Mapping susceptibility genes for bipolar disorder: a pharmacogenetic approach based on excellent response to lithium


Genetic mapping studies in bipolar disorder (BD) have been hampered by the unclear boundaries of the phenotypic spectrum, and possibly, by the complexity of the underlying genetic mechanisms, and heterogeneity. Among the suggested approaches to circumvent these problems, a pharmacogenetic strategy has been increasingly proposed. Several studies have indicated that patients with BD who respond well to lithium prophylaxis constitute a biologically distinct subgroup. In this study we have conducted a complete genome scan using 378 markers spaced at an average distance of 10 cM in 31 families ascertained through excellent lithium responders. Response to lithium was evaluated prospectively with an average follow-up of 12 years. Evidence for linkage was found with a locus on chromosome 15q14 (ACTC, lod score = 3.46, locus-specific P-value = 0.000014) and suggestive results were observed for another marker on chromosome 7q11.2 (D7S1816, lod score = 2.68, locus-specific P-value = 0.00011). Other interesting findings were obtained with markers on chromosomes 6 and 22, namely D6S1050 (lod score = 2.0, locus-specific P-value = 0.00004) and D22S420 (lod score = 1.91). Nonparametric linkage analysis provided additional support for the role of these loci. Further analyses of these results suggested that the locus on chromosome 15q14 may be implicated in the etiology of BD, whereas the 7q11.2 locus may be relevant for lithium response. In conclusion, our results provide original evidence suggesting that loci on 15q14 and 7q11.2 may be implicated in the pathogenesis of BD responsive to lithium.


Since the late 1950s, researchers have been aware of a relationship between drug response and genetic variation.1 Genes make humans more or less likely to respond to a given pharmacological treatment and/or to present a certain side-effect profile.2,3,4 Thus, selecting research subjects based on common patterns of response or non-response to a given pharmacological treatment may be useful to define genetically more homogeneous subgroups. We have been using such an approach, focusing on bipolar patients who present an excellent response to lithium prophylaxis.

After five decades of use in mood disorders, lithium remains the first-choice therapy for preventing recurrences. Although some authors have questioned its effectiveness, lithium is the best-studied and clearly effective mood stabilizer currently available.5 However, its effectiveness tends to vary depending on the clinical presentation. It is more effective in forms of BD characterized by typical symptomatology, absence of comorbidity and a specific family history of bipolar disorder.6,7,8 There is also evidence that responders and nonresponders to lithium treatment differ in certain neuroendocrine responses involving the serotonergic and endorphin systems.9 Over the last 20 years, several studies have suggested that lithium-responsive patients are more likely to have relatives affected with BD than lithium nonresponders.10,11,12,13,14,15 Moreover, segregation analysis in this population indicates a major gene effect consistent with an autosomal recessive mode of inheritance.16,17 Taken together, these findings suggest that response to lithium prophylaxis may help define a distinct bipolar phenotype with less genetic heterogeneity and stronger genetic effect. Therefore, we collected a sample of subjects and their families prospectively ascertained according to their response to lithium (see Table 1) to carry out genetic studies. Here we report the results of the first stage of a complete genome scan.

Table 1 Criteria used to diagnose bipolar patients as excellent lithium responders



Families for this study were ascertained through a cohort of bipolar probands who were followed prospectively and met strict criteria for excellent lithium response (see the criteria in Table 1). The probands, 18 males and 13 females, were recruited from the affective disorder clinics of the Hamilton Psychiatric Hospital and the Royal Ottawa Hospital, Ontario, Canada. Their mean age was 45.1 ± 12.9 years and the mean age-at-onset was 23.8 ± 7.2 years. On average, they had 8.9 ± 6.1 episodes of the illness before lithium and 11.9 ± 8.0 years of full stability on lithium monotherapy. After obtaining informed consent, all available relatives were personally interviewed by two psychiatrists who were blind to the proband's diagnosis.

To be included in this study, families had to meet the following criteria. At least four family members (including the proband) had to be available for an interview and to provide a blood sample. Of these, at least two subjects must have been affected. Finally, only subjects older than 15 years were asked to participate. The pedigrees were extended in such a way as to include all first-degree relatives of all affected subjects. The procedure for an extended sampling of families for linkage analysis was based on the rule proposed by Cannings and Thompson.18

Overall, 31 multiplex families were included, from which a total of 247 individuals were interviewed, sampled and included in this study. Of these, 106 were considered affected. Best estimate diagnoses were made by a panel of experienced psychiatrists (other than the interviewers) who reviewed blindly the data from the Schedule for Affective Disorders and Schizophrenia (SADS-L) interviews19 and available medical records. Diagnoses were based on the Research Diagnostic Criteria (RDC).20 All families were Caucasian of European descent. All individuals provided written informed consent.

Only one phenotypic schedule was used in the genome-scan analysis. Relatives were considered affected if they met diagnostic criteria for BD, manic disorder, schizoaffective disorder or recurrent major depression (with the additional criterion of functional incapacitation). Rates of psychopathology other than these disorders were notably low in these families, similar to the rates observed in the general population. In addition, we observed almost no comorbidity in probands and affected family members. For instance, all probands had exclusively BD diagnosis and only one schizophrenia diagnosis was made among relatives. Panic disorder was similarly rare. The best results observed in the genome-scan analysis were further investigated using two additional phenotypic schedules. These were: (a) BD only, where only those subjects who met criteria for BD were considered affected; and (b) lithium-responders, where only relatives who responded to lithium were considered. Those subjects who have never been treated with lithium (including psychiatrically normal individuals) were considered as phenotype unknown. The total number of relatives treated with lithium was 23, but only 20 were treated in a way allowing treatment response assessment according to the same strict criteria used for probands. Of these relatives, 17 (85%) were excellent responders.


DNA was extracted from peripheral blood following standard procedures.21 The genotyping was performed at the Montreal Genome Centre, Montreal General Hospital, where the process was streamlined using a modified Packard Multiprobe robot and two ABI 377 sequencers. A genome-wide scan was performed with a set of 378 simple sequence repeat polymorphisms. This marker panel is a modified version of the Cooperative Human Linkage Center (CHLC) Human Screening set version 6.022 that also includes selected Genethon markers.23 Fluorescent genotyping gels were analyzed in an automated system using the BASS/GRACE software developed at the Whitehead Institute/MIT Center for Genome Research. Subsequently, the gel was run through an automatic allele binning system (PEDMANAGER) and the identified alleles were submitted to a process called allele quality checking, which controlled for adequacy of signal intensity and large scale errors such as gel misloading, sample mix-ups and gel mislabeling. This was done through checking the allele calls of control DNA, which were stored in the database, as well as checking for segregation errors in the families.

Statistical analysis

Parametric linkage analysis

Parametric linkage analysis was conducted using the MLINK program from FASTLINK.24,25 Five major genetic models were explored in order to maximize the evidence for linkage (Table 2). These models were: (a) Dominant 1 (allele frequency (q) 0.012, male penetrance (fM) 0.4, female penetrance (fF) 0.7, and normal penetrance 0.005 for males (fM0) and 0.009 for females (fF0)); (b) Dominant 2 (q = 0.012, fM = 0.2, fF = 0.35, fM0 = 0.005 and fF0 = 0.009); (c) Intermediate (q = 0.024, fM = 0.4/0.1, fF = 0.7/0.175, fM0 = 0.005, fF0 = 0.009); (d) Recessive 1 (q = 0.11, fM = 0.35, fF = 0.65, fM0 = 0.005, fF0 = 0.009); (e) Recessive 2 (q = 0.16, fM = 0.18, fF = 0.33, fM0 = 0.005; fF0 = 0.009). The parameters for the Recessive 1 model were based on segregation analysis of this sample. The remaining models maintain sex-specific penetrances and were defined so as to allow for different modes of inheritance. Allele frequencies were estimated based on the observed allele frequencies using PEDMANAGER After completion of the parametric analysis, homogeneity analysis was carried out using the HOMOG program.26

Table 2 Genetic models used in the parametric linkage analysis

For some selected loci (those that provided lod scores above 2.0), empirical, locus-specific P-values were determined by computer simulations, using the SIMULATE program,27 to generate between 1000 and 500 000 replicates of our sample under the hypothesis of no linkage. These were analyzed using the MSIM program.28,29

Nonparametric linkage analysis

Nonparametric linkage analysis was conducted using the SimIBD 2.130 program. SimIBD calculates a simulation-based nonparametric statistic that is based on IBD sharing and provides a powerful test for linkage in general pedigrees. WZObs is the score statistic provided by SimIBD, which measures IBD sharing between all relative pairs. P-values are based on empirical null distributions. One of the major advantages of this method is that there are no constraints imposed on family size. All analyses were carried out using the 1/sqrt (p) weighting function, where p is the frequency of the shared marker allele, with 100 observed replicates, 1000 bootstraps per simulated null replicate and 500 simulated null distribution replicates.


Figure 1 shows the distribution of maximum lod scores per chromosome for three of the five models tested. A number of loci showed lod scores above 1. Table 3 lists these loci and the models that provided maximum evidence for linkage in the parametric analysis. Marker ACTC on chromosome 15q14 (31.46 cM) gave a lod score of 3.43 under model Recessive 1. Using the guidelines suggested by Lander and Kruglyak, this result may be considered significant.31 In addition, a simulation with 500 000 replicates assuming the null hypothesis of no linkage suggests an empirical, locus-specific P-value associated with this result of 0.000014. The test of heterogeneity was not significant (L ratio = 1.0) and multipoint linkage analysis in this region did not provide additional information. The second largest lod score observed in our study was on chromosome 7q11.2 (84 cM). Locus D7S1816 provided a maximum lod score of 2.68 under the Intermediate model (locus-specific P-value = 0.00011). Following the proposed guidelines,31 this result may be considered as providing suggestive evidence of linkage. Of the other positive lod scores listed in Table 3, only those observed for loci D6S1050 and D22S420, respectively 2.0 (locus-specific P-value = 0.00004) and 1.91, both under the Recessive 2 model, may also be considered suggestive of linkage. Locus D6S1050 is located on 6p23 (42.27 cM), whereas locus D22S420 is on 22q11.2 (4.06 cM).

Figure 1

Maximum lod score distribution for all chromosomes, according to each of the following parametric models: .... Dominant 1, --X-- Intermediate, Recessive 1.

Table 3 List of markers that provided maximum lod scores above 1.00 in the parametric linkage analysis

The nonparametric analysis was, as expected, less informative than parametric linkage. Table 4 shows loci that provided Z statistics with empirical P-values less than 0.05. It is noteworthy that among the 16 loci listed in this table, only three yielded lod scores above 1 in the parametric analysis. Namely, these are D6S1050 (WZObs = 64.97; locus-specific P-value = 0.017), ACTC (WZObs = 74.43; locus-specific P-value = 0.0037) and D21S1437 (WZObs = 96.13; locus-specific P-value = 0.041). Overall, in addition to ACTC, only two loci yielded P-values less than 0.01. These were D14S587 (WZObs = 50.06; locus-specific P-value = 0.0055), located on 14q21, and D20S115 (WZObs = 16.96; locus-specific P-value = 0.0036), located on 20p13.

Table 4 List of markers that provided an empirical P-value below 0.05 in the nonparametric linkage analysis

In order to investigate the specificity of our results, we further studied those loci that provided lod scores above 2.0 (ACTC, D7S1816 and D6S1050), exploring different phenotypic models. These additional models considered as affected either subjects diagnosed only with BD or lithium-responders only (in the case of subjects for whom evidence of response to lithium was available). In both cases, all other subjects were considered as phenotype unknown. These analyses yielded the results presented in Table 5. For the ACTC locus, the results were most significant (Zmax = 1.40; locus-specific P-value = 0.00024) when the bipolar-only phenotype was considered. For locus D7S1816, on the other hand, the analysis with lithium-responders provided the most significant results (Zmax = 1.53; locus-specific P-value = 0.003).

Table 5 Maximum lod scores and empirical locus-specific P-values observed for models BP (bipolars only), Li (lithium responders only) and BP/Li (original phenotypic model used in the genome scan—see Methods section) in those loci that provided lod scores above 2.0


Our results indicate that lithium-responsive BD may be linked to a locus on chromosome 15q14. In addition, the data suggest that a locus on 7q11.2 could be involved as well. The results observed for marker ACTC were most significant in parametric linkage analysis, whereas the results of the nonparametric analysis, although still congruent with linkage, were less significant. In linkage studies of complex traits such as BD, nonparametric methods are used more commonly than parametric methods. However, it has been repeatedly shown that the lod score method is generally more powerful to detect linkage, even when the precise genetic models is not known. Recently, Durner et al32 demonstrated that even under complex genetic models, the lod score method generally outperforms nonparametric allele-sharing methods, if the assumed mode of inheritance at the disease locus tested is approximately correct. This explains the difference in significance we observed between parametric and nonparametric analyses. In addition, given the variability found between the different models tested, it seems reasonable to speculate that the Recessive 1 model is the most accurate locus-specific model for ACTC. This is consistent with segregation analysis carried out in this population of families that suggested an autosomal recessive mode of inheritance.16,17 In fact, the parameters used in the recessive model were based on this analysis.

Questions related to the specificity of these findings remain to be answered. The design employed in this study does not allow us to determine if our results are related to lithium response or to BD. However, further analyses of the main positive loci under different phenotypic models provided interesting observations that suggest that the locus on chromosome 15 may be more involved in the etiology of BD, whereas the locus on chromosome 7 might be related to the response to lithium treatment. However, it is important to stress that assessing the lithium response in relatives may present some difficulties, particularly among those who are unaffected, apparently non-responders or who have not been treated with lithium. Thus, restricting the analysis to lithium responders only led to a considerable reduction of power.

A few previous studies have specifically investigated a possible role of 15q11–q15 genes in bipolar disorder.33,34,35,36,37,38,39 Most of these studies are reports of chromosomal aberrations in patients with bipolar disorder34,36,38,39,40 and one genome-wide study in a large Amish family that provided suggestive evidence with a marker on ch15.35 Finally, positive linkage results were found between a gene that codes for α7-nicotinic receptor and sensory gating deficits in schizophrenic patients.41 On the other hand, in our genome scan, with the possible exception of 22q11, we were unable to observe results that are consistent with previous positive linkage findings reported in bipolar disorder, most notably loci on 4p16, 11p15, 12q24, 18p11, 18q22, 21q21, and Xq26.42 In this regard, the results observed with marker D22S420 are particularly interesting. Moreover, this locus is close to the gene that codes for catechol-O-methyltransferase (COMT), an important candidate gene for bipolar disorder.

The marker that provided maximum evidence for linkage is located within a gene coding for cardiac actin, alpha type, which does not seem to be, a priori, a candidate gene for BD. Nevertheless, a few other interesting candidate genes are present in the same region of chromosome 15. SGNE1 (pituitary polypeptide 7B2), the expression of which is restricted to the brain and endocrine tissue, is implicated in the processing of neuroendocrine precursors and seems to be involved in the regulation of pituitary hormone secretion. Westphal et al43 have recently reported that mice lacking SGNE1 presented increased ACTH and corticosterone levels with adrenocortical expansion, features that are quite interesting if one considers that an important percentage of patients with affective disorders show cortisol nonsuppression following dexamethasone administration. PLCB2 (phospholipase C beta 2) is specially interesting because it codes for a phosphoinositide-specific phospholipase C, and alterations in the phosphatidylinositol second messenger system are among the most accepted hypotheses for etiological mechanisms in BD. In addition, our previous studies with a different phospholipase C isozyme (gamma 1) have already indicated that this enzyme may be involved in the pathogenesis of BD.44 Other interesting candidate genes in the region include a putative G-protein coupled receptor (GPR) and GTP cyclohydrolase I feedback regulatory protein gene (GCHFR).

Further evidence supporting the involvement of these loci in the etiology of lithium-responsive BD will have to come through independent replication. As it has been repeatedly advocated in the field of psychiatric genetics, consistency between studies is an important way to validate findings. In this context, it is important to note that the families used in our study were selected according to a systematic and prospective evaluation of lithium response in probands.


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The authors wish to thank Drs Kenneth Morgan and Mary Fujiwara for their help in different phases of this project. We would also like to thank Corinne Darmond-Zwaig for technical help. The genotyping facility at the Montreal Genome Centre is supported by the Canadian Genetic Disease Network. This study was funded by the Medical Research of Council of Canada grant 14041. MA is a NARSAD Independent Investigator.

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Correspondence to M Alda.

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Turecki, G., Grof, P., Grof, E. et al. Mapping susceptibility genes for bipolar disorder: a pharmacogenetic approach based on excellent response to lithium. Mol Psychiatry 6, 570–578 (2001).

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  • lithium response
  • bipolar disorder
  • genome-scan
  • linkage analysis
  • chromosome 15
  • chromosome 7

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