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to elucidate the mechanisms involved, which may ultimately guide to the discovery of novel drug targets and predictive markers.
Disclosure: Nothing to disclose. http://dx.doi.org/10.1016/j.euroneuro.2017.08.387
M81. SCHIZOPHRENIA POLYGENIC RISK SCORE PREDICTS ANTIPSYCHOTIC TREATMENT RESPONSE IN PATIENTS WITH FIRST EPISODE PSYCHOSIS n
Jianping Zhang ,1, Delbert Robinson1, Jin Yu1, Juan Gallego2, W. Wolfgang Fleischhacker3, Rene S. Kahn4, Benedicto Crespo-Facorro5, Javier Vazquez-Bourgon5, John Kane1, Anil Malhotra1, Todd Lencz6 1
Zucker Hillside Hospital Hofstra North Shore LIJ School of Medicine 3 Medical University Innsbruck 4 University Medical Center Utrecht 5 CIBERSAM, IDIVAL, University Hospital Marqués de Valdecilla, Santander 6 Hofstra Northwell School of Medicine 2
Background: The genetic basis of antipsychotic drug efficacy is likely polygenic in nature. Genetic risks of schizophrenia may also be related to antipsychotic drug response. The Psychiatric Genomics Consortium (PGC) Genome-Wide Association Study (GWAS) provided evidence of association with schizophrenia risk for many Single Nucleotide Polymorphism (SNP) across the genome. We examined whether Polygenic Risk Scores (PRS) based on the PGC GWAS are predictive of antipsychotic efficacy in four cohorts of patients with first episode psychosis (total n= 510): 1) Zucker Hillside Hospital First Episode schizophrenia trial (ZHH-FE), 2) European First Episode Schizophrenia Trial (EUFEST), 3) Spanish First Episode Psychosis study (SFEP), and 4) the clinical trial as part of the Center for Intervention Development and Applied Research at ZHH (CIDAR). Methods: The discovery cohort was the ZHH-FE with 77 patients (mixed ethnicity) randomized to risperidone or olanzapine. Three replication cohorts were: 1) EUFEST with 141 patients (all Caucasian) randomized to five antipsychotics; 2) SFEP with 192 patients (all Caucasian) on various antipsychotics; and 3) CIDAR with 100 patients (mixed ethnicity) randomized to risperidone or aripiprazole. Genotyping was performed using the Illumina Omni-1Quad (EUFEST and ZHH-FE) or Illumina Infinium HumanOmniExpress Exome platform (CIDAR and SFEP). SNP imputation was conducted with IMPUTE2 against the full 1000 Genomes v3 reference panel. PRS was computed based on the results of the PGC GWAS using PRSice software for the discovery cohort with thresholds at PTo5E-8, 0.001, 0.01, 0.05, 0.10, 0.20, 0.50. Based on the findings from the discovery
cohort, PRS was computed for the three replication cohorts using a threshold of PTo0.01. Symptom measure was the total score of Brief Psychiatric Rating Scale (BPRS) for ZHHFE, SFEP, and CIDAR, or Positive and Negative Symptoms Scale (PANSS) for EUFEST. Results: Hierarchical linear regression was performed on the 3-month symptom score with the PRS as the predictor while controlling for age, sex, and baseline symptom score. Genomic principal component scores were also covaried to control for population stratification for ZHH-FE and CIDAR. In the ZHH-FE cohort, higher PRS at the thresholds of PTo0.01, 0.05, 0.10, 0.20, and 0.50 significantly predicted higher symptom scores at 3-month follow-up, explaining 6– 8% of the variance (all p'so0.05). PTo0.01 gave the strongest result in the discovery sample, and was used to replicate the findings in the other three cohorts. Higher PRS significantly predicted worse symptoms in EUFEST and SFEP cohorts, explaining 3.5% and 3.7% of variance, (p'so0.01), but not in the CIDAR sample. Combining the four cohorts in a meta-analysis, PRS was significantly predictive of 3-month symptom scores (pooled partial r = 0.18, p= 0.002). Higher PRS was associated with higher symptom scores at 3-month follow-up, suggestive of less improvement in treatment. The overall results remained significant when only European ancestry individuals were included in the analysis. Discussion: These findings suggest that polygenic risk scores for schizophrenia may also be related to antipsychotic drug response. Patients with higher polygenic risk scores tended to have less improvement with antipsychotic drug treatment. Further analysis is needed to elucidate a more refined genomic profile for antipsychotic drug response.
Disclosure: Nothing to disclose. http://dx.doi.org/10.1016/j.euroneuro.2017.08.388
M82. RARE CODING VARIANTS IN TREATMENT-RESISTANT DEPRESSION n
Christopher Song ,1, Liping Hou1, Nirmala Akula1, Carlos Zarate2, Brian Mickey3, Francis McMahon1 1
National Institute of Mental Health - Human Genetics Branch 2 National Institute of Mental Health - Experimental Therapeutics & Pathophysiology Branch (ETPB) 3 University of Utah
Background: Approximately 30% of patients with Major Depressive Disorder (MDD) meet criteria for TreatmentResistant Depression (TRD), defined as non-response to two or more different classes of antidepressants. Pharmacogenomics research has shown that genetic factors can impact medication effectiveness and tolerability by modulating drug levels or target affinity. Antidepressant
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response is heritable, but genome-wide association studies of common variants have failed to find consistent signals. Since patients with TRD occupy the extreme end of the treatment response distribution, they may also carry alleles that occupy an extreme of frequency and functional impact. Methods: Study participants included 146 unrelated patients, 130 with MDD or bipolar TRD and 16 with good response to antidepressants, drawn from several sources. All participants had experienced at least two failed antidepressant trials not explained by non-adherence or severe comorbidities. Exomes were captured with various arrays and sequenced by use of Illumina or SOLiD platforms. Variants were called and assessed for quality using the GATK best practices pipeline. Results: After quality control, 348,077 variants were identified, of which 99,711 were exonic and had allele frequencies o1% in ExAC. We sought to remove false positives by filtering out variants that were not found in dbSNP, appeared in only one individual, or had missing genotypes in all typical responders. Functional filtering of variants that were called as synonymous or non-frameshift left 4,456 qualifying variants in 3,370 genes. Very few qualifying variants were found in 41 TRD patient, but LIST GENES each carried the same qualifying variant in 42 individuals. A total of 820 genes carried at least 2 qualifying variants. This set of genes was tested for overlap with genes implicated in MDD (Hyde et al., 2016) or antidepressant treatment response (GENDEP, MARS, and STARn D Investigators, 2013). Significant excess overlap of genes was detected for both MDD (OR= 1.5, p = 0.035) and response to 2 wks of antidepressant treatment (OR= 2.16; P = 1 105). The latter gene set was enriched for “actin cytoskeleton” by ENRICHR (q = 0.01). Discussion: Some of the same genes implicated in MDD or antidepressant treatment response through common variants may also harbor rare, functional variants in patients with TRD. Further study of these genes could yield new insights into the pathophysiology of treatment-resistant depression.
Background: Weight change is a common adverse effect of antidepressant treatment. The purpose of this study was to identify genetic variants associated with change in body weight during antidepressant treatment for Major Depressive Disorder (MDD) with Selective Serotonin Reuptake Inhibitors (SSRIs). Methods: Genotyping and objectively measured weight change data were available for 340 unrelated adults with Major Depressive Disorder (MDD) treated with escitalopram over a 26week period in the Genome-Based Therapeutic Drugs for Depression (GENDEP) project. We performed a genome-wide association analysis of change in Body Mass Index (BMI). Next, we meta-analyzed the GENDEP results with those obtained from two cohorts of 195 and 144 individuals treated with SSRIs with objective measurement of weight and genotyped as part of the Partners HealthCare Biobank initiative. Finally, we conducted gene-wide analyses to identify genes associated with weight change during SSRI treatment. Results: We identified 3 independent associations (p o 5 10-8) with weight change in the GENDEP analysis, including single nucleotide polymorphisms (SNPs) in CEP41. The meta-analysis identified 2 independent associations meeting genome-wide significance with consistent signal across samples, including uncommon SNPs in a phosphodiesterase-encoding gene, PDE8A. We have further identified 7 suggestively significant associations involving common variants, including associations in or near genes GATA4, CDH8, and NMNAT3. Discussion: We provide preliminary evidence that uncommon and common genetic variation contributes to weight change during treatment with antidepressant drugs. Confirmation in larger samples is needed to identify clinically useful variants.
Disclosure: Nothing to disclose.
M84. EXOCYTOSIS-RELATED GENE-SETS AND RESPONSE TO METHYLPHENIDATE TREATMENT IN ADULTS WITH ADHD
Disclosure: Nothing to disclose. http://dx.doi.org/10.1016/j.euroneuro.2017.08.390
http://dx.doi.org/10.1016/j.euroneuro.2017.08.389 n
M83. GENETIC DETERMINANTS OF WEIGHT CHANGE DURING ANTIDEPRESSANT TREATMENT WITH SELECTIVE SEROTONIN RE-UPTAKE INHIBITORS: GENOMEWIDE STUDY AND META-ANALYSIS
Bruna da Silva ,1, Djenifer B. Kappel1, Renata B. Cupertino1, Diego L. Rovaris1, Jaqueline B. Schuch1, Cibele Bandeira1, Alana C. Panzenhagen1, Diana Müller1, Nina R. Mota2, Eduardo S. Vitola3, Marcelo M. Victor3, Eugenio H. Grevet3, Verônica Contini4, Claiton H.D. Bau1 1
n,1
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Universidade Federal do Rio Grande do Sul Radboud University Nijmegen Medical Centre 3 Hospital de Clínicas de Porto Alegre 4 UNIVATES
Alyson Zwicker , Chiara Fabbri , Daniel Gaston , Eileen M. Denovan-Wright1, Katherine Aitchison3, McGuffin Peter2, Cathryn M. Lewis2, Roy Perlis4, Victor Castro5, Rudolf Uher1
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Background: Substantial interindividual variability is observed in treatment response to Methylphenidate (MPH), which is considered the first-line pharmacological treatment for adults with ADHD. In addition to its main known mechanism of action involving the blockade of the dopamine transporter, experimental studies have also
Dalhousie University Kings College London 3 University of Alberta 4 Massachusetts General Hospital 5 Partners Healthcare 2