Genetic risk prediction and neurobiological understanding of alcoholism.

We have used a translational Convergent Functional Genomics (CFG) approach to discover genes involved in alcoholism, by gene-level integration of genome-wide association study (GWAS) data from a German alcohol dependence cohort with other genetic and gene expression data, from human and animal model studies, similar to our previous work in bipolar disorder and schizophrenia. A panel of all the nominally significant P-value SNPs in the top candidate genes discovered by CFG  (n=135 genes, 713 SNPs) was used to generate a genetic  risk prediction score (GRPS), which showed a trend towards significance (P=0.053) in separating  alcohol dependent individuals from controls in an independent German test cohort. We then validated and prioritized our top findings from this discovery work, and subsequently tested them in three independent cohorts, from two continents. A panel of all the nominally significant P-value single-nucleotide length polymorphisms (SNPs) in the top candidate genes discovered by CFG (n=135 genes, 713 SNPs) were used to generate a Genetic Risk Prediction Score (GRPS), which showed a trend towards significance (P=0.053) in separating alcohol-dependent individuals from controls in an independent German test cohort. In order to validate and prioritize the key genes that drive behavior without some of the pleiotropic environmental confounds present in humans, we used a stress-reactive animal model of alcoholism developed by our group, the D-box binding protein (DBP) knockout mouse, consistent with the surfeit of stress theory of addiction proposed by Koob and colleagues. A much smaller panel (n=11 genes, 66 SNPs) of the top CFG-discovered genes for alcoholism, cross-validated and prioritized by this stress-reactive animal model showed better predictive ability in the independent German test cohort (P=0.041). The top CFG scoring gene for alcoholism from the initial discovery step, synuclein alpha (SNCA) remained the top gene after the stress-reactive animal model cross-validation. We also tested this small panel of genes in two other independent test cohorts from the United States, one with alcohol dependence (P=0.00012) and one with alcohol abuse (a less severe form of alcoholism; P=0.0094). SNCA by itself was able to separate alcoholics from controls in the alcohol-dependent cohort (P=0.000013) and the alcohol abuse cohort (P=0.023). So did eight other genes from the panel of 11 genes taken individually, albeit to a lesser extent and/or less broadly across cohorts. SNCA, GRM3 and MBP survived strict Bonferroni correction for multiple comparisons. Taken together, these results suggest that our stress-reactive DBP animal model helped to validate and prioritize from the CFG-discovered genes some of the key behaviorally relevant genes for alcoholism. These genes fall into a series of biological pathways involved in signal transduction, transmission of nerve impulse (including myelination) and cocaine addiction. Overall, our work provides leads towards a better understanding of illness, diagnostics and therapeutics, including treatment with omega-3 fatty acids. We also examined the overlap between the top candidate genes for alcoholism from this work and the top candidate genes for bipolar disorder, schizophrenia, anxiety from previous CFG analyses conducted by us, as well as cross-tested genetic risk predictions. This revealed the significant genetic overlap with other major psychiatric disorder domains, providing a basis for comorbidity and dual diagnosis, and placing alcohol use in the broader context of modulating the mental landscape.

Discovery and validation of blood biomarkers for suicidality.

Suicides are a leading cause of death in psychiatric patients, and in society at large. Developing more quantitative and objective ways (biomarkers) for predicting and tracking suicidal states would have immediate practical applications and positive societal implications. We undertook such an endeavor. First, building on our previous blood biomarker work in mood disorders and psychosis, we decided to identify blood gene expression biomarkers for suicidality, looking at differential expression of genes in the blood of subjects with a major mood disorder (bipolar disorder), a high-risk population prone to suicidality. We compared no suicidal ideation (SI) states and high SI states using a powerful intrasubject design, as well as an intersubject case-case design, to generate a list of differentially expressed genes. Second, we used a comprehensive Convergent Functional Genomics (CFG) approach to identify and prioritize from the list of differentially expressed gene biomarkers of relevance to suicidality. CFG integrates multiple independent lines of evidence-genetic and functional genomic data-as a Bayesian strategy for identifying and prioritizing findings, reducing the false-positives and false-negatives inherent in each individual approach. Third, we examined whether expression levels of the blood biomarkers identified by us in the live bipolar subject cohort are actually altered in the blood in an age-matched cohort of suicide completers collected from the coroner's office, and report that 13 out of the 41 top CFG scoring biomarkers (32%) show step-wise significant change from no SI to high SI states, and then to the suicide completers group. Six out of them (15%) remained significant after strict Bonferroni correction for multiple comparisons. Fourth, we show that the blood levels of SAT1 (spermidine/spermine N1-acetyltransferase 1), the top biomarker identified by us, at the time of testing for this study, differentiated future as well as past hospitalizations with suicidality, in a live cohort of bipolar disorder subjects, and exhibited a similar but weaker pattern in a live cohort of psychosis (schizophrenia/schizoaffective disorder) subjects. Three other (phosphatase and tensin homolog (PTEN), myristoylated alanine-rich protein kinase C substrate (MARCKS), and mitogen-activated protein kinase kinase kinase 3 (MAP3K3)) of the six biomarkers that survived Bonferroni correction showed similar but weaker effects. Taken together, the prospective and retrospective hospitalization data suggests SAT1, PTEN, MARCKS and MAP3K3 might be not only state biomarkers but trait biomarkers as well. Fifth, we show how a multi-dimensional approach using SAT1 blood expression levels and two simple visual-analog scales for anxiety and mood enhances predictions of future hospitalizations for suicidality in the bipolar cohort (receiver-operating characteristic curve with area under the curve of 0.813). Of note, this simple approach does not directly ask about SI, which some individuals may deny or choose not to share with clinicians. Lastly, we conducted bioinformatic analyses to identify biological pathways, mechanisms and medication targets. Overall, suicidality may be underlined, at least in part, by biological mechanisms related to stress, inflammation and apoptosis.

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.

Convergent functional genomics of anxiety disorders: translational identification of genes, biomarkers, pathways and mechanisms.

Anxiety disorders are prevalent and disabling yet understudied from a genetic standpoint, compared with other major psychiatric disorders such as bipolar disorder and schizophrenia. The fact that they are more common, diverse and perceived as embedded in normal life may explain this relative oversight. In addition, as for other psychiatric disorders, there are technical challenges related to the identification and validation of candidate genes and peripheral biomarkers. Human studies, particularly genetic ones, are susceptible to the issue of being underpowered, because of genetic heterogeneity, the effect of variable environmental exposure on gene expression, and difficulty of accrual of large, well phenotyped cohorts. Animal model gene expression studies, in a genetically homogeneous and experimentally tractable setting, can avoid artifacts and provide sensitivity of detection. Subsequent translational integration of the animal model datasets with human genetic and gene expression datasets can ensure cross-validatory power and specificity for illness. We have used a pharmacogenomic mouse model (involving treatments with an anxiogenic drug--yohimbine, and an anti-anxiety drug--diazepam) as a discovery engine for identification of anxiety candidate genes as well as potential blood biomarkers. Gene expression changes in key brain regions for anxiety (prefrontal cortex, amygdala and hippocampus) and blood were analyzed using a convergent functional genomics (CFG) approach, which integrates our new data with published human and animal model data, as a translational strategy of cross-matching and prioritizing findings. Our work identifies top candidate genes (such as FOS, GABBR1, NR4A2, DRD1, ADORA2A, QKI, RGS2, PTGDS, HSPA1B, DYNLL2, CCKBR and DBP), brain-blood biomarkers (such as FOS, QKI and HSPA1B), pathways (such as cAMP signaling) and mechanisms for anxiety disorders--notably signal transduction and reactivity to environment, with a prominent role for the hippocampus. Overall, this work complements our previous similar work (on bipolar mood disorders and schizophrenia) conducted over the last decade. It concludes our programmatic first pass mapping of the genomic landscape of the triad of major psychiatric disorder domains using CFG, and permitted us to uncover the significant genetic overlap between anxiety and these other major psychiatric disorders, notably the under-appreciated overlap with schizophrenia. PDE10A, TAC1 and other genes uncovered by our work provide a molecular basis for the frequently observed clinical co-morbidity and interdependence between anxiety and other major psychiatric disorders, and suggest schizo-anxiety as a possible new nosological domain.

Prevalence of Pterygium in a Rural Community of District; Southern Ethiopia

Background: Pterygium is principally prevalent in tropical Africa. Though Ethiopia is located in the tropics; the prevalence of pterygium has not been well studied previously. Aim: To determine the prevalence of pterygium among people older than 20 years and its association with age; sex; smoking and exposure to ultraviolet light (UVL) in Meskan District of Gurage zone; Southern Ethiopia. Methods: This cross-sectional study was conducted from March 15 to May 15; 2006. Two peasant associations (PA) were randomly selected from the total of 42 in the district. Based on the sampling fraction; every third household in the selected peasant associations was included in the study. All individuals above 20 years of age in the selected households were interviewed and examined for the presence of pterygium. Measurements of pterygium were made with ruler. Visual acuity was taken for those individuals with pterygium and checked with pinhole if found to be less than 6/18. Results: A total of 922 individuals; 400 (43.4) males and 522 (56.6) females; were involved in the study. The mean age was 38.2 ranging from 21-95 years. Pterygium was found in 81 (8.8; 95CI: 7.0;10.6) of the study subjects. For those individuals whose age was above 40 years; the prevalence was found to be 17.4indicating an increased risk with age (p0.001). It also occurred among 50 (12.5) males and 31 (5.9) females and the difference was statistically significant (p0.001). Males have about twice the risk of developing pterygium compared to females. Conclusion: The prevalence of pterygium obtained (8.8) from this study was relatively low compared to many other reports from tropical areas. However; in agreement with other reports; advancing age and males have been identified as risk factors for the development of pterygium. Pterygium was associated with visual impairment in 13.1of cases

First Report of Soybean Dwarf Virus Infecting Lentil and Beet Western Yellows Virus Infecting Lentil and Chickpea Crops in Ethiopia.

A survey conducted during November 14-23, 1998, to identify viruses infecting chickpea (Cicer arietinum) and lentil (Lens culinaris) crops in the Shewa province of Ethiopia covered 33 chickpea and 32 lentil fields randomly selected. Identity of the viruses present and virus incidence were determined on the basis of laboratory testing of 100 to 200 randomly collected samples in addition to 15 to 20 symptomatic samples from each field. A total of 5,427 lentil and 3,836 chickpea samples were collected and tested for the presence of 12 different viruses by tissue blot immunoassay (1) at the Plant Pathology Laboratory in Debre Zeit Agriculture Research Center, Ethiopia. All antisera were virus specific, including those for beet western yellows virus (BWYV; ATCC PVAS-647) and soybean dwarf virus (SbDV; ATCC PVAS-650). More than 21% of the samples from 5 chickpea fields were infected; the most common virus was BWYV. Also, at least 21% of the samples from 11 lentil fields were virus positive; the most widespread virus was PSbMV. Highest rates of infection: of lentil in a single field, PSbMV in 58.5% of the samples; in a chickpea field, 41.3% of the samples positive for BWYV. Other viruses such as faba bean necrotic yellows nanovirus (FBNYV) and broad bean wilt fabavirus in chickpea and FBNYV, broad bean stain comovirus, bean yellow mosaic potyvirus, and cucumber mosaic cucumovirus in lentil were detected at very low incidence. Reference: (1) K. M. Makkouk and A. Comeau. Eur. J. Plant Pathol. 100:71, 1994.