Speaker 1: Siegfried Kasper, Austria

s | 9 Purpose: We aimed to investigate the genetics of antidepressant response in patients with obsessive-compulsive disorder (OCD) in two ethnic groups. Hypothesis: We postulated that different genetic variations across known OCD candidate genes may predict antidepressant response in OCD patients with different ethnic background. Method: We examined two independent and ethnically different OCD samples. The Canadian sample comprises of 222 Caucasian OCD subjects and we investigated 32 single nucleotide polymorphisms (SNPs) across 14 OCD candidate genes and their regulatory regions with antidepressant response data using a custom-made 32-SNP QuantStudio Flex Real-Time PCR System Chip. Individuals were grouped into those who improved following an adequate trial of antidepressant as compared with those who reported “minimal” improvement, “no change”, or “worsening” using the Clinical Global Impression – Improvement scale. Pearson χ2 test was performed to detect differences in the number of responders versus non-responders across genotype groups. The Brazilian sample consists of 192 Brazilian OCD individuals and 45 SNPs across 18 OCD candidate genes were genotyped. Of the 192 Brazilian OCD participants, 74 completed an adequate antidepressant trial and change of the Yale-Brown Obsessive-Compulsive Scale severity scores preand post-treatment were compared between genotype distributions of each examined SNP. Results: For the Canadian sample, interesting associations (P<0.05) were detected for the serotonin genes, HTR2A and HTR1B in antidepressant response. For the Brazilian sample, significant associations were detected for a gabaergic system gene, GABRA3, and antidepressant response (P<0.05). Conclusions: These variants may be clinically useful in predicting treatment resistance versus response in patients with OCD, thereby, reducing their duration of suffering via trial-and-error method of prescribing and improving clinical outcome. Speaker 4: James Kennedy, Canada Title: The IMPACT Study “Psychiatry Pharmacogenomics Testing in Clinical Practice” Abstract In genomics the amount of information available is increasing rapidly. At the same time our knowledge of inter-individual differences in terms of response and side effects to medicationsIn genomics the amount of information available is increasing rapidly. At the same time our knowledge of inter-individual differences in terms of response and side effects to medications is at an early stage. For example, an important dilemma facing psychiatrists when they need to select an antipsychotic medication for their patient is the forced choice between risk for weight gain and diabetes with the newer generation drugs versus the risk of tardive dyskinesia and other motor side effects with first generation antipsychotics. We have developed a model of seven genes (melanocortin-4 receptor, serotonin 2C, neuropeptide Y, others) that predicts 67% of the variance in risk for this weight gain (Tiwari et al, 2015). In terms of antidepressant treatment there are now several replicated studies showing a significant benefit of gene guided medication selection over treatment as usual (Altar et al, 2015). Genes tested include CYP450, 5HT2A, and 5HTTLPR. In addition to significant clinical improvement and reduction of side effects there is also a documented reduction in health care costs when genetic guidance is used in antidepressant treatment. Regarding the concern that physicians will not be able to efficiently translate complex genetic information into clinical decision-making, we have surveyed over 200 psychiatrists and family practitioners in our Toronto-based pharmacogenetics study (www.IM-PACT.ca; n=4,900 patients tested) and found that the overwhelming majority of physicians found our user-friendly genetic report to be readily understandable, and over 90% believe that pharmacogenetics testing will become standard of care in the future. We are now working on relevant epigenetic changes in the cortisol system genes, and in the serotonin transporter gene, that may affect medication response and side effects.