Association Study of Wnt Signaling Pathway Genes in Bipolar Disorder FREE

Wnt proteins are a family of molecules that locally activate cell signaling pathways, which regulate cell fate and play an important role in development.¹ Aberrations in these pathways have been implicated in a number of chronic diseases, such as cancer. There are several lines of evidence to suggest that these pathways may also be involved in the etiology of bipolar disorder.² First, Wnt signaling pathways influence neuroplasticity, cell survival, and adult neurogenesis, and recent studies have suggested that bipolar disorder may involve impairments in these functions. For example, Wnt7a was found to be critical to axon and growth cone remodeling in the cerebellum³ ; mice with inactivated Wnt1 genes failed to develop large portions of their brain⁴ ; and Wnt3 increased neurogenesis in the adult rat hippocampus.⁵

Second, mood-stabilizing drugs and antipsychotic medications used to treat bipolar disorder are known to affect Wnt signaling pathways, particularly through glycogen synthase kinase 3β (GSK3β), a key enzyme in these pathways. Lithium inhibits this enzyme, leading to activation of Wnt signaling,⁶ and haploinsufficiency of GSK3β mimics lithium effects in a mouse model.⁷ Clozapine⁸ and haloperidol⁹ have also been found to inhibit GSK3β, and 1 study showed that valproic acid similarly activates Wnt signaling.¹⁰ Furthermore, another study showed that dopamine increases GSK3β activity and that this increase is reversed by dopamine receptor D₂ blockade and by lithium.¹¹

Third, it has been shown that monozygotic twins who are discordant for bipolar disorder have differential expression of genes in Wnt signaling pathways.¹² Among 292 genes found to be differentially expressed, 8 were in the Wnt pathway. One of these was TCF7, a transcription factor activated by β-catenin. This supports a role for the Wnt pathway in bipolar disorder pathophysiology. At the level of etiology, this study directly suggests a role for environmental and epigenetic factors given that monozygotic twins are genetically identical, though it also indirectly suggests that variations in genes that alter Wnt signaling could be etiologic factors in bipolar disorder.

Finally, in an association study of bipolar disorder with 22 genes on chromosome 22,¹³ evidence for association was found with HMG2L1, a gene that influences Wnt signaling by interacting with NLK, a negative regulator of TCF7.¹⁴ Motivated by these considerations, we sought to systematically test whether variation in candidate genes of the Wnt signaling pathways was associated with susceptibility for bipolar disorder in a family-based study.

The families in the current study were ascertained through 1 of 3 projects: (1) the National Institute of Mental Health (NIMH) Genetics Initiative Bipolar Disorder Consortium involving 9 different sites across the United States (Johns Hopkins University, Indiana University, Washington University in St Louis, University of California–San Diego, University of Iowa, University of Pennsylvania, University of Chicago, Rush-Presbyterian Medical Center, University of California–Irvine, and the NIMH Intramural Program); (2) a separate collaboration involving the University of Chicago, Johns Hopkins University, and the NIMH Intramural Program (here referred to as CHIP); or (3) the Clinical Neurogenetics (CNG) collection. In all 3 samples, the families were recruited opportunistically through probands with bipolar disorder type I and 1 or more relatives affected with a mood disorder. The NIMH Genetics Initiative families were assessed with the Diagnostic Interview for Genetic Studies (DIGS),¹⁵ and diagnoses were assigned using best-estimate procedures based on DSM-III-R or DSM-IV criteria. The CHIP and CNG families were assessed with the Schedule for Affective Disorders and Schizophrenia, Lifetime Version (SAD-L), and diagnoses were made using similar best-estimate procedures based on Research Diagnostic Criteria (RDC). We have previously reported high diagnostic reliability for major mood episodes and diagnoses using both SAD-L (κ = 0.72-1.0)¹⁶ and DIGS (κ = 0.78-0.96).¹⁵ In a small sample of 33 individuals with original diagnoses of bipolar disorder type I by RDC after assessment with the SAD-L, we found that on reinterview a mean 10 years later using DIGS, 31 of them were rediagnosed with bipolar disorder type I and 2 of them were rediagnosed with bipolar disorder type II, both by RDC and DSM-IV criteria (P.P.Z, unpublished data, 2002). We used all independent quads (2 parents and 2 affected offspring) and trios (2 parents and 1 affected offspring) from these families with DNA available for genotyping. This yielded 1118 participants in 237 quads and 80 trios from 317 families. Among the affected offspring, there were 491 with bipolar disorder type I, 39 with bipolar disorder type II, and 24 with schizoaffective manic disorder or schizoaffective disorder, bipolar type. Of these, 60.5% were female, the mean age of onset was 18 years, and the mean age at interview was 41 years. Ninety-six percent of participants had been treated. Of the 634 parents in our samples, 613 (97%) were of European ancestry (defined as > 90% European), as assessed by a STRUCTURE analysis in which genotype data from 250 single-nucleotide polymorphisms (SNPs) in our samples were analyzed along with data from HapMap Centre d'Etude du Polymorphisme Humain (CEPH), Yoruban, Chinese, and Japanese samples. This broke down by study sample as follows: NIMH, 458 of 470 (97% with European ancestry); CHIP, 147 of 154 (95%); and CNG, 10 of 10. The other 21 parents came from 14 families and included 5 with African ancestry (defined as > 90% African), 2 with Chinese ancestry (defined > 90% Chinese), and 14 with mixed ancestry. All participants provided informed consent at the institutions where they were seen, and all protocols were approved by the local institutional review boards.

In these individuals, we genotyped 34 candidate genes from the Wnt signaling pathways (Table 1). We compiled a list of genes reflecting various aspects of Wnt signaling, including Wnt ligands, receptors, interactors, and targets, that were present on the Wnt Homepage (http://www.stanford.edu/˜rnusse/wntwindow.html) as of June 2005. We localized 131 of these genes to chromosomal locations using the University of California–Santa Cruz Genome Browser. We selected candidates from these based on whether they were (1) expressed in the brain and (2) located in a chromosomal region that had been implicated by previous linkage studies of bipolar disorder and/or schizophrenia. Evidence for linkage was a significant result from 1 of 2 meta-analyses,^{17 - 18} genome-wide significance in at least 1 family study, or consistent replication of a suggestive finding¹⁹ across more than 1 independent family study. Two Wnt signaling genes were included, though they did not meet all of these criteria. These were GSK3B, because of its well-established inhibition by lithium,⁶ and NLK, because it interacts with HMG2L1, which was previously associated with psychotic bipolar disorder.^{13 - 14}

We selected for genotyping nonsynonymous coding SNPs identified from the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov) as well as a set of tagging SNPs chosen to capture the known common genetic variation (minor allele frequency [MAF] > 0.1) across each gene ± 10 kilobases (kb) (r² ≥ 0.8). The tagging SNPs were selected using LDSelect²⁰ with HapMap Phase I data (http://www.hapmap.org), which were available at the time of the study's design. These SNPs were genotyped by the Illumina Integrated BeadArray System (Illumina, San Diego, California). A total of 265 SNPs were originally selected for genotyping. Of these, 34 SNPs could not be genotyped using the Illumina system. Another 6 replacement SNPs were identified, resulting in 237 successfully genotyped SNPs. Of these, 10 SNPs were excluded because they were either monomorphic (n = 2) or not in Hardy-Weinberg equilibrium (P < .05) among the founders (n = 8), leaving 227 SNPs for the initial analyses.

For follow-up, we sought to do denser genotyping in our most significant gene, PPARD (OMIM 600409). We used the Web-based tool QuickSNP²¹ to select an additional 18 tagging SNPs from HapMap Phase II data, which captured the known common variation in the gene (MAF > 0.05; r² > 0.9). We used the TaqMan-5′ nuclease assay (Applied Biosystems, Foster City, California) to genotype these SNPs. Two of the assays failed and 3 SNPs were excluded because more than 5.0% of data were missing, leaving 13 additional SNPs for analyses.

We carried out 2 main analyses. First, we conducted a single-locus analysis in which we individually tested each SNP for association with bipolar disorder using FBAT (http://www.biostat.harvard.edu/˜fbat/default.html; Harvard School of Public Health, Boston, Massachusetts), a flexible program for testing allelic associations with family data. Under certain conditions, it reduces to the commonly used transmission disequilibrium test. However, FBAT is more general and allows tests of association that are robust to population confounds in the case in which parental data are missing and/or other offspring are included in the analysis.²² We used the biallelic mode and examined the additive model for counting alleles. We specified the option to calculate the variance empirically to provide valid tests of association in the presence of linkage,²³ because each of the candidate genes was selected based on prior evidence of linkage. We used permutations to estimate the significance of our best finding after accounting for all the tests carried out with FBAT. After randomly shuffling the haplotypes across each of the genes that were transmitted to the offspring, we recalculated the FBATs for each SNP and counted the number of replicates out of 10 000 in which there was a more significant finding than what we observed.

Second, we conducted a multilocus analysis in which we tested haplotypes of adjacent SNPs for association with bipolar disorder using HBAT.²⁴ In some conditions (such as when r² = 1 between the risk variant and a particular multi-SNP haplotype), it has been shown that haplotypes may provide more information for association than corresponding single-locus tests²⁵ ; HBAT is an elaboration of FBAT that allows for family-based association tests of haplotypes, even when the phase is ambiguous. We used a sliding window approach to test haplotypes of 2, 3, and 4 adjacent SNPs. Haplotypes were only allowed to cross intergenic regions when less than 1 kb separated SNPs in the 2 genes. This happened 3 times for the following gene pairs: WNT9A/WNT3A, WNT2A/RHOU, and MMP7/MMP3. Significance values were obtained for tests of each specific haplotype.

In exploratory analyses of our most significant findings in the PPARD gene, we separately tested for subgroup differences in the evidence of association by certain clinical features or for interactions with SNPs from other genes in the Wnt signaling pathways. For both analyses, we used the method implemented by the program GENASSOC in Stata, version 9.0 (Stata Corp, College Station, Texas) (http://www-gene.cimr.cam.ac.uk/clayton/software/stata/genassoc/), which allows for tests of interactions. Here, the family data set is converted into cases and matched pseudocontrols and analyzed for genotypic associations with an SNP of interest using conditional logistic regression models. These models may then be expanded to incorporate interactions between the SNP and other covariates. The significance of the interactions is determined by comparing models with and models without the interaction terms using likelihood ratio tests (LRTs). For the analysis looking at clinical features, we assumed the patients and pseudocontrols were matched on the feature under investigation. This is artificial because the pseudocontrols do not have clinical features, but it allows us to test for subgroup differences in the association with an SNP among the cases by including interaction terms between the SNP and a covariate for the feature. The different features tested in this way, selected to capture various aspects of severity, included the following: number of depressive episodes, number of manic episodes, age at onset (dichotomized at ≤ 21 years), having ever attempted suicide, having ever been hospitalized, history of psychosis, history of mood incongruent psychosis, and poor functioning in the past month (as assessed by the Global Assessment Scale [GAS], which is a measure of general functioning with a range of 0-100; a score lower than the mean of 68 represents poor functioning). For the analyses looking at 2-way interactions between PPARD and the other genes examined in the Wnt signaling pathway, we individually tested interaction terms for the best SNPs in PPARD with the single best SNP from each of the other genes. In all models for both analyses, we used a dominant coding for the SNPs being tested.

The results of the single SNP and the 2-, 3-, and 4-SNP haplotype analyses are shown in Figure 1. The most significant single SNP was rs2267665 in PPARD (nominal P < .001). The association with this SNP was significant (P = .05) by permutation after accounting for all the tests carried out with the SNPs. The most significant haplotype association was with a 2-SNP haplotype that included rs2267665 (P < .001). No other single SNP or haplotype was associated with bipolar disorder at the nominal P < .01.

To corroborate the finding with PPARD, we consulted the publicly available results from the Wellcome Trust Case-Control Consortium Genome-Wide Association Study (WTCCC GWAS). The SNP rs2267665 was not genotyped in this study, but an SNP (rs9470015) that was in perfect linkage disequilibrium with it was (r² = 1.0), and this SNP was associated with bipolar disorder (P = .02) in the analysis in which all available controls were used to maximize the power of the sample. In both the SNP from our study and from WTCCC GWAS, the common allele was over-represented in bipolar disorder.

Encouraged by these findings, we sought to genotype a denser panel of SNPs in PPARD to provide better coverage of the gene and refine the boundaries of the apparent association. The PPARD gene, which is located on chromosome 6p21.2-p21.1, has 8 exons and spans approximately 85.6 kb (http://www.ncbi.nlm.nih.gov/RefSeq/). In the initial genotyping effort, we successfully genotyped only 2 intronic SNPs (including rs2267665, as described) that captured approximately 44% of the known variation in the gene according to the HapMap Phase II data (MAF > 0.05, r² > 0.80). With the successful genotyping of 13 additional tagging SNPs, we increased the coverage to capture 86% of the known variation (MAF > 0.01, r² > 0.90).

The results of the single SNP and 2-, 3-, and 4-SNP haplotype analyses with all 15 SNPs in PPARD are shown in Figure 2 and Table 2. Of the 13 new tagging SNPs, 4 were significantly associated with bipolar disorder (P < .01). The most significant of these (rs9462082) was associated with bipolar disorder at P < .001. The most significant haplotype was a 2-SNP haplotype that included rs9462082 (P < .001). All of the SNPs associated at P < .01 resided in a single haplotype block that spanned exons 3 to 7 of the gene. The 2 most significant SNPs overall, rs2267665 and rs9462082, were highly correlated (r² = 0.84).

In an exploratory analysis, we tested whether the association with the 2 best SNPs in the PPARD gene varied depending on certain covariates related to the clinical presentation of bipolar disorder. Of the different clinical covariates examined, only poor functioning as measured by the GAS showed a significant interaction (LRT, P < .01) with these 2 SNPs. The SNP rs9462082 was significantly associated with bipolar disorder among all participants with an odds ratio (OR) of 1.46 (95% confidence interval [CI], 1.16-1.85) for those who were homozygous for the common allele compared with others. However, among participants with poor functioning (GAS score < 68), this risk increased to an OR of 3.36 (95% CI, 1.85-6.08). There was no apparent association of this SNP with bipolar disorder among those who were high functioning (OR, 1.28; 95% CI, 0.85-1.93). The results were nearly identical for rs2267665.

In further exploratory analyses, we tested for 2-way interactions between the 2 best SNPs in PPARD and the best SNP from each of the other Wnt signaling pathway genes. Evidence for significant interactions (LRT, P < .01) was detected with WNT2B and WNT7A. In particular, rs9462082 was significantly associated with bipolar disorder only among those who were carriers of the rare allele at rs3790606 (MAF = 0.31) in WNT2B (OR, 2.56; 95% CI, 1.67-4.00), but not among those who were homozygous for the common allele (OR, 1.16; 95% CI, 0.79-1.72). The results were nearly identical for rs2267665. Similarly, rs9462082 was significantly associated with bipolar disorder only among those who were carriers of the rare allele at rs4685048 (MAF = 0.49) in WNT7A (OR, 1.79; 95% CI, 1.23-2.63) but not among those who were homozygous for the common allele (OR, 0.75; 95% CI, 0.42-1.32).

Several converging lines of evidence suggest that Wnt signaling pathways, which play an important role in a number of cellular functions, may also contribute to the etiology of bipolar disorder. We sought to test whether variation in genes of the Wnt signaling pathways is associated with susceptibility to bipolar disorder using a family-based design. We observed an association with an SNP in the PPARD gene that remained significant after correcting for the multiple tests carried out. Further genotyping in our sample suggested that the association was delimited within a single haplotype block that spanned exons 3 to 7 of the gene. Moreover, the association appeared to be strongest among patients with bipolar disorder who had the poorest functioning and among those carrying a rare allele at rs3790606 in WNT2B or at rs4685048 in WNT7A. These findings merit further investigation to identify the putative causal variant(s) within this region of the gene and to confirm their effects on poor functioning in bipolar disorder in particular.

The PPARD gene is located on chromosome 6p21. Chromosome 6p has not been implicated directly by linkage studies of bipolar disorder, though 1 study found evidence of interaction between loci on 6q and 6p.²⁷ By contrast, this region has been implicated in schizophrenia by a number of linkage studies. A rigorous meta-analysis of 20 different genome-wide linkage scans²⁸ identified 2 broad loci on 6p, one stretching 6pter-6p22.3 and another 6p22.3-21.1, that were genome-wide significant and among the top 10 signals across the entire genome. This region contains several interesting candidate genes, including one (DTNBP1) that has been associated with both schizophrenia and psychotic bipolar disorder.^{29 - 30} The PPARD gene itself has not previously been reported to be associated with either bipolar disorder or schizophrenia.

The PPARD gene encodes a member of the peroxisome proliferator-activated receptor (PPAR) family. The PPARs are nuclear hormone receptors that mediate a wide variety of cellular and biochemical processes, including peroxisomal functioning, lipid oxidation, lipid synthesis, cell proliferation, and inflammation.³¹ They act by dimerizing with the retinoid X receptor and, on binding with various ligands, serve as transcription factors for a number of different target genes that have peroxisome proliferation response elements within their promoters. The PPARD gene is expressed in a wide range of tissues, most notably the brain, adipose, and skin.³² Studies have shown that in the murine brain, it is expressed at particularly high levels in the entorhinal cortex, hypothalamus, and hippocampus as well as the corpus callosum and neostriatum.³¹ Interestingly, PPARD appears to be expressed at its highest levels in the embryonic brain, suggesting that it may help regulate differentiation of cells during neurodevelopment.³² Consistent with this, several studies have shown that PPAR delta (PPARD) agonists augment differentiation and myelogenesis of cultured murine oligodendrocytes^{33 - 34} and that PPARD-null mice have diminished myelination levels of the corpus callosum and other neurodevelopmental abnormalities.³³ These findings suggest that if PPARD influences susceptibility to bipolar disorder, it could be through neurodevelopmental processes. Furthermore, agonists of PPARD were neuroprotective in rat models of stroke and of neurodegenerative disease.³⁵

The increased evidence of association for PPARD among those with poor functioning is consistent with a potential role for Wnt dysfunction in severe bipolar disorder. The GAS score captures both social-occupational and interpersonal functioning in the month before the interview. We have previously shown that social functioning was the most highly familial feature of bipolar disorder among 40 variables tested and that loss of employment was also familial.³⁶ We have previously hypothesized³⁷ that psychotic features in bipolar disorder represent clinical manifestations of etiologic overlap between bipolar disorder and schizophrenia; we found no evidence to support that hypothesis in these data. However, functional impairment represents another potential clinical indicator of etiologic overlap, given that the most impaired patients with bipolar disorder are, by this metric, the most similar to schizophrenia patients, who are typically chronically impaired.

Several studies have implicated Wnt signaling genes in bipolar disorder as well as in schizophrenia. For schizophrenia, the frizzled 3 gene (FZD3), which encodes a receptor for Wnt ligands, has been associated in 3 different samples,^{38 - 40} and Proitsi et al,⁴¹ employing a Wnt pathway approach similar to ours, found evidence for an association with DKK4. We did not find any support for the former gene and did not study the latter. For bipolar disorder, 1 study reported a positive association with GSK3B,⁴² but we did not observe a similar finding with this gene in our sample. There are 3 potential explanations for our failure to find signals in FZD3 and GSK3B. First, these genes may not be related to bipolar disorder etiology. Second, these genes may be involved in bipolar disorder, but we may have failed to assay the relevant SNPs. This possibility is particularly relevant for GSK3B, for which we assayed only 56% of the known common variations. Third, we could have assayed SNPs that are etiologically relevant to bipolar disorder (or are in high linkage disequilibrium with relevant SNPs), but our sample may have had insufficient power to detect the potentially very modest effect sizes the risk alleles confer. More recently, a GWAS using pooled samples of patients with bipolar disorder,⁴³ some of which were also used in the current study, reported replicated associations with several genes involved with the Wnt signaling pathway, including NXN, A2BP1, and DFNB31. Unfortunately, we did not study these genes. Baum et al⁴³ did not detect significant associations with PPARD, WNT7A, or WNT2B. There are several potential reasons for this. Despite some overlap in the patients with bipolar disorder included in the 2 studies, there were substantial differences in sample composition, which may have introduced heterogeneity. Additionally, Baum et al⁴³ used a pooling approach for genotyping, which likely reduced the power to detect genetic variants with modest effect sizes.

We also found evidence for interactions of PPARD with WNT2B and WNT7A. These Wnt ligand genes are at the upstream end of the Wnt signaling pathways, while PPARD is on the downstream end of the β-catenin Wnt pathway. There are 3 Wnt signaling pathways: the β-catenin pathway (the canonical pathway), the planar cell polarity pathway, and the Wnt/Ca²⁺ pathway; PPARD is a target gene in the β-catenin pathway, which is the one that has been implicated in the mechanism of action of bipolar disorder medications. The current findings may suggest that multiple hits along the Wnt β-catenin pathway are needed to substantially influence bipolar disorder susceptibility. We note, however, that the findings of statistical interactions are exploratory and further confirmation is required.

The current study has several limitations that merit attention. First, the sample size may not have been sufficient to detect associations with loci of smaller effects on bipolar disorder. We estimated that this sample had 80% power to detect association with a locus exerting a genotypic relative risk of approximately 1.6 (assuming an additive model, a disease prevalence of 1%, and a conservative α = .00022 that is Bonferroni adjusted for the 227 SNPs tested). However, bipolar disorder appears to be a very genetically complex disease, and it is possible that the genotypic relative risks of bipolar disorder–susceptibility genes may be smaller than 1.6. Second, when we designed the experiment, the HapMap Phase II data were not yet available and as a result our selection of tagging SNPs provided incomplete coverage of the currently know common variation in the candidate genes studied. Consequently, we may have missed some of the relevant associations in these candidate genes. Third, we only studied 34 of at least 131 known Wnt-related genes, chosen largely because of their location in bipolar disorder and/or schizophrenia linkage regions. Because of the limited robustness of linkage studies, we may have omitted genes with an etiologic role in bipolar disorder. This problem should soon be overcome because the results of several large whole-genome association studies in bipolar disorder and schizophrenia will soon be available. Combining these data sets, as planned, will allow for comprehensive study of all Wnt-related genes.

The study also has several important strengths. Most notably, we used a family-based design with data available on both parents. Thus, we were able to extract the most information from this sample for association testing, and the findings are robust to potential confounding by population stratification.

In summary, we found evidence for association between bipolar disorder and a gene that is a downstream target of Wnt signaling, PPARD. If this association can be confirmed in additional samples, it may illuminate a new avenue for understanding the pathogenesis of severe bipolar disorder and developing more effective treatments.

Correspondence: Peter P. Zandi, PhD, MHS, Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Hampton House, Room 857, 624 N Broadway, Baltimore, MD 21205 (pzandi@jhsph.edu).

Submitted for Publication: November 14, 2007; final revision received January 31, 2008; accepted February 8, 2008.

Author Contributions: Drs Zandi and Belmonte contributed equally to the work.

Financial Disclosure: None reported.

Funding/Support: This work was supported by grants R01 MH-042243 (Dr Potash), R01 MH-061613 (Dr Gershon), and K01 MH-072866 (Dr Zandi) from NIMH; by the National Alliance for Research on Schizophrenia and Depression; by the Stanley Medical Research Institute; and by the NIMH Intramural Research Program (Dr McMahon). Dr Belmonte was supported by a gift from the Alex Brown Foundation. Drs Willour and Potash were supported by Margaret Price Investigatorships. Some DNA samples were prepared and distributed by The Rutgers University Cell and DNA Repository under a contract from the NIMH.

Additional Contributions: We thank Barbara Schweizer, RN, Yuqing Huo, MD, Kuangyi Miao, MS, and Brandie Craighead, MS, for their contributions. We also thank the many interviewers and diagnosticians who contributed to this project and the families who devoted their time and effort to the study.

Some of the data and biomaterials were collected in 4 projects that participated in the NIMH Genetics Initiative Bipolar Disorder Consortium from 1991 to 1998. The principal investigators and coinvestigators were: Indiana University: Dr Nurnberger, M. J. Miller, MD, and E. S. Bowman, MD; Washington University at St Louis: T. Reich, MD, A. Goate, PhD, and Dr Rice; Johns Hopkins University: Dr DePaulo, Dr Simpson, and C. Stine, PhD; NIMH Intramural Research Program, Clinical Neurogenetics Branch: Dr Gershon, D. Kazuba, BA, and E. Maxwell, MSW.

Other data and biomaterials were collected in 10 NIMH Genetics Initiative Bipolar Disorder Consortium projects from 1999 to 2003. The principal investigators and coinvestigators were: Indiana University: Dr Nurnberger, Dr Miller, Dr Bowman, N. L. Rau, MD, P. R. Moe, MD, N. Samavedy, MD, R. El-Mallakh, MD (at University of Louisville), H. Manji, MD, D.A. Glitz, MD (at Wayne State University), E. T. Meyer, MS, C. Smiley, RN, T. Foroud, PhD, L. Flury, MS, D. M. Dick, PhD, and H. Edenberg, PhD; Washington University at St Louis: Dr Rice, Dr Reich, Dr Goate, and L. Bierut, MD; Johns Hopkins University: M. McInnis MD, Dr DePaulo, D. F. MacKinnon, MD, F. M. Mondimore, MD, Dr Potash, Dr Zandi, D. Avramopoulos, PhD, and J. Payne, MD; University of Pennsylvania, Philadelphia: W. Berrettini, MD, PhD; University of California–Irvine, Irvine: Dr Byerley and M. Vawter, MD; University of Iowa: Dr Coryell and R. Crowe, MD; University of Chicago, Chicago, Illinois: Dr Gershon, Dr Badner, Dr McMahon, C. Liu, PhD, A. Sanders, MD, M. Caserta, S. Dinwiddie, MD, T. Nguyen, and D. Harakal; University of California–San Diego, San Diego: Dr Kelsoe and R. McKinney, BA; Rush University, Chicago: W. Scheftner, MD, H. M. Kravitz, DO, MPH, D. Marta, BS, A. Vaughn-Brown, MSN, RN, and L. Bederow, MA; and NIMH Intramural Research Program, Bethesda: Dr McMahon, L. Kassem, PsyD, S. Detera-Wadleigh, PhD, L. Austin, PhD, and D. L. Murphy, MD.