), pathways and mechanisms of likely importance in pathophysiology. Some of the pathways identified suggest possible avenues for augmentation pharmacotherapy of schizophrenia with other existing agents, such as benzodiazepines, anticonvulsants and lipid modulating agents. Other pathways are new potential targets for drug development. Lastly, a comparison with our earlier work on bipolar disorder illuminates the significant molecular overlap between schizophrenia and bipolar disorder.

Candidate genes, pathways and mechanisms for alcoholism: an expanded convergent functional genomics approach.

We describe a comprehensive translational approach for identifying candidate genes for alcoholism. The approach relies on the cross-matching of animal model brain gene expression data with human genetic linkage data, as well as human tissue data and biological roles data, an approach termed convergent functional genomics. An analysis of three animal model paradigms, based on inbred alcohol-preferring (iP) and alcohol-non-preferring (iNP) rats, and their response to treatments with alcohol, was used. A comprehensive analysis of microarray gene expression data from five key brain regions (frontal cortex, amygdala, caudate-putamen, nucleus accumbens and hippocampus) was carried out. The Bayesian-like integration of multiple independent lines of evidence, each by itself lacking sufficient discriminatory power, led to the identification of high probability candidate genes, pathways and mechanisms for alcoholism. These data reveal that alcohol has pleiotropic effects on multiple systems, which may explain the diverse neuropsychiatric and medical pathology in alcoholism. Some of the pathways identified suggest avenues for pharmacotherapy of alcoholism with existing agents, such as angiotensin-converting enzyme (ACE) inhibitors. Experiments we carried out in alcohol-preferring rats with an ACE inhibitor show a marked modulation of alcohol intake. Other pathways are new potential targets for drug development. The emergent overall picture is that physical and physiological robustness may permit alcohol-preferring individuals to withstand the aversive effects of alcohol. In conjunction with a higher reactivity to its rewarding effects, they may able to ingest enough of this nonspecific drug for a strong hedonic and addictive effect to occur.

Molecular genetics of bipolar disorder.

Bipolar disorder (BPD) is an often devastating illness characterized by extreme mood dysregulation. Although family, twin and adoption studies consistently indicate a strong genetic component, specific genes that contribute to the illness remain unclear. This study gives an overview of linkage studies of BPD, concluding that the regions with the best evidence for linkage include areas on chromosomes 2p, 4p, 4q, 6q, 8q, 11p, 12q, 13q, 16p, 16q, 18p, 18q, 21q, 22q and Xq. Association studies are summarized, which support a possible role for numerous candidate genes in BPD including COMT, DAT, HTR4, DRD4, DRD2, HTR2A, 5-HTT, the G72/G30 complex, DISC1, P2RX7, MAOA and BDNF. Animal models related to bipolar illness are also reviewed, with special attention paid to those with clear genetic implications. We conclude with suggestions for strategies that may help clarify the genetic bases of this complex illness.