Leveraging electronic health records to study pleiotropic effects on bipolar disorder and medical comorbidities.

Patients with bipolar disorder (BD) have a high prevalence of comorbid medical illness. However, the mechanisms underlying these comorbidities with BD are not well known. Certain genetic variants may have pleiotropic effects, increasing the risk of BD and other medical illnesses simultaneously. In this study, we evaluated the association of BD-susceptibility genetic variants with various medical conditions that tend to co-exist with BD, using electronic health records (EHR) data linked to genome-wide single-nucleotide polymorphism (SNP) data. Data from 7316 Caucasian subjects were used to test the association of 19 EHR-derived phenotypes with 34 SNPs that were previously reported to be associated with BD. After Bonferroni multiple testing correction, P<7.7 × 10(-5) was considered statistically significant. The top association findings suggested that the BD risk alleles at SNP rs4765913 in CACNA1C gene and rs7042161 in SVEP1 may be associated with increased risk of 'cardiac dysrhythmias' (odds ratio (OR)=1.1, P=3.4 × 10(-3)) and 'essential hypertension' (OR=1.1, P=3.5 × 10(-3)), respectively. Although these associations are not statistically significant after multiple testing correction, both genes have been previously implicated with cardiovascular phenotypes. Moreover, we present additional evidence supporting these associations, particularly the association of the SVEP1 SNP with hypertension. This study shows the potential for EHR-based analyses of large cohorts to discover pleiotropic effects contributing to complex psychiatric traits and commonly co-occurring medical conditions.

TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics.

Millions of patients suffer from major depressive disorder (MDD), but many do not respond to selective serotonin reuptake inhibitor (SSRI) therapy. We used a pharmacometabolomics-informed pharmacogenomics research strategy to identify genes associated with metabolites that were related to SSRI response. Specifically, 306 MDD patients were treated with citalopram or escitalopram and blood was drawn at baseline, 4 and 8 weeks for blood drug levels, genome-wide single nucleotide polymorphism (SNP) genotyping and metabolomic analyses. SSRI treatment decreased plasma serotonin concentrations (P<0.0001). Baseline and plasma serotonin concentration changes were associated with clinical outcomes (P<0.05). Therefore, baseline and serotonin concentration changes were used as phenotypes for genome-wide association studies (GWAS). GWAS for baseline plasma serotonin concentrations revealed a genome-wide significant (P=7.84E-09) SNP cluster on chromosome four 5' of TSPAN5 and a cluster across ERICH3 on chromosome one (P=9.28E-08) that were also observed during GWAS for change in serotonin at 4 (P=5.6E-08 and P=7.54E-07, respectively) and 8 weeks (P=1.25E-06 and P=3.99E-07, respectively). The SNPs on chromosome four were expression quantitative trait loci for TSPAN5. Knockdown (KD) and overexpression (OE) of TSPAN5 in a neuroblastoma cell line significantly altered the expression of serotonin pathway genes (TPH1, TPH2, DDC and MAOA). Chromosome one SNPs included two ERICH3 nonsynonymous SNPs that resulted in accelerated proteasome-mediated degradation. In addition, ERICH3 and TSPAN5 KD and OE altered media serotonin concentrations. Application of a pharmacometabolomics-informed pharmacogenomic research strategy, followed by functional validation, indicated that TSPAN5 and ERICH3 are associated with plasma serotonin concentrations and may have a role in SSRI treatment outcomes.

The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response.

Response to treatment with selective serotonin reuptake inhibitors (SSRIs) varies considerably between patients. The International SSRI Pharmacogenomics Consortium (ISPC) was formed with the primary goal of identifying genetic variation that may contribute to response to SSRI treatment of major depressive disorder. A genome-wide association study of 4-week treatment outcomes, measured using the 17-item Hamilton Rating Scale for Depression (HRSD-17), was performed using data from 865 subjects from seven sites. The primary outcomes were percent change in HRSD-17 score and response, defined as at least 50% reduction in HRSD-17. Data from two prior studies, the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study (PGRN-AMPS) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, were used for replication, and a meta-analysis of the three studies was performed (N=2394). Although many top association signals in the ISPC analysis map to interesting candidate genes, none were significant at the genome-wide level and the associations were not replicated using PGRN-AMPS and STAR*D data. The top association result in the meta-analysis of response represents SNPs 5' upstream of the neuregulin-1 gene, NRG1 (P = 1.20E - 06). NRG1 is involved in many aspects of brain development, including neuronal maturation and variations in this gene have been shown to be associated with increased risk for mental disorders, particularly schizophrenia. Replication and functional studies of these findings are warranted.

Pharmacogenomics of selective serotonin reuptake inhibitor treatment for major depressive disorder: genome-wide associations and functional genomics.

A genome-wide association (GWA) study of treatment outcomes (response and remission) of selective serotonin reuptake inhibitors (SSRIs) was conducted using 529 subjects with major depressive disorder. While no SNP associations reached the genome-wide level of significance, 14 SNPs of interest were identified for functional analysis. The rs11144870 SNP in the riboflavin kinase (RFK) gene on chromosome 9 was associated with 8-week treatment response (odds ratio (OR)=0.42, P=1.04 × 10⁻6). The rs915120 SNP in the G protein-coupled receptor kinase 5 (GRK5) gene on chromosome 10 was associated with 8-week remission (OR=0.50, P=1.15 × 10⁻5). Both SNPs were shown to influence transcription by a reporter gene assay and to alter nuclear protein binding using an electrophoretic mobility shift assay. This report represents an example of joining functional genomics with traditional GWA study results derived from a GWA analysis of SSRI treatment outcomes. The goal of this analytical strategy is to provide insights into the potential relevance of biologically plausible observed associations.

Multivariate models to detect genomic signatures for a class of drugs: application to thiopurines pharmacogenomics.

Often, analysis for pharmacogenomic studies involving multiple drugs from the same class is completed by analyzing each drug individually for association with genomic variation. However, by completing the analysis of each drug individually, we may be losing valuable information. When studying multiple drugs from the same drug class, one may wish to determine genomic variation that explains the difference in response between individuals for the drug class, as opposed to each individual drug. Therefore, we have developed a multivariate model to assess whether genomic variation impacts a class of drugs. In addition to determine genomic effects that are similar for the drugs, we will also be able to determine genomic effects that differ between the drugs (that is, interaction). We will illustrate the utility of this multivariate model for cytotoxicity and genomic data collected on the Coriell Human Variation Panel for the class of anti-purine metabolites (6-mercaptopurine and 6-thioguanine).