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. Author manuscript; available in PMC: 2016 Jun 1.
Published in final edited form as: J Affect Disord. 2015 Mar 14;178:206–214. doi: 10.1016/j.jad.2015.03.011

Characteristics of Bipolar I Patients Grouped by Externalizing Disorders

Shanker Swaminathan a, Daniel L Koller a, Tatiana Foroud a, Howard J Edenberg a,b, Xiaoling Xuei b, Alexander B Niculescu III c; the Bipolar Genome Study (BiGS) Consortium, John I Nurnberger Jr a,c,*
PMCID: PMC4433475  NIHMSID: NIHMS678458  PMID: 25827505

Abstract

Background

Bipolar disorder co-occurs with a number of disorders with externalizing features. The aim of this study is to determine whether Bipolar I (BPI) subjects with comorbid externalizing disorders and a subgroup with externalizing symptoms prior to age 15 have different clinical features than those without externalizing disorders and whether these could be attributed to specific genetic variations.

Methods

A large cohort (N=2505) of Bipolar I subjects was analyzed. Course of illness parameters were compared between an Externalizing Group, an Early-Onset Subgroup and a Non-Externalizing group in the Discovery sample (N=1268). Findings were validated using an independent set of 1237 BPI subjects (Validation sample). Genetic analyses were carried out.

Results

Subjects in the Externalizing Group (and Early-Onset Subgroup) tended to have a more severe clinical course, even in areas specifically related to mood disorder such as cycling frequency and rapid mood switching. Regression analysis showed that the differences are not completely explainable by substance use. Genetic analyses identified nominally associated SNPs; calcium channel genes were not enriched in the gene variants identified.

Limitations

Validation in independent samples is needed to confirm the genetic findings in the present study.

Conclusions

Our findings support the presence of an externalizing disorder subphenotype within BPI with greater severity of mood disorder and possible specific genetic features.

Keywords: Bipolar disorder, Externalizing disorders, Early onset, Comorbidity, Genome-wide association study (GWAS)

1. Introduction

Bipolar disorder (BP) includes distinct episodes with altered mood, activity, and thought patterns. The mean age at onset for Bipolar Disorder Type I (BPI) is 18.4 years and the lifetime prevalence is 0.6% (Merikangas et al., 2011). BP may occur in conjunction with a number of other disorders such as substance use and anxiety disorders (Merikangas et al., 2011). Substance use has been shown to be highly prevalent in BP patients (Merikangas et al., 2011; Regier et al., 1990). Prior studies have identified more severe outcomes among BP patients with co-occurring substance use disorders (Cardoso et al., 2008; Elizabeth Sublette et al., 2009; Frye and Salloum, 2006; Grunebaum et al., 2006). The presence of substance abuse also makes it more difficult to treat BP (Swann, 2010). The disorder has a substantial genetic component. Monozygotic twin concordance rates range from 45 to 70% and sibling recurrence risk ranges from 5 to 10% (Craddock and Forty, 2006). Genome Wide Association Studies (GWAS) have identified ten common variants with modest effects (Chen et al., 2013; Ferreira et al., 2008; Seifuddin et al., 2012; Sklar, 2013; Sklar et al., 2011; Smith et al., 2009; Smith et al., 2011), and analyses using the entire set of variants tested with common GWAS platforms suggest that many additional vulnerability genes remain to be identified as larger samples become available (Cross-Disorder Group of the Psychiatric Genomics et al., 2013; Lee et al., 2011). Many course of illness parameters have also been shown to be heritable (Potash et al., 2007). The aim of this study is to determine whether a subphenotype of BPI subjects can be defined based on the presence of externalizing disorders, and whether these subjects are clinically and/or genetically different from those who did not have externalizing disorders. We were particularly interested in whether the characteristics of the mood disorder itself (apart from externalizing symptoms) differentiated the subgroups. Two sets of BPI subjects ascertained by the National Institute of Mental Health (NIMH) Bipolar Disorder Genetics Initiative and evaluated with the Diagnostic Instrument for Genetic Studies (Nurnberger et al., 1994) were included (a Discovery sample and a Validation sample). We also performed GWAS analyses using the combined sample to identify genetic variations that may help characterize these groups.

2. Methods

2.1. Clinical parameters

BPI subjects were selected from those collected and characterized by the National Institute of Mental Health (NIMH) Bipolar Disorder Genetics Initiative over the past 18 years. Subjects were from Indiana University, Johns Hopkins University, the National Institute of Mental Health Intramural (NIMH) Program, Washington University at St. Louis, University of Pennsylvania, University of California at San Diego, University of California at Irvine, University of California at San Francisco, University of Iowa, University of Chicago, Rush University, and Howard University.

The study protocol was approved by the Institutional Review Board of the respective universities. After description of the study to the subjects, written informed consent was obtained.

2.1.1. Discovery sample

Subjects totaled 951 unrelated European American (EA) individuals and 317 unrelated African-American (AA) individuals. EA status was determined based on the subject’s self-report that all four grandparents were of EA heritage. AA status was based on self-report of at least one grandparent being of AA heritage. The 1268 BPI subjects were divided into the following groups: 1) the Non-Externalizing Group-472 subjects; 2) the Externalizing Group-796 subjects who had at least one externalizing disorder and 3) the Early-Onset Subgroup-329 subjects in the Externalizing Group who had two or more symptoms of conduct disorder (CD) prior to age 15 (Table 1). Externalizing disorders included one or more of the following DSM-IV diagnoses: alcohol abuse/dependence, drug abuse/dependence, pathological gambling, anti-social personality disorder (ASPD), attention-deficit hyperactivity disorder (ADHD) and CD (Table 2). Of note, about 70% of subjects in this sample came from multiplex families (additional affected relatives with BPI disorder).

Table 1.

BPI subject distribution.

Non-Externalizing Group Externalizing Group Total N Early-Onset Subgroup
N % of Total N N % of Total N N % of Externalizing Group N
Discovery subjects 472 37.2 796 62.8 1268 329 41.3
European Americans 385 40.5 566 59.5 951 235 41.5
African Americans 87 27.4 230 72.6 317 93 40.4
Validation sample subjects 436 35.2 801 64.8 1237 307 38.3
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Table 2.

Breakdown of externalizing disorders in the Externalizing Group of Discovery and Validation sample BPI subjects.

Discovery and Validation sample subjects
Externalizing disorder Discovery sample (N=796) Validation sample (N=801) χ2 df P (two-sided)
N % N %
Alcohol abuse/dependence 591 74.2 607 75.8 0.501 1 0.479
Drug abuse/dependence 528 66.3 475 59.3 8.448 1 0.004
Pathological gambling 43 5.4 44 5.5 0.006 1 0.936
Anti-social personality disorder 110 13.8 58 7.2 18.353 1 <0.001
Attention-deficit hyperactivity disorder 153 19.2 263 32.8 38.407 1 <0.001
Conduct disorder 29 3.6 28 3.5 0.025 1 0.874
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2.1.2. Validation sample

The validation sample consisted of 1237 unrelated EA BPI subjects from the same study. Based on the above mentioned criteria, these subjects were also divided into 1) the Non-Externalizing Group-436 subjects; 2) the Externalizing Group-801 subjects and 3) the early-onset sub-group-307 subjects (Table 1). Of note, about 20% of subjects in this sample came from multiplex families.

2.1.3. Clinical assessment

All subjects were interviewed with the Diagnostic Interview for Genetic Studies (DIGS) (Nurnberger et al., 1994), a diagnostic instrument developed for determining mood disorders and related conditions and shown to have excellent test-retest reliability. Final diagnoses were made by two independent clinicians incorporating all available information using a best-estimate procedure.

2.2. Genome-wide association analyses

2.2.1. Genotyping and quality control of data available on dbGaP

Genotyping was carried out at the The Broad Institute Center for Genotyping and Analysis. PicoGreen fluorometry was used to check DNA quantity, and sample quality was initially assessed by genotyping a 24-single nucleotide polymorphism (SNP) panel on the Sequenom iPLEX platform containing a sex determining assay. Samples were plated at 50ng/ul in 96 well plates at the Rutgers University Cell and DNA Repository. The Centre d’Etude du Polymorphisme Human (CEPH; http://www.cephb.fr/en/cephdb/) sample NA12144 was placed on each production plate at the Broad Institute. Genotyping was carried out separately for the EA and AA samples using the Affymetrix Genome-Wide Human SNP Array 6.0. Allele calling was performed using the BirdSeed algorithm Affymetrix Power Tools version apt-1.8.6 and cluster models (‘priors’) file. Concordance between genotypes from the array and those from the initial quality control (QC) panel was evaluated to confirm sample ID. BPI EA Discovery and Validation samples were pooled together for the GWAS.

Samples were not used in the analysis if they had a low call rate (<98.5%) or incompatibility between reported gender and genetically determined gender. Pairwise identity-by-descent estimation was used to check for unexpected familial relationships in PLINK v1.07 (Purcell et al., 2007). SNPs were not analyzed if the minor allele frequency was <0.01, call rate <95%, Hardy Weinberg Equilibrium was violated (P<10−6) in control samples, if there were three or more Mendelian errors, or if there was more than one discrepancy among duplicate samples. 2064 samples and 677,171 SNPs passed all QC tests. Further information on QC can be obtained in (Smith et al., 2009; Smith et al., 2011).

2.3. Statistical analyses

2.3.1. Clinical parameter analyses

Statistical analyses were performed between the Non-Externalizing Group and the Externalizing Group of BPI subjects (including both early-onset and later-onset subjects), and between the Non-Externalizing Group and the Early-Onset Subgroup. Categorical variables were analyzed using the Pearson chi-square test (two-sided) and continuous variables were analyzed using an independent samples t-test (two-sided). Levene’s Test for Equality of Variances was performed to check the equality of variances assumption. All statistical analyses were performed in SPSS Statistics.

2.3.2. Genetic analyses

All genetic analyses were conducted using PLINK v1.07. The analyses involved testing the association of each SNP coded additively with externalizing and early-onset status, with subject sex included as a covariate in the logistic model employed.

3. Results

3.1. Subject distribution in the non-externalizing and Externalizing Groups

1268 BPI subjects (951 EA, 317 AA) were included in the Discovery phase of the analysis and the findings were validated using 1237 BPI subjects in the Validation sample (Table 1). The distribution of BPI subjects among the non-externalizing and Externalizing Groups, and the Early-Onset Subgroup was similar for the two samples.

3.2. Prevalence of externalizing disorders

We then examined the distribution of externalizing disorders in the Externalizing Group of BPI subjects in the two studies (Table 2). A majority of subjects had alcohol abuse/dependence and/or drug abuse/dependence. A higher proportion (P<0.05) of Discovery subjects had drug abuse/dependence and ASPD compared to Validation sample subjects. Conversely, a higher proportion (P<0.05) of Validation sample subjects had ADHD compared to Discovery subjects, but this may be related to assessment methodology (see Discussion). Further information on the breakdown of substance use disorders in the sample cohort can be found in Table S1.

3.3. Clinical characteristics

A number of clinical parameters were found to be significantly (P<0.05) different when comparing the Externalizing Group to the Non-Externalizing Group of BPI subjects in the Discovery sample (Tables 3 and 4). Subjects in the Externalizing Group were more likely to be male (χ2=9.012, df=1, P=0.003), to be disabled (χ2=6.932, df=1, P=0.008) and to have fewer years of schooling (t=6.694, df=1217, P<0.001). They had an earlier age at onset of BPI (t=7.250, df=776.393, P<0.001) (including earlier onset ages for both depression (t=5.533, df=796.274, P<0.001) and mania (t=5.807, df=837.128, P<0.001)), a higher number of depressive (χ2=20.902, df=1, P<0.001) and manic episodes (χ2=12.613, df=1, P<0.001, excluding episodes judged to be secondary to substance use) and a higher frequency of episodes (χ2=9.763, df=1, P=0.002; χ2=4.179, df=1, P=0.041). The subjects in the Externalizing Group had an increased frequency of incidents of (non-suicidal) self-harm (χ2=25.797, df=1, P<0.001) and more suicide attempts (t=−2.680, df=478.609, P=0.008). They were rated as more impaired on the interepisode Global Assessment Scale (GAS) (t=3.771, df=474, P<0.001) and were more likely to report a history of rapid switching (χ2=21.696, df=1, P<0.001) and rapid cycling (χ2=25.516, df=1, P<0.001). We have investigated the effect of ethnicity in externalizing disorders and we find similar results for EA and AA except for disability, frequency of depressive episodes, number and frequency of manic episodes, and age of first tobacco use in case of AA (Tables S2, S3, S4 and S5). The findings in BPI subjects in the overall Discovery sample (Tables 3 and 4) were replicated in BPI subjects in the Validation sample (Tables S6 and S7).

Table 3.

Clinical features in Discovery sample BPI subjects grouped by externalizing disorders (categorical parameters).

Non-Externalizing Group vs. Externalizing Group
Parameter Non-Externalizing Externalizing χ2 (1 df) P (two-sided) Odds Ratio: Value Odds Ratio: 95% CI
N % N % Lower Upper
Gender
Male 185 39.2 381 47.9 9.012 0.003 0.702 0.557 0.885
Female 287 60.8 415 52.1
Disabled
Yes 92 20.4 208 27.2 6.932 0.008 0.688 0.521 0.910
No 358 79.6 557 60.9
Self harm
Yes 25 10.0 122 26.0 25.797 <0.001 0.316 0.199 0.501
No 226 90.0 348 74.0
Rapid switching
Yes 205 48.6 434 62.8 21.696 <0.001 0.559 0.438 0.715
No 217 51.4 257 37.2
Rapid cycling
Yes 182 44.6 403 60.4 25.516 <0.001 0.528 0.411 0.677
No 226 55.4 264 39.6
Number of clean depressive episodesa
1 to 8 267 64.5 364 50.5 20.902 <0.001 1.781 1.389 2.284
>8 147 35.5 357 49.5
Number of clean depressive episodes per yearb
0 to 0.4 224 55.3 317 45.5 9.763 0.002 1.480 1.157 1.893
>0.4 181 44.7 379 54.5
Number of clean manic episodesb
1 to 4 254 57.2 343 46.5 12.613 <0.001 1.536 1.211 1.947
>4 190 42.8 394 53.5
Number of clean manic episodes per yearb
0 to 0.4 240 55.7 345 49.4 4.179 0.041 1.286 1.010 1.636
>0.4 191 44.3 353 50.6
Non-Externalizing Group vs. Early-Onset Subgroup
Parameter Non-Externalizing Early-Onset χ2 (1 df) P (two-sided) Odds Ratio: Odds Ratio: 95% CI
N % N % Lower Upper
Gender
Male 185 39.2 172 52.3 13.435 <0.001 0.588 0.443 0.782
Female 287 60.8 157 47.7
Disabled
Yes 92 20.4 97 30.0 9.355 0.002 0.599 0.430 0.833
No 358 79.6 226 70.0
Self harm
Yes 25 10.0 73 32.0 35.719 <0.001 0.235 0.143 0.386
No 226 90.0 155 68.0
Rapid switching
Yes 205 48.6 218 70.6 35.321 <0.001 0.394 0.289 0.538
No 217 51.4 91 29.4
Rapid cycling
Yes 182 44.6 201 68.1 38.216 <0.001 0.377 0.275 0.515
No 226 55.4 94 31.9
Number of clean depressive episodesa
1 to 8 267 64.5 130 43.3 31.548 <0.001 2.375 1.752 3.220
>8 147 35.5 170 56.7
Number of clean depressive episodes per yeara
0 to 0.4 224 55.3 130 44.5 7.901 0.005 1.542 1.139 2.087
>0.4 181 44.7 162 55.5
Number of clean manic episodesb
1 to 4 254 57.2 136 44.9 10.960 0.001 1.642 1.223 2.203
>4 190 42.8 167 55.1
Number of clean manic episodes per yearb
0 to 0.4 240 55.7 144 49.5 2.682 0.101 1.283 0.952 1.728
>0.4 191 44.3 147 50.5
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a

According to the DSM-IV, a clean depressive episode is a depressive episode without prior physical illness, drug or alcohol abuse, organic precipitants, or bereavement.

b

According to the DSM-IV, a clean manic episode is a manic episode without prior physical illness, drug or alcohol abuse, or organic precipitants.

Table 4.

Clinical features in Discovery sample BPI subjects grouped by externalizing disorders (continuous parameters).

Non-Externalizing Group vs. Externalizing Group
Parameter Non-Externalizing Externalizing t df P (two-sided)b
Mean SD Mean SD
Age 44.01 (N=467) 13.49 41.06 (N=782) 11.52 3.954 862.231 <0.001
Years of school 15.18 (N=452) 2.81 14.06 (N=767) 2.84 6.694 1217 <0.001
Age at onset of illness 21.36 (N=447) 9.92 17.38 (N=774) 7.94 7.250 776.393 <0.001
Age at onset of depression 21.93 (N=425) 10.70 18.48 (N=746) 9.46 5.533 796.274 <0.001
Age at onset of mania 26.61 (N=452) 11.52 22.83 (N=750) 9.84 5.807 837.128 <0.001
Age of first drink 16.72 (N=197) 4.68 14.06 (N=422) 4.88 6.392 617 <0.001
Age of first tobacco use 16.03 (N=244) 5.35 14.89 (N=579) 5.23 2.855 821 0.004
Number of suicide attempts 2.62 (N=144) 2.55 3.51 (N=380) 4.99 −2.680 478.609 0.008
Age of first suicide attempt 24.46 (N=74) 10.32 21.26 (N=229) 8.44 2.676 301 0.008
Interepisode GASa 75.30 (N=206) 14.94 70.14 (N=270) 14.64 3.771 474 <0.001
Non-Externalizing Group vs. Early-Onset Subgroup
Parameter Non-Externalizing Early-Onset t df P (two-sided)b
Mean SD Mean SD
Age 44.01 (N=467) 13.49 39.80 (N=325) 11.00 4.825 770.462 <0.001
Years of school 15.18 (N=452) 2.81 13.45 (N=325) 2.82 8.464 775 <0.001
Age at onset of illness 21.36 (N=447) 9.92 16.03 (N=322) 7.55 8.460 764.467 <0.001
Age at onset of depression 21.93 (N=425) 10.70 16.93 (N=312) 9.10 6.840 719.155 <0.001
Age at onset of mania 26.61 (N=452) 11.52 21.29 (N=309) 9.46 6.962 733.888 <0.001
Age of first drink 16.72 (N=197) 4.68 12.98 (N=206) 4.32 8.345 401 <0.001
Age of first tobacco use 16.03 (N=244) 5.35 14.02 (N=269) 5.47 4.208 511 <0.001
Number of suicide attempts 2.62 (N=144) 2.55 3.64 (N=181) 4.27 −2.661 301.360 0.008
Age of first suicide attempt 24.46 (N=74) 10.32 20.30 (N=115) 8.54 3.012 187 0.003
Interepisode GASa 75.30 (N=206) 14.94 69.29 (N=108) 14.89 3.389 312 0.001
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a

GAS, Global Assessment Scale.

b

P-values were determined after performing Levene’s Test for Equality of Variances to check equality of variances assumption.

Examination of the clinical variables in the Early-Onset Subgroup shows the same pattern as in the Externalizing Group as a whole, but in general the differences are greater. Non-suicidal self-harm was seen in 30.2% of early-onset subjects as compared to 10.0% of non-externalizing subjects (and 26.0% of externalizing subjects as a whole). Rapid switching was seen in 70.6% of early-onset subjects as compared to 48.6% of non-externalizing subjects (and 62.8% of externalizing subjects). Although they are younger, 56.7% of early-onset externalizing subjects have had >8 non-substance-related depressive episodes in their lifetime compared with 35.5% of non-externalizing subjects (and 49.5% of externalizing subjects); 55.1% have had >4 non-substance-related manic episodes in their lifetime compared with 42.8% of non-externalizing subjects (and 53.5% of externalizing subjects). They have an earlier age of onset of bipolar disorder by more than 5 years (at 16.0 years compared to 20.4 years) and these ages are similar to the ages of first depressive episode; age of first mania is also advanced by more than 5 years (21.3 years compared to 26.6 years). Number of suicide attempts is greater (3.6 vs 2.6) and age of first attempt is >4 years earlier (20.3 years vs 24.5 years). Patterns are very similar in the Validation sample.

To determine whether the differences in the clinical parameters between subjects in the non-externalizing and Externalizing Groups can be accounted for by substance use disorders, separate linear regression analyses were performed using frequency of clean depressive episodes/clean manic episodes as the dependent variable, and externalizing disorder status, presence of alcohol abuse/dependence, presence of drug abuse/dependence, ethnicity, presence/absence of first degree relatives with BPI and cohort (years of ascertainment) as independent variables controlling for age and gender in the Discovery sample. The frequency of depressive episodes was found to be a function of externalizing disorder status (standardized β=0.119, P=0.021), but was not completely explained by the presence of alcohol abuse/dependence (P>0.05) or drug abuse/dependence (P>0.05). The frequency of manic episodes was not found to be a function of externalizing disorder status (P>0.05), the presence of alcohol abuse/dependence (P>0.05) or the presence of drug abuse/dependence (P>0.05), but appeared to be related to cohort (P<0.05). This may be related to the preponderance of multiplex families in the earlier cohorts. We further examined the effect of externalizing disorder status controlling for alcohol abuse/dependence in one analysis and drug abuse/dependence in a separate analysis on age at onset of BPI, one of the clinical parameters observed to be significantly different between the non-externalizing and Externalizing Groups. A significant (P<0.05) effect of externalizing disorder status was still observed even when controlling for alcohol and drug use disorders in the two analyses (data not shown). This suggests that other externalizing disorders such as ASPD (Swann et al., 2010), pathological gambling (Kim et al., 2006), ADHD and CD (Masi et al., 2008) may explain a substantial part of the differences seen in the clinical parameters between the externalizing and Non-Externalizing Groups.

3.4. Genetic association analyses

Two GWAS analyses were performed comparing: 1) 784 subjects in the Non-Externalizing Group vs. 1280 subjects in the Externalizing Group; 2) 784 subjects in the Non-Externalizing Group vs. 502 subjects in the Early-Onset Subgroup. QC was performed on all subjects and all SNPs, as summarized above. Although no SNP reached genome-wide significance (P<5 × 10−8) from the two analyses, a number of SNPs were found to be associated at a P-value of 10−5 or lower. Details of 20 SNPs with the strongest association signals from each of the two analyses are shown in Table 5. Since gene variants associated with bipolar disorder have been reported to be enriched for calcium channel genes, we examined that pathway (Cross-Disorder Group of the Psychiatric Genomics, 2013; Sklar et al., 2011) but saw no evidence for enrichment of calcium channel genes in either analysis. We also performed GWAS analyses comparing each of the three groups with the control group, but did not identify any genome-wide significant SNPs in any of these comparisons (data not shown).

Table 5.

SNPs with the strongest association signals from the GWAS analyses of BPI subjects.

SNP Chromosome Base pair positiona Closest genea,b SNP location relative to genea Minor Allele Frequency P Odds Ratio
Externalizing Group vs. Non-Externalizing Group
rs17099448 1 76921007 ST6GALNAC3 51.79kb downstream 0.025 2.56E-06 0.4633
rs17622252 6 21439886 CDKAL1 100.15kb downstream 0.1333 4.39E-06 1.658
rs2764655 1 76942171 ST6GALNAC3 72.96kb downstream 0.025 6.62E-06 0.4774
rs10090419 8 124097812 DERL1 Intron 0.175 1.94E-05 0.7077
rs2071842 22 41313726 POLDIP3 Intron 0.1667 2.16E-05 0.661
rs2958709 8 106838246 ZFPM2 Intron 0.1417 2.31E-05 0.6872
rs2273238 6 90383081 ANKRD6 Coding region 0.0417 2.33E-05 0.5065
rs11015506 10 27409689 ANKRD26 Intron 0.025 2.65E-05 3.33
rs2997227 10 122575842 WDR11 24.85kb upstream 0.0778 2.98E-05 0.7426
rs6461161 7 15239240 TMEM195 Intron 0.3083 3.00E-05 1.327
rs4869963 6 151341417 MTHFD1L Intron 0.2833 3.48E-05 0.7507
rs7913464 10 23551309 PTF1A 28.13kb downstream 0.0583 3.76E-05 0.5527
rs16851722 2 166741590 SCN9A 18.36kb downstream 0.0583 4.40E-05 1.655
rs333846 2 117695664 - - 0.025 4.44E-05 4.72
rs4341952 2 166728262 SCN9A 31.68kb downstream 0.1917 4.47E-05 1.393
rs1779518 14 40797200 - - 0.3222 4.61E-05 1.424
rs7739248 6 151323361 MTHFD1L Intron 0.15 4.85E-05 0.7254
rs11997383 8 40574573 ZMAT4 Intron 0.1889 4.85E-05 0.618
rs16831019 2 135259447 ACMSD 53.21kb upstream 0.025 4.91E-05 0.4319
rs11013397 10 23660815 C10orf67 Intron 0.0667 5.10E-05 0.5823
Early-Onset Subgroup vs. Non-Externalizing Group
rs9359856 6 90364704 ANKRD6 Intron 0.2 4.52E-06 0.6091
rs4761053 12 127072154 - - 0.2583 6.95E-06 1.525
rs6934804 6 90372739 ANKRD6 Intron 0.1833 1.18E-05 0.6201
rs1438108 20 1792858 SIRPA 29.96kb upstream 0.0417 1.45E-05 0.4555
rs1526303 2 174151593 - - 0.3778 1.93E-05 0.7042
rs1871946 2 213587652 IKZF2 Intron 0.4407 2.24E-05 1.413
rs2665452 7 97285019 ASNS 34.36kb downstream 0.2333 2.39E-05 0.6398
rs17622252 6 21439886 CDKAL1 100.15kb downstream 0.1333 2.71E-05 1.746
rs13080973 3 140078740 FOXL2 67.02kb downstream 0.1583 2.89E-05 1.553
rs12538214 7 154969302 CNPY1 17.42kb downstream 0.2167 2.91E-05 1.484
rs2289439 15 87486790 ABHD2 Intron 0.1083 3.15E-05 0.5478
rs4726457 7 139269639 TBXAS1 Intron 0.275 3.45E-05 0.6856
rs2242400 12 24989341 BCAT1 Intron 0.1167 3.48E-05 1.733
rs742002 22 25765086 - - 0.2917 4.14E-05 0.683
rs4339696 9 4285880 GLIS3 Intron 0.4333 4.27E-05 1.403
rs2216316 7 53451934 - - 0.1083 4.31E-05 1.539
rs2530132 7 97286662 ASNS 32.72kb downstream 0.2333 4.37E-05 0.6502
rs10868082 9 85828929 RMI1 20.12kb downstream 0.1833 4.38E-05 0.6671
rs17099448 1 76921007 ST6GALNAC3 51.79kb downstream 0.025 4.51E-05 0.3724
rs1560651 5 167530850 ODZ2 Intron 0.4833 4.59E-05 1.392
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a

Genomic locations are based on NCBI Build 36.3.

b

Closest genes were genes in which the SNP was present or genes located within 200kb (kilobases) of the SNP.

4. Discussion

The purpose of this study was to determine clinical and genetic differences between BPI subjects who had externalizing disorders and those who did not have externalizing disorders. We found that a majority of Discovery and Validation sample BPI subjects had externalizing disorders. We believe that this is the largest cohort of BPI subjects (total N=2505) analyzed for factors associated with externalizing symptomatology. Two factors: internalizing and externalizing, appear to substantially account for psychiatric comorbidity among BPI subjects (Monahan et al., in press).

A significant difference in a majority of clinical parameters were found when we compared subjects in the non-externalizing and Externalizing Groups as well as subjects in the Non-Externalizing Group and the Early-Onset Subgroup (Tables 3 and 4, online Tables S6 and S7). A possible explanation for the differences seen between these groups is the high proportion of subjects with alcohol abuse/dependence and/or drug abuse/dependence (Table 2). However, our regression studies show that the differences in clinical parameters are not completely explained by the presence or absence of substance use disorders.

The most remarkable result of this study was the increased severity and frequency of mood disorder symptoms and episodes in the Externalizing Group and particularly in the early-onset externalizing subgroup. In every parameter tested, subjects with externalizing disorders show evidence of greater symptomatology, earlier onset, and more impairment. This is true even when care is taken to exclude the direct effects of substances (e.g. the exclusion of substance-related depressive or manic episodes). In general the chronology suggests that substance use precedes episodes of major mood disorder but it is not clear whether minor mood problems may precede substance use or abuse. In any case, the divisions highlighted in this report appear to be clinically salient.

We noted a higher proportion of subjects in the Validation sample with ADHD compared to the Discovery sample. A section for adult ADHD was included in DIGS 4.0 used for later cohorts but the earlier versions had only a retrospective report of childhood ADHD. Inclusion of the adult ADHD section may be expected to have increased the frequency of ADHD reports (including retrospective childhood ADHD). It is not clear to us how to explain the decrease in ASPD diagnoses and drug abuse/dependence diagnoses in the Validation Sample compared to the Discovery sample. Site effects cannot be ruled out (as the four original sites contributed to a larger proportion of the Discovery sample in comparison to the Validation sample), and such effects have been demonstrated in other studies of bipolar subphenotypes (Saunders et al., 2008). However it is reassuring to note the similarity of the proportions of externalizing and early-onset subjects in the two samples, as well as the very similar results with respect to clinical variables (Tables 3 and 4, Tables S6 and S7).

Although no SNP reached genome-wide significance (P<5 × 10−8) in the two GWAS analyses, a number of potential candidate SNPs were found to be nominally associated (P<10−5) (Table 5). Two intronic SNPs (rs9359856 and rs6934804) and one coding SNP (rs2273238) were identified in the ANKRD6 (ankyrin repeat domain 6) gene on chromosome 6. Also known as Diversin, the gene has been shown to be an essential component of the Wnt-signaling pathway, controlling fusion of heart precursors and gastrulation movements in zebrafish embryogenesis (Moeller et al., 2006; Schwarz-Romond et al., 2002). It has been shown to be prominently expressed in the developing mouse brain suggesting a role during brain development and variants in the gene have been associated with muscle performance and habitual physical activity (Tissir et al., 2002; Van Deveire et al., 2012). A SNP in a gene in the same family (ANKRD26) was also noted in this short list. Variants in other identified genes have been associated with fasting glucose-related traits (GLIS3, TMEM195 and ZMAT4) (Dupuis et al., 2010; Meigs et al., 2007) and Alzheimer’s disease (MTHFD1L) (Naj et al., 2010). Cerebral glucose metabolic rates are known to be altered in mood disorders (Baxter et al., 1985; Drevets et al., 1997). SNPs in or near ST6GALNAC3, associated with carbohydrate metabolism, are seen three times in this list of 40. Thus, a number of SNPs associated with other diseases/complex traits may play a role in predisposition to externalizing disorders in the presence of bipolar illness. There is no evidence of an enrichment of calcium channel genes, suggesting that this pathway is not likely to explain the difference between externalizing and non-externalizing subtypes of BPI disorder.

The present study includes 2505 unrelated BPI subjects in two samples with clinical and genetic data. Subjects from two different ethnic populations (EA and AA) were analyzed for clinical parameters. However, this is a modest sample size for GWAS analyses and possible significant associations are likely to have been missed. Validation at genome-wide significance levels would be needed to confirm the suggestive associations identified in the present study.

5. Conclusion

In sum, we have demonstrated that BPI subjects with externalizing disorders tend to have a poorer clinical outcome then those without externalizing disorders; this is particularly true of those subjects with early onset of conduct disorder symptoms. Although no SNP reached genome-wide significance in the GWAS analyses, it does seem important to us that calcium channel genes do not appear to explain the genetic variance between externalizing and Non-Externalizing Groups of BPI subjects. The clinical results in particular suggest the presence of an externalizing disorder subphenotype within BPI warranting further investigation.

Supplementary Material

1
2

Acknowledgments

Role of the Funding Source

This work was supported by grants from the NIMH and NHGRI to the BiGS Consortium (Coordinating Center PI John R Kelsoe) (MH078151, MH081804, MH059567 supplement), and by the Genetic Association Information Network (GAIN). This work was additionally supported by the NIMH Intramural Research Program (Francis J McMahon and Thomas G Schulze). Follow-up genotyping was performed in the laboratory of Howard J Edenberg at Indiana University School of Medicine. This research was also supported, in part, by the Intramural Research Program of the NIH, National Library of Medicine. Data and biomaterials were collected in four projects that participated in the National Institute of Mental Health (NIMH) Bipolar Disorder Genetics Initiative. From 1991 to 1998, the principal investigators and coinvestigators were: Indiana University, Indianapolis, IN, U01 MH46282, John Nurnberger, Marvin Miller and Elizabeth Bowman; Washington University, St Louis, MO, U01 MH46280, Theodore Reich, Allison Goate and John Rice; Johns Hopkins University, Baltimore, MD U01 MH46274, J Raymond DePaulo, Jr, Sylvia Simpson and Colin Stine; NIMH Intramural Research Program, Clinical Neurogenetics Branch, Bethesda, MD, Elliot Gershon, Diane Kazuba and Elizabeth Maxwell. Data and biomaterials were collected as part of 10 projects that participated in the National Institute of Mental Health (NIMH) Bipolar Disorder Genetics Initiative. From 1999 to 2007, the principal investigators and coinvestigators were: Indiana University, Indianapolis, IN, R01 MH59545, John Nurnberger, Marvin J Miller, Elizabeth S Bowman, N Leela Rau, P Ryan Moe, Nalini Samavedy, Rif El-Mallakh (at University of Louisville), Husseini Manji (at Wayne State University), Debra A Glitz (at Wayne State University), Eric T Meyer, Carrie Smiley, Tatiana Foroud, Leah Flury, Danielle M Dick, Howard Edenberg; Washington University, St Louis, MO, R01 MH059534, John Rice, Theodore Reich, Allison Goate, Laura Bierut; Johns Hopkins University, Baltimore, MD, R01 MH59533, Melvin McInnis, J Raymond DePaulo, Jr, Dean F MacKinnon, Francis M Mondimore, James B Potash, Peter P Zandi, Dimitrios Avramopoulos and Jennifer Payne; University of Pennsylvania, PA, R01 MH59553, Wade Berrettini; University of California at Irvine, CA, R01 MH60068, William Byerley and Mark Vawter; University of Iowa, IA, R01 MH059548, William Coryell and Raymond Crowe; University of Chicago, IL, R01 MH59535, Elliot Gershon, Judith Badner, Francis McMahon, Chunyu Liu (now at the University of Illinois at Chicago), Alan Sanders, Maria Caserta, Steven Dinwiddie, Tu Nguyen, Donna Harakal; University of California at San Diego, CA, R01 MH59567, John Kelsoe, Rebecca McKinney; Rush University, IL, R01 MH059556, William Scheftner, Howard M Kravitz, Diana Marta, Annette Vaughn-Brown and Laurie Bederow; NIMH Intramural Research Program, Bethesda, MD, 1Z01MH002810-01, Francis J McMahon, Layla Kassem, Sevilla Detera-Wadleigh, Lisa Austin, Dennis L Murphy.

The sponsors had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

We thank the participants in the study, as without them this work would not have been possible. For best estimate diagnostic work, we thank Vegas Coleman, Robert Schweitzer, N Leela Rau and Kelly Rhoadarmer. For data management, we thank Mariano Erpe and for study coordination Carrie Fisher RN.

The Bipolar Genome Study (BiGS) Consortium members

University of California, San Diego: John R. Kelsoe, Tiffany A. Greenwood, Caroline M. Nievergelt, Rebecca McKinney, Paul D. Shilling

Scripps Translational Science Institute: Nicholas J. Schork, Erin N. Smith, Cinnamon S. Bloss

Indiana University: John I. Nurnberger, Jr., Howard J. Edenberg, Tatiana Foroud, Daniel L. Koller

University of Chicago: Elliot S. Gershon, Chunyu Liu, Judith A. Badner

Rush University Medical Center: William A. Scheftner

Howard University: William B. Lawson, Evaristus A. Nwulia, Maria Hipolito

University of Iowa: William Coryell

Washington University: John Rice

University of California, San Francisco: William Byerley

NIMH: Francis J. McMahon, Thomas G. Schulze

University of Pennsylvania: Wade H. Berrettini

John Hopkins: James B. Potash, Peter P. Zandi, Pamela B. Mahon

University of Michigan: Melvin G. McInnis, Sebastian Zollner, Peng Zhang

The Translational Genomics Research Institute: David W. Craig, Szabolcs Szelinger

Portland Veterans Affairs Medical Center: Thomas B. Barrett

Georg-August-University Gottingen: Thomas G. Schulze

Footnotes

Contributors

Shanker Swaminathan contributed to the analysis and interpretation of data for the work; drafting the work, and approval of final article

Daniel L. Koller contributed to the acquisition, analysis and interpretation of data for the work; drafting and critically revising the work, and approval of final article

Tatiana Foroud contributed to the acquisition, analysis and interpretation of data for the work; critically revising the work, and approval of final article

Howard J. Edenberg contributed to the conception and design of the work, acquisition, analysis and interpretation of data for the work; critically revising the work, and approval of final article

Xialong Xuei contributed to the conception and design of the work, acquisition, analysis and interpretation of data for the work; drafting, critically revising the work, and approval of final article

Alexander B. Niculescu, III contributed to the conception and design of the work, acquisition, analysis and interpretation of data for the work; critically revising the work, and approval of final article

The Bipolar Genome Study (BiGS) Consortium contributed to the conception and design of the work, acquisition, analysis and interpretation of data for the work; critically revising the work, and approval of final article

John I Nurnberger, Jr. contributed to the conception and design of the work, acquisition, analysis and interpretation of data for the work; drafting and critically revising the work, and approval of final article

Conflict of Interest

None

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