Discussion As individual polymorphisms are likely to have small effect sizes, identifying multiple genes may be necessary to generate a complete picture of genetic variability and responsiveness to SSRIs. By identifying likely targets based on current knowledge regarding the mechanisms of SSRI action, a variety of potential candidates have been culled. This review has attempted to suggest multiple candidate genes based on known and putative mechanisms of SSRI action. Notably, the number of targeted genes represents less than 0.5% of the genome. With current high-throughput methodologies for assessing polymorphisms, examining this large but significantly limited number of genes is feasible (Cowan et al 2002). However, studies of variable antidepressant response are affected by several methodologic issues that are inherent in antidepressant trials. There are fundamental considerations regarding the phenotypic definition of antidepressant response (Nierenberg 2003). For example, although depression is a complex and polythetic disorder (Kendler et al 1992; Kendell 2001), it is often treated as a single entity. Similarly, defining response is problematic (Nierenberg and DeCecco 2001). Response can be quantified as the rate of response or the magnitude of response. It can also be qualitatively defined as achieving a predetermined level of symptomatology. Different genes may specifically affect different aspects and/or subtypes of depression, or they may specifically affect different elements of response. That is, there may be a number of “mood disorders”, or even subtypes of “major depression”, that may respond differently to SSRIs. Additionally, some genetic variation may be associated with different types of side effects and different rates of developing tolerance to side effects, rather than antidepressant response itself (Murphy GM et al 2003b). Unfortunately, there is extremely little data to date regarding the association of polymorphisms with different measures of response, tolerance to SSRIs, or different side effects of SSRIs. Moreover, determining true drug responders is also complicated by partial or non-adherence to the medication (Thompson et al 2000). In addition to variable adherence, the concentration of SSRIs can be influenced by dosage and environmental factors. The effect of some poly- morphisms may be evident at low SSRI concentrations but not at higher concentrations, or vice versa. For example, we have noted that 5-HTTLPR is associated with variable response to paroxetine in the first few weeks, but that Neuropsychiatric Disease and Treatment 2005:1(1) Candidate genes for SSRI response response to the SSRI did not differ after several weeks (Pollock et al 2000). Given that paroxetine can inhibit its own metabolism, we observed that paroxetine levels increased after several weeks in elderly individuals, to concentrations potentially sufficient for noradrenergic involvement (Gilmor et al 2002). This is of interest given that 5-HTTLPR was not associated with response to a noradrenergic reuptake inhibitor (Pollock et al 2000). For this reason, as well as statistical reasons, accounting for adherence and SSRI exposure may be essential in association studies of treatment response (Judson 2003; Selinger- Leneman et al 2003). Accounting for adherence and SSRI exposure in these types of studies has been extremely limited to date. There is also the ever-escalating problem of placebo response rates in clinical trials (Walsh et al 2002). This is complicated by the aggregation of potential subtypes of depression in clinical studies and the relative exclusion of other subtypes, with some subtypes possibly having variable drug responsiveness (Parker et al 2002). If these confounding elements are not accounted for, erroneous conclusions are possible. Population stratification is another concern. Methods such as genomic control and structured association may be used to correct for population substructuring (Devlin et al 2001). However, ethnic and cultural difference may confound results in other ways (Balant and Balant-Gorgia 2000). For example, adherence, social background, and ethnic differences may all interact to affect treatment response (Lotrich et al 2003), and the frequency of many polymorphisms varies with ancestry. Given these methods are now available for accounting for population stratification (eg, Devlin et al 2001), they should be included in these types of association studies. There are also statistical issues pertaining to testing multiple polymorphisms. More sophisticated modeling may be necessary to accurately determine appropriate significance levels when testing multiple associations (Lucek et al 1998). For example, one suggested approach is to control for the false discovery rate (FDR), rather than focus on controlling the rate of false positives or false negatives (van den Oord and Sullivan 2003). That is, rather than stringently protecting against a single false rejection of a hypothesis, FDR limits the percentage of false rejections, providing improved power when testing many likely candidate hypotheses (Devlin et al 2003). Further, there are additional methodological issues such as appropriate sample sizes (McCarthy and Hilfiker 2000) and 27