Short Courses: Flexible Learning Opportunities in Informatics

In today’s fast-paced, data-driven world, researchers need to have a good foundation in informatics to store, organize, process, and analyze growing amounts of data. However, not all degree programs offer such training. Obtaining training in informatics on your own can be a daunting task for both new and established researchers who have little informatics experience. Providing educational opportunities appropriate for various skill levels and that mesh with a full-time schedule can remove barriers and foster a collaborative, informatics-savvy community that is better equipped to push science forward. To enhance informatics education in bioinformatics, VCUs Wright Center for Clinical and Translational Research of- fers a complementary series of seminars and workshops. These short course offerings introduce attendees to bioinformatics concepts and applications, and provide hands-on experience using online Bioinformatics databases. Bioinformatics 101 (B101) is an 8-week long series of 1-hour seminars focused on introducing topics in bioinformatics related to Next Generation Sequencing (NGS). Lectures are application focused and include overviews of NGS technology, practical bioinformatics pipelines, and examples of how the technology can influence downstream bioinformatics analyses. Bioinformatics 102 (B102) is a 5-day, 2 hours per day workshop developed in collaboration with VCU Libraries that provides attendees with hands-on experience accessing and using public data repositories. Sessions include a brief lecture followed by hands-on exercises. A Certificate of Completion is awarded upon meeting certain criteria for either the 101 or 102 courses. Bioinformatics 101 has been offered 3 times with a combined total of 246 registrants, and Bioinformatics 102 has been offered twice with a total of 78 registrants (limited to 30 per session per day). From course surveys, 82% (n=108) and 95% (n=47) of respondents gave B101 and B102 a positive rating, respectively. In addition, 89% of B101 respondents indicated their knowledge was improved, with 100% of B102 respondents indicating the same. A total of 84 and 33 certificates have been awarded for B101 and B102, respectively. The Bioinformatics 101 and 102 courses have become highly anticipated across the university, and have gained the external attention of surrounding businesses and colleges. Registrants have diverse backgrounds including biological, clinical, computational, administrative, librarian, business, and others with a total of 77 departments across VCU and VCU Health represented. Due to this interest, Bioinformatics 101 began offering live online attendance to accommodate those who were unable to travel across campus, or who are attending from outside VCU. This past year, 50% of attendance was online indicating a growing need for flexible education opportunities in informatics. Increasing researcher knowledge of Bioinformatics along with awareness of university resources for informatics support fosters an informatics-savvy research community that is empowered to take advantage of existing and new data sources in the pursuit of new insights and scientific discoveries for the betterment of human health. Future work will include the development of a more comprehensive educational framework by creating new and flexible learning opportunities that will make informatics education easy and convenient for our dedicated researchers

Providing Hands-on Training with Bioinformatics Databases: A Collaboration Between VCU Libraries & Wright Center for Clinical and Translational Research

BackgroundWith the goal of increasing specialized services for researchers, Virginia Commonwealth University (VCU) Libraries sent its basic science librarians to an intensive training on bioinformatics databases, “A Librarian’s Guide to NCBI.” VCU’s Wright Center for Clinical and Translational Research (Wright CCTR) was expanding the educational component of its bioinformatics support around the same time. This year, the librarians partnered with the Wright CCTR to offer an introductory bioinformatics database workshop introducing researchers to genetic/genomic databases. MethodsFor one week in June, sessions were conducted introducing up to 30 faculty and staff to The Cancer Genome Atlas and NCBI’s Gene, BLAST, Variation Viewer and Gene Expression Omnibus. Librarians taught resources they learned in the NCBI training, and Wright CCTR staff taught resources they use often. Each day’s 1.5 hour session included presentations, demonstrations, and hands-on assignment time. Certificates were awarded to participants who completed 4 out of 5 assignments. ResultsRegistration for the workshop was full in under a week with a waiting list. All survey respondents (n=27) evaluated the overall quality of the workshop as good or excellent and indicated that they would recommend the workshop to a colleague or student. ConclusionsThis successful partnership between VCU Libraries and the Wright CCTR allowed for a broader range of bioinformatics topics to be covered, in addition to easing the planning and teaching workload for each group. The strong interest in this series across a variety of disciplines from both VCU and VCU Health indicates a need for staff and faculty-oriented bioinformatics training within the university

Time-Course Analysis of Brain Regional Expression Network Responses to Chronic Intermittent Ethanol and Withdrawal: Implications for Mechanisms Underlying Excessive Ethanol Consumption

Long lasting abusive consumption, dependence, and withdrawal are characteristic features of alcohol use disorders (AUD). Mechanistically, persistent changes in gene expression are hypothesized to contribute to brain adaptations leading to ethanol toxicity and AUD. We employed repeated chronic intermittent ethanol (CIE) exposure by vapor chamber as a mouse model to simulate the cycles of ethanol exposure and withdrawal commonly seen with AUD. This model has been shown to induce progressive ethanol consumption in rodents. Brain CIE-responsive expression networks were identified by microarray analysis across five regions of the mesolimbic dopamine system and extended amygdala with tissue harvested from 0-hours to 7-days following CIE. Weighted Gene Correlated Network Analysis (WGCNA) was used to identify gene networks over-represented for CIE-induced temporal expression changes across brain regions. Differential gene expression analysis showed that long-lasting gene regulation occurred 7-days after the final cycle of ethanol exposure only in prefrontal cortex (PFC) and hippocampus. Across all brain regions, however, ethanol-responsive expression changes occurred mainly within the first 8-hours after removal from ethanol. Bioinformatics analysis showed that neuroinflammatory responses were seen across multiple brain regions at early time-points, whereas co-expression modules related to neuroplasticity, chromatin remodeling, and neurodevelopment were seen at later time-points and in specific brain regions (PFC or HPC). In PFC a module containing Bdnf was identified as highly CIE responsive in a biphasic manner, with peak changes at 0 hours and 5 days following CIE, suggesting a possible role in mechanisms underlying long-term molecular and behavioral response to CIE. Bioinformatics analysis of this network and several other modules identified Let-7 family microRNAs as potential regulators of gene expression changes induced by CIE. Our results suggest a complex temporal and regional pattern of widespread gene network responses involving neuroinflammatory and neuroplasticity related genes as contributing to physiological and behavioral responses to chronic ethanol

Molecular Genetic Influences on Normative and Problematic Alcohol Use in a Population-Based Sample of College Students

Background: Genetic factors impact alcohol use behaviors and these factors may become increasingly evident during emerging adulthood. Examination of the effects of individual variants as well as aggregate genetic variation can clarify mechanisms underlying risk. Methods: We conducted genome-wide association studies (GWAS) in an ethnically diverse sample of college students for three quantitative outcomes including typical monthly alcohol consumption, alcohol problems, and maximum number of drinks in 24 h. Heritability based on common genetic variants (h2SNP) was assessed. We also evaluated whether risk variants in aggregate were associated with alcohol use outcomes in an independent sample of young adults. Results: Two genome-wide significant markers were observed: rs11201929 in GRID1 for maximum drinks in 24 h, with supportive evidence across all ancestry groups; and rs73317305 in SAMD12 (alcohol problems), tested only in the African ancestry group. The h2SNP estimate was 0.19 (SE = 0.11) for consumption, and was non-significant for other outcomes. Genome-wide polygenic scores were significantly associated with alcohol outcomes in an independent sample. Conclusions: These results robustly identify genetic risk for alcohol use outcomes at the variant level and in aggregate. We confirm prior evidence that genetic variation in GRID1impacts alcohol use, and identify novel loci of interest for multiple alcohol outcomes in emerging adults. These findings indicate that genetic variation influencing normative and problematic alcohol use is, to some extent, convergent across ancestry groups. Studying college populations represents a promising avenue by which to obtain large, diverse samples for gene identification

Genetic Dissection of Acute Ethanol Responsive Gene Networks in Prefrontal Cortex: Functional and Mechanistic Implications

Background Individual differences in initial sensitivity to ethanol are strongly related to the heritable risk of alcoholism in humans. To elucidate key molecular networks that modulate ethanol sensitivity we performed the first systems genetics analysis of ethanol-responsive gene expression in brain regions of the mesocorticolimbic reward circuit (prefrontal cortex, nucleus accumbens, and ventral midbrain) across a highly diverse family of 27 isogenic mouse strains (BXD panel) before and after treatment with ethanol. Results Acute ethanol altered the expression of ~2,750 genes in one or more regions and 400 transcripts were jointly modulated in all three. Ethanol-responsive gene networks were extracted with a powerful graph theoretical method that efficiently summarized ethanol\u27s effects. These networks correlated with acute behavioral responses to ethanol and other drugs of abuse. As predicted, networks were heavily populated by genes controlling synaptic transmission and neuroplasticity. Several of the most densely interconnected network hubs, including Kcnma1 and Gsk3β, are known to influence behavioral or physiological responses to ethanol, validating our overall approach. Other major hub genes like Grm3, Pten and Nrg3 represent novel targets of ethanol effects. Networks were under strong genetic control by variants that we mapped to a small number of chromosomal loci. Using a novel combination of genetic, bioinformatic and network-based approaches, we identified high priority cis-regulatory candidate genes, including Scn1b,Gria1, Sncb and Nell2. Conclusions The ethanol-responsive gene networks identified here represent a previously uncharacterized intermediate phenotype between DNA variation and ethanol sensitivity in mice. Networks involved in synaptic transmission were strongly regulated by ethanol and could contribute to behavioral plasticity seen with chronic ethanol. Our novel finding that hub genes and a small number of loci exert major influence over the ethanol response of gene networks could have important implications for future studies regarding the mechanisms and treatment of alcohol use disorders

No Reliable Association between Runs of Homozygosity and Schizophrenia in a Well-Powered Replication Study

It is well known that inbreeding increases the risk of recessive monogenic diseases, but it is less certain whether it contributes to the etiology of complex diseases such as schizophrenia. One way to estimate the effects of inbreeding is to examine the association between disease diagnosis and genome-wide autozygosity estimated using runs of homozygosity (ROH) in genome-wide single nucleotide polymorphism arrays. Using data for schizophrenia from the Psychiatric Genomics Consortium (n = 21,868), Keller et al. (2012) estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is autozygous (β = 16.1, CI(β) = [6.93, 25.7], Z = 3.44, p = 0.0006). Here we describe replication results from 22 independent schizophrenia case-control datasets from the Psychiatric Genomics Consortium (n = 39,830). Using the same ROH calling thresholds and procedures as Keller et al. (2012), we were unable to replicate the significant association between ROH burden and schizophrenia in the independent PGC phase II data, although the effect was in the predicted direction, and the combined (original + replication) dataset yielded an attenuated but significant relationship between Froh and schizophrenia (β = 4.86,CI(β) = [0.90,8.83],Z = 2.40,p = 0.02). Since Keller et al. (2012), several studies reported inconsistent association of ROH burden with complex traits, particularly in case-control data. These conflicting results might suggest that the effects of autozygosity are confounded by various factors, such as socioeconomic status, education, urbanicity, and religiosity, which may be associated with both real inbreeding and the outcome measures of interest

Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (OR=1.11, P=5.7×10−15), which persisted after excluding loci implicated in previous studies (OR=1.07, P=1.7 ×10−6). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 ×10−11) and neurobehavioral phenotypes in mouse (OR = 1.18, P= 7.3 ×10−5). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by non-allelic homologous recombination

Genetic correlation between amyotrophic lateral sclerosis and schizophrenia

A. Palotie on työryhmän Schizophrenia Working Grp Psychiat jäsen.We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P = 1 x 10(-4)) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P = 8.4 x 10(-7)). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies.Peer reviewe

Age at first birth in women is genetically associated with increased risk of schizophrenia

Prof. Paunio on PGC:n jäsenPrevious studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank sample. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.Peer reviewe

Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants with Treatment Resistance in Schizophrenia

Importance: About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts. Objective: To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples. Design, Setting, and Participants: Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10501) and individuals with non-TRS (n = 20325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]). Main Outcomes and Measures: GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition. Results: The study included a total of 85490 participants (48635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P =.001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P =.04). Conclusions and Relevance: In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance