LAUSR

Background Major depressive disorder is a high-prevalence disease associated with a heavy burden on both personal well-being and socio-economical welfare, partly as a result of lacking tailored treatment options [1]. Common single nucleotide polymorphisms (SNPs) were estimated to account for 0.42 of the variance in antidepressant response [2], confirming the hypothesis that genetic polymorphisms may be used as effective markers to provide tailored antidepressant treatments. Candidate gene and genome-wide analyses can provide a complementary strategy, since the former can be applied to clarify the role of SNPs with high pre-test probability of association with the trait and the latter is useful to study the joint effects of a number of SNPs in a gene or a set of genes [3]. Aim We applied such a complementary strategy to the study of eight genes that are very strong candidates with previous evidence of pleiotropic effect across psychiatric traits. The genes of interest are involved in the regulation of neurotransmission (CACNA1C, CACNB2, ANK3), neural differentiation, synaptic plasticity, adhesion processes and structural organization (GRM7, TCF4, ITIH3, SYNE1) and glucocorticoid signaling (FKBP5). Methods Three samples with major depressive disorder (total n=671) were genotyped for 44 SNPs in strong candidate genes based on biological function and previous genome-wide association studies (CACNA1C, CACNB2, ANK3, GRM7, TCF4, ITIH3, SYNE1, FKBP5). Phenotypes were response/remission after 4 weeks of treatment and treatment-resistant depression (TRD: non response/non remission to at least two antidepressant treatments). Genome-wide data from STAR*D were used to replicate findings for response/remission (Level 1, n=1409) and TRD (Level 2, n=620). Pathways including the most promising candidate genes for involvement in TRD were investigated in STAR*D Level 2. Top pathway(s) were investigated using machine learning models. Results FKBP5 rs3800373, rs1360780 and rs9470080 showed replicated associations with response, remission or TRD. CACNA1C SNPs showed contradictory direction of association across samples. ANK3 rs1049862 AA genotype showed a replicated association with better outcome. In STAR*D the best pathway associated with TRD included CACNA1C (GO:0006942, permutated p=0.15). Neural networks and gradient boosted machine showed that independent SNPs in this pathway predicted TRD with a mean sensitivity of 0.83 and specificity of 0.56 after 10-fold cross validation repeated 100 times. Conclusions FKBP5 polymorphisms should be considered for inclusion in antidepressant pharmacogenetic tests. CACNA1C is a good candidate and GO:0006942 includes several genes coding for ion channels expressed in the central nervous system and other genes relevant for excitatory mechanisms. CACNB2 and ANK3 showed replicated associations with phenotypes and further investigations could help in clarifying their role. This study may pave the way to the identification of sets of genetic predictors in specific pathways able to predict the risk of TRD. It is reasonable to hypothesize a certain degree of variability in the genetic variants involved in TRD across different patients, but the involved pathways are expected to be more stable. Validated genetic markers of TRD could have a pivotal role in the implementation of targeted antidepressant treatments.