| | The FKBP5-Gene in Depression and Treatment Response—an Association Study in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) CohortReceived 14 May 2007; received in revised form 30 October 2007; accepted 30 October 2007. published online 15 January 2008. BackgroundIn a recent study of several antidepressant drugs in hospitalized, non-Hispanic White patients, Binder et al. reported association of markers located within the FKBP5 gene with treatment response after 2 and 5 weeks. Individuals homozygous for the TT-genotype at one of the markers (rs1360780) reported more depressive episodes and responded better to antidepressant treatment. There was no association between markers in FKBP5 and disease. The present study aimed at studying the associated FKBP5 markers in the ethnically diverse Sequenced Treatment Alternatives to Relieve Depression (STAR*D) sample of non-hospitalized patients treated with citalopram. MethodsWe used clinical data and DNA samples from 1809 outpatients with non-psychotic major depressive disorder (DSM-IV criteria), who received up to 14 weeks of citalopram. A subset of 1523 patients of White non-Hispanic or Black race was matched with 739 control subjects for a case-control analysis. The markers rs1360780 and rs4713916 were genotyped on the Illumina platform. TaqMan-assay was used for marker rs3800373. ResultsIn the case-control analysis, marker rs1360780 was significantly associated with disease status in the White non-Hispanic sample after correction for multiple testing. A significant association was also found between rs4713916 and remission. Markers rs1360780 and rs4713916 were in strong linkage disequilibrium in the White non-Hispanic but not in the Black population. There was no significant difference in the number of previous episodes of depression between genotypes at any of the three markers. ConclusionsThese results indicate that FKBP5 is an important target for further studies of depression and treatment response. Major depressive disorder (MDD) is a debilitating disease with a high prevalence (16.2% according to the most recent National Comorbidity Survey [1]). In the year 2020, depressive disorders are estimated to be one of the top ranked disease burdens worldwide (2, 3). Finding more effective interventions for depression must therefore have high priority for clinical and basic research efforts. With pharmacological treatment options available today, only a minority of the patients disabled by depression experience full remission (4, 5, 6). In addition, clinical and pharmacogenetic studies have shown strong individual variation in treatment outcome and side effects with antidepressant drugs (7, 8, 9, 10). Different methods are used to elucidate possible genetic determinants of susceptibility for depression as well as prediction of success when treating the disease. Although whole-genome assays will shortly become available, up to now most studies have employed candidate-gene approaches on the basis of known gene function in mood and depression. Candidate genes regularly include members of the main neurotransmitter systems such as serotonin, dopamine, and glutamate (11, 12, 13). However, other pathways that have influence over several neuroendocrine systems have recently received considerable attention. One such pathway, the hypothalamic–pituitary–adrenal axis (HPA-axis), plays a major role in stress hormone regulation (14, 15). A study by Binder et al. (16) has suggested that single-nucleotide polymorphisms (SNPs) in the FKBP5-gene are associated with outcome of antidepressant treatment and recurrence in major depression. The FKBP5-gene codes for a chaperone protein important for fine-tuning of the HPA-axis. In further analyses, it was shown that the TT-homozygote genotype of the marker rs1360780 was most strongly associated with better treatment response and more previous depressive episodes. No association of FKBP5 markers with disease status was observed. Although the Binder et al. study provided a very interesting new lead on involvement of FKBP5 in treatment response, the study had several limitations. The sample size was relatively modest for detecting risk loci of small effect, the study only included hospitalized patients and contained both psychotic and non-psychotic depression, and the sample was limited to individuals of White non-Hispanic ancestry. The objective of the present study was to investigate correlation of the associated markers with treatment-response in an ethnically diverse sample of non-hospitalized, non-psychotic depressed patients with data generated within the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, the largest treatment-response study carried out to date. The genetic sub-study of STAR*D was carried out following a pre-agreed analysis plan employing a two-split design in order to correct for multiple testing. Findings of significant associations have been summarized in two reports (9, 10) that did not include the FKBP5-gene among the reported associations. However, the split sample design we employed might have reduced power to detect a genuine association with FKBP5. To maximize power to detect such association, this report therefore did not employ a two-split design. Rather, data from all 1809 treated individuals with available genotypes were analyzed together herein. Correction for multiple testing was carried out with the Bonferroni method. We also tested, similarly to the Binder et al. study, whether FKBP5 markers might show association with the diagnosis of depression itself and the number of depressive episodes. Methods and Materials  Patients and Study Design This study presents data obtained from level 1 of the STAR*D study, whose overall study design has been described elsewhere (17, 18, 19). Diagnoses were established according to DSM-IV criteria, and only non-psychotic MDD was included. A diagnosis of bipolar disorder led to exclusion from the study. The treatment regime at level 1 aimed to evaluate outcome of treatment with the antidepressant drug citalopram (18, 20). To reduce the risk of inadequate dosing and to ensure that patients who progressed to the next level of treatment were truly resistant to the level-1 treatment, the study was carried out with a desired end point after 14 weeks of treatment, which is regarded as a long enough period for adjusting an optimal dosage and evaluating the corresponding effects. If deemed necessary, patients were, however, allowed to proceed into level 2 before 14 weeks of treatment and included in this analysis if they had received at least 6 weeks of treatment with citalopram. Participating individuals were recruited without regard to race or ethnicity. Self-reported race and ethnicity data were used for group assignment. For this report, race was collapsed into “White,” “Black,” or “other”; the White sample was further divided into “Hispanic” or “Non-Hispanic” (10). The group of individuals reporting “other” ethnic origin was too small and inhomogeneous to be analyzed separately. Thus, these individuals only appear in the “total” sample. The STRUCTURE software package (21, 22, 23) was used to assess possible population structure as described previously (9). No evidence for population structure was seen in the sample self-described as “White, non-Hispanic.” The total number of available individuals at each visit is presented in Supplement 1. The numbers were reduced from each time point to the next by those who went to follow-up with a satisfactory outcome as well as those who dropped out of the study or moved on to level-2 treatment. Note also that not all individuals attended each planned study visit. Those patients, for example, that failed to attend the visit after 2 weeks were still included in the calculations of 16-item Quick Inventory of Depressive Symptomatology (QIDS-C16) score means for the following study visits. For the case-control analysis, DNA samples were used only from the White non-Hispanic and Black subpopulations (see Supplement 2). Control Subjects Control DNAs (n = 739) were provided by the Rutgers Cell Repository and originated from the collection of normal individuals collected under auspices of the National Institute of Mental Health (NIMH) genetic initiative (Supplement 2). All control samples were screened by self-report (DSM-IV) for major depression, bipolar disorder, and psychosis, and affected individuals were excluded (10). DNA Samples and Genotyping After written informed consent and approval of the study protocol by Medical Centers, Data Coordinating Centers, and the Data Safety and Monitoring Board of NIMH (17, 24), blood samples were drawn from 1953 of the total of 4041 participants in the STAR*D study. Ninety-five individuals had to be excluded from the study, owing to missing clinical data, insufficient baseline depression (QIDS-C16 < 10), noncompliance with treatment protocol, or suspected sample mix-ups (Supplement 2). Of the remaining 1858 samples, 15 were excluded because of late baseline score (baseline QIDS-C16 score later than within 2 weeks of treatment); genotypes were not available from 34 individuals; and a total of 1809 eligible and successfully genotyped DNAs were included in the analyses presented herein. Genotypes for the two FKBP5-markers rs1360780 and rs4713916 were determined at Illumina (San Diego, California) with a standard protocol with a success rate of 99.78%. TaqMan assay was used in-house (FJM lab) for determination of genotypes at the marker rs3800373. Measurements of Change of Depression Severity The clinician-rated QIDS-C16 (25, 26, 27, 28) was used to measure treatment response at each treatment visit and at the end of the treatment period of up to 14 weeks. In the categorized outcome definition (9), remitters were defined as having a QIDS-C16 score ≤ 5 at end point of the level-1 treatment, whereas non-remitters still had a QIDS-C16 score ≥ 10 at the last visit, as long as they were not classified as intolerant of the medication. In a parallel analysis, response was defined as a 50% reduction of QIDS-C16 score at the last treatment visit and non-response as < 40% reduction. Individuals that received scores in between these ranges or had < 6 weeks of treatment were excluded from the respective analyses to minimize the risk for misclassification. After such exclusions, a total of 1370 individuals were eligible for the treatment-response study when response was the outcome, and 1190 individuals were eligible when the outcome was remission. The quantitative analyses presented herein used the raw QIDS-C16 scores at each visit. Analysis of the number of previous depressive episodes was carried out on the basis of self-reported estimates. Statistical Analysis In the case-control and treatment-response studies, tests for significance were calculated in UNPHASED 3.0.5 (http://www.mrc-bsu.cam.ac.uk/personal/frank/software/unphased/) and Haploview 3.2 (http://www.broad.mit.edu/mpg/haploview/), which gave virtually identical results for the allele-wise tests. The UNPHASED software was also used for genotype-based analyses. Determination of statistical significance was made by a two-sided likelihood-ratio test at an α level of 5%. Hardy-Weinberg Equilibrium was tested for by a χ2 goodness-of-fit test. Correction for multiple testing was carried out with a factor of 12 in the case-control study (3 markers × 2 populations × 2 tests, allele- and genotype-wise) and a factor of 18 in the treatment-response study (3 markers × 3 populations × 2 tests, allele- and genotype-wise). For the analysis of QIDS-C16 scores at different time points during the study, a quantitative association test was performed by one-way analysis of variance (ANOVA). In addition, a Tukey-Kramer test (α level of 5%) was used as post hoc analysis, and correction for multiple testing was carried out with the Bonferroni method (owing to substantially different composition of the sample at the different time points). Association with number of episodes of depression in the past was calculated with one-way ANOVA. As a post hoc test, Tukey-Kramer procedure was applied with an α level of 5%. Because three markers were tested in three groups, Bonferroni correction was carried out with a factor of 9. Results Case-Control Study In the comparison of cases and control subjects, nominally significant associations of markers rs1360780 and rs4713916 with disease were identified in the White non-Hispanic population in a genotype-wise test (Table 1). However, only the finding in rs1360780 remained significant after correction for multiple testing (p = .046). The CC-genotype was more frequent in control subjects than in cases (50% vs. 44%), whereas the TC-heterozygote genotype was over-represented in cases (46% vs. 38%). No significant association was seen in Blacks. No significant association was seen in an allele-wise test (Table 1). Both markers were found to be in Hardy-Weinberg Equilibrium in cases and control subjects within each population (data not shown). | | |  | Genotype-Wise Test | |
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| Ethnic Group | SNP | Cases | Control Subjects | OR (CI, Lo–Hi) | Nominal p | Corrected p | |
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| White n-H | rs1360780 | TT, 125 (.10a) | TC,581 (.46) | CC,547 (.44) | TT,72 (.11) | TC,243 (.38) | CC,319 (.50) | TT/TC,.72 (.51–1.01) | TT/CC,1.01 (.74–1.40) | TC/CC,1.39 (1.14–1.70) | .0038 | .046 | | | | rs4713916 | AA,115 (.092) | AG,557 (.44) | GG,584 (.47) | AA,60 (.095) | AG,244 (.39) | GG,330 (.52) | AA/AG,.84 (.59–1.19) | AA/GG,1.08 (.77–1.52) | AG/GG,1.29 (1.05–1.58) | .046 | ns | | | | rs3800373 | AA,590 (.48) | AC,531 (.43) | CC,111 (.09) | AA,308 (.52) | AC,228 (.39) | CC,55 (.09) | AA/AC,.82 (.67–1.01) | AA/AC,.95 (.67–1.35) | AC/CC,1.16 (.80–1.66) | .18 | ns | | | Black | rs1360780 | TT,45 (.17) | TC,131 (.49) | CC,91 (.34) | TT,17 (.16) | TC,47 (.45) | CC,41 (.39) | TT/TC,.95 (.49–1.82) | TT/CC,1.19 (.62–2.29) | TC/CC,1.26 (.76–2.07) | .66 | ns | | | | rs4713916 | AA,1 (.004) | AG,49 (.18) | GG,217 (.81) | AA,1 (.01) | AG,20 (.19) | GG,84 (.80) | AA/AG,.37 (.02–7.88) | AA/GG,.34 (.02–7.38) | AG/GG,.95 (.53–1.70) | .8 | ns | | | | rs3800373 | AA,83 (.31) | AC,135 (.51) | CC,46 (.17) | AA,41 (.41) | AC,44 (.44) | CC,15 (.15) | AA/AC,.66 (.40–1.09) | AA/CC,.67 (.34–1.30) | AC/CC,1.0 (.51–1.96) | .23 | ns | | | | |
| Allele-Wise Test | |
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| Ethnic Group | SNP | Cases | Control Subjects | OR(CI, Lo–Hi) | Nominal p | Corrected p | |
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| White n-H | rs1360780 | T,831(.33a) | C,1675(.67) | T,387(.31) | C,881(.69) | T/C,1.13(.98–1.30) | .1 | ns | | | | rs4713916 | A,787(.31) | G,1725(.69) | A,364(.29) | G,904(.71) | A/G,1.13(.98–1.31) | .097 | ns | | | | rs3800373 | A,1711(.69) | C,753(.31) | A,844(.71) | C,338(.29) | A/C,.91(.78–1.06) | .22 | ns | | | Black | rs1360780 | T,221(.41) | C,313(.59) | T,81(.39) | C,129(.61) | T/C,1.12(.82–1.55) | .49 | ns | | | | rs4713916 | A,51(.096) | G,483(.905) | A,22(.10) | G,188(.90) | A/G,.9(.53–1.54) | .7 | ns | | | | rs3800373 | A,301(.57) | C,227(.43) | A,126(.63) | C,74(.37) | A/C,.78(.56–1.09) | .14 | ns | | | | |
| a Frequencies in parentheses. |
Treatment-Response Analysis A genotype-based association test with treatment-response showed significant association of rs4713916 with remission but not with response when all racial groups were analyzed together (Table 2). Dividing the sample into the different racial groups revealed that the association was mainly driven by an association within the white non-Hispanic population. | | | | Remiters vs. Non-Remitters | |
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| Ethnic Group | SNP | Remitter | Non-Remitter | OR(CI,Lo–Hi) | Nominal p | Corrected p | |
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| All | rs1360780 | TT,81 (.11a) | TC,334 (.46) | CC,307 (.43) | TT,54 (.12) | TC,199 (.43) | CC,213 (.46) | TT/TC,.89 (.61–1.32) | TT/CC,1.04 (.71–1.53) | TC/CC,1.16 (.91–1.49) | .47 | ns | | | | rs4713916 | AA,69 (.10) | AG,295 (.41) | GG,359 (.50) | AA,33 (.07) | AG,155 (.33) | GG,279 (.60) | AA/AG,1.10 (.70–1.73) | AA/GG,1.60 (1.04–2.43) | AG/GG,1.47 (1.15–1.88) | .002708 | .049 | | | | rs3800373 | AA,324 (.46) | AC,311 (.44) | CC,76 (.11) | AA,220 (.48) | AC,189 (.41) | CC,50 (.11) | AA/AC,.90 (.70–1.15) | AA/CC,.97 (.65–1.44) | AC/CC,1.08 (.72–1.62) | .68 | ns | | | White n-H | rs1360780 | TT,52 (.10) | TC,249 (.48) | CC,215 (.42) | TT,30 (.11) | TC,110 (.39) | CC,143 (.51) | TT/TC,.76 (.45–1.27) | TT/CC,1.15 (.70–1.88) | TC/CC,1.50 (1.11–2.04) | .032 | ns | | | | rs4713916 | AA,56 (.11) | AG,242 (.47) | GG,219 (.42) | AA,25 (.09) | AG,107 (.38) | GG,152 (.54) | AA/AG,.99 (.59–1.67) | AA/GG,1.53 (.93–2.50) | AG/GG,1.56 (1.15–2.12) | .01 | ns | | | | rs3800373 | AA,234 (.46) | AC,225 (.44) | CC,48 (.09) | AA,149 (.53) | AC,106 (.38) | CC,25 (.09) | AA/AC,.74 (.55–1.01) | AA/CC,.82 (.49–1.37) | AC/CC,1.11 (.64–1.90) | .15 | ns | | | Black | rs1360780 | TT,14 (.16) | TC,42 (.48) | CC,31 (.36) | TT,13 (.16) | TC,48 (.58) | CC,22 (.27) | TT/TC,1.23 (.52–2.90) | TT/CC,.77 (.30–1.94) | TC/CC,.63 (.32–1.23) | .39 | ns | | | | rs4713916 | AA,0 (.0) | AG,15 (.174) | GG,71 (.826) | AA,0 (.0) | AG,14 (.169) | GG,69 (.831) | n.a. | n.a. | AG/GG,1.04 (.47–2.31) | .92 | ns | | | | rs3800373 | AA,27 (.31) | AC,47 (.54) | CC,13 (.15) | AA,22 (.27) | AC,44 (.54) | CC,16 (.20) | AA/AC,1.15 (.57–2.30) | AA/CC,1.50 (.60–3.74) | AC/CC,1.31 (.57–3.01) | .68 | ns | | | | |
| Responders vs. Non-Responders | |
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| Ethnic Group | SNP | Responder | Non-Responder | OR(CI,Lo–Hi) | Nominal p | Corrected p | |
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| All | rs1360780 | TT,104 (.11a) | TC,437 (.46) | CC,412 (.432) | TT,48 (.12) | TC,187 (.45) | CC,180 (.434) | TT/TC,.93 (.63–1.36) | TT/CC,.95 (.64–1.39) | TC/CC,1.02 (.80–1.30) | .93 | ns | | | | rs4713916 | AA,79 (.083) | AG,386 (.41) | GG,489 (.51) | AA,33 (.08) | AG,140 (.34) | GG,243 (.58) | AA/AG,.87 (.55–1.37) | AA/GG,1.18 (.78–1.81) | AG/GG,1.36 (1.07–1.74) | .042 | ns | | | | rs3800373 | AA,433 (.462) | AC,410 (.437) | CC,94 (.10) | AA,187 (.455) | AC,179 (.436) | CC,45 (.11) | AA/AC,1.01 (.79–1.29) | AA/CC,1.11 (.74–1.65) | AC/CC,1.10 (.74–1.64) | .88 | ns | | | White n-H | rs1360780 | TT,69 (.10) | TC,314 (.48) | CC,278 (.42) | TT,28 (.11) | TC,104 (.42) | CC,116 (.47) | TT/TC,.81 (.49–1.35) | TT/CC,1.03 (.63–1.67) | TC/CC,1.26 (.92–1.72) | .32 | ns | | | | rs4713916 | AA,64 (.10) | AG,308 (.47) | GG,290 (.44) | AA,25 (.10) | AG,100 (.40) | GG,124 (.50) | AA/AG,.83 (.49–1.40) | AA/GG,1.09 (.66–1.80) | AG/GG,1.32 (.97–1.79) | .21 | ns | | | | rs3800373 | AA,304 (.47) | AC,283 (.44) | CC,61 (.09) | AA,122 (.50) | AC,101 (.41) | CC,23 (.09) | AA/AC,.89 (.65–1.21) | AA/CC,.94 (.56–1.58) | AC/CC,1.06 (.62–1.80) | .76 | ns | | | Black | rs1360780 | TT,18 (.15) | TC,58 (.48) | CC,46 (.38) | TT,11 (.14) | TC,47 (.61) | CC,19 (.25) | TT/TC,1.32 (.58–3.01) | TT/CC,.67 (.26–1.71) | TC/CC,.52 (.28–.98) | .13 | ns | | | | rs4713916 | AA,0 (.0) | AG,23 (.19) | GG,98 (.81) | AA,0 (.0) | AG,12 (.16) | GG,65 (.84) | n.a. | n.a. | AG/GG,1.26 (.60–2.67) | .54 | ns | | | | rs3800373 | AA,41 (.34) | AC,65 (.53) | CC,16 (.13) | AA,20 (.26) | AC,43 (.56) | CC,14 (.18) | AA/AC,1.35 (.71–2.58) | AA/CC,1.80 (.73–4.42) | AC/CC,1.32 (.58–3.00) | .41 | ns | | | | |
| a Frequencies in parentheses. |
In addition to the genotype-wise test chosen by Binder et al., we also tested an additive disease model by carrying out an allele-wise association test (Table 3). Results from this test were very similar to the genotype-wise tests. The A-allele of rs4713916 was significantly over-represented in remitters in the combined sample (Table 3). | | | | Remitters vs. Non-Remitters | |
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| Ethnic Group | SNP | Remitter | Non-Remitter | OR (CI, Lo–Hi) | Nominal p | Corrected p | |
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| All | rs1360780 | T,496(.34a) | C,948(.66) | T,307(.33) | C,625(.67) | T/C,1.01(.89–1.27) | .48 | ns | | | | rs4713916 | A,433(.30) | G,1013(.70) | A,221(.24) | G,713(.76) | A/G,1.37(1.14–1.65) | .00074 | .013 | | | | rs3800373 | A,959(.67) | C,463(.33) | A,629(.69) | C,289(.31) | A/C,.95(.80–1.14) | .59 | ns | | | White n-H | rs1360780 | T,353(.34) | C,679(.66) | T,170(.30) | C,396(.70) | T/C,1.21(.97–1.50) | .088 | ns | | | | rs4713916 | A,354(.34) | G,680(.66) | A,157(.28) | G,411(.72) | A/G,1.35(1.09–1.69) | .0064 | ns | | | | rs3800373 | A,693(.68) | C,321(.32) | A,404(.72) | C,156(.28) | A/C,.84(.67–1.05) | .12 | ns | | | Black | rs1360780 | T,70(.40) | C,104(.60) | T,74(.45) | C,92(.55) | T/C,.84(.54–1.29) | .42 | ns | | | | rs4713916 | A,17(.10) | G,157(.90) | A,14(.08) | G,152(.92) | A/G,1.17(.56–2.46) | .67 | ns | | | | rs3800373 | A,101(.58) | C,73(.42) | A,88(.54) | C,76(.46) | A/C,1.19(.78–1.84) | .42 | ns | | | | |
| Responder vs. Non-Responders | |
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| Ethnic Group | SNP | Responder | Non-Responder | OR (CI,Lo–Hi) | Nominal p | Corrected p | |
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| All | rs1360780 | T,654(.338a) | C,1261(.662) | T,283(.341) | C,547(.659) | T/C,.99(.83–1.17) | .9 | ns | | | | rs4713916 | A,544(.29) | G,1364(.72) | A,206(.25) | G,626(.72) | A/G,1.21(1.01–1.45) | .042 | ns | | | | rs3800373 | A,1276(.68) | C,598(.32) | A,553(.67) | C,269(.33) | A/C,1.04(.87–1.24) | .68 | ns | | | White n-H | rs1360780 | T,452(.34) | C,870(.66) | T,160(.33) | C,336(.68) | T/C,1.09(.88–1.36) | .44 | ns | | | | rs4713916 | A,436(.33) | G,888(.67) | A,150(.30) | G,348(.70) | A/G,1.14(.91–1.42) | .25 | ns | | | | rs3800373 | A,891(.69) | C,405(.31) | A,345(.70) | C,147(.30) | A/C,.94(.75–1.17) | .57 | ns | | | Black | rs1360780 | T,94(.39) | C,150(.61) | T,69(.45) | C,85(.55) | T/C,.77(.51–1.16) | .22 | ns | | | | rs4713916 | A,25(.10) | G,219(.90) | A,12(.08) | G,142(.92) | A/G,1.34(.67–2.68) | .41 | ns | | | | rs3800373 | A,147(.60) | C,97(.40) | A,83(.54) | C,71(.46) | A/C,1.30(.86–1.95) | .21 | ns | | | | |
| a Frequencies in parentheses. |
Odds Ratio Comparisons Table 3 shows odds ratios, allele frequencies, and results from the allele-wise association tests. The direction of association was reversed for markers rs1360780 and rs3800373 in the Black population. This led us to carry out a test for determining the degree of linkage disequilibrium (LD) between the markers in the White non-Hispanic and the Black subpopulations. In the White non-Hispanic population the markers rs1360780 and rs4713916 were in strong LD (r2 of .67). Markers rs4713916 and rs3800373 were also in strong LD (r2 of .54). In the Black population these markers were in much weaker LD: the r2 was only of .09 between rs1360780 and rs4713916 and .08 between rs4713916 and rs3800373. Markers rs1360780 and rs3800373 were in strong LD both in the White non-Hispanics and Blacks (r2 = .86 in both populations). This suggests that in White non-Hispanics all three markers reside in one haplotype block, as suggested by Binder et al., whereas in Blacks there are at least two different blocks in this genomic interval, where rs1360780 and rs3800373 belong to one block and rs4713916 belongs to a different block. Quantitative Analysis Over 14 Weeks Binder et al. presented a difference in depression scores between genotypes over the treatment period. Our study has several limitations that do not allow us to directly aim to replicate that finding (see Discussion for further details). We here only present genotype-wise QIDS-C16 scores over time in the White non-Hispanic population, which was the largest sub-sample in our study (Figures 1A–1C). For each genotype, mean QIDS-C16 scores were plotted against time during the 14 weeks of treatment for markers rs1360780, rs4713916, and rs3800373. Carriers of all genotypes responded to the treatment with citalopram with a similar slope of the curve as observed by Binder et al. A nominally significant difference between genotype groups was only seen in homozygote carriers of the C-allele in the marker rs3800373 after 4 weeks of treatment (p = .02 by ANOVA). This p value, however, did not hold up for correction for multiple testing (see Methods). Prior Episodes of Depression In an analysis of prior episodes of depression, none of the observed differences (Figures 2A–2C) between genotypes in the markers rs1360780, rs4713916, or rs3800373 was of sufficient magnitude to reach significance when analyzed with one-way ANOVA. Discussion Case-Control Study Sufficient power to detect association of genotype to phenotype can only be obtained with large sample sizes (29), especially when genes that only partially contribute to the disease susceptibility shall be identified. Furthermore, association signals must be strong enough to survive correction for multiple testing, as carried out in this study as well as in the original FKBP5-study. The present case-control analysis was carried out including 1256 White non-Hispanic individuals matched with 634 control subjects as well as 267 Black individuals matched with 105 control subjects. Our analyses showed significant association (corrected p value = .046) of the marker rs1360780 with disease status. This association was only seen in White non-Hispanics. In contrast to our results, the Binder et al. study did not reveal significant association after correction for multiple testing, when 294 hospitalized individuals were studied together with 339 matched control subjects. Failure to detect such association in the original study might be due to insufficient power due to a relatively small sample size as well as the need for correction for a large number of independent tests. Treatment-Response Analysis Given the prior finding by Binder et al., we herein did not correct for all markers that were included in the original STAR*D study of treatment response but only for those tests that specifically aimed at replicating the analysis plan employed by Binder et al., focusing on three previously associated markers in the FKBP5-gene. Association tests for evaluation of treatment outcome showed nominally significant association of marker rs4713916 with both remission and response when all racial/ethnic populations were studied together. However, only the association with remission survived correction for multiple testing (Table 2, Table 3). Tests of LD of the three markers in FKBP5 revealed the presence of at least two different haplotype blocks in this genomic interval in the Black population, compared with the single block of strong LD observed in the Whites. Inclusion of Blacks in our study might thus provide an indication toward the functional allele causing the observed associations, because in the Black sample, rs4713916 and rs1360780 segregate almost independently. On the basis of the observed LD patterns, our study points to the promoter-SNP rs4713916 as the putative functional region rather than the intronic rs1360780 or rs3800373, located in the 3-prime untranslated region (3′-UTR). Quantitative Analysis Over 14 Weeks The STAR*D study design has some severe limitations regarding analyses at different time points, because data from individual outcome in treatment response are not available from all weeks during the period of up to 14 weeks of treatment. Enrolled individuals might end earlier than the defined end point, owing to remission, side effects, or drop outs. In addition, all individuals did not attend each clinic visit at 2, 4, 6, 9, 12, and 14 weeks. Our results did not show any significant differences between genotypes in either the total sample or in the sub-samples. The fact that no significant association was seen in our analysis might, however, simply be due to the previously described limitations of our study design regarding such calculations. Number of Previous Episodes The number of prior episodes of depression was assessed in relation to the different genotypes. Our results did not show any differences between genotypes and number of lifetime depressive episodes in any of the three markers. This is contrary to the results of Binder et al., who observed more depressive episodes in TT-homozygous carriers of rs1360780. This discrepancy between the studies can, however, be due to the method used to ascertain the actual number of episodes, which is often difficult, because it relies on patients' recall abilities and different definitions as to when an episode has truly ended. General Remarks Clinical trials designed to determine the efficacy of antidepressant agents need to be able to differentiate between placebo and true pharmacological effects (30). However, this is difficult to realize in the pharmacogenetic setting. The genetic analysis is performed at the level of the individual rather than the group, and it is impossible to predict how an individual with a certain genotype would have responded if they had been given a placebo only. Therefore, pharmacogenetic studies such as the present study and the Binder et al. study have limitations. None of the studies has included a positive or a negative control for the antidepressant treatment and thus could not distinguish pharmacological response from placebo response to assess the absolute effect as well as the relative effect. Our study provides further evidence for an association of FKBP5-markers with treatment response to antidepressant treatment when using the categorical “responder” and “remitter” outcomes, which were the primary outcomes in the STAR*D study design. Our definitions of remission and response were designed to allow for sufficiently long treatment with citalopram, aiming at eliminating as much of a confounding placebo response as possible in the primary outcomes. Association of a marker in FKBP5 with depression and remission after citalopram treatment provides further evidence that dysregulation of the HPA axis in depression is not only an epiphenomenon of the disease (31). Rather, knowledge about individual variation in components of HPA-axis regulation might lead to prediction of clinical phenotypes and outcome of antidepressant treatment. To summarize, the results provide further modest evidence for association of FKBP5 markers with treatment response to the selective serotonin reuptake inhibitor (SSRI) citalopram and evidence for an association with depression itself. Odds ratios identified in the present report for the treatment response phenotype are considerably lower than those reported by Binder et al., which might be owing to the fact that our study used outpatients who might have had a higher likelihood of response to the nonspecific (“placebo”) effects of treatment as compared with hospitalized individuals. Another possible explanation could be the “winners curse” phenomenon, according to which the first study identifying an effect tends to over-estimate effect sizes. We conclude that FKBP5 remains an interesting gene target both for depression and treatment response to SSRIs. This study was funded by the Intramural Research Programs of the National Institute of Mental Health (NIMH), the National Institute on Alcohol Abuse and Alcoholism, the National Human Genome Research Institute, National Institutes of Health (NIH), National Alliance for Research on Schizophrenia and Depression (FJM and SP), the Swedish Foundation for Strategic Research, Karolinska Institutet, and the Swedish Research Council. We thank the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) research team for acquisition of clinical data and DNA samples and Forest Laboratories for providing citalopram at no cost for the STAR*D study. Data and sample collection were funded with federal funds from the NIMH, NIH, under contract N01MH90003 to University of Texas Southwestern Medical Center at Dallas (Principal Investigator, A. John Rush). We thank Nirmala Akula and Jo Steele for technical assistance, the Rutgers University Cell and DNA Repository for extracting DNA and providing samples to our laboratories, and Luana Galver at Illumina for supervising the genotyping. The content of this publication does not necessarily reflect the views or policies of the Department of Health & Human Services nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. Most importantly, we thank the participants of the STAR*D trial, without whom this study would not be possible. Control subjects from the NIMH Schizophrenia Genetics Initiative (NIMH-GI), data, and biomaterials were collected by the “Molecular Genetics of Schizophrenia II” (MGS-2) collaboration. The investigators and coinvestigators are: ENH/Northwestern University, Evanston, Illinois, MH059571, Pablo V. Gejman, M.D. (Collaboration Coordinator; PI), Alan R. Sanders, M.D.; Emory University School of Medicine, Atlanta, Georgia, MH59587, Farooq Amin, M.D. (PI); Louisiana State University Health Sciences Center; New Orleans, Louisiana, MH067257, Nancy Buccola APRN, BC, MSN (PI); University of California-Irvine, Irvine, California, MH60870, William Byerley, M.D. (PI); Washington University, St. Louis, Missouri, U01, MH060879, C. Robert Cloninger, M.D. (PI); University of Iowa, Iowa, MH59566, Raymond Crowe, M.D. (PI), Donald Black, M.D.; University of Colorado, Denver, Colorado, MH059565, Robert Freedman, M.D. (PI); University of Pennsylvania, Philadelphia, Pennsylvania, MH061675, Douglas Levinson M.D. (PI); University of Queensland, Queensland, Australia, MH059588, Bryan Mowry, M.D. (PI); Mt. Sinai School of Medicine, New York, New York, MH59586, Jeremy Silverman, Ph.D. (PI). Drs. Laje, Wilson, Lipsky, Charney, Manji, McMahon, and Paddock as well as Ms. Sorant and Mr. Lekman report no competing interests. Dr. Rush has served as an advisor, consultant, or speaker for or received research support from Advanced Neuromodulation Systems; Best Practice Project Management; Bristol-Myers Squibb Company; Cyberonics; Eli Lilly & Company; Forest Pharmaceuticals; Gerson Lehman Group; GlaxoSmithKline; Healthcare Technology Systems; Jazz Pharmaceuticals; Merck & Company; the NIMH; Neuronetics; Ono Pharmaceutical; Organon USA; Personality Disorder Research Corporation; Pfizer; the Robert Wood Johnson Foundation; the Stanley Medical Research Institute; the Urban Institute; and Wyeth-Ayerst Laboratories. He has equity holdings in Pfizer and receives royalty/patent income from Guilford Publications and Healthcare Technology Systems. Dr. Wisniewski has received research support from the NIMH and served as an advisor/consultant for Cyberonics. Supplementary data References 1. 1Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, et al. 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31. 31Nemeroff CB. Clinical significance of psychoneuroendocrinology in psychiatry: Focus on the thyroid and adrenal. J Clin Psychiatry. 1989;50(suppl):13–20. a Department of Neuroscience, Karolinska Institute, Stockholm, Sweden b Genetic Basis of Mood & Anxiety Disorders, Mood & Anxiety Program, National Institute of Mental Health, National Institutes of Health (NIH), Department of Health & Human Services (DHHS), Bethesda c Laboratory of Molecular Pathophysiology, Mood & Anxiety Program, National Institute of Mental Health, National Institutes of Health (NIH), Department of Health & Human Services (DHHS), Bethesda d Genometrics Section, Inherited Disease Research Branch, National Human Genome Research Institute, NIH, DHHS, Baltimore e Section of Molecular Genetics, Laboratory of Neurogenetics, National Institute on Alcohol Abuse & Alcoholism, NIH, DHHS, Rockville, Maryland f Department of Psychiatry, Neuroscience, and Pharmacology & Biological Chemistry, Mount Sinai School of Medicine, New York, New York g Departments of Clinical Sciences and Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas h Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania  Address reprint requests to Silvia Paddock, Ph.D., Department of Neuroscience, Retzius Väg 8, B2 plan 4, 17177 Stockholm, Sweden
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