UCSF

Major depressive disorder is one of the most common and debilitating psychiatric disorders. Psychopharmacological agents are the most widely used form of treatment, although they are not universally effective and can produce significant side effects in some patients. The most common psychopharmacological agents used to treat major depression are the selective serotonin reuptake inhibitors, or SSRIs. Often, these drugs take several weeks to relieve depressive symptoms. Individualized therapy would have great clinical utility by identifying patients that are likely to respond positively to SSRI therapy a priori. The goal of this thesis is to investigate the use of genetic markers for guiding treatment with SSRIs.

We utilized several complementary pharmacogenetic approaches and two depressed populations treated with SSRIs. The first was a small (N=96) population given fluoxetine, and the second was a large (N=1,953) population taking citalopram. We used the fluoxetine population and a variant discovery approach to uncover novel variation and previously unknown tagging SNPs in the molecular target of SSRIs, the serotonin transporter, then employed a linkage disequilibrium mapping approach to investigate variants for association to response. Several variants in the promoter region of the gene were associated with fluoxetine outcome. No markers were associated with response when investigated in our citalopram population.

We also investigated relevant candidate genes for association with citalopram response and tolerance. Variants within the FEV gene, a master transcription factor in the serotonin pathway, were associated with a number of response phenotypes and mouse work implicates this gene in citalopram response. None of our other candidate genes demonstrated association with citalopram response.

Utilizing a panel of approximately 20,000 non-synonymous cSNPs for association with citalopram response, one SNP in the gene LRP2 was significantly associated with response in the African American population. We also performed a whole genome association study using over 500,000 SNPs from across the genome. Using a two-stage study design, none of the most highly associated markers in the discovery sample were also associated in the validation sample.