PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2012 August 1.
Published in final edited form as:
PMCID: PMC3143361
NIHMSID: NIHMS296350

Genome-Wide Association Study of the Child Behavior Checklist Dysregulation Profile

Abstract

Objective

A potentially useful tool for understanding the distribution and determinants of emotional dysregulation in children is a Child Behavior Checklist profile comprised of the Attention Problems, Anxious/Depressed, and Aggressive Behavior clinical subscales (CBCL-DP). The CBCL-DP indexes a heritable trait that increases susceptibility for later psychopathology, including severe mood problems and aggressive behavior. We have conducted a genome-wide association study of the CBCL-DP in children with attention-deficit/hyperactivity disorder (ADHD).

Method

Families were ascertained at Massachusetts General Hospital and University of California, Los Angeles. Genotyping was conducted with the Illumina Human1M or Human1M-Duo BeadChip platforms. Genome-wide association analyses were conducted with the MQFAM multivariate extension of PLINK.

Results

CBCL data were available for 341 ADHD offspring from 339 ADHD affected trio families from the UCLA (N=128) and the MGH (N=213) sites. We found no genome-wide statistically significant associations but identified several plausible candidate genes among findings at p<5E-05: TMEM132D, LRRC7, SEMA3A, ALK, and STIP1.

Conclusions

We found suggestive evidence for developmentally expressed genes operant in hippocampal dependent memory and learning with the CBCL-DP.

Keywords: ADHD, Emotional Dysregulation, CBCL, GWAS

Introduction

Decades of research demonstrate that individuals with attention-deficit/hyperactivity disorder are at increased risk of additional psychiatric morbidity with mood, anxiety, behavioral, or substance use disorders 1,2. While the mechanism underlying this spectrum of psychiatric morbidity is not known, it has been hypothesized that these disorders reflect impaired self-regulation of affect, cognition and behavior 3. One of the most well-studied clinical and research tools for indexing dysregulation across these three domains is the Child Behavior Checklist Dysregulation Profile (CBCL-DP) 4,5 defined from the Anxious/Depressed (A/D), Attention Problems (AP) and Aggression (AGG) scales. Referred children with elevated CBCL-DP scores are at an increased risk of psychopathology, psychiatric hospitalization, and suicidality 610. In the general community, impairment on the CBCL-DP is observed in 1–5% 1113 of children and it is associated with a similarly elevated risk of anxiety, mood, disruptive behavior, and drug abuse disorders 14 years later in adulthood 3.

Several lines of evidence suggest that the susceptibility conveyed by the CBCL-DP may be strongly influenced by genetic factors. For example, the CBCL-DP is highly heritable with additive genetic effects consistently explaining up to 67% of its variance 1114. Candidate gene association studies of the CBCL-DP have suggested possible association with genes coding the dopamine transporter (SLC6A3) and brain-derived neurotrophic factor (BDNF) 13,15. Genome-wide linkage scans have implicated potential loci (LOD scores>2.5) on chromosome 2q23 16 and 1p21.1, 6p21.3 and 8q21.13 17. The suggestive linkage findings are noteworthy as they partially overlap loci implicated in bipolar spectrum disorders (2q22-2q24 and 6q23-6q24) 18 that are associated with dysregulation of affect, behavior, and cognition, as well.

To extend this small literature, we conducted a genome-wide association study of the CBCL-DP in an affected offspring trio study of ADHD 19. We also focused on loci at previously identified linkage signals for the CBCL-DP itself (2q23, 1p21.1, 6p21.3, and 8q21.13), those from meta-analyses of bipolar disorder (8q24 and 6q21) and, owing to the association between the CBCL-DP and ADHD 7,9,11,13,16,20, ADHD (16q23.1) 21.

Method

All study procedures were reviewed and approved by the subcommittee for human subjects of each respective institution. All subjects’ parents or guardians signed written permission forms and children older than 7 years of age signed written assent forms.

ADHD families were ascertained at Massachusetts General Hospital (MGH, N=309 trios) and University of California, at Los Angeles (UCLA, N=156 trios) 19. Children were 6–17 years of age at initial assessment and met criteria for DSM-IV-TR attention-deficit hyperactivity disorder. Psychiatric assessment of ADHD criteria was made with the Schedule for Affective Disorders and Schizophrenia for School-Age Children Epidemiologic Version (K-SADS-E) at MGH and with the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime version (K-SADS-PL) at UCLA.

Genotyping was conducted by Genizon BioSciences Inc. with funding from Pfizer Inc. Genomic DNA samples from the MGH were genotyped using the Illumina Human1M BeadChip (N=1,057,265 SNPs) while the UCLA samples were genotyped using the Illumina Human 1M-Duo array (N=1,151,846 SNPs). Genotyping calls were generated after clustering all available data within platform at Genizon and then merged into a single file of 1,172,613 SNPs. To generate a data set of markers common to all sites, we removed SNPs that were either not included on both arrays (N=128,718 SNPs) or failed preliminary quality-control (QC) procedures conducted at Genizon (99% call rate for all samples and for all SNPs, gender check, Mendelian errors) on both the 1M and 1M-Duo arrays (N=9,500 SNPs), the 1M array only (N=39,753 SNPs) or the 1M-Duo array only (N=11,201 SNPs). For association analyses we included only SNPs with 0.01 ≤ MAF < 0.05 and call rate >99%; 0.05 ≤ MAF <0.1 and call rate >97%, MAF ≥0.1 and call rate >95% (N=142,386 SNPs excluded). Any SNPs found to be out of Hardy-Weinberg Equilibrium (p<1.0E-6) were excluded from further consideration (N=5,908 SNPs excluded). We checked for sample duplication by examining identity-by-state for all pairs of individuals and found none. After applying quality control filters, the final sample of ADHD trios across all three sites was 835,136 SNPs in 735 DSM-IV-TR ADHD trios from 732 families.

The Child Behavior Checklist-Parent Form (CBCL) 4 is a standardized assessment of child behavior problems and social competence. The CBCL records, in standardized format, the behavioral problems and competencies of children ages 6 to 18, as reported by their parents. A T-score above 70 is considered to be a clinically meaningful indicator of childhood psychopathology. The CBCL-DP 10 is comprised of the Attention Problems (AP), Aggressive Behavior (AGG), and the Anxious/Depressed (A/D) clinical subscales.

Statistical Analysis

We selected a family-based, quantitative, multivariate test of association for this GWAS of the component subscales of the CBCL-DP. A family-based test was necessary to account for background genetic heterogeneity in the sample. We focused on a quantitative trait analysis because the sample size was small and prohibited a categorical analysis of CBCL-DP classes. A multivariate test of association was preferable because the CBCL-DP is a composite of multiple (N=3) correlated subscales (r2 ranged from 0.41 – 0.57). We wished to maintain the information contained in the covariation amongst the AP, A/D, and AGG subcomponents (rather than testing a summation of the scales) and to avoid the additional number statistical tests required to test each sub-scale separately. Therefore, we used the MQFAM multivariate extension of PLINK 22,23 on the Genetic Cluster Computer (http://www.geneticcluster.org) which is financially supported by the Netherlands Scientific Organization (NWO 480-05-003).

This multivariate test is described in detail in the original publication 23 but we describe it here briefly. MQFAM uses canonical correlation analysis (CCA) to identify correlation between two sets of correlated variables: X containing one variable (the bi-allelic SNP), and Y containing the three correlated CBCL-DP subscales. For each test conducted, CCA finds the linear combination of X and Y that maximizes correlation between the SNP and all traits simultaneously. This is an additive test of association but is flexible as the CBCL-DP subscales are not restricted to the same linear combination for each SNP tested. Because the phenotype loadings produced are not inherently interpretable, we also estimated the change of each component subscale with univariate linear regression to generate beta coefficients that convey direction and magnitude of effect.

Multiple, within-family permutation was used to conduct association tests that are robust to population stratification 24,25. Because this is computationally intensive, we employed adaptive permutation to maintain family structure of the data and the permuted p-values do not adjust for multiple comparisons. To control for the number of SNPs tested, we adopted the conservative recommendation of Dudbridge et al and Pe’er et al 26,27 and considered p-values less than 5.0E-08 to be statistically significant genome-wide.

Because the power to detect a genome-wide significant associations in our sample, is quite low at 11% (assuming a quantitative trait locus explaining 2.5% of the variance) 28, we anticipate that many false negative findings will be observed amongst our top hits. Functional annotation clustering and gene enrichment tests were conducted online with the Database for Annotation, Visualization and Integrated Discovery (DAVID v6.7; http://david.abcc.ncifcrf.gov/home.jsp) clustering algorithm 29,30 in an attempt to distinguish falsely negative from falsely positive findings. Genes were submitted to DAVID if at least one SNP located within that gene (i.e., 3′ UTR, 5′ UTR, intron or exon) was nominally significant at p<0.001. Each cluster is assigned an EASE (Expression Analysis Systematic Explorer) score representing the statistical significance of gene enrichment in the selected gene list (i.e., -log10 of the p-value). This p-value is calculated by comparing the percent of submitted genes associated with a particular functional category against the percent of genes associated with that functional category in the relevant genomic background (i.e., the human genome). Although any EASE score ≥1.3 is nominally statistically significant at p<0.05, we applied a sequential Bonferroni correction 31 to the EASE scores to account for the number of clusters identified.

Results

CBCL data were available for 341 ADHD offspring of 339 ADHD affected trio families from UCLA (N=128) and MGH (N=213). Children were 10.8±3.2 years of age at ascertainment and predominantly male (64%, N=219). Most children met DSM-IV criteria for ADHD combined type (64%, N=212), followed by ADHD inattentive (30%, N=99) and hyperactive/impulsive (6%, N=22) subtypes. The mean T-score for the Anxious/Depressed (A/D; 62.2±10.5), Aggressive Behavior (AGG; 63.8±11.6), and Attention Problems (AP; 67.9±9.6) components of the CBCL-DP were of borderline clinical significance; 11% (N=39) of ADHD children scored above 70 on all three subscales.

Because we used family-based tests, the lambda statistic (defined as the observed median F statistic divided by the expected median F statistic) was 1.0008 indicating that there was no evidence of bias associated with population genetic heterogeneity. As illustrated in the Manhattan plot of association results, however, the smallest p-value observed (Figure 1, p=1.0E-06) was not statistically significant genome-wide. Also highlighted in Figure 1 are SNPs within 1Mb of suggestive leakage peaks for the CBCL-DP (1p21.1, 2q23, 6p21.3, 8q21.13) and meta-analyses of bipolar disorder (6q21 and 8q24.22) or ADHD (16q23.1). Association results under these loci were generally unremarkable with the exception of the ADHD loci on 16q23.1 with 2 SNPs at p≤5E-04.

Figure 1
Manhattan Plot of Association Results. Note: Single Nucleotide Polymorphisms (SNPs) within 1 million base-pairs of suggestive leakage peaks for the Child Behavior Checklist – Dysregulation Profile (CBCL-DP) (1p21.1, 2q23, 6p21.3, 8q21.13) and ...

Six CNS expressed genes were implicated in the top 50 genome-wide associations or within 1Mb of prior linkage peaks at p<0.001 (full results presented in Table S1, available online; regional association results presented in Figure 2). Although the strongest signal for association on chromosome 11q13.1 was driven by rs12575642 (p=1.0E-6; Table S1, available online), the most functionally relevant gene at this locus is STIP1 (stress-inducible protein 1). STIP1 is a ligand for the cellular prion protein 32 that is highly expressed in the brain and mediates astrocyte differentiation, survival, and proliferation 33. LRRC7 (also referred to as Densin-180) is a brain-specific intracellular membrane-bound protein involved in cell adhesion, dendritic branching and neuronal excitability 34. SEMA3A is a secreted axonal guidance protein that demonstrates increased neuronal transcription during postnatal development 35 and functions as a chemo-repellant collapsing neuronal growth cones 36. ALK is a receptor tyrosine kinase associated with several forms of cancer 37 but also plays an important role in brain development and function. Not much is known about the function of TMEM132D, but it is a novel candidate for anxiety-related behavior 38. CDH13 (cadherin 13, heart) on chromosome 16q24 is a calcium dependent cell-cell adhesion glycoprotein and may act as a negative regulator of neural cell growth. 39

Figure 2
Regional Association Plots for Genes Implicated in the Top 50 Associations. Note: The sentinel Single Nucleotide Polymorphism (SNP) (i.e. with the smallest p-value) is indicated by the green square. Any SNPs in linkage disequilibrium (LD) (r2≥0.8) ...

For each of the CNS expressed candidate genes implicated in the top genome-wide results, the Bonferonni corrected p-value for the sentinel SNP (i.e. the smallest p-value, see Table 1) was <0.05 after correcting for the number of SNPs genotyped for that gene. Of these, LRRC7 was associated with the most clinically relevant impact on the CBCL-DP: a 10-point increase in each of the A/D, AGG, and AP subscales. TMEM132D SNP was associated with a similar but more moderate increase in each the components of the CBCL-DP, but STIP1 (increased A/D and AGG scores) and ALK (lower A/D scores only) affected more specific behavioral domains (Table 1).

Table 1
Candidate Genes from the Top Genome-Wide Association Results and from Gene Enrichment Analysis

Of the 835,136 SNPs tested for association, 928 (0.11% 95%CI [0.105%-0.118%]) in 506 genes were nominally statistically significant at p<0.001 and were clustered into 18 non-mutually exclusive functional categories nominally significant at p<0.05 (i.e. EASE score ≥1.3) (for details on the top-ranked categories see Table S2, available online). Using a sequential Bonferroni correction for the number of clusters identified, two were had corrected p<0.05. The enrichment cluster identified by the DAVID algorithm with the smallest p-value (p=5.7E-04) was for genes involved with neuron development/differentiation and the second cluster surviving correction for the number of clusters identified was for membrane lipoproteins. The sentinel SNP and associated gene for these top two categories are presented in Table 1.

Discussion

The “dysregulatory syndrome” measured by the CBCL-DP encompasses affective, behavioral, and cognition-related behaviors that are impaired across several psychiatric diagnoses of childhood. The CBCL-DP is attractive for genetic research because it is heritable and indexes underlying susceptibility for a severe phenotype (explosive and emotionally dysregulated) with a poor prognosis (serious psychopathology, suicidality, psychiatric hospitalization). In study of the CBCL-DP in children with ADHD, we failed to identify any genome-wide statistically significant associations. However, results from the top-ranked, suggestive findings or via gene enrichment analysis support the hypothesis that the CBCL-DP captures a heritable neurodevelopmental syndrome in children. In particular, a number of the top-ranked genes are involved in hippocampal dependent learning and memory.

Of the top ranked findings from the genome-wide association results, SNPs in LRRC7 were associated with largest clinical impact on CBCL-DP subscales. LRRC7 is scaffolding protein expressed in the post-synaptic density 40 and is used as postsynaptic marker of hippocampal glutamatergic synapse integrity. In the post-synaptic membrane LRRC7 anchors a calcium dependent protein kinase (CAMK2A) that is critical for the initiation of early long-term potentiation 34. Furthermore, the tyrosine kinase receptor gene (NTRKB2) activated by BDNF was included in the neuronal differentiation/development gene enrichment category. NTRKB2 activation in the hippocampus initiates intra-cellular signaling cascades that partially rely on LRRC7-anchored CAMK2A activity to mediate calcium release required for long-term potentiation 41. The possible involvement of this signaling pathway is consistent with prior research suggesting association with BDNF and both bipolar disorder 4244 and the CBCL-DP 15. Additional research suggests that the BDNF-NTRKB2-CREB pathway may interact with childhood adversity to impact depressive symptoms in adulthood 45 and anti-depressant induced suicidality 46.

We also found nominal evidence of association with several SNPs in STIP1 on chromosome 11q13.1. STIP1 is highly expressed in the brain and is a ligand for cellular prion protein (PRPN; included the lipoproteins/integral to membrane enrichment category listed in Table 1). STIP1-PRPN binding mediates astrocyte differentiation and survival through ERK activation and the PKA signaling pathway, respectively 32,33. Direct infusion of polyclonal antibodies against the STIP1-PRPN binding site into the dorsal hippocampus blocked memory consolidation during an inhibitory avoidant step-down paradigm in rats 47.

A third gene implicated amongst our top-ranked association results has been implicated in brain development and hippocampal neurogenesis 37. Several SNPs on chromosome 2p23.1 in the ALK gene were nominally associated with the CBCL-DP. Loss of ALK function is associated with improved performance on hippocampal-dependent learning tasks and an anti-depressant behavioral profile on the tail suspension and Porsolt swim tests 48. The anti-depressant effects demonstrated with inhibition of ALK activity in animal models mirrors our study in which we observed a 5-point reduction with each minor allele of the ALK SNP (Table 1) for the CBCL-AD T-score but no appreciable effect for either the AP or AGG T-scores.

The pathophysiology underlying dysregulated affect, cognition and behavior is unknown and the small extant literature has not specifically addressed hippocampal dependent memory and learning 49. Nonetheless, the hypotheses generated from these genome-wide data are consistent with a learning-disordered/transactional model of explosive behavior in which lagging higher-order cognitive skills interfere with a child’s ability to comply with authoritarian demands 50,51. Specific deficits in learning and memory could contribute to explosive reactivity through inefficient encoding of previous consequences of noncompliance, thereby interfering with the ability to anticipate consequences of potential actions 51.

Although preliminary, the genetic enrichment analysis suggests that the CBCL-DP phenotype may be associated with genes mediating developmental neuromorphology, as well. For example, SEMA3A was one of our top-ranked association results and was included in the neuron developmental/differentiation enrichment category. SEMA3A is a secreted axonal guidance protein that demonstrates increased neuronal transcription during postnatal development 35 and functions as a chemo-repellant collapsing neuronal growth cones 36. Additional neurodevelopmental candidate genes in this category include EPHA7 that is amongst the earliest markers of subcortical parcellation of the neocortex 52 and cerebral volume 53. DLX2 also regulates subcortical development in the telencephalon and has previously been associated with autism 54.

It may also be noteworthy, however, that these and other neurodevelopmental candidates from this category also mediate synaptic plasticity and memory/learning. DLX2, for example, interacts with the promoter region of the NTRKB2 gene in the mouse retina 55. Although EPHA7 is widely expressed during embryonic development, it is also expressed postnatally and has been hypothesized to impact ongoing synaptic plasticity 52. SEMA3A is highly expressed in in the entorhinal cortex 56 and SEMA3A induced neuronal growth cone collapse is significantly reduced in hippocampal neurons in the FMR1 knock-out model of Fragile X syndrome 57. Additionally, NRXN1 is a cell membrane protein that regulates calcium triggered neurotransmitter release and has been associated with both schizophrenia and developmental disorders 5861.

This collaborative effort improves upon our earlier linkage studies 16,17 by utilizing the Illumina Human1M array and improving the genomic coverage afforded by our prior linkage studies. Despite pooling samples from the two previous CBCL-DP linkage studies 16,17, we failed to identify any nominally significant candidate genes under the suggestive linkage peaks reported. While this clearly could be due to small sample size, additional differences may explain the lack of replication of prior findings. Because the inclusion criteria for the primary ADHD genome-wide study 19 required independent ADHD trios and substantial quantities of DNA for genotyping, many children from the UCLA (N=412) and MGH (N=235) linkage analyses were not included in the current study while additional (N=129) trios from MGH were included. Thus, the current report is neither an independent replication nor a focused follow-up of the previous work.

Rather, our results represent the next step in trying to understand genes contributing to dysregulated affect, cognition, and behavior measured with the CBCL-DP. Clearly, larger independent samples are needed to test the hypotheses that developmentally expressed genes operant in hippocampal-dependent learning and memory may be associated with the CBCL-DP. If replicated, these findings may be useful for identifying children at risk for severe psychopathology and developing novel interventions for this impairing dysregulatory syndrome.

Supplementary Material

01

02

Acknowledgments

Genotyping was supported with funding from Pfizer Inc. Subject ascertainment and assessment was supported by the following sources: National Institute of Health (NIH) Grants R13MH059126, R01MH62873, U01MH085518 and R01MH081803 (S.V.F.); National Institute of Neurological Disorders and Stroke (NINDS) grant NS054124 (S. L.); National Institute of Mental Health (NIMH) grant MH01966 (J. McGough); NIMH grant MH01805 (J. McCracken); National Institute on Drug Abuse (NIDA) grant K24 DA016264 (T. W.); NIMH grant MH63706 (S.S.); and NIMH grants K08MH001503 and R01MH066237 (J.W.).

Footnotes

Supplemental material cited in this article is available online.

Disclosure: Dr. Mick has received research support from Ortho-McNeil Janssen Scientific Affairs, Pfizer, and Shire Pharmaceuticals, and has served on the advisory board for Shire Pharmaceuticals. Dr. Biederman receives research support from Elminda, Janssen, McNeil, and Shire. He serves on the speakers’ bureau for Fundacion Areces, Medice Pharmaceuticals, and the Spanish Child Psychiatry Association. He has received research support, served as a consultant, or served on the speakers’ bureau for/from Abbott, Alza, AstraZeneca, Bristol Myers Squibb, Celltech, Cephalon, Eli Lilly and Co., Esai, Forest, Glaxo, Gliatech, Janssen, McNeil, Merck, the National Alliance for Research on Schizophrenia and Depression (NARSAD), the National Institute on Drug Abuse (NIDA), New River, the National Institute of Child Health and Human Development (NICHD), the National Institute of Mental Health (NIMH), Novartis, Noven, Neurosearch, Organon, Otsuka, Pfizer, Pharmacia, the Prechter Foundation, Shire, the Stanley Foundation, UCB Pharma, Inc. and Wyeth. Dr. McCracken has received research support from Bristol-Myers Squibb, Aspect, and Seaside Pharmaceuticals. He has served as a consultant for Novopharm and BioMarin, and has served on the speakers’ bureau for the Tourette Syndrome Association, Veritas, and CME Outfitters. Dr. McGough has served as a consultant for Eli Lilly and Co. and Shire Pharmaceuticals. He has received research support from Eli Lilly and Co. Dr. Wilens receives or has received grant support from Abbott, McNeil, Eli Lilly and Co., the National Institutes of Health (NIH) - NIDA, Merck, and Shire. He has served on the speakers’ buearu for Eli Lilly and Co., McNeil, Novartis, and Shire. He has served as a consultant for Abbott, Astra-Zeneca, McNeil, Eli Lilly and Co., NIH, Novartis, Merck, and Shire. He receives royalties from Guilford Press. Dr. Wozniak receives royalties from Bantam Books. She has served on the speakers’ bureau for McNeil, Primedia/Massachusetts General Hospital Psychiatry Academy, and Eli Lilly and Co. She has served on the advisory board or as a consultant for Pfizer and Shire. She receives research support for McNeil, Shire, and Eli Lilly and Co. Her spouse has served on the speakers’ bureau for Boehringer-Ingelheim, Cephalon, GlaxoSmithKline, King, Sanofi-Aventis, Sepracor, and Takeda, is on the Advisory Board/Consulting for Axon Labs, Boehringer-Ingelheim, GlaxoSmithKline, Jazz Pharmaceuticals, Novartis, Neurogen, Novadel Pharma, Pfizer, UCB (Schwarz) Pharma, Sanofi-Aventis, Sepracor, and Takeda, and receives research support from Boehringer-Ingelheim, GlaxoSmithKline, UCB (Schwarz) Pharma, Pfizer, and Sepracor. Dr. Faraone has served on the advisory board, served on the speakers’ bureau, or served as a consultant for Eli Lilly and Co., Janssen, McNeil, Novartis, Ortho-McNeil, Pfizer, and Shire. He has received research support from Eli Lilly and Co., Pfizer, Shire, and NIH. Drs. Loo, Doyle, and Smalley report no biomedical financial interests or potential conflicts of interest.

This article was reviewed under and accepted by Ad Hoc Editor Robert Althoff, MD, PhD.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributor Information

Dr. Eric Mick, University of Massachusetts Medical School.

Dr. James McGough, Division of Child and Adolescent Psychiatry at the University of California Los Angeles (UCLA) Semel Institute for Neuroscience and Human Behavior and David Geffen School of Medicine.

Dr. Sandra Loo, Center for Neurobehavioral Genetics the UCLA Semel Institute for Neuroscience and Human Behavior and David Geffen School of Medicine.

Dr. Alysa E. Doyle, Massachusetts General Hospital and Harvard Medical School.

Dr. Janet Wozniak, Massachusetts General Hospital and Harvard Medical School.

Dr. Timothy E. Wilens, Massachusetts General Hospital and Harvard Medical School.

Dr. Susan Smalley, Center for Neurobehavioral Genetics the UCLA Semel Institute for Neuroscience and Human Behavior and David Geffen School of Medicine.

Dr. James McCracken, Division of Child and Adolescent Psychiatry at the University of California Los Angeles (UCLA) Semel Institute for Neuroscience and Human Behavior and David Geffen School of Medicine.

Dr. Joseph Biederman, Massachusetts General Hospital and Harvard Medical School.

Dr. Stephen V. Faraone, State University of New York (SUNY) Upstate Medical University.

References

1. Angold A, Costello J, Erkanli A. Comorbidity. J Child Psychol Psychiatry. 1999;40(1):57–87. [PubMed]
2. Biederman J, Newcorn J, Sprich S. Comorbidity of attention deficit hyperactivity disorder with conduct, depressive, anxiety, and other disorders. American Journal of Psychiatry. 1991;148(5):564–577. [PubMed]
3. Althoff RR, Verhulst FC, Rettew DC, Hudziak JJ, van der Ende J. Adult outcomes of childhood dysregulation: a 14-year follow-up study. J Am Acad Child Adolesc Psychiatry. 2010 Nov;49(11):1105–1116. [PMC free article] [PubMed]
4. Achenbach T. Manual for the Child Behavior Checklist/4–18 and 1991 profile. Burlington: University of Vermont, Department of Psychiatry; 1991.
5. Althoff RR, Rettew DC, Ayer LA, Hudziak JJ. Cross-informant agreement of the Dysregulation Profile of the Child Behavior Checklist. Psychiatry Res. 2010 Aug 15;178(3):550–555. [PMC free article] [PubMed]
6. Youngstrom E, Youngstrom JK, Starr M. Bipolar diagnoses in community mental health: Achenbach Child Behavior Checklist profiles and patterns of comorbidity. Biol Psychiatry. 2005 Oct 1;58(7):569–575. [PubMed]
7. Diler RS, Birmaher B, Axelson D, et al. The Child Behavior Checklist (CBCL) and the CBCL-bipolar phenotype are not useful in diagnosing pediatric bipolar disorder. J Child Adolesc Psychopharmacol. 2009 Feb;19(1):23–30. [PMC free article] [PubMed]
8. Meyer SE, Carlson GA, Youngstrom E, et al. Long-term outcomes of youth who manifested the CBCL-Pediatric Bipolar Disorder phenotype during childhood and/or adolescence. J Affect Disord. 2009 Mar;113(3):227–235. [PubMed]
9. Biederman J, Petty CR, Monuteaux MC, et al. The child behavior checklist-pediatric bipolar disorder profile predicts a subsequent diagnosis of bipolar disorder and associated impairments in ADHD youth growing up: a longitudinal analysis. J Clin Psychiatry. 2009 May;70(5):732–740. [PMC free article] [PubMed]
10. Ayer L, Althoff R, Ivanova M, et al. Child Behavior Checklist Juvenile Bipolar Disorder (CBCL-JBD) and CBCL Posttraumatic Stress Problems (CBCL-PTSP) scales are measures of a single dysregulatory syndrome. J Child Psychol Psychiatry. 2009 Oct;50(10):1291–1300. [PubMed]
11. Hudziak J, Althoff RR, Rettew DC, Derks EM, Faraone SV. The prevalence and genetic architecture of CBCL-juvenile bipolar disorder. Biol Psychiatry. 2005;58(7):562–568. [PubMed]
12. Althoff RR, Rettew DC, Faraone SV, Boomsma DI, Hudziak JJ. Latent class analysis shows strong heritability of the child behavior checklist-juvenile bipolar phenotype. Biol Psychiatry. 2006 Nov 1;60(9):903–911. [PubMed]
13. Volk HE, Todd RD. Does the Child Behavior Checklist juvenile bipolar disorder phenotype identify bipolar disorder? Biol Psychiatry. 2007 Jul 15;62(2):115–120. [PubMed]
14. Hudziak JJ, Derks EM, Althoff RR, Rettew DC, Boomsma DI. The genetic and environmental contributions to attention deficit hyperactivity disorder as measured by the conners’ rating scales--revised. Am J Psychiatry. 2005 Sep;162(9):1614–1620. [PubMed]
15. Mick E, Monuteaux M, Wilens TE, Wozniak J, Byrne D, Faraone SV. A Genetic association study of emotional dysregulation indexed by the Child Behavior Checklist (CBCL) in Children with Attention-Deficit/Hyperactivity Disorder (ADHD). Paper presented at: American Academy of Child and Adolescent Psychiatry 57th Annual 10/2010; 2010; New York.
16. McGough JJ, Loo SK, McCracken JT, et al. CBCL Pediatric Bipolar Disorder Profile and ADHD: Comorbidity and Quantitative Trait Loci Analysis. J Am Acad Child Adolesc Psychiatry. 2008 Aug 21;47(10):1151–1157. [PMC free article] [PubMed]
17. Doyle AE, Biederman J, Ferreira MA, Wong P, Smoller JW, Faraone SV. Suggestive linkage of the child behavior checklist juvenile bipolar disorder phenotype to 1p21, 6p21, and 8q21. J Am Acad Child Adolesc Psychiatry. 2010 Apr;49(4):378–387. [PMC free article] [PubMed]
18. Abou Jamra R, Fuerst R, Kaneva R, et al. The first genomewide interaction and locus-heterogeneity linkage scan in bipolar affective disorder: strong evidence of epistatic effects between loci on chromosomes 2q and 6q. Am J Hum Genet. 2007 Nov;81(5):974–986. [PubMed]
19. Mick E, Todorov A, Smalley S, et al. Family-based genome-wide association scan of attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2010 Sep;49(9):898–905. e893. [PubMed]
20. Galanter CA, Carlson GA, Jensen PS, et al. Response to methylphenidate in children with attention deficit hyperactivity disorder and manic symptoms in the multimodal treatment study of children with attention deficit hyperactivity disorder titration trial. J Child Adolesc Psychopharmacol. 2003;13(2):123–136. [PubMed]
21. Zhou K, Dempfle A, Arcos-Burgos M, et al. Meta-analysis of genome-wide linkage scans of attention deficit hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet. 2008 Dec 5;147B(8):1392–1398. [PMC free article] [PubMed]
22. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007 Sep;81(3):559–575. [PubMed]
23. Ferreira MA, Purcell SM. A multivariate test of association. Bioinformatics. 2009 Jan 1;25(1):132–133. [PubMed]
24. Abecasis GR, Cardon LR, Cookson WO. A general test of association for quantitative traits in nuclear families. Am J Hum Genet. 2000 Jan;66(1):279–292. [PubMed]
25. Fulker DW, Cherny SS, Sham PC, Hewitt JK. Combined linkage and association sib-pair analysis for quantitative traits. Am J Hum Genet. 1999 Jan;64(1):259–267. [PubMed]
26. Dudbridge F, Gusnanto A. Estimation of significance thresholds for genomewide association scans. Genet Epidemiol. 2008 Apr;32(3):227–234. [PMC free article] [PubMed]
27. Pe’er I, Yelensky R, Altshuler D, Daly MJ. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol. 2008 May;32(4):381–385. [PubMed]
28. Purcell S, Cherny SS, Sham PC. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics. 2003 Jan;19(1):149–150. [PubMed]
29. Dennis G, Jr, Sherman BT, Hosack DA, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4(5):P3. [PubMed]
30. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. [PubMed]
31. Holm S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics. 1979;6:65–70.
32. Lopes MH, Hajj GN, Muras AG, et al. Interaction of cellular prion and stress-inducible protein 1 promotes neuritogenesis and neuroprotection by distinct signaling pathways. J Neurosci. 2005 Dec 7;25(49):11330–11339. [PubMed]
33. Arantes C, Nomizo R, Lopes MH, Hajj GN, Lima FR, Martins VR. Prion protein and its ligand stress inducible protein 1 regulate astrocyte development. Glia. 2009 Oct;57(13):1439–1449. [PubMed]
34. Thalhammer A, Trinidad JC, Burlingame AL, Schoepfer R. Densin-180: revised membrane topology, domain structure and phosphorylation status. J Neurochem. 2009 Apr;109(2):297–302. [PMC free article] [PubMed]
35. Cheung I, Shulha HP, Jiang Y, et al. Developmental regulation and individual differences of neuronal H3K4me3 epigenomes in the prefrontal cortex. Proc Natl Acad Sci U S A. 2010 May 11;107(19):8824–8829. [PubMed]
36. Luo Y, Raible D, Raper JA. Collapsin: a protein in brain that induces the collapse and paralysis of neuronal growth cones. Cell. 1993 Oct 22;75(2):217–227. [PubMed]
37. Palmer RH, Vernersson E, Grabbe C, Hallberg B. Anaplastic lymphoma kinase: signalling in development and disease. Biochem J. 2009 Jun 15;420(3):345–361. [PMC free article] [PubMed]
38. Erhardt A, Czibere L, Roeske D, et al. TMEM132D, a new candidate for anxiety phenotypes: evidence from human and mouse studies. Mol Psychiatry. 2010 Apr 6; [PubMed]
39. Takeuchi T, Misaki A, Liang SB, et al. Expression of T-cadherin (CDH13, H-Cadherin) in human brain and its characteristics as a negative growth regulator of epidermal growth factor in neuroblastoma cells. J Neurochem. 2000 Apr;74(4):1489–1497. [PubMed]
40. Apperson ML, Moon IS, Kennedy MB. Characterization of densin-180, a new brain-specific synaptic protein of the O-sialoglycoprotein family. J Neurosci. 1996 Nov 1;16(21):6839–6852. [PubMed]
41. Minichiello L. TrkB signalling pathways in LTP and learning. Nat Rev Neurosci. 2009 Dec;10(12):850–860. [PubMed]
42. Sklar P, Gabriel SB, McInnis MG, et al. Family-based association study of 76 candidate genes in bipolar disorder: BDNF is a potential risk locus. Brain-derived neutrophic factor. Mol Psychiatry. 2002;7(6):579–593. [PubMed]
43. Geller B, Badner JA, Tillman R, Christian SL, Bolhofner K, Cook EH., Jr Linkage disequilibrium of the brain-derived neurotrophic factor Val66Met polymorphism in children with a prepubertal and early adolescent bipolar disorder phenotype. The American Journal of Psychiatry. 2004;161(9):1698. [PubMed]
44. Mick E, Wozniak J, Wilens TE, Biederman J, Faraone SV. Family-based association study of the BDNF, COMT and serotonin transporter genes and DSM-IV bipolar-I disorder in children. BMC Psychiatry. 2009;9:2. [PMC free article] [PubMed]
45. Juhasz G, Dunham JS, McKie S, et al. The CREB1-BDNF-NTRK2 Pathway in Depression: Multiple Gene-Cognition-Environment Interactions. Biol Psychiatry. 2011 Jan 5; [PubMed]
46. Perroud N, Uher R, Ng MY, et al. Genome-wide association study of increasing suicidal ideation during antidepressant treatment in the GENDEP project. The pharmacogenomics journal. 2010 Sep 28; [PubMed]
47. Coitinho AS, Lopes MH, Hajj GN, et al. Short-term memory formation and long-term memory consolidation are enhanced by cellular prion association to stress-inducible protein 1. Neurobiol Dis. 2007 Apr;26(1):282–290. [PubMed]
48. Bilsland JG, Wheeldon A, Mead A, et al. Behavioral and neurochemical alterations in mice deficient in anaplastic lymphoma kinase suggest therapeutic potential for psychiatric indications. Neuropsychopharmacology. 2008 Feb;33(3):685–700. [PubMed]
49. Leibenluft E. Severe mood dysregulation, irritability, and the diagnostic boundaries of bipolar disorder in youths. The American journal of psychiatry. 2011 Feb;168(2):129–142. [PMC free article] [PubMed]
50. Greene RW, Doyle AE. Toward a transactional conceptualization of oppositional defiand disorder: Implications for assessment and treatment. Clinical Child and Family Psychology Review. 1999;2(3):129–148. [PubMed]
51. Greene RW, Ablon JS, Goring JC. A transactional model of oppositional behavior: underpinnings of the Collaborative Problem Solving approach. J Psychosom Res. 2003 Jul;55(1):67–75. [PubMed]
52. Yun ME, Johnson RR, Antic A, Donoghue MJ. EphA family gene expression in the developing mouse neocortex: regional patterns reveal intrinsic programs and extrinsic influence. The Journal of comparative neurology. 2003 Feb 10;456(3):203–216. [PubMed]
53. Depaepe V, Suarez-Gonzalez N, Dufour A, et al. Ephrin signalling controls brain size by regulating apoptosis of neural progenitors. Nature. 2005 Jun 30;435(7046):1244–1250. [PubMed]
54. Liu X, Novosedlik N, Wang A, et al. The DLX1and DLX2 genes and susceptibility to autism spectrum disorders. European journal of human genetics : EJHG. 2009 Feb;17(2):228–235. [PMC free article] [PubMed]
55. de Melo J, Zhou QP, Zhang Q, et al. Dlx2 homeobox gene transcriptional regulation of Trkb neurotrophin receptor expression during mouse retinal development. Nucleic acids research. 2008 Feb;36(3):872–884. [PMC free article] [PubMed]
56. Giger RJ, Pasterkamp RJ, Heijnen S, Holtmaat AJ, Verhaagen J. Anatomical distribution of the chemorepellent semaphorin III/collapsin-1 in the adult rat and human brain: predominant expression in structures of the olfactory-hippocampal pathway and the motor system. J Neurosci Res. 1998 Apr 1;52(1):27–42. [PubMed]
57. Li C, Bassell GJ, Sasaki Y. Fragile X Mental Retardation Protein is Involved in Protein Synthesis-Dependent Collapse of Growth Cones Induced by Semaphorin-3A. Front Neural Circuits. 2009;3:11. [PMC free article] [PubMed]
58. Sudhof TC. Neuroligins and neurexins link synaptic function to cognitive disease. Nature. 2008 Oct 16;455(7215):903–911. [PMC free article] [PubMed]
59. O’Dushlaine C, Kenny E, Heron E, et al. Molecular pathways involved in neuronal cell adhesion and membrane scaffolding contribute to schizophrenia and bipolar disorder susceptibility. Mol Psychiatry. 2011 Mar;16(3):286–292. [PubMed]
60. Ghahramani Seno MM, Hu P, Gwadry FG, et al. Gene and miRNA expression profiles in autism spectrum disorders. Brain research. 2011 Mar 22;1380:85–97. [PubMed]
61. Ching MS, Shen Y, Tan WH, et al. Deletions of NRXN1 (neurexin-1) predispose to a wide spectrum of developmental disorders. American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics. 2010 Jun 5;153B(4):937–947. [PMC free article] [PubMed]